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, doi: 10.14135/j.cnki.1006-3080.20201022001
Abstract:
By using the feature extraction strategy of local extraction and global integration, this paper propose a multi-block convolutional variational information bottleneck (MBCVIB) for the fault diagnosis of multivariate dynamic processes. Firstly, according to the process mechanism, all variables are divided into sub-blocks and the variables in the same operation unit will be put into the same block. Secondly, one-dimension convolutional neural network (1-D CNN) is used to extract the local dynamic features of each operating unit in the process, which considers the temporal correlation between samples. Besides, a global feature representation is constructed by concatenating the local dynamic features of all operating units. On the basis of global features, the most relevant fault information is further extracted according to the variational information bottleneck principle. Finally, the proposed model is validated via Continuous Stirred Tank Reactor (CSTR) and Tennessee Eastman Process (TEP), which achieves an average fault diagnosis accuracy of 0.983 on CSTR and 0.955 on TEP.
By using the feature extraction strategy of local extraction and global integration, this paper propose a multi-block convolutional variational information bottleneck (MBCVIB) for the fault diagnosis of multivariate dynamic processes. Firstly, according to the process mechanism, all variables are divided into sub-blocks and the variables in the same operation unit will be put into the same block. Secondly, one-dimension convolutional neural network (1-D CNN) is used to extract the local dynamic features of each operating unit in the process, which considers the temporal correlation between samples. Besides, a global feature representation is constructed by concatenating the local dynamic features of all operating units. On the basis of global features, the most relevant fault information is further extracted according to the variational information bottleneck principle. Finally, the proposed model is validated via Continuous Stirred Tank Reactor (CSTR) and Tennessee Eastman Process (TEP), which achieves an average fault diagnosis accuracy of 0.983 on CSTR and 0.955 on TEP.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201022003
Abstract:
In order to solve the multimodal multi-objective optimization problem and find all solutions equivalent to the Pareto optimal solution, this paper proposes a novel group search optimization algorithm (MMO_LTSGSO) based on spatial learning mechanism and emotion tracking behavior by introducing social behavior into the basic group search algorithm. Firstly, a spatial learning mechanism is established and the decision of the population distribution state (discrete state and concentrated state) is made according to the real-time information of the learned individual's own position and the best individual position. When the population is in a discrete state, the following and wandering way is adopted to enhance the space exploration ability of the algorithm. With the optimization process, individuals interact with each other, and the spatial distance gradually decreases. At this time, the population gradually aggregates, the dynamic step search strategy is used to update the individual position, which can explore the solution around the optimal solution in real time and accelerate the convergence speed of the algorithm. Secondly, in order to prevent the algorithm from falling into stagnation and improve the accuracy of the algorithm, the emotion factor is introduced to make certain individuals track their moving behavior along their preferred direction. Then, special congestion distance calculation and guided evolution strategy are used to ensure the diversity of the algorithm in decision space and target space. Finally, the convergence of the algorithm is proved theoretically, and its performance is verified via 15 multimodal multi-objective optimization test benchmark functions, and is also compared with several existing multimodal multi-objective optimization algorithms. It is shown via the experiments results that the proposed algorithm can effectively solve multimodal multi-objective optimization problems.
In order to solve the multimodal multi-objective optimization problem and find all solutions equivalent to the Pareto optimal solution, this paper proposes a novel group search optimization algorithm (MMO_LTSGSO) based on spatial learning mechanism and emotion tracking behavior by introducing social behavior into the basic group search algorithm. Firstly, a spatial learning mechanism is established and the decision of the population distribution state (discrete state and concentrated state) is made according to the real-time information of the learned individual's own position and the best individual position. When the population is in a discrete state, the following and wandering way is adopted to enhance the space exploration ability of the algorithm. With the optimization process, individuals interact with each other, and the spatial distance gradually decreases. At this time, the population gradually aggregates, the dynamic step search strategy is used to update the individual position, which can explore the solution around the optimal solution in real time and accelerate the convergence speed of the algorithm. Secondly, in order to prevent the algorithm from falling into stagnation and improve the accuracy of the algorithm, the emotion factor is introduced to make certain individuals track their moving behavior along their preferred direction. Then, special congestion distance calculation and guided evolution strategy are used to ensure the diversity of the algorithm in decision space and target space. Finally, the convergence of the algorithm is proved theoretically, and its performance is verified via 15 multimodal multi-objective optimization test benchmark functions, and is also compared with several existing multimodal multi-objective optimization algorithms. It is shown via the experiments results that the proposed algorithm can effectively solve multimodal multi-objective optimization problems.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201107001
Abstract:
In the ultrasonic imaging of polyethylene (PE) pipe joints, the detection of characteristic line, resistance wire and echo of pipe inner wall is the key technology to realize automatic identification of welding defects. Aiming at the characteristics of obvious longitudinal stratification and more global interference in ultrasonic image, an adaptive ultrasonic signal line detection algorithm for polyethylene pipeline is proposed. Firstly, the grayscale image is adaptively blurred and projected in the horizontal direction for identifying the hierarchical area. Secondly, based on the target feature and spatial information, an improved adaptive threshold segmentation algorithm is proposed to detect feature lines. Thirdly, the Otsu-CRF algorithm is proposed to detect the signal line of the resistance wires. Finally, the detection of pipeline inner wall echo is fulfilled by color space conversion and threshold segmentation. It is shown via experimental results that the proposed adaptive algorithm can completely detect three signal lines and enhances the detection effect and efficiency than the existing algorithms. It also verifies the feasibility of applying the algorithm to the automatic detection of ultrasonic signal line of polyethylene pipe electrofusion welding joint.
In the ultrasonic imaging of polyethylene (PE) pipe joints, the detection of characteristic line, resistance wire and echo of pipe inner wall is the key technology to realize automatic identification of welding defects. Aiming at the characteristics of obvious longitudinal stratification and more global interference in ultrasonic image, an adaptive ultrasonic signal line detection algorithm for polyethylene pipeline is proposed. Firstly, the grayscale image is adaptively blurred and projected in the horizontal direction for identifying the hierarchical area. Secondly, based on the target feature and spatial information, an improved adaptive threshold segmentation algorithm is proposed to detect feature lines. Thirdly, the Otsu-CRF algorithm is proposed to detect the signal line of the resistance wires. Finally, the detection of pipeline inner wall echo is fulfilled by color space conversion and threshold segmentation. It is shown via experimental results that the proposed adaptive algorithm can completely detect three signal lines and enhances the detection effect and efficiency than the existing algorithms. It also verifies the feasibility of applying the algorithm to the automatic detection of ultrasonic signal line of polyethylene pipe electrofusion welding joint.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201027003
Abstract:
In recent years, with the continuous breakthrough in the field of algorithms, the current target detection algorithms has higher and higher computational complexity. In the forward inference stage, many practical applications often face low latency and strict power consumption restrictions. How to realize a low-power, low-cost, and high-performance target detection platform has gradually attracted more attentions. As a high-performance, reconfigurable and low-cost embedded platform, Field Programmable Gate Array (FPGA) is becoming the key technology of algorithm application. In view of the above requirements, this paper proposes a low-power target detection accelerator architecture based on FPGA+SOC (System On Chip) heterogeneous platform by adopting various hardware acceleration methods such as coarse and fine granularity optimization, parameter fixed-point and reordering. Aiming at the design limitation of existing researches on Zynq 7000 series FPGA, this paper proposes a new multi-dimensional hardware acceleration of YOLOv2 (You Only Look Once) algorithm, and deeply analyzes and models the accelerator performance and resource consumption to verify the rationality of the architecture. In order to make full use of the on-chip hardware resources to optimize the design of each module, the accelerator data access mechanism is improved to effectively reduce the transmission delay of the system and improve the actual utilization rate of bus bandwidth. The fixed-point processing of floating-point numbers can reduce the processing load of FPGA and further accelerate the processing speed. It is shown via experiments that the architecture achieves 26.98 GOPs performance on PYNQ-Z2 platform, which is about 38.71% higher than the existing FPGA-based target detection platform, and the power consumption is only 2.96W. Moreover, it has far-reaching significance for the application of target detection algorithm.
In recent years, with the continuous breakthrough in the field of algorithms, the current target detection algorithms has higher and higher computational complexity. In the forward inference stage, many practical applications often face low latency and strict power consumption restrictions. How to realize a low-power, low-cost, and high-performance target detection platform has gradually attracted more attentions. As a high-performance, reconfigurable and low-cost embedded platform, Field Programmable Gate Array (FPGA) is becoming the key technology of algorithm application. In view of the above requirements, this paper proposes a low-power target detection accelerator architecture based on FPGA+SOC (System On Chip) heterogeneous platform by adopting various hardware acceleration methods such as coarse and fine granularity optimization, parameter fixed-point and reordering. Aiming at the design limitation of existing researches on Zynq 7000 series FPGA, this paper proposes a new multi-dimensional hardware acceleration of YOLOv2 (You Only Look Once) algorithm, and deeply analyzes and models the accelerator performance and resource consumption to verify the rationality of the architecture. In order to make full use of the on-chip hardware resources to optimize the design of each module, the accelerator data access mechanism is improved to effectively reduce the transmission delay of the system and improve the actual utilization rate of bus bandwidth. The fixed-point processing of floating-point numbers can reduce the processing load of FPGA and further accelerate the processing speed. It is shown via experiments that the architecture achieves 26.98 GOPs performance on PYNQ-Z2 platform, which is about 38.71% higher than the existing FPGA-based target detection platform, and the power consumption is only 2.96W. Moreover, it has far-reaching significance for the application of target detection algorithm.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201218002
Abstract:
Ice nucleation proteins (INPs), which exist widely in nature, can induce water molecules to arrange regularly at micro scale resulting in elevated freezing point, but their tertiary structures have not been determined by experiments. The latest research shows that INPs may interact with water molecules to promote the formation of ice nuclei through TXT template of central repeat region, which shares identical structural feature of antifreeze proteins but with larger template area and opposite functionalities. INPs also have tyrosine (TYR) ladders to form new β−helix dimer along dimerization interface, thus increasing the active surface area of protein ice. At the same time, in a series of control experiments, it was found that polyglycerol at a certain concentration obviously combined with the INP of ice nucleation bacteria Pseudomonas syringae and inhibited its ice nucleation activity. In this work, molecular simulation software AutoDock was used to study the binding interaction of ice nucleation protein model of Pseudomonas borealis with glycerol and triglycerol molecules, in order to discover the corresponding inhibition mechanism on ice nucleation proteins and its universality with other INPs. The binding information shows that the ligand molecules express different binding abilities to TXT template and tyrosine ladder, and other residues of ice nucleation protein may participate in the binding with TXT freezing template.
Ice nucleation proteins (INPs), which exist widely in nature, can induce water molecules to arrange regularly at micro scale resulting in elevated freezing point, but their tertiary structures have not been determined by experiments. The latest research shows that INPs may interact with water molecules to promote the formation of ice nuclei through TXT template of central repeat region, which shares identical structural feature of antifreeze proteins but with larger template area and opposite functionalities. INPs also have tyrosine (TYR) ladders to form new β−helix dimer along dimerization interface, thus increasing the active surface area of protein ice. At the same time, in a series of control experiments, it was found that polyglycerol at a certain concentration obviously combined with the INP of ice nucleation bacteria Pseudomonas syringae and inhibited its ice nucleation activity. In this work, molecular simulation software AutoDock was used to study the binding interaction of ice nucleation protein model of Pseudomonas borealis with glycerol and triglycerol molecules, in order to discover the corresponding inhibition mechanism on ice nucleation proteins and its universality with other INPs. The binding information shows that the ligand molecules express different binding abilities to TXT template and tyrosine ladder, and other residues of ice nucleation protein may participate in the binding with TXT freezing template.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210102001
Abstract:
Recent years have witnessed autonomous outdoor robots have prevailed. The outdoor navigation is more difficult than the indoor one because the outdoor environment is more complicated. Various methods for outdoor navigation were proposed. Vision-based methods are vulnerable to weather, road marker-based methods lack flexibility and machine learning-based methods are unable to cope with the complex outdoor environments. A navigation method is proposed in this paper, which includes a real-time local grid map constructer, road direction-based trajectory planner, and model predictive control-based tracking controller. During the navigation task, the point cloud of the surrounding environment is obtained through a 16 thread radar, and the local grid map is constructed in real time. The mission goal is transformed to the robot coordinates, then the improved A-star algorithm based on the road direction is used to search the local obstacle avoidance path. Finally, a differential robot model predictive controller is designed to track the trajectory. Both simulation and experimental results are presented and discussed. It is revealed in the simulation tests that the designed planner can ensure collision avoidance with fewer turns and the designed motion controller has good tracking performance, which helps to reduce total mission execution time. It is shown in the outdoor experiment that the navigation method drives the robot to preselected mission point in turn with a stable motion. The distance between the robot and the obstacle is more than 1 meter in the process of obstacle avoidance. Furthermore, there is no need to build a map in advance, the proposed method is more applicable and efficient in various outdoor environments.
Recent years have witnessed autonomous outdoor robots have prevailed. The outdoor navigation is more difficult than the indoor one because the outdoor environment is more complicated. Various methods for outdoor navigation were proposed. Vision-based methods are vulnerable to weather, road marker-based methods lack flexibility and machine learning-based methods are unable to cope with the complex outdoor environments. A navigation method is proposed in this paper, which includes a real-time local grid map constructer, road direction-based trajectory planner, and model predictive control-based tracking controller. During the navigation task, the point cloud of the surrounding environment is obtained through a 16 thread radar, and the local grid map is constructed in real time. The mission goal is transformed to the robot coordinates, then the improved A-star algorithm based on the road direction is used to search the local obstacle avoidance path. Finally, a differential robot model predictive controller is designed to track the trajectory. Both simulation and experimental results are presented and discussed. It is revealed in the simulation tests that the designed planner can ensure collision avoidance with fewer turns and the designed motion controller has good tracking performance, which helps to reduce total mission execution time. It is shown in the outdoor experiment that the navigation method drives the robot to preselected mission point in turn with a stable motion. The distance between the robot and the obstacle is more than 1 meter in the process of obstacle avoidance. Furthermore, there is no need to build a map in advance, the proposed method is more applicable and efficient in various outdoor environments.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210223004
Abstract:
The rarely used spare parts’ demand usually changes sharply, and the demand interval is long and uncertain, which results in inaccurate prediction of spare part demand and the difficulty to make a reasonable inventory decision. A novel method is proposed for demand forecasting and inventory optimization. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then combined to an augmented sample statistical method called by Bootstrap, to predict the probability distribution of spare parts demand. Based on the demand probability statistics, the stochastic inventory model on the total inventory cost can be established. The particle swarm algorithm is used to solve the problem of minimizing the total cost for inventory decision. The experimental results from two sets of real data show that the proposed method has higher prediction accuracy. Besides, the inventory decision achieves the goal of minimum inventory cost subject to service level of spare parts, which shows the practicality of the proposed method.
The rarely used spare parts’ demand usually changes sharply, and the demand interval is long and uncertain, which results in inaccurate prediction of spare part demand and the difficulty to make a reasonable inventory decision. A novel method is proposed for demand forecasting and inventory optimization. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then combined to an augmented sample statistical method called by Bootstrap, to predict the probability distribution of spare parts demand. Based on the demand probability statistics, the stochastic inventory model on the total inventory cost can be established. The particle swarm algorithm is used to solve the problem of minimizing the total cost for inventory decision. The experimental results from two sets of real data show that the proposed method has higher prediction accuracy. Besides, the inventory decision achieves the goal of minimum inventory cost subject to service level of spare parts, which shows the practicality of the proposed method.
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Abstract:
Multiple cycles of experiments and reaction performance tests respectively were conducted using a batch fluidized bed reactor and a thermogravimetric analyzer. The catalytic effect of Fe2O3 on CaSO4/Ben oxygen carriers (OCs) during chemical looping combustion (CLC) was analyzed and the reaction activation energy of OCs with different Fe2O3 content and CO were compared to verify. The experimental results showed that the specific surface area and pore volume of CaSO4/Ben OCs increase with the addition of Fe2O3, which improved the reduction reaction rate and maintained the high CO2 concentration in the system. Fe2O3 can inhibit CaSO4 reaction to generate CaO and sulfur-containing gases, and improve the stability of CaSO4/Ben OC circulation reaction. w=15% Fe2O3 addition was the best choice. The addition of w=15% Fe2O3 reduced the activation energy of CaSO4/Ben OCs reacting with CO from 88.72 kJ/ mol to 43.08 kJ/mol, and the reactivity of CaSO4/Ben OCs were significantly improved.
Multiple cycles of experiments and reaction performance tests respectively were conducted using a batch fluidized bed reactor and a thermogravimetric analyzer. The catalytic effect of Fe2O3 on CaSO4/Ben oxygen carriers (OCs) during chemical looping combustion (CLC) was analyzed and the reaction activation energy of OCs with different Fe2O3 content and CO were compared to verify. The experimental results showed that the specific surface area and pore volume of CaSO4/Ben OCs increase with the addition of Fe2O3, which improved the reduction reaction rate and maintained the high CO2 concentration in the system. Fe2O3 can inhibit CaSO4 reaction to generate CaO and sulfur-containing gases, and improve the stability of CaSO4/Ben OC circulation reaction. w=15% Fe2O3 addition was the best choice. The addition of w=15% Fe2O3 reduced the activation energy of CaSO4/Ben OCs reacting with CO from 88.72 kJ/ mol to 43.08 kJ/mol, and the reactivity of CaSO4/Ben OCs were significantly improved.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210407001
Abstract:
Cucurbiturils (CB[n]s) is a hollow macrocyclic molecule formed by the condensation of glycoluril and formaldehyde under acidic conditions. The glycoluril units are linked together by methylene bridges, and cucurbiturils have hydrophobic cavity and polar carbonyl groups on both portals. Cucurbituril has strong inclusion ability for positively charged guest molecules such as protonated organic amines, pyridinium, and viologen. The study of the inclusion behaviors of aryl substituted viologen with cucurbit[8]uril urea (CB[8]) is of great significance for the further construction of related supramolecular polymers and even stimulus responsive materials. In this work, asymmetric 1-ethyl-1'-benzyl-4,4'-bipyridine bromide (EBV) was used to investigate its inclusion behaviors with CB[8] in aqueous solution by means of 1H-NMR spectroscopy, isothermal titration calorimetry (ITC) and high resolution electrospray ionization mass spectrometry (ESI-HRMS). The results show that the benzyl unit of EBV will firstly enter into the cavity of CB[8] to form a 1∶1 inclusion complex, and a 1∶2 supramolecular system where one CB[8] molecule encircles two benzyl groups will ultimately form. The constant of the first 1∶1 inclusion process is 1.65(±1.22)×107 M−1, the corresponding ΔH and −TΔS are −26.2(±1.26) kJ/mol and 14.6 kJ/mol, respectively. and the apparent inclusion constant of the whole process is 1.34(±0.193)×1013 M−2, the corresponding ΔH and −TΔS are −64.4(±3.19) kJ/mol and −9.43 kJ/mol, respectively, indicating that the host-guest complexation is driven by both enthalpy and entropy.
Cucurbiturils (CB[n]s) is a hollow macrocyclic molecule formed by the condensation of glycoluril and formaldehyde under acidic conditions. The glycoluril units are linked together by methylene bridges, and cucurbiturils have hydrophobic cavity and polar carbonyl groups on both portals. Cucurbituril has strong inclusion ability for positively charged guest molecules such as protonated organic amines, pyridinium, and viologen. The study of the inclusion behaviors of aryl substituted viologen with cucurbit[8]uril urea (CB[8]) is of great significance for the further construction of related supramolecular polymers and even stimulus responsive materials. In this work, asymmetric 1-ethyl-1'-benzyl-4,4'-bipyridine bromide (EBV) was used to investigate its inclusion behaviors with CB[8] in aqueous solution by means of 1H-NMR spectroscopy, isothermal titration calorimetry (ITC) and high resolution electrospray ionization mass spectrometry (ESI-HRMS). The results show that the benzyl unit of EBV will firstly enter into the cavity of CB[8] to form a 1∶1 inclusion complex, and a 1∶2 supramolecular system where one CB[8] molecule encircles two benzyl groups will ultimately form. The constant of the first 1∶1 inclusion process is 1.65(±1.22)×107 M−1, the corresponding ΔH and −TΔS are −26.2(±1.26) kJ/mol and 14.6 kJ/mol, respectively. and the apparent inclusion constant of the whole process is 1.34(±0.193)×1013 M−2, the corresponding ΔH and −TΔS are −64.4(±3.19) kJ/mol and −9.43 kJ/mol, respectively, indicating that the host-guest complexation is driven by both enthalpy and entropy.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210224001
Abstract:
In the process of pressurized feeding, the powders will be compacted under gas pressurization, which will cause the cohesive arching and flow blockage. The main reason for these problems is that the mechanism of consolidation of powders under gas pressurization and the effective regulation method have not been mastered yet. In this work, the consolidation characteristics of powders under gas pressurization and mechanical pressurization were investigated, providing a reference for optimizing pressurized powder feeding. The density distribution of the powder bed under different consolidation states was characterized by using the forces on an intruder immersed in the powder bed, and the influence mechanism of gas pressurization on the consolidation characteristics of powders was analyzed. The results show that a smaller increase in the compressive stress under gas pressurization can make the compaction density increase significantly, but the gas will penetrate into the bed, weakening the mechanical force generated by the pressurized gas on the bed, so the consolidation characteristics under gas pressurization and mechanical pressurization are markedly different: the compressive stress on the powder bed under gas pressurization increases linearly with the pressurization rate, and the critical value of compaction density under gas pressurization is only 85% of that of mechanical pressurization, thus it is relatively easier to compact the powder bed under mechanical pressurization. And the final pressure of gas pressurization hardly affects the compaction density of the powder bed. The research on the mechanical properties of the powder bed shows that the density distribution under gas pressurization is more uniform than that under mechanical pressurization. The dimensionless resistance force Fb/Fb,0 is used to characterize the consolidation characteristics of the powder bed, which increases linearly with applied normal stress and exponentially with the compaction density.
In the process of pressurized feeding, the powders will be compacted under gas pressurization, which will cause the cohesive arching and flow blockage. The main reason for these problems is that the mechanism of consolidation of powders under gas pressurization and the effective regulation method have not been mastered yet. In this work, the consolidation characteristics of powders under gas pressurization and mechanical pressurization were investigated, providing a reference for optimizing pressurized powder feeding. The density distribution of the powder bed under different consolidation states was characterized by using the forces on an intruder immersed in the powder bed, and the influence mechanism of gas pressurization on the consolidation characteristics of powders was analyzed. The results show that a smaller increase in the compressive stress under gas pressurization can make the compaction density increase significantly, but the gas will penetrate into the bed, weakening the mechanical force generated by the pressurized gas on the bed, so the consolidation characteristics under gas pressurization and mechanical pressurization are markedly different: the compressive stress on the powder bed under gas pressurization increases linearly with the pressurization rate, and the critical value of compaction density under gas pressurization is only 85% of that of mechanical pressurization, thus it is relatively easier to compact the powder bed under mechanical pressurization. And the final pressure of gas pressurization hardly affects the compaction density of the powder bed. The research on the mechanical properties of the powder bed shows that the density distribution under gas pressurization is more uniform than that under mechanical pressurization. The dimensionless resistance force Fb/Fb,0 is used to characterize the consolidation characteristics of the powder bed, which increases linearly with applied normal stress and exponentially with the compaction density.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210221001
Abstract:
Sophorolipids (SLs) is one of the most promising biosurfactants, which has been widely used in cosmetics, petroleum, pharmaceutical and other industrial fields. In this study, n-hexadecane was used as hydrophobic substrate to investigate the effect of different oxygen supply levels on SLs synthesis. Through the integrated analyses of metabolic flux distribution and key enzyme activities during the fermentation process, it was found that the oxygen supply level significantly affected the syntheses of fatty acids and hydroxyl fatty acids, and simultaneously caused significant changes in the utilization of glucose and alkanes.The on-line physiological parameter respiratory auotient (RQ) could well represent the changes of intracellular metabolic flux, so as to be expected to act as a key process parameter to regulate the cell metabolism in the further. Finally, by providing the appropriate oxygen supply level during the synthesis period of SLs, it can not only enhance the utilization rate of relatively expensive substrate alkane, but also significantly reduce the power input, thus improving the production economy. The results of this study would be easily extended to other hydrophobic substrates such as rapeseed oil and oleic acid for SLs fermentation, and would provide a solid theoretical basis for the development and application of industrial-scale SLs fermentation process control strategies.
Sophorolipids (SLs) is one of the most promising biosurfactants, which has been widely used in cosmetics, petroleum, pharmaceutical and other industrial fields. In this study, n-hexadecane was used as hydrophobic substrate to investigate the effect of different oxygen supply levels on SLs synthesis. Through the integrated analyses of metabolic flux distribution and key enzyme activities during the fermentation process, it was found that the oxygen supply level significantly affected the syntheses of fatty acids and hydroxyl fatty acids, and simultaneously caused significant changes in the utilization of glucose and alkanes.The on-line physiological parameter respiratory auotient (RQ) could well represent the changes of intracellular metabolic flux, so as to be expected to act as a key process parameter to regulate the cell metabolism in the further. Finally, by providing the appropriate oxygen supply level during the synthesis period of SLs, it can not only enhance the utilization rate of relatively expensive substrate alkane, but also significantly reduce the power input, thus improving the production economy. The results of this study would be easily extended to other hydrophobic substrates such as rapeseed oil and oleic acid for SLs fermentation, and would provide a solid theoretical basis for the development and application of industrial-scale SLs fermentation process control strategies.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210331002
Abstract:
Performing photocatalysis in continuous microflow offers great advantages including uniform light distribution, high mass transfer rate and improved safety. Nevertheless, a large amount of photocatalytic reactions involve solids which will block the microreactor. A new photo-microreactor, multistage consecutive stirred-tank microreactor, is developed in this paper to manipulate solids occurring in photocatalytic reactions. The microreactor has 16 miniaturized stirred-tanks connected in series, each of which possesses a volume of 390 µL and contains a micro stirred-bar. By applying a magnetic stirrer below the reactor, good mixing was achieved in each unit stirred-tank. The residence time distribution curves for liquid single-phase flow and liquid-solid two-phase flow almost overlapped and both exhibited satisfying symmetry, indicating solid particles was able be suspended homogeneously in the flow in the new photo-microreactor. The degradation of methylene blue photo-catalyzed by TiO2 particles (about 400 nm) was conducted in a multistage consecutive stirred-tank microreactor made of glass. No clogging was observed during long time operation of the photo-microreactor (about 10 h). Moreover, the photocatalytic reaction was accelerated by about 24 times as compared to the batch reactor and the reaction rate was not slowed down as the content of TiO2 particles was decreased in the photo-microreactor. The multistage consecutive stirred-tank reactor developed in this work can be applied to photocatalytic reactions involving or not involving solids, and helps to push forward the development of continuous microflow photocatalysis.
Performing photocatalysis in continuous microflow offers great advantages including uniform light distribution, high mass transfer rate and improved safety. Nevertheless, a large amount of photocatalytic reactions involve solids which will block the microreactor. A new photo-microreactor, multistage consecutive stirred-tank microreactor, is developed in this paper to manipulate solids occurring in photocatalytic reactions. The microreactor has 16 miniaturized stirred-tanks connected in series, each of which possesses a volume of 390 µL and contains a micro stirred-bar. By applying a magnetic stirrer below the reactor, good mixing was achieved in each unit stirred-tank. The residence time distribution curves for liquid single-phase flow and liquid-solid two-phase flow almost overlapped and both exhibited satisfying symmetry, indicating solid particles was able be suspended homogeneously in the flow in the new photo-microreactor. The degradation of methylene blue photo-catalyzed by TiO2 particles (about 400 nm) was conducted in a multistage consecutive stirred-tank microreactor made of glass. No clogging was observed during long time operation of the photo-microreactor (about 10 h). Moreover, the photocatalytic reaction was accelerated by about 24 times as compared to the batch reactor and the reaction rate was not slowed down as the content of TiO2 particles was decreased in the photo-microreactor. The multistage consecutive stirred-tank reactor developed in this work can be applied to photocatalytic reactions involving or not involving solids, and helps to push forward the development of continuous microflow photocatalysis.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210104002
Abstract:
Gender identification is a relevant task in speaker verification. It can also be used as an auxiliary tool in Automatic Speech Recognition(ASR) to improve model performance. To improve the accuracy of gender identification, some researchers have proposed some scheme based on deep learning. However, compared to the acoustic conditioned data in training, speech data in application scenarios is usually masked by the background noise, such as music, environmental noise, or background chatter, etc. Thus the performance of gender identification model is seriously degraded due to the data difference between training and actual use. In order to solve this problem, we propose a domain adaptive model combining Generative Adversarial Network(GAN) and GhostVLAD layer. The introduction of GhostVLAD can effectively reduce the interference of noise and irrelevant information in speech. Besides, the domain adaptation with GANs can realize the adaptation of the model to the target domain data. Furthermore, to maintain the network’s ability of gender discrimination, we use an auxiliary loss during the adversarial training. Voxceleb1 is chosen as the source domain data, while Audioset and Movie as the target domain data. The experimental results show that the proposed model achieves a relative improvement of 5.13% and 7.72% over the baseline in target domain data.
Gender identification is a relevant task in speaker verification. It can also be used as an auxiliary tool in Automatic Speech Recognition(ASR) to improve model performance. To improve the accuracy of gender identification, some researchers have proposed some scheme based on deep learning. However, compared to the acoustic conditioned data in training, speech data in application scenarios is usually masked by the background noise, such as music, environmental noise, or background chatter, etc. Thus the performance of gender identification model is seriously degraded due to the data difference between training and actual use. In order to solve this problem, we propose a domain adaptive model combining Generative Adversarial Network(GAN) and GhostVLAD layer. The introduction of GhostVLAD can effectively reduce the interference of noise and irrelevant information in speech. Besides, the domain adaptation with GANs can realize the adaptation of the model to the target domain data. Furthermore, to maintain the network’s ability of gender discrimination, we use an auxiliary loss during the adversarial training. Voxceleb1 is chosen as the source domain data, while Audioset and Movie as the target domain data. The experimental results show that the proposed model achieves a relative improvement of 5.13% and 7.72% over the baseline in target domain data.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210308003
Abstract:
The effects of reaction conditions on hydrogen production from methanol steam reforming were discussed. The experimental results showed that the optimum temperature of the reaction was about 240 ℃. The high temperature would make the CO selectivity higher, and the low temperature would make the conversion of CH3OH lower.When H2O/CH3OH molar ratio increases, the conversion of CH3OH increases and the selectivity of CO decreases. However, if H2O/CH3OH molar ratio is too high, more energy will be consumed.Under the premise of ensuring the conversion rate of CH3OH, the reaction efficiency can be improved by appropriately increasing the liquid hourly space velocity of feed liquid.The Langmuir-Hinshelwood two-rate dynamics model equation was used to fit the experimental data of intrinsic dynamics. The calculated values of molar flow rates of CO and CO2 in the gas products at the reactor outlet were in good agreement with the experimental values, and the two-rate model could be applied.The deactivation of CuO/ZnO/Al2O3 modified catalysts at 200 ℃ and 300 ℃ was investigated. The catalysts were characterized by BET, XRF, XRD and CO-TPD, the results showed that the main reasons for the deactivation of the catalysts were besides hot sintering. The reduction of specific surface area, the reduction of mesoporous ratio, CuO loss and the increase of CuO grain size are also the specific reasons for catalyst deactivation. The high content of CO produced in the high temperature has no obvious effect on catalyst deactivation.
The effects of reaction conditions on hydrogen production from methanol steam reforming were discussed. The experimental results showed that the optimum temperature of the reaction was about 240 ℃. The high temperature would make the CO selectivity higher, and the low temperature would make the conversion of CH3OH lower.When H2O/CH3OH molar ratio increases, the conversion of CH3OH increases and the selectivity of CO decreases. However, if H2O/CH3OH molar ratio is too high, more energy will be consumed.Under the premise of ensuring the conversion rate of CH3OH, the reaction efficiency can be improved by appropriately increasing the liquid hourly space velocity of feed liquid.The Langmuir-Hinshelwood two-rate dynamics model equation was used to fit the experimental data of intrinsic dynamics. The calculated values of molar flow rates of CO and CO2 in the gas products at the reactor outlet were in good agreement with the experimental values, and the two-rate model could be applied.The deactivation of CuO/ZnO/Al2O3 modified catalysts at 200 ℃ and 300 ℃ was investigated. The catalysts were characterized by BET, XRF, XRD and CO-TPD, the results showed that the main reasons for the deactivation of the catalysts were besides hot sintering. The reduction of specific surface area, the reduction of mesoporous ratio, CuO loss and the increase of CuO grain size are also the specific reasons for catalyst deactivation. The high content of CO produced in the high temperature has no obvious effect on catalyst deactivation.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201229002
Abstract:
Tobermorite (TOB) was major phase of calcium silicate board and autoclaved aerated concrete, which were widely used refractory materials and thermal insulation materials in the building engineering field. However, these materials were easy to wear so that large quantities of solid wastes have been produced. The problem of functionalized utilization of TOB needed to be resolved urgently. In the present paper, TOB samples with good crystal morphology were prepared by hydrothermal synthesis and were calcined at different temperature (H-TOB). Then the porous SiO2 material (300AH-TOB) was successfully prepared by acid treatment of H-TOB, and the porous SiO2 material was characterized by XRD, N2 adsorption-desorption, SEM, TEM and other testing methods to investigated the formation mechanism. The BET and pore size distribution test analysis showed that the specific surface area of 300AH-TOB after thermal activation at 300 ℃ and hydrochloric acid modification was 570.25 m2/g, and the total pore volume was 0.747 m3/g. After heat treatment and hydrochloric acid treatment, the calcium ions in TOB were selectively dissolved, and the main component of the acid-insoluble material was silica. Therefore, it was inferred that the silicon oxide exhibits an eight-membered ring double-chain deformation structure composed of a silicon-oxygen tetrahedron, resulting in an increase in mesopores and micropores. The adsorption performance of TOB, 300H-TOB and 300AH-TOB on two organic dyes, Safranine T and crystal violet, was investigated by static adsorption experiments. The adsorption rates were 56.35% and 47.69% for TOB; 25.57% and 42.69% for 300H-TOB; and 91.46% and 88.86% for 300AH-TOB, respectively, with the addition amount of 0.4 g. The porous material prepared from TOB had favorable adsorption of organic dyes, indicating its promising potential in adsorption application.
Tobermorite (TOB) was major phase of calcium silicate board and autoclaved aerated concrete, which were widely used refractory materials and thermal insulation materials in the building engineering field. However, these materials were easy to wear so that large quantities of solid wastes have been produced. The problem of functionalized utilization of TOB needed to be resolved urgently. In the present paper, TOB samples with good crystal morphology were prepared by hydrothermal synthesis and were calcined at different temperature (H-TOB). Then the porous SiO2 material (300AH-TOB) was successfully prepared by acid treatment of H-TOB, and the porous SiO2 material was characterized by XRD, N2 adsorption-desorption, SEM, TEM and other testing methods to investigated the formation mechanism. The BET and pore size distribution test analysis showed that the specific surface area of 300AH-TOB after thermal activation at 300 ℃ and hydrochloric acid modification was 570.25 m2/g, and the total pore volume was 0.747 m3/g. After heat treatment and hydrochloric acid treatment, the calcium ions in TOB were selectively dissolved, and the main component of the acid-insoluble material was silica. Therefore, it was inferred that the silicon oxide exhibits an eight-membered ring double-chain deformation structure composed of a silicon-oxygen tetrahedron, resulting in an increase in mesopores and micropores. The adsorption performance of TOB, 300H-TOB and 300AH-TOB on two organic dyes, Safranine T and crystal violet, was investigated by static adsorption experiments. The adsorption rates were 56.35% and 47.69% for TOB; 25.57% and 42.69% for 300H-TOB; and 91.46% and 88.86% for 300AH-TOB, respectively, with the addition amount of 0.4 g. The porous material prepared from TOB had favorable adsorption of organic dyes, indicating its promising potential in adsorption application.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210208001
Abstract:
According to the periodic dynamic characteristics of traffic flow, a traffic flow probability combination model method based on similarity clustering is proposed. It fully mines and exploits the potential similarity of traffic flow data. First, use adaptive k-means++ clustering method to classify the historical traffic flow data with time-period similarity and build a combined model for each sub-dataset with different characteristics correspondingly. Second, for the newly input traffic flow state data, analyze the similarity between the input data and the classified datasets to calculate the probability weight of each combined model. Finally, the predicted output is obtained through the probability fusion of the combined models. The effectiveness and accuracy of the proposed prediction model are verified by carrying out the simulation experiments.
According to the periodic dynamic characteristics of traffic flow, a traffic flow probability combination model method based on similarity clustering is proposed. It fully mines and exploits the potential similarity of traffic flow data. First, use adaptive k-means++ clustering method to classify the historical traffic flow data with time-period similarity and build a combined model for each sub-dataset with different characteristics correspondingly. Second, for the newly input traffic flow state data, analyze the similarity between the input data and the classified datasets to calculate the probability weight of each combined model. Finally, the predicted output is obtained through the probability fusion of the combined models. The effectiveness and accuracy of the proposed prediction model are verified by carrying out the simulation experiments.
, Available online
, doi: 10.14135/j.cnki.1006-3080.2021-01-11
Abstract:
The electroluminescence(EL) performance of the blue light phosphorescent white organic light emitting diode(WOLED) incorporating Au NPs, Ag NPs and their mixed NPs beneath the PEDOT: PSS hole injection layer(HIL) has been investigated, and the surface plasmon-enhanced EL efficiency has been demonstrated. The EL properties of OLED sample with either Ag or Au NPs has been improved compared to the samples without Metal Nanoparticles(MNPs). The OLED with mixed NPs of volume ratio of 3: 1(Au NPs: Ag NPs) shows best EL properties: External Quantum Efficiency(EQE) of 15.19% and Power Efficiency(PE) of 15.03 lm/W, which are improved by 29.06% and 23.00% respectively compared to the samples without MNPs. The influence of localized surface plasmon resonance(LSPR) from MNPs in the various EML on the EL and spectra properties is discussed in details. The improvement of OLED with mixed MNPs is due to the sufficient coupling between the LSPR of mixed MNPs and the excitons in the emission layer without significant exciton quenching from the MNPs.
The electroluminescence(EL) performance of the blue light phosphorescent white organic light emitting diode(WOLED) incorporating Au NPs, Ag NPs and their mixed NPs beneath the PEDOT: PSS hole injection layer(HIL) has been investigated, and the surface plasmon-enhanced EL efficiency has been demonstrated. The EL properties of OLED sample with either Ag or Au NPs has been improved compared to the samples without Metal Nanoparticles(MNPs). The OLED with mixed NPs of volume ratio of 3: 1(Au NPs: Ag NPs) shows best EL properties: External Quantum Efficiency(EQE) of 15.19% and Power Efficiency(PE) of 15.03 lm/W, which are improved by 29.06% and 23.00% respectively compared to the samples without MNPs. The influence of localized surface plasmon resonance(LSPR) from MNPs in the various EML on the EL and spectra properties is discussed in details. The improvement of OLED with mixed MNPs is due to the sufficient coupling between the LSPR of mixed MNPs and the excitons in the emission layer without significant exciton quenching from the MNPs.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210127003
Abstract:
CuFe catalyst is an important catalyst for higher alcohols formation from syngas. In order to gain insight to the reaction mechanism, spin-polarized density functional theory calculations were performed to investigate the growth mechanism of carbon chains on CuFe (100) and (110) surfaces. The calculated results show that Cu atoms prefer to aggregate rather than homogeneously distribute on the Fe(100) and (110) surfaces. With the increase of Cu atoms, the surface energy decreases gradually, indicating that the surface is more stable. The dominant activation mechanism of CO on CuFe(100) surfaces is that H-assisted CO dissociation via CHO intermediate, then the intermediate CHO is progressively hydrogenated to form CH2O and CH3O. and CH3O is dominantly hydrogenated to CH3OH.The pathway of carbon chain growth is CHO rather than CO insertion. The activation mechanism of CO on CuFe(110) surface is the same as CuFe(100) surface. The pathway of CH3O formation is that CO+3H→CHO+2H→CH2O+H→CH3O. On the contrary of CuFe (100) surface, CH3 formation is more favorable thermodynamically than CH3OH formation, which leads to more CH3 available for CO insertion to form C2+ higher alcohols. It provides an idea for improving the production of higher alcohols on CuFe catalyst. If an ingenious structure, such as the step surface, can be designed to have the two advantages of CH3O on CuFe(110) surface being more inclined to generate CH3 and the low CHO insertion reaction energy barrier on CuFe(100) surface, the higher alcohols selectivity of CuFe catalyst will be greatly improved.
CuFe catalyst is an important catalyst for higher alcohols formation from syngas. In order to gain insight to the reaction mechanism, spin-polarized density functional theory calculations were performed to investigate the growth mechanism of carbon chains on CuFe (100) and (110) surfaces. The calculated results show that Cu atoms prefer to aggregate rather than homogeneously distribute on the Fe(100) and (110) surfaces. With the increase of Cu atoms, the surface energy decreases gradually, indicating that the surface is more stable. The dominant activation mechanism of CO on CuFe(100) surfaces is that H-assisted CO dissociation via CHO intermediate, then the intermediate CHO is progressively hydrogenated to form CH2O and CH3O. and CH3O is dominantly hydrogenated to CH3OH.The pathway of carbon chain growth is CHO rather than CO insertion. The activation mechanism of CO on CuFe(110) surface is the same as CuFe(100) surface. The pathway of CH3O formation is that CO+3H→CHO+2H→CH2O+H→CH3O. On the contrary of CuFe (100) surface, CH3 formation is more favorable thermodynamically than CH3OH formation, which leads to more CH3 available for CO insertion to form C2+ higher alcohols. It provides an idea for improving the production of higher alcohols on CuFe catalyst. If an ingenious structure, such as the step surface, can be designed to have the two advantages of CH3O on CuFe(110) surface being more inclined to generate CH3 and the low CHO insertion reaction energy barrier on CuFe(100) surface, the higher alcohols selectivity of CuFe catalyst will be greatly improved.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210128001
Abstract:
Zinc ingots are the main raw material for the production of galvanized sheets. The consumption of zinc ingots fluctuates greatly due to the impact of contract orders and product structure, resulting in fluctuating demand. Material demand often presents the characteristics of small sample size and large variation range. Its non-stationarity and non-linearity make the problem of demand forecasting more difficult. The inaccuracy of demand forecasting will be gradually amplified in the information transmission of the supply chain, which will inevitably affect the material procurement plan and inventory management. Therefore, accurate material demand forecasting has important practical significance for the optimization of raw material procurement and production management adjustments of iron and steel enterprises. In order to improve the prediction accuracy of zinc ingot demand, this paper proposes a zinc consumption prediction modeling method based on Support Vector Regression (SVR) optimized by Improved Grey Wolf Optimization (IGWO). Aiming at the shortcomings of fast convergence and premature maturity of traditional gray wolf algorithm, firstly, the chaotic Tent mapping strategy is adopted to enhance the diversity and distribution uniformity of the initial population; secondly, an adaptive adjustment strategy of control parameters is introduced to balance the search ability and development ability; Finally, the differential evolution is integrated in the location update process to reduce the possibility of the algorithm misconvergence. For the improved gray wolf algorithm, a simulation experiment is carried out with a typical benchmark test function, and the result shows that its comprehensive performance is superior. Based on the production performance data of a certain unit of a steel plant, the zinc ingot consumption was modeled and predicted, and the SVR was optimized using the IGWO algorithm. The experimental results showed that IGWO-SVR has higher prediction accuracy, better stability and better generalization ability.
Zinc ingots are the main raw material for the production of galvanized sheets. The consumption of zinc ingots fluctuates greatly due to the impact of contract orders and product structure, resulting in fluctuating demand. Material demand often presents the characteristics of small sample size and large variation range. Its non-stationarity and non-linearity make the problem of demand forecasting more difficult. The inaccuracy of demand forecasting will be gradually amplified in the information transmission of the supply chain, which will inevitably affect the material procurement plan and inventory management. Therefore, accurate material demand forecasting has important practical significance for the optimization of raw material procurement and production management adjustments of iron and steel enterprises. In order to improve the prediction accuracy of zinc ingot demand, this paper proposes a zinc consumption prediction modeling method based on Support Vector Regression (SVR) optimized by Improved Grey Wolf Optimization (IGWO). Aiming at the shortcomings of fast convergence and premature maturity of traditional gray wolf algorithm, firstly, the chaotic Tent mapping strategy is adopted to enhance the diversity and distribution uniformity of the initial population; secondly, an adaptive adjustment strategy of control parameters is introduced to balance the search ability and development ability; Finally, the differential evolution is integrated in the location update process to reduce the possibility of the algorithm misconvergence. For the improved gray wolf algorithm, a simulation experiment is carried out with a typical benchmark test function, and the result shows that its comprehensive performance is superior. Based on the production performance data of a certain unit of a steel plant, the zinc ingot consumption was modeled and predicted, and the SVR was optimized using the IGWO algorithm. The experimental results showed that IGWO-SVR has higher prediction accuracy, better stability and better generalization ability.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201228003
Abstract:
The effects of nozzle screw structure on liquid jet breakup were investigated with a high-speed camera. Five nozzles with different diameters (4.00, 4.80, 7.50, 8.75 and 10.80 mm) were used in the experiment. The thread depth range was 0.40—1.25 mm, the liquid jet Reynolds number was within the scope of 500—22 600, and the Weber number was within the scope of 0.000 3~1.2. The experimental results show the screw structure has a strong disturbance to the jet and promotes the breakup of jet. By comparing the jet breakup length under different exprerimental conditions, it can be obtained that increasing Reynolds number leads to the decrease of breakup length when the Reynolds number is less than 1 600. The structure of nozzle screw has little influence on the fracture length of liquid jet. The breakup length of liquid jet increases first and then decreases with the Reynolds numbers raised. In this condition, the influence of screw structure of nozzle is significant, the breakup length is shorter than the smooth one. When the Reynolds number is in the 7 000 range above, the breakup length of liquid jet rises with the increase of Reynolds number and the screw structure of nozzle continues to promote the decrease of the fracture length of liquid jet. The experimental results show that the influence of nozzle screw structure on small diameter nozzle (diameter less than 5 mm) is more significant than the larger nozzle. By using dimensionless thread depth, Reynolds number and Weber number, the relationship for predicting the rupture length of liquid jet was established.
The effects of nozzle screw structure on liquid jet breakup were investigated with a high-speed camera. Five nozzles with different diameters (4.00, 4.80, 7.50, 8.75 and 10.80 mm) were used in the experiment. The thread depth range was 0.40—1.25 mm, the liquid jet Reynolds number was within the scope of 500—22 600, and the Weber number was within the scope of 0.000 3~1.2. The experimental results show the screw structure has a strong disturbance to the jet and promotes the breakup of jet. By comparing the jet breakup length under different exprerimental conditions, it can be obtained that increasing Reynolds number leads to the decrease of breakup length when the Reynolds number is less than 1 600. The structure of nozzle screw has little influence on the fracture length of liquid jet. The breakup length of liquid jet increases first and then decreases with the Reynolds numbers raised. In this condition, the influence of screw structure of nozzle is significant, the breakup length is shorter than the smooth one. When the Reynolds number is in the 7 000 range above, the breakup length of liquid jet rises with the increase of Reynolds number and the screw structure of nozzle continues to promote the decrease of the fracture length of liquid jet. The experimental results show that the influence of nozzle screw structure on small diameter nozzle (diameter less than 5 mm) is more significant than the larger nozzle. By using dimensionless thread depth, Reynolds number and Weber number, the relationship for predicting the rupture length of liquid jet was established.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201222004
Abstract:
Taking the linear ball bearing subjected to transverse load as the research object, the relationship between the contact force and deformation of the balls is established based on the mechanical analysis. Combined with the Hertz Theory and deformation coordination relation, an accurate mechanical model for calculating the contact force and deformation of each ball in linear bearing is constructed, which considers the dimensional error of the balls and the clearance of the bearing. Using MATLAB software to form the algorithm and taking the LM8UU linear bearing as an example, the factors affecting the contact force of balls such as the size and position of lateral load, the dimensional error and clearance between balls and raceways are deeply studied. Finally, a series of regular relationship curves are obtained. The result shows that the contact force of each ball increases with the increase of transverse load. As the transverse load moves from the middle to the edge, the distribution of force on the balls starts to become uneven, with more circular columns bearing the force, but less balls bearing in each column. Clearance makes the contact force decrease among most of the balls, while a small part of the balls bear more with the increase of clearance. The negative dimensional error of one ball reduces its contact force and the positive one increases its contact force. When all balls have dimensional error, the contact force exerted on each ball will be significantly different from that without considering dimensional error. These conclusions provide a theoretical basis for further accurate calculation of the stress distribution, deformation and fatigue life of linear bearings.
Taking the linear ball bearing subjected to transverse load as the research object, the relationship between the contact force and deformation of the balls is established based on the mechanical analysis. Combined with the Hertz Theory and deformation coordination relation, an accurate mechanical model for calculating the contact force and deformation of each ball in linear bearing is constructed, which considers the dimensional error of the balls and the clearance of the bearing. Using MATLAB software to form the algorithm and taking the LM8UU linear bearing as an example, the factors affecting the contact force of balls such as the size and position of lateral load, the dimensional error and clearance between balls and raceways are deeply studied. Finally, a series of regular relationship curves are obtained. The result shows that the contact force of each ball increases with the increase of transverse load. As the transverse load moves from the middle to the edge, the distribution of force on the balls starts to become uneven, with more circular columns bearing the force, but less balls bearing in each column. Clearance makes the contact force decrease among most of the balls, while a small part of the balls bear more with the increase of clearance. The negative dimensional error of one ball reduces its contact force and the positive one increases its contact force. When all balls have dimensional error, the contact force exerted on each ball will be significantly different from that without considering dimensional error. These conclusions provide a theoretical basis for further accurate calculation of the stress distribution, deformation and fatigue life of linear bearings.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210110002
Abstract:
Aiming at the localization problem of illegal whistle vehicles, a fast moving sound source location system based on distributed microphone array is proposed. The system uses GNSS clock to realize the time synchronization between microphones, and transmits the synchronously collected sound information to the cloud database. The cloud computing technology is applied to realize the sound source localization algorithm. Compared with the centralized microphone array, the system can greatly reduce the number of microphones and computing resources, and has the advantages of cost-effective and flexible deployment. The system adopts a fast location algorithm based on time difference of arrival and frequency of arrival, which makes full use of the difference information of arrival frequency between distributed microphones caused by Doppler effect to overcome the bottleneck that the time of arrival method is difficult to adapt to moving sound source. This method avoids the process of eliminating Doppler effect with complex calculation and large amount of calculation, and has low computational complexity and can adapt to high-speed moving sound source The advantages of. The results of system simulation and field experiment show that the system can achieve fast and accurate positioning of high-speed moving sound source, and can be well applied to the scene of car whistle positioning.
Aiming at the localization problem of illegal whistle vehicles, a fast moving sound source location system based on distributed microphone array is proposed. The system uses GNSS clock to realize the time synchronization between microphones, and transmits the synchronously collected sound information to the cloud database. The cloud computing technology is applied to realize the sound source localization algorithm. Compared with the centralized microphone array, the system can greatly reduce the number of microphones and computing resources, and has the advantages of cost-effective and flexible deployment. The system adopts a fast location algorithm based on time difference of arrival and frequency of arrival, which makes full use of the difference information of arrival frequency between distributed microphones caused by Doppler effect to overcome the bottleneck that the time of arrival method is difficult to adapt to moving sound source. This method avoids the process of eliminating Doppler effect with complex calculation and large amount of calculation, and has low computational complexity and can adapt to high-speed moving sound source The advantages of. The results of system simulation and field experiment show that the system can achieve fast and accurate positioning of high-speed moving sound source, and can be well applied to the scene of car whistle positioning.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201207003
Abstract:
BiOI is considered as one of most promising visible light photocatalyst because of its narrow band gap and layered structure. BiOI photocatalyst was synthesized by hydro-solvo-thermal process using bismuth nitrate and potassium iodide as raw materials, and a mixed solution of water and 2- methoxyethanol as a solvent. The crystal structure, micro-morphology, light absorption performance and specific surface area of the samples were characterized by XRD, SEM, UV-Vis-DRS and BET respectively. The photocatalytic activities of BiOI samples under visible-light irradiation were evaluated by the degradation of methyl orange. The results showed that the volume ratio of 2-methoxyethanol in the mixture solvent had a significant influence on the morphology and performance of the BiOI. The morphology of the BiOI photocatalyst prepared at 50% volume ratio of 2-methoxyethanol of the mixture solvent is a flower microsphere. The BiOI microspheres were fabricated by nanosheets with the characteristics of mesoporous structure. Because of adding of 2-methoxyethanol, the crystal growth of BiOI was strongly restrained, and the dominant growth along (110) plane was very obvious. The photocatalytic activity of BiOI has been significantly improved because of the larger specific surface area and the flower spherical structure. After 150 minutes of visible light irradiation, 77.9% methyl orange were degraded, and the degradation rate was 14 times that of the nanosheet BiOI photocatalyst. The photocatalytic mechanism of BiOI under visible light was proposed. The photoinduced holes(h+) and ∙O2− are the active species in the MO photocatalytic degradation process. Furthermore, the photo-generated holes (h+) were the most important photocatalytic active species.
BiOI is considered as one of most promising visible light photocatalyst because of its narrow band gap and layered structure. BiOI photocatalyst was synthesized by hydro-solvo-thermal process using bismuth nitrate and potassium iodide as raw materials, and a mixed solution of water and 2- methoxyethanol as a solvent. The crystal structure, micro-morphology, light absorption performance and specific surface area of the samples were characterized by XRD, SEM, UV-Vis-DRS and BET respectively. The photocatalytic activities of BiOI samples under visible-light irradiation were evaluated by the degradation of methyl orange. The results showed that the volume ratio of 2-methoxyethanol in the mixture solvent had a significant influence on the morphology and performance of the BiOI. The morphology of the BiOI photocatalyst prepared at 50% volume ratio of 2-methoxyethanol of the mixture solvent is a flower microsphere. The BiOI microspheres were fabricated by nanosheets with the characteristics of mesoporous structure. Because of adding of 2-methoxyethanol, the crystal growth of BiOI was strongly restrained, and the dominant growth along (110) plane was very obvious. The photocatalytic activity of BiOI has been significantly improved because of the larger specific surface area and the flower spherical structure. After 150 minutes of visible light irradiation, 77.9% methyl orange were degraded, and the degradation rate was 14 times that of the nanosheet BiOI photocatalyst. The photocatalytic mechanism of BiOI under visible light was proposed. The photoinduced holes(h+) and ∙O2− are the active species in the MO photocatalytic degradation process. Furthermore, the photo-generated holes (h+) were the most important photocatalytic active species.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201209001
Abstract:
A key problem in speaker verification task is the condition mismatch between the training data and the testing data, which may significantly affect the performance. In most of the speaker recognition application scenarios, it may be impossible to obtain enough samples to retrain the speaker recognition model. At the same time, the samples used to train the original model usually may be quite different from those obtained in real applications due to the variability caused by the intrinsic factors (such as the changes in emotion, language, vocal effect, speaking style, and aging, etc.) or extrinsic ones (such as background noise, transmission channel, microphone, room acoustics, and distance from the microphone, etc.). In this paper, an adversarial domain adaptation strategy is designed and applied to the X-Vector-based speaker verification scheme to enhance its domain adaptation ability. First, the X-Vector scheme is trained on the source dataset (AISHELL1). Then, the domain adaptation strategy is applied on the obtained X-Vector scheme to make it adapt to the target dataset (VoxCeleb1 or CN-Celeb). Finally, the performances of the X-Vector schemes obtained before and after adaptation are compared on the target dataset to verify the effectiveness of the proposed adaption strategy. Experimental results demonstrate that the adaptation strategy achieves 21.46% and 19.24% Equal Error Rate (EER) reduction on VoxCeleb1 and CN-Celeb dataset, respectively.
A key problem in speaker verification task is the condition mismatch between the training data and the testing data, which may significantly affect the performance. In most of the speaker recognition application scenarios, it may be impossible to obtain enough samples to retrain the speaker recognition model. At the same time, the samples used to train the original model usually may be quite different from those obtained in real applications due to the variability caused by the intrinsic factors (such as the changes in emotion, language, vocal effect, speaking style, and aging, etc.) or extrinsic ones (such as background noise, transmission channel, microphone, room acoustics, and distance from the microphone, etc.). In this paper, an adversarial domain adaptation strategy is designed and applied to the X-Vector-based speaker verification scheme to enhance its domain adaptation ability. First, the X-Vector scheme is trained on the source dataset (AISHELL1). Then, the domain adaptation strategy is applied on the obtained X-Vector scheme to make it adapt to the target dataset (VoxCeleb1 or CN-Celeb). Finally, the performances of the X-Vector schemes obtained before and after adaptation are compared on the target dataset to verify the effectiveness of the proposed adaption strategy. Experimental results demonstrate that the adaptation strategy achieves 21.46% and 19.24% Equal Error Rate (EER) reduction on VoxCeleb1 and CN-Celeb dataset, respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201126003
Abstract:
ObjectiveTo screen out the potential targets and molecular mechanisms of Epigallocatechin gallate (EGCG) in the treatment of triple negative breast cancer (MDA-MB-231). MethodDatabases was used to explore the potential targets between EGCG and MDA-MB-231. The “target-pathway” networks of common targets were constructed using Cytoscape 3.8.0 software, while the String database was used to draw and analyze the PPI network. Subsequently, the core genes were submitted to the Metascape database for GO and KEGG enrichment analysis; Finally, the prediction results were verified through the in vitro experiment. ResultsIn our study, a total of 537 EGCG targets and 181 disaster targets were obtained, 30 key targets were retained by further screening from 88 common potential targets. The result of the enrichment analysis showed that the active targets were involved in 20 core GO biological processes and 17 KEGG signaling pathways, including cancer signaling pathways, toxic tolerance pathways, pancreatic cancer pathways, rectal cancer pathways, small cell lung cancer pathways and so on. Molecular docking illuminated that EGCG could interact with β-catenin in a non-covalent manner. The in vitro experiment revealed that HGF could induce the expression of β-catenin, and EGCG could repress the HGF-induced over-expression of β-catenin. ConclusionEGCG inhibits cell viability through multiple targets and multiple pathways, among them, it has been initially confirmed that EGCG can affect the HGF / β-catenin pathway, providing a theoretical and practical basis for further mechanism exploration.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201229001
Abstract:
The essential factor to consistently improve the teaching quality of college education relies on the quality of curriculum, which is the basis to realize the reform idea of all higher education. Colleges and universities have accumulated a large amount of curriculum data in long-term teaching activities. How to use data to evaluate the course and provide decision support for improving the quality of course teaching has become an important research field. This paper designs a curriculum evaluation system based on association rules and cluster analysis, then analyzes the functional requirements of the curriculum evaluation and preprocesses the course evaluation data. The FP-growth algorithm is used to analyze the association rules of the score of student course and K-means++ algorithm is used for cluster analysis, which improves the analysis accuracy of course data, realizes the automation of course evaluation, and improves the efficiency and objectivity of evaluation.
The essential factor to consistently improve the teaching quality of college education relies on the quality of curriculum, which is the basis to realize the reform idea of all higher education. Colleges and universities have accumulated a large amount of curriculum data in long-term teaching activities. How to use data to evaluate the course and provide decision support for improving the quality of course teaching has become an important research field. This paper designs a curriculum evaluation system based on association rules and cluster analysis, then analyzes the functional requirements of the curriculum evaluation and preprocesses the course evaluation data. The FP-growth algorithm is used to analyze the association rules of the score of student course and K-means++ algorithm is used for cluster analysis, which improves the analysis accuracy of course data, realizes the automation of course evaluation, and improves the efficiency and objectivity of evaluation.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201130005
Abstract:
High energy and cost consumption of wastewater treatment has always been a huge problem to chemical industries, especially coal chemical industry since it generates a large amount of high-concentration organic wastewater. Fixed-bed Lurgi gasifier wastewater is employed as example to perform the research of wastewater treatment to value added chemicals for cost reduction. The process design of coupling the coal gasification wastewater treatment and the solid oxide cells (SOCs) system with renewable energy is proposed to synthesize dimethyl ether (DME) by comparison of one-step and two-step scenarios. Afterwards, process simulation, integration, tech-economic and carbon emission are all performed to analyze the coupling system. The economy analysis and carbon dioxide emission show that although the carbon emission reduction of unit productivity for one-step scenario is better than that of two-step case, the carbon sequestration rate of the latter is much higher than that of the former and the two step scenario is superior in unit production cost with only 2.32% more CO2 emission. Besides, two-step method for DME production under wind power is better in economy and carbon emission reduction. Investment breakdown reveals that the cost of renewable energy is the main contributor of the annual production cost and therefore looking for cheap alternative energy is an effective way to reduce the production cost and improve economic performance. In summary, it is economically and environmentally feasible to deepen and efficiently use wastewater as resources by coupling the energy efficient SOCs system and producing value added chemicals.
High energy and cost consumption of wastewater treatment has always been a huge problem to chemical industries, especially coal chemical industry since it generates a large amount of high-concentration organic wastewater. Fixed-bed Lurgi gasifier wastewater is employed as example to perform the research of wastewater treatment to value added chemicals for cost reduction. The process design of coupling the coal gasification wastewater treatment and the solid oxide cells (SOCs) system with renewable energy is proposed to synthesize dimethyl ether (DME) by comparison of one-step and two-step scenarios. Afterwards, process simulation, integration, tech-economic and carbon emission are all performed to analyze the coupling system. The economy analysis and carbon dioxide emission show that although the carbon emission reduction of unit productivity for one-step scenario is better than that of two-step case, the carbon sequestration rate of the latter is much higher than that of the former and the two step scenario is superior in unit production cost with only 2.32% more CO2 emission. Besides, two-step method for DME production under wind power is better in economy and carbon emission reduction. Investment breakdown reveals that the cost of renewable energy is the main contributor of the annual production cost and therefore looking for cheap alternative energy is an effective way to reduce the production cost and improve economic performance. In summary, it is economically and environmentally feasible to deepen and efficiently use wastewater as resources by coupling the energy efficient SOCs system and producing value added chemicals.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201126004
Abstract:
With the explosive growth of video data, video intelligent analysis has become the current academic and industrial research hotspot. Video Action Detection is to obtain the location and time information of actions based on Action Recognition. In this paper, we propose a region spatiotemporal two-in-one action detection network based on single shot multi-box detector(SSD) with the combination of RGB space flow and optical flow. To design the improved nonlocal spatiotemporal module in the network, a pixel filter is proposed in optical flow to extract the information of key motion regions, and then the correlation calculation is only performed on the selected key motion regions in the spatial flow. The proposed module can get long-range dependence of actions effectively and improve the computational cost of the nonlocal module, and reduce the interference of video background noise. The proposed network is tested on the benchmark dataset UCF101-24, and the video_map reaches 43.17% @ 0.5. The results show that the proposed network has better performance.
With the explosive growth of video data, video intelligent analysis has become the current academic and industrial research hotspot. Video Action Detection is to obtain the location and time information of actions based on Action Recognition. In this paper, we propose a region spatiotemporal two-in-one action detection network based on single shot multi-box detector(SSD) with the combination of RGB space flow and optical flow. To design the improved nonlocal spatiotemporal module in the network, a pixel filter is proposed in optical flow to extract the information of key motion regions, and then the correlation calculation is only performed on the selected key motion regions in the spatial flow. The proposed module can get long-range dependence of actions effectively and improve the computational cost of the nonlocal module, and reduce the interference of video background noise. The proposed network is tested on the benchmark dataset UCF101-24, and the video_map reaches 43.17% @ 0.5. The results show that the proposed network has better performance.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201124001
Abstract:
Acoustic Scene Classification (ASC) is one of the most challenging tasks in the field of Computational Auditory Scene Analysis (CASA). Most of the traditional ASC models are based on the combination of handcrafted feature extraction strategy and deep learning-based classifiers. On the one hand, this type of method cannot mimic the nonlinear frequency selectivity of the human basilar membrane, which results in lower feature resolution. On the other hand, such classifiers cannot solve the problem of low classification accuracy caused by complex sound sources and highly overlapping of sound events. To solve these problems, this paper proposes an ASC model based on multi-instance learning (MIL) of the cochleargram. On the one hand, equivalent rectangular bandwidth cosine filterbank is adopted to analyze the signal spectrum to simulate the acoustic perception property of human beings; On the other hand, multi-instance learning strategy is introduced to characterize the entire data structure of acoustic scenes to improve classification accuracy. In addition, to enhance the robustness of the proposed system against to frequency shift of sound events, the average pooling method is chosen in the prediction integrator of the model. Experimental results on the DCASE 2018 and DCASE 2019 Challenge Task1a dataset demonstrated that the model proposed in this article achieved higher classification accuracy than the baseline model provided by the DCASE 2018 Challenge and the traditional model based on Log Mel spectrogram and multi-instance learning. It also verified that the average pooling helped to enhance the performance of the proposed model.
Acoustic Scene Classification (ASC) is one of the most challenging tasks in the field of Computational Auditory Scene Analysis (CASA). Most of the traditional ASC models are based on the combination of handcrafted feature extraction strategy and deep learning-based classifiers. On the one hand, this type of method cannot mimic the nonlinear frequency selectivity of the human basilar membrane, which results in lower feature resolution. On the other hand, such classifiers cannot solve the problem of low classification accuracy caused by complex sound sources and highly overlapping of sound events. To solve these problems, this paper proposes an ASC model based on multi-instance learning (MIL) of the cochleargram. On the one hand, equivalent rectangular bandwidth cosine filterbank is adopted to analyze the signal spectrum to simulate the acoustic perception property of human beings; On the other hand, multi-instance learning strategy is introduced to characterize the entire data structure of acoustic scenes to improve classification accuracy. In addition, to enhance the robustness of the proposed system against to frequency shift of sound events, the average pooling method is chosen in the prediction integrator of the model. Experimental results on the DCASE 2018 and DCASE 2019 Challenge Task1a dataset demonstrated that the model proposed in this article achieved higher classification accuracy than the baseline model provided by the DCASE 2018 Challenge and the traditional model based on Log Mel spectrogram and multi-instance learning. It also verified that the average pooling helped to enhance the performance of the proposed model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201214001
Abstract:
With the vigorous development and application of distributed energy, major challenges have been raised to the planning and operation of traditional power grids. The intermittent influence of distributed energy such as wind power and photovoltaic power generation on energy dispatch and the impact of uncertainty on the power grid are issues that need to be solved urgently. In view of the intermittency and uncertainty of distributed power generation, the wind-solar energy-storage joint optimization model is established with the goal of minimizing wind-solar forecast power generation error and energy storage output, and a method of real-time online optimization of wind-solar complementary power generation and energy storage is proposed. Improved PSO (Particle Swarm Optimization) algorithm is used to optimize the model in real time. Based on the establishment of the wind-solar-storage complementary model, the co-generation of wind-solar storage is regarded as an equivalent node to construct an optimal power flow model with the optimal output of diesel generators as the objective function. GA (Genetic Algorithm) is used to solve the optimal power flow model. Finally, the IEEE30-node system is taken as an example to verify the correctness and effectiveness of the strategy.
With the vigorous development and application of distributed energy, major challenges have been raised to the planning and operation of traditional power grids. The intermittent influence of distributed energy such as wind power and photovoltaic power generation on energy dispatch and the impact of uncertainty on the power grid are issues that need to be solved urgently. In view of the intermittency and uncertainty of distributed power generation, the wind-solar energy-storage joint optimization model is established with the goal of minimizing wind-solar forecast power generation error and energy storage output, and a method of real-time online optimization of wind-solar complementary power generation and energy storage is proposed. Improved PSO (Particle Swarm Optimization) algorithm is used to optimize the model in real time. Based on the establishment of the wind-solar-storage complementary model, the co-generation of wind-solar storage is regarded as an equivalent node to construct an optimal power flow model with the optimal output of diesel generators as the objective function. GA (Genetic Algorithm) is used to solve the optimal power flow model. Finally, the IEEE30-node system is taken as an example to verify the correctness and effectiveness of the strategy.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201125001
Abstract:
In this paper, the process models of the shale gas chemical looping reforming to methanol combined with the solid oxide fuel cell for power generation is established by the means of the system decomposition, unit modeling, and process simulation. The technical analysis of the new process is carried out through technical indexes, which consists of four aspects of raw material consumption, product output, process energy consumption, and exergy efficiency. In this paper, the efficient utilization of the shale gas resource is realized through the chemical looping reforming for simultaneously producing the syngas-hydrogen and then syngas used for methanol synthesis. After adjusting the composition of the syngas for methanol production, the remaining hydrogen is fueled to solid oxide fuel cell unit and the purge gas of the methanol synthesis is fueled to chemical looping combustion unit for power generation, by which the self-sufficiency as well as surplus of the electric energy can be achieved. Through the mass and energy integration between chemical looping reforming, chemical looping combustion, methanol synthesis, and solid oxide fuel cell, the technical and environmental performance of the shale gas chemical looping reforming to methanol combined with solid oxide fuel cell process can be significantly improved. This paper also discusses the influence of different methane conversion rates on the technical performance of the new process. In a word, the exergy efficiency of the process with 60%-methane conversion rate is only 57%, while the exergy efficiency of the process with 80%—99.3% methane conversion rate can be as high as 71%—74%.
In this paper, the process models of the shale gas chemical looping reforming to methanol combined with the solid oxide fuel cell for power generation is established by the means of the system decomposition, unit modeling, and process simulation. The technical analysis of the new process is carried out through technical indexes, which consists of four aspects of raw material consumption, product output, process energy consumption, and exergy efficiency. In this paper, the efficient utilization of the shale gas resource is realized through the chemical looping reforming for simultaneously producing the syngas-hydrogen and then syngas used for methanol synthesis. After adjusting the composition of the syngas for methanol production, the remaining hydrogen is fueled to solid oxide fuel cell unit and the purge gas of the methanol synthesis is fueled to chemical looping combustion unit for power generation, by which the self-sufficiency as well as surplus of the electric energy can be achieved. Through the mass and energy integration between chemical looping reforming, chemical looping combustion, methanol synthesis, and solid oxide fuel cell, the technical and environmental performance of the shale gas chemical looping reforming to methanol combined with solid oxide fuel cell process can be significantly improved. This paper also discusses the influence of different methane conversion rates on the technical performance of the new process. In a word, the exergy efficiency of the process with 60%-methane conversion rate is only 57%, while the exergy efficiency of the process with 80%—99.3% methane conversion rate can be as high as 71%—74%.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201207001
Abstract:
A variable-length particle swarm optimization (VLPSO) shows good performance for feature selection on large data sets. However, its completely random particle initialization leads to certain blindness in the initial stage. At the same time, the single updating mechanism of VLPSO and the information isolation between subpopulations also affect the classification performance. In order to solve the defect of VLPSO, a co-evolutionary feature selection method based on variable-length particle and multi-behavior interaction(M-CVLPSO) is proposed. Firstly, to improve the blindness caused by random initialization, the multidirectional initialization strategy in continuous space is adopted to shorten the distance between the initial solution and the optimal solution from the perspective of expectation. Secondly, particles are divided into leaders, followers and weeders according to fitness, and multiple updating strategies are adopted in the process of iteration to balance the diversity and convergence of dynamic algorithms. At the same time, the dimension reduction index is added to the fitness function to further enhance the performance of the algorithm on some datasets. The convergence of the proposed algorithm is proved theoretically, and the experimental analysis on the classification accuracy, dimension reduction and calculation time based on 11 large-scale feature selection data sets is carried out. The results show that the proposed model has better comprehensive performance than the four comparison algorithms.
A variable-length particle swarm optimization (VLPSO) shows good performance for feature selection on large data sets. However, its completely random particle initialization leads to certain blindness in the initial stage. At the same time, the single updating mechanism of VLPSO and the information isolation between subpopulations also affect the classification performance. In order to solve the defect of VLPSO, a co-evolutionary feature selection method based on variable-length particle and multi-behavior interaction(M-CVLPSO) is proposed. Firstly, to improve the blindness caused by random initialization, the multidirectional initialization strategy in continuous space is adopted to shorten the distance between the initial solution and the optimal solution from the perspective of expectation. Secondly, particles are divided into leaders, followers and weeders according to fitness, and multiple updating strategies are adopted in the process of iteration to balance the diversity and convergence of dynamic algorithms. At the same time, the dimension reduction index is added to the fitness function to further enhance the performance of the algorithm on some datasets. The convergence of the proposed algorithm is proved theoretically, and the experimental analysis on the classification accuracy, dimension reduction and calculation time based on 11 large-scale feature selection data sets is carried out. The results show that the proposed model has better comprehensive performance than the four comparison algorithms.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201128002
Abstract:
Object detection has been a research hotspot in the field of computer vision in recent years. Due to the extensive application of deep learning, the target detection technology combined with deep learning is also developing year by year and making continuous breakthroughs. In the field of target detection, few methods can solve the problem of target detection with few sample categories, and can detect small targets with high accuracy on the basis of training with few sample categories in the detection algorithm of integrated convolutional network. According to the Model-Agnostic Meta-Learning(MAML) algorithm in meta learning, this paper improves the information transmission form of the backbone network in YOLOv3, so that Darknet-53 can achieve two stages of parameter internal update and external update in the aspect of gradient descent. Through its loss function in the case of multi-step gradient adjustment, the weight parameters trained by the model can focus more on the feature information of the target. Even when learning a small number of sample categories and encountering a new task, it can also maintain the sensitivity to the target in the new task. The experiment shows that the mean Average Precision(mAP) value of YOLOv3 model is 74.81%, and the mAP value of YOLOv3 model improved based on MAML algorithm can reach 80.05%, which improves the accuracy by about 5%. The network structure and training mechanism of YOLOv3 improved on the basis of MAML can improve the accuracy of detection in training and prediction, and the weight parameters trained finally can make the model high detection accuracy and high generalization.
Object detection has been a research hotspot in the field of computer vision in recent years. Due to the extensive application of deep learning, the target detection technology combined with deep learning is also developing year by year and making continuous breakthroughs. In the field of target detection, few methods can solve the problem of target detection with few sample categories, and can detect small targets with high accuracy on the basis of training with few sample categories in the detection algorithm of integrated convolutional network. According to the Model-Agnostic Meta-Learning(MAML) algorithm in meta learning, this paper improves the information transmission form of the backbone network in YOLOv3, so that Darknet-53 can achieve two stages of parameter internal update and external update in the aspect of gradient descent. Through its loss function in the case of multi-step gradient adjustment, the weight parameters trained by the model can focus more on the feature information of the target. Even when learning a small number of sample categories and encountering a new task, it can also maintain the sensitivity to the target in the new task. The experiment shows that the mean Average Precision(mAP) value of YOLOv3 model is 74.81%, and the mAP value of YOLOv3 model improved based on MAML algorithm can reach 80.05%, which improves the accuracy by about 5%. The network structure and training mechanism of YOLOv3 improved on the basis of MAML can improve the accuracy of detection in training and prediction, and the weight parameters trained finally can make the model high detection accuracy and high generalization.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201127005
Abstract:
Compared with traditional machine learning algorithms, deploying deep neural networks in embedded systems can significantly improve the performance of robot system object recognition. However, the computing resources and memory capacity of the embedded platform are limited. It is necessary to simplify the network structure and improve the system efficiency through methods such as model pruning and parameter quantification. It is also necessary to prevent overfitting through Dropout regularization and improve the accuracy of system recognition. In order to further improve the object recognition performance of the deep neural network algorithm in the embedded robot system, this paper proposes a deep neural network dropout regularization method based on constant false alarm detection (CFAR-Dropout). First, by quantizing the weights, the weights and activations are reduced from floating point numbers to binary values; then, a constant false alarm detector (CFAR) is designed to maintain a certain false alarm rate and adaptively delete some neuron nodes. Optimize the neuron nodes involved in the calculation; finally, on the embedded platform PYNQ-Z2, an optimization model based on VGG16 is used to experimentally verify the object recognition performance of the algorithm. Experimental results show that, compared with the classic Dropout regularization method, the error rate of the CFAR-Dropout regularization method is reduced by 2%, effectively preventing overfitting; compared with the original network structure, the amount of memory occupied by the parameters is reduced to Around 8%, effectively preventing over-parameterization.
Compared with traditional machine learning algorithms, deploying deep neural networks in embedded systems can significantly improve the performance of robot system object recognition. However, the computing resources and memory capacity of the embedded platform are limited. It is necessary to simplify the network structure and improve the system efficiency through methods such as model pruning and parameter quantification. It is also necessary to prevent overfitting through Dropout regularization and improve the accuracy of system recognition. In order to further improve the object recognition performance of the deep neural network algorithm in the embedded robot system, this paper proposes a deep neural network dropout regularization method based on constant false alarm detection (CFAR-Dropout). First, by quantizing the weights, the weights and activations are reduced from floating point numbers to binary values; then, a constant false alarm detector (CFAR) is designed to maintain a certain false alarm rate and adaptively delete some neuron nodes. Optimize the neuron nodes involved in the calculation; finally, on the embedded platform PYNQ-Z2, an optimization model based on VGG16 is used to experimentally verify the object recognition performance of the algorithm. Experimental results show that, compared with the classic Dropout regularization method, the error rate of the CFAR-Dropout regularization method is reduced by 2%, effectively preventing overfitting; compared with the original network structure, the amount of memory occupied by the parameters is reduced to Around 8%, effectively preventing over-parameterization.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201201005
Abstract:
No-idle production Scheduling is widespread in modern industry, thus, there is of great significance to study the flow shop scheduling problem with no-idle constrains. This paper proposes a multi-objective discrete sine optimization algorithm (MDSOA) to solve the mixed no-idle permutation flow shop scheduling problem (MNPFSP) with the goal of minimizing the makespan and the maximum tardiness. Firstly, an external archive set (AS) is established to store Pareto front and update after each iteration. Secondly, based on the basic sine optimization algorithm, the destruction reconstruction mechanism of the iterative greedy (IG) algorithm is introduced to redefine a location update strategy, which is suitable for discrete scheduling problems. Finally, fast non-dominate sorting method and crowding distance are utilized to screen the population, aim to retain the elite solutions and ensure the diversity and distribution of solutions. Simulation experiments based on 11 instances with different scales in Taillard Benchmark and comparisons with NSGA-II and NSGA-III demonstrate the effectiveness of the proposed MDSOA algorithm for solving MNPFSP.
No-idle production Scheduling is widespread in modern industry, thus, there is of great significance to study the flow shop scheduling problem with no-idle constrains. This paper proposes a multi-objective discrete sine optimization algorithm (MDSOA) to solve the mixed no-idle permutation flow shop scheduling problem (MNPFSP) with the goal of minimizing the makespan and the maximum tardiness. Firstly, an external archive set (AS) is established to store Pareto front and update after each iteration. Secondly, based on the basic sine optimization algorithm, the destruction reconstruction mechanism of the iterative greedy (IG) algorithm is introduced to redefine a location update strategy, which is suitable for discrete scheduling problems. Finally, fast non-dominate sorting method and crowding distance are utilized to screen the population, aim to retain the elite solutions and ensure the diversity and distribution of solutions. Simulation experiments based on 11 instances with different scales in Taillard Benchmark and comparisons with NSGA-II and NSGA-III demonstrate the effectiveness of the proposed MDSOA algorithm for solving MNPFSP.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200325001
Abstract:
Shale gas is an unconventional natural gas resource, and the hydraulic fracturing technology is frequently used in the exploitation process of shale gas, resulting in a large amount of wastewater. The gas field water with high calcium and magnesium contents has some obvious characteristics, such as high salinity, high turbidity, high chloride ion content and low pH value. Scaling problem is the most challenging task, especially the scaling problem of calcium carbonate in the post-treatment process of the water produced from shale gas. Due to the ignorance of the polycrystalline nature of calcium carbonate, it has been found that the prediction accuracy of the existing scaling prediction methods is low. Moreover, the influences of impurity ions on both the scaling crystal form and the prediction accuracy of model have not been explored yet. In this work, the scaling potential prediction model was established to provide effective guidance for the subsequent scale inhibition based on the investigation of the scaling behavior of calcium carbonate in brine with high calcium and magnesium contents. Through the analysis of the change of the key ion concentration before and after scaling and characterization of scaling by X ray polycrystalline diffraction and scanning electron microscopy, it was found that scaling occured with bicarbonate dissociation in vaterite crystal form. According to Langelier's theory, the scaling potential prediction model of the vaterite saturation index(VLSI) was established by using the soluble product of vaterite and the activity coefficient of bicarbonate as the key model parameters, which can predict the calcium carbonate scaling in the gas field water with more than 90% accuracy, and the prediction accuracy is higher than other models, such as the other calcium carbonate polycrystal saturation index, GB recommended saturation index and GB SDSI.
Shale gas is an unconventional natural gas resource, and the hydraulic fracturing technology is frequently used in the exploitation process of shale gas, resulting in a large amount of wastewater. The gas field water with high calcium and magnesium contents has some obvious characteristics, such as high salinity, high turbidity, high chloride ion content and low pH value. Scaling problem is the most challenging task, especially the scaling problem of calcium carbonate in the post-treatment process of the water produced from shale gas. Due to the ignorance of the polycrystalline nature of calcium carbonate, it has been found that the prediction accuracy of the existing scaling prediction methods is low. Moreover, the influences of impurity ions on both the scaling crystal form and the prediction accuracy of model have not been explored yet. In this work, the scaling potential prediction model was established to provide effective guidance for the subsequent scale inhibition based on the investigation of the scaling behavior of calcium carbonate in brine with high calcium and magnesium contents. Through the analysis of the change of the key ion concentration before and after scaling and characterization of scaling by X ray polycrystalline diffraction and scanning electron microscopy, it was found that scaling occured with bicarbonate dissociation in vaterite crystal form. According to Langelier's theory, the scaling potential prediction model of the vaterite saturation index(VLSI) was established by using the soluble product of vaterite and the activity coefficient of bicarbonate as the key model parameters, which can predict the calcium carbonate scaling in the gas field water with more than 90% accuracy, and the prediction accuracy is higher than other models, such as the other calcium carbonate polycrystal saturation index, GB recommended saturation index and GB SDSI.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201227001
Abstract:
Cholinium-amino acid based ionic liquids, CHAAILs, which were shown to be “practically harmless” and considered as truly green chemicals, have drawn much attention recently. The high viscosity of pure ionic liquid limits its application. Solutions containing ionic liquids are more frequently used in various fields. The mixtures of CHAAILs and water has already been used in biomass processing and extraction. However, there are few studies on the physicochemical properties of aqueous solutions of CHAAILs, especially for the migration properties like viscosities (η) and electrical conductivities (к), which are important physical and chemical data for the industrialization of ionic liquids and are crucial for mass transfer rate. Herein, we synthesized two CHAAILs, cholinium glycinate[Ch][Gly] and cholinium analinate [Ch][Ala]. The viscosities and the electrical conductivities of water + [Ch][Gly] and water + [Ch][Ala] systems were determined at different temperatures from 288.15K to 323.15K. The change of both the viscosities and electrical conductivities with temperature can be well described by Arrhenius equation and VFT equation, respectively. The viscosites of the water + CHAAILs mixtures decreased with the increasing temperature while the electrical conductivites increased with temperature. The excess viscosities (Δη) were also calculated and correlated by Redlich-Kister equation, which showed obvious negative deviation and the deviation increased with decreasing temperature and the increasing of alkyl chain length of amino acid anion. The relationship between molar electrical conductivities and viscosities of water + CHAAILs mixtures were correlated by Walden rule. The results show that the binary system of water +[Ch][Ala] has a good ionicity.
Cholinium-amino acid based ionic liquids, CHAAILs, which were shown to be “practically harmless” and considered as truly green chemicals, have drawn much attention recently. The high viscosity of pure ionic liquid limits its application. Solutions containing ionic liquids are more frequently used in various fields. The mixtures of CHAAILs and water has already been used in biomass processing and extraction. However, there are few studies on the physicochemical properties of aqueous solutions of CHAAILs, especially for the migration properties like viscosities (η) and electrical conductivities (к), which are important physical and chemical data for the industrialization of ionic liquids and are crucial for mass transfer rate. Herein, we synthesized two CHAAILs, cholinium glycinate[Ch][Gly] and cholinium analinate [Ch][Ala]. The viscosities and the electrical conductivities of water + [Ch][Gly] and water + [Ch][Ala] systems were determined at different temperatures from 288.15K to 323.15K. The change of both the viscosities and electrical conductivities with temperature can be well described by Arrhenius equation and VFT equation, respectively. The viscosites of the water + CHAAILs mixtures decreased with the increasing temperature while the electrical conductivites increased with temperature. The excess viscosities (Δη) were also calculated and correlated by Redlich-Kister equation, which showed obvious negative deviation and the deviation increased with decreasing temperature and the increasing of alkyl chain length of amino acid anion. The relationship between molar electrical conductivities and viscosities of water + CHAAILs mixtures were correlated by Walden rule. The results show that the binary system of water +[Ch][Ala] has a good ionicity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201204003
Abstract:
The Claus process is one of the technologies for efficient acid gas processing and sulfur recovering. In industry, it is necessary to add combustion-supporting gas for combustion to ensure flame stability when processing low concentration acid gas, which leads to problems such as increased production of harmful substances like polycyclic aromatic hydrocarbons and organic sulfur. The use of pure oxygen for combustion can not only increase the temperature of the furnace and the removal rate of hydrocarbon impurities, but also avoid the problems caused by the addition of combustion-supporting gas. In this paper, a full Claus process model was built in the Aspen Plus and was validated by industrial data. The influence of the inlet acid gas composition, oxygen concentration, oxygen preheating temperature, furnace pressure, oxygen gas intake and the temperature of the second catalytic stage reactor on the Clause process were studied. The optimization tool in Aspen Plus was used to calculate the best operating parameters. The optimized results showed that the sulfur recovery efficiency increased from 98.31% — 99.08% and the emission of SO2, the main pollutant in the tail gas, reduced from 0.350 kmol/h to 0.278 kmol/h, reduction rate at 20.6%. Moreover, it saved 9133.38 kJ/kmol(acid gas) heat required for the preheating of the acid gas and air.
The Claus process is one of the technologies for efficient acid gas processing and sulfur recovering. In industry, it is necessary to add combustion-supporting gas for combustion to ensure flame stability when processing low concentration acid gas, which leads to problems such as increased production of harmful substances like polycyclic aromatic hydrocarbons and organic sulfur. The use of pure oxygen for combustion can not only increase the temperature of the furnace and the removal rate of hydrocarbon impurities, but also avoid the problems caused by the addition of combustion-supporting gas. In this paper, a full Claus process model was built in the Aspen Plus and was validated by industrial data. The influence of the inlet acid gas composition, oxygen concentration, oxygen preheating temperature, furnace pressure, oxygen gas intake and the temperature of the second catalytic stage reactor on the Clause process were studied. The optimization tool in Aspen Plus was used to calculate the best operating parameters. The optimized results showed that the sulfur recovery efficiency increased from 98.31% — 99.08% and the emission of SO2, the main pollutant in the tail gas, reduced from 0.350 kmol/h to 0.278 kmol/h, reduction rate at 20.6%. Moreover, it saved 9133.38 kJ/kmol(acid gas) heat required for the preheating of the acid gas and air.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200903030
Abstract:
In this work, we proposed a computational fluid dynamics model for simulating gas-liquid two-phase flow in a shake flask, which we named the centrifugal acceleration model method. The model was used to investigate the mass transfer and shearing environment in the 3 L flask flow field, and the results showed that the presence of the baffle increased the gas-liquid oxygen transfer capacity inside the flask and provided a larger shearing environment than the unbaffled flask. The speed of rotate is favorable for the gas-liquid mass transfer ability, and the optimal liquid volume exists for a specific rotation speed that provides the best gas-liquid oxygen mass transfer capacity. Finally, the effects of different flask culture conditions on the seed and fermentation culture of aerobic bacteria streptomyces clavuligerus were investigated. and the morphological characteristics of the hyphae and the synthesis of clavulanic acid were compared. Using the flow field simulation results, the flow field of the baffled shake flask was analyzed for interpreting the better result for clavulanic acid seed and fermentation process in baffled flask. The method formed in this paper can be extended to the fermentation analysis of other products.
In this work, we proposed a computational fluid dynamics model for simulating gas-liquid two-phase flow in a shake flask, which we named the centrifugal acceleration model method. The model was used to investigate the mass transfer and shearing environment in the 3 L flask flow field, and the results showed that the presence of the baffle increased the gas-liquid oxygen transfer capacity inside the flask and provided a larger shearing environment than the unbaffled flask. The speed of rotate is favorable for the gas-liquid mass transfer ability, and the optimal liquid volume exists for a specific rotation speed that provides the best gas-liquid oxygen mass transfer capacity. Finally, the effects of different flask culture conditions on the seed and fermentation culture of aerobic bacteria streptomyces clavuligerus were investigated. and the morphological characteristics of the hyphae and the synthesis of clavulanic acid were compared. Using the flow field simulation results, the flow field of the baffled shake flask was analyzed for interpreting the better result for clavulanic acid seed and fermentation process in baffled flask. The method formed in this paper can be extended to the fermentation analysis of other products.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201110002
Abstract:
With the development of target detection algorithms, intrusion detection based on surveillance video has attracted more and more attention. At present, the traditional target detection algorithm is more complex, and can not be detected in real time in the scene of limited computing power and storage space.Aiming at this pain point, a lightweight intrusion detection algorithm is proposed: firstly extract the preliminary screening target through the adaptive update rate of the mixed Gaussian foreground extraction algorithm, and then identify the preliminary screening target based on the improved residual squeeze network (R-SqueezeNet) classification. Experimental results show that, without reducing the detection accuracy, the algorithm can increase the detection speed by an average of 30 times compared with the traditional algorithm, and the model size is reduced to 1/40 of YOLOv3-tiny.
With the development of target detection algorithms, intrusion detection based on surveillance video has attracted more and more attention. At present, the traditional target detection algorithm is more complex, and can not be detected in real time in the scene of limited computing power and storage space.Aiming at this pain point, a lightweight intrusion detection algorithm is proposed: firstly extract the preliminary screening target through the adaptive update rate of the mixed Gaussian foreground extraction algorithm, and then identify the preliminary screening target based on the improved residual squeeze network (R-SqueezeNet) classification. Experimental results show that, without reducing the detection accuracy, the algorithm can increase the detection speed by an average of 30 times compared with the traditional algorithm, and the model size is reduced to 1/40 of YOLOv3-tiny.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201009002
Abstract:
Experiments on dense phase pneumatic conveying of pulverized coal were carried out in the self-built pneumatic conveying facility with a Industrial pipe diameter of 50 mm. The gas-solid two-phase flow characteristics through the bend were studied. Firstly, With the help of Enick and Klinzing model, the entrance length of gas-solid two-phase flow in a bend outlet is calculated. It is found that electrical capacitance tomography (ECT) is in the underdeveloped area, where the flow is affected by the bend. The flow patterns of the outlet section of the bend were analyzed in the means of ECT, and it was found that the flow pattern changed from the packed bed flow to the unstable/stable plug flow with the increase of the superficial gas velocity. Due to the influence of inertial force and centrifugal force, there was an obvious radial distribution in the concentration of the pipe section when the pulverized coal flowed through the bend, while the coal concentration on lateral wall surface was relatively high. Next, regression analysis of experimental data is carried out by using pressure drop model and dimensional analysis method, a pressure drop model of pulverized coal dense phase pneumatic conveying bend was established by providing errors most smaller than ±10%. Finally, based on micro element analysis and pressure drop distribution along the bend, the distribution characteristics of pulverized coal concentration along the bend were obtained. The research in this work can provide important guiding significance in reducing the wear of bend.
Experiments on dense phase pneumatic conveying of pulverized coal were carried out in the self-built pneumatic conveying facility with a Industrial pipe diameter of 50 mm. The gas-solid two-phase flow characteristics through the bend were studied. Firstly, With the help of Enick and Klinzing model, the entrance length of gas-solid two-phase flow in a bend outlet is calculated. It is found that electrical capacitance tomography (ECT) is in the underdeveloped area, where the flow is affected by the bend. The flow patterns of the outlet section of the bend were analyzed in the means of ECT, and it was found that the flow pattern changed from the packed bed flow to the unstable/stable plug flow with the increase of the superficial gas velocity. Due to the influence of inertial force and centrifugal force, there was an obvious radial distribution in the concentration of the pipe section when the pulverized coal flowed through the bend, while the coal concentration on lateral wall surface was relatively high. Next, regression analysis of experimental data is carried out by using pressure drop model and dimensional analysis method, a pressure drop model of pulverized coal dense phase pneumatic conveying bend was established by providing errors most smaller than ±10%. Finally, based on micro element analysis and pressure drop distribution along the bend, the distribution characteristics of pulverized coal concentration along the bend were obtained. The research in this work can provide important guiding significance in reducing the wear of bend.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200928001
Abstract:
Noise is an important problem which need to be resolved in people's life. The modeling accuracy and control result in active noise control are affected by the nonlinear factors. Secondary path identification is optimized according to the active noise control (ANC) principle, to improve the accuracy and effect of noise control. The finite impulse response (FIR) model used to identify is replaced by the back propagation (BP) neural network, which performs better on the nonlinear factors. Based on least mean square (LMS) algorithm, the ANC algorithm under the secondary path model of neural network is deduced, and the iteration formula of coefficients is derived. The active noise control platform in a duct is built with the TMS320VC5509A as the core processor and the duct as the noise environment. The platform includes input, output and processing modules. The neural network model is trained with input and output signals as training samples. The signals of secondary path are generated by the addictive white noise. The neural network improves the accuracy of secondary path identification model as shown in the training results, which means that the nonlinear factors of secondary path can be described by the neural network. The coefficients computed offline are loaded into the DSP and taken as the filtering parameters of the input signals. Under 500 Hz and 500+800 Hz noise source, the noise control experiment of FIR secondary path model and traditional ANC algorithm is compared with neural network model and optimized ANC algorithm. The results show that the algorithm is effective with good performance under the low-frequency noise of single and two-mixed frequency.
Noise is an important problem which need to be resolved in people's life. The modeling accuracy and control result in active noise control are affected by the nonlinear factors. Secondary path identification is optimized according to the active noise control (ANC) principle, to improve the accuracy and effect of noise control. The finite impulse response (FIR) model used to identify is replaced by the back propagation (BP) neural network, which performs better on the nonlinear factors. Based on least mean square (LMS) algorithm, the ANC algorithm under the secondary path model of neural network is deduced, and the iteration formula of coefficients is derived. The active noise control platform in a duct is built with the TMS320VC5509A as the core processor and the duct as the noise environment. The platform includes input, output and processing modules. The neural network model is trained with input and output signals as training samples. The signals of secondary path are generated by the addictive white noise. The neural network improves the accuracy of secondary path identification model as shown in the training results, which means that the nonlinear factors of secondary path can be described by the neural network. The coefficients computed offline are loaded into the DSP and taken as the filtering parameters of the input signals. Under 500 Hz and 500+800 Hz noise source, the noise control experiment of FIR secondary path model and traditional ANC algorithm is compared with neural network model and optimized ANC algorithm. The results show that the algorithm is effective with good performance under the low-frequency noise of single and two-mixed frequency.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200614001
Abstract:
A detailed study of the dispersion, rheological and adsorption behaviors between multi-walled carbon nanotubes (MWNTs) and sodium carboxymethylcellulose (CMC) at different metal ions solutions were presented. The experimental results suggested that the chelation between metal ions and CMC governed the adsorption amount and adsorption conformation of CMC onto MWNTs, which had a great influence on the dispersion stability of MWNTs slurries. The MWNTs slurry with Fe2+ had smaller average size, lower viscosity and better stability, which led the slurry to evolving from shear-thinning fluid. It can be seen form UV adsorption experiment that the chelation between Fe3+ and CMC was stronger than that of other divalent ion. And the chelation increased with the increase of the radius of the divalent ion. Raman and Thermo-gravimetry (TGA) results showed that the adsorption amount of Fe3+ was lower, which provided a lower electrostatic repulsive force. In the slurry with divalent ions, adsorption amount of CMC onto MWNTs were higher in the order of Ni2+, Co2+ and Fe2+, providing higher repulsive force, larger zeta potential on MWNTs surface. That’s the reason why Fe2+ had better dispersion stability. The microstructures were measured by TEM. It was found that uniform CMC adsorption layers were formed on the surface of MWNTs with divalent ions. However, for the MWNTs with Fe3+, MWNTs were wrapped by CMC agglomerates, resulting in poor dispersion stability.
A detailed study of the dispersion, rheological and adsorption behaviors between multi-walled carbon nanotubes (MWNTs) and sodium carboxymethylcellulose (CMC) at different metal ions solutions were presented. The experimental results suggested that the chelation between metal ions and CMC governed the adsorption amount and adsorption conformation of CMC onto MWNTs, which had a great influence on the dispersion stability of MWNTs slurries. The MWNTs slurry with Fe2+ had smaller average size, lower viscosity and better stability, which led the slurry to evolving from shear-thinning fluid. It can be seen form UV adsorption experiment that the chelation between Fe3+ and CMC was stronger than that of other divalent ion. And the chelation increased with the increase of the radius of the divalent ion. Raman and Thermo-gravimetry (TGA) results showed that the adsorption amount of Fe3+ was lower, which provided a lower electrostatic repulsive force. In the slurry with divalent ions, adsorption amount of CMC onto MWNTs were higher in the order of Ni2+, Co2+ and Fe2+, providing higher repulsive force, larger zeta potential on MWNTs surface. That’s the reason why Fe2+ had better dispersion stability. The microstructures were measured by TEM. It was found that uniform CMC adsorption layers were formed on the surface of MWNTs with divalent ions. However, for the MWNTs with Fe3+, MWNTs were wrapped by CMC agglomerates, resulting in poor dispersion stability.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201127006
Abstract:
The evolution characteristics of the reduction process of single iron concentrate particles under high temperature and CO atmospheres were studied in the in-situ experiment of a high temperature stage microscope. The high-temperature reduction process of single iron concentrate was recorded via in-situ experiment, and the reduced product (elemental iron) was verified by using Raman spectrometer. The results showed that the initial formation time of the elemental iron on the particle surface was mainly affected by temperature, and this influence from the gas flowrate was smaller. The initial formation time decreased 75% when the reducing temperature reduced from 1 100 ℃ to 1 300 ℃. But a unchanged result of this formation time was found in the temperature range from 1 300 ℃ to 1 400 ℃. Nodular structures were found on the surface of iron concentrate particles during the reduction process between 1 100 ℃ and 1 350 ℃, and their sizes increased with the rising reduction temperature. A characteristic number l, which was self-defined as the mean value of the length and width, increased from 6 μm at 1 100 ℃ to 15 μm at 1 350 ℃. When the reduction temperature was above 1 400 ℃, layered melting products were observed for the iron concentrate particle: the product on the core is reduced iron, the second layer was the root-shaped metal iron with reduced molten ferrous oxide, and the outer layer was the iron slag containing Al, Ca, Si, and other elements.
The evolution characteristics of the reduction process of single iron concentrate particles under high temperature and CO atmospheres were studied in the in-situ experiment of a high temperature stage microscope. The high-temperature reduction process of single iron concentrate was recorded via in-situ experiment, and the reduced product (elemental iron) was verified by using Raman spectrometer. The results showed that the initial formation time of the elemental iron on the particle surface was mainly affected by temperature, and this influence from the gas flowrate was smaller. The initial formation time decreased 75% when the reducing temperature reduced from 1 100 ℃ to 1 300 ℃. But a unchanged result of this formation time was found in the temperature range from 1 300 ℃ to 1 400 ℃. Nodular structures were found on the surface of iron concentrate particles during the reduction process between 1 100 ℃ and 1 350 ℃, and their sizes increased with the rising reduction temperature. A characteristic number l, which was self-defined as the mean value of the length and width, increased from 6 μm at 1 100 ℃ to 15 μm at 1 350 ℃. When the reduction temperature was above 1 400 ℃, layered melting products were observed for the iron concentrate particle: the product on the core is reduced iron, the second layer was the root-shaped metal iron with reduced molten ferrous oxide, and the outer layer was the iron slag containing Al, Ca, Si, and other elements.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200927001
Abstract:
In the process of using reverse osmosis membrane method to treat wastewater, the surface of membrane is easily scaled, which shortens the service life of membrane and reduces the efficiency of wastewater treatment. Corresponding membrane antiscalant are usually used to prolong the service life of reverse osmosis membrane, the quality of membrane antiscalant directly affects the effectiveness and efficiency of wastewater treatment. Nano-sized spherical polyelectrolyte brush is a kind of polymer assembly with core-shell structure, under the influence of static electricity and Donnan effect, has the characteristics of selectively adsorbing counter ions and inhibiting the crystallization of inorganic salts. Therefore, nano-sized spherical polyelectrolyte brush can be used in scale inhibition of membrane systems for wastewater treatment. The nano-sized spherical polyelectrolyte brush was prepared by photo-emulsion polymerization as a great membrane antiscalant shows excellent scale inhibition performance than the best imported membrane antiscalant ASD-200 and MDC-220. Nano-sized spherical polyelectrolyte brush’s scale inhibition performance on CaCO3, CaSO4, Al3+ and the influence of mass concentration evaluated by dynamic evaluation experiment. The results provide an important reference for its practical application in industrial wastewater treatment.
In the process of using reverse osmosis membrane method to treat wastewater, the surface of membrane is easily scaled, which shortens the service life of membrane and reduces the efficiency of wastewater treatment. Corresponding membrane antiscalant are usually used to prolong the service life of reverse osmosis membrane, the quality of membrane antiscalant directly affects the effectiveness and efficiency of wastewater treatment. Nano-sized spherical polyelectrolyte brush is a kind of polymer assembly with core-shell structure, under the influence of static electricity and Donnan effect, has the characteristics of selectively adsorbing counter ions and inhibiting the crystallization of inorganic salts. Therefore, nano-sized spherical polyelectrolyte brush can be used in scale inhibition of membrane systems for wastewater treatment. The nano-sized spherical polyelectrolyte brush was prepared by photo-emulsion polymerization as a great membrane antiscalant shows excellent scale inhibition performance than the best imported membrane antiscalant ASD-200 and MDC-220. Nano-sized spherical polyelectrolyte brush’s scale inhibition performance on CaCO3, CaSO4, Al3+ and the influence of mass concentration evaluated by dynamic evaluation experiment. The results provide an important reference for its practical application in industrial wastewater treatment.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201009001
Abstract:
Two kinds of Kex2 protease mutants Kex2-K291L and Kex2-K291H were successfully expressed in Pichia. pastoris, which were induced by methanol, and purified with anion exchange chromatography (Q-FF). Finally, the enzymatic characteristics of these Kex2 proteases were characterized. It was showed that compared with the wild-type Kex2, the degradation of the two mutants Kex2-K291H and Kex2-K291L has been significantly improved. The wild-type Kex2 protease was degraded during the purification, and a non-single band appeared, while the mutants were not degraded during the purification, and it was still a single band. The optimum pH and optimum temperature of these two mutants were the same as those of the wild-type Kex2. Their optimal pH and temperature were at pH 9.0 and 37 ℃. Compared with the wild-type Kex2 protease, the pH stability range of the mutant Kex2-K291H was expanded, from the range of pH 5.0 to 6.0 to the range of pH 5.0 to 7.0; compared with the wild-type Kex2, Kex2-K291H was more stable at 4 ℃ to 37 ℃. Enzymatic reaction kinetics studies showed that the Kcat/Km values of mutant Kex2-K291H and Kex2-K291L were 1.8 fold and 2.0 fold higher than that of the wild-type Kex2 protease, respectively.
Two kinds of Kex2 protease mutants Kex2-K291L and Kex2-K291H were successfully expressed in Pichia. pastoris, which were induced by methanol, and purified with anion exchange chromatography (Q-FF). Finally, the enzymatic characteristics of these Kex2 proteases were characterized. It was showed that compared with the wild-type Kex2, the degradation of the two mutants Kex2-K291H and Kex2-K291L has been significantly improved. The wild-type Kex2 protease was degraded during the purification, and a non-single band appeared, while the mutants were not degraded during the purification, and it was still a single band. The optimum pH and optimum temperature of these two mutants were the same as those of the wild-type Kex2. Their optimal pH and temperature were at pH 9.0 and 37 ℃. Compared with the wild-type Kex2 protease, the pH stability range of the mutant Kex2-K291H was expanded, from the range of pH 5.0 to 6.0 to the range of pH 5.0 to 7.0; compared with the wild-type Kex2, Kex2-K291H was more stable at 4 ℃ to 37 ℃. Enzymatic reaction kinetics studies showed that the Kcat/Km values of mutant Kex2-K291H and Kex2-K291L were 1.8 fold and 2.0 fold higher than that of the wild-type Kex2 protease, respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200929002
Abstract:
The pesticide reducing has been a key issue in pesticides area in recent years. The use of nano-carrier technology to incorporate pesticides into nanoparticle provides new route for solving the problem. Different from most nano-carrier technologies which are based on thermodynamic equilibrium self-assembly, the emerging Flash Nanoprecipitation (FNP) method is based on kinetic control, preparing nanoparticles through turbulent mixing of chemical engineering fluids. It has advantages like high drug loading efficiency, short preparation time (milliseconds), easy to scale-up and continuously production, etc. Moreover, it could also systematically control the microstructures of nanoparticle, such as morphology, internal structure and surface structure, which could provide help for further improving the efficiency and low-toxic utilization of pesticide nanoparticle.
The pesticide reducing has been a key issue in pesticides area in recent years. The use of nano-carrier technology to incorporate pesticides into nanoparticle provides new route for solving the problem. Different from most nano-carrier technologies which are based on thermodynamic equilibrium self-assembly, the emerging Flash Nanoprecipitation (FNP) method is based on kinetic control, preparing nanoparticles through turbulent mixing of chemical engineering fluids. It has advantages like high drug loading efficiency, short preparation time (milliseconds), easy to scale-up and continuously production, etc. Moreover, it could also systematically control the microstructures of nanoparticle, such as morphology, internal structure and surface structure, which could provide help for further improving the efficiency and low-toxic utilization of pesticide nanoparticle.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200728001
Abstract:
The traffic accidents are mainly related to unfavorable driving status, e. g, fatigue, stress, distraction and sleepiness, resulting in a considerable amount of vehicle collisions and casualties every year. Commonly, stress can be viewed as a normal response of human body to dangerous or difficult events. With the advance of wearable sensor and wireless technique, researchers are paying increasing amount of attention to physiological measures, which have been proved to be highly correlated to driver’s mental states. In-vehicle intelligent systems could utilize these physiological measures automatically in various manners to help drivers better manage their negative driving status. So we focus on the research of physiological measures in this study. Commonly, driving stress detection based on multimodal physiological signals will affect the driving comfort of drivers and traditional physiological feature extraction techniques largely rely on prior knowledge. In this paper, single module physiological signal, i.e., GSR signal on the foot (FGSR) is used and abstract features are generated by unsupervised feature learning using 1D-convolutional auto-encoder (CAE). Then, the features are sent to four different base classifiers, namely k-nearest neighbor (KNN), gradient boosting decision tree (GBDT),support vector machine (SVM) and random forest (RF).Finally, the outputs of different base classifiers are integrated with voting method to improve the stability and accuracy of driving stress detection. The proposed model is validated on the MIT-drivedb data set, which shows that the feature extracted from GSR using convolutional auto-encoder has good representational ability for the driving stress and the ensemble of different base classifiers can improve the accuracy of final recognition results.
The traffic accidents are mainly related to unfavorable driving status, e. g, fatigue, stress, distraction and sleepiness, resulting in a considerable amount of vehicle collisions and casualties every year. Commonly, stress can be viewed as a normal response of human body to dangerous or difficult events. With the advance of wearable sensor and wireless technique, researchers are paying increasing amount of attention to physiological measures, which have been proved to be highly correlated to driver’s mental states. In-vehicle intelligent systems could utilize these physiological measures automatically in various manners to help drivers better manage their negative driving status. So we focus on the research of physiological measures in this study. Commonly, driving stress detection based on multimodal physiological signals will affect the driving comfort of drivers and traditional physiological feature extraction techniques largely rely on prior knowledge. In this paper, single module physiological signal, i.e., GSR signal on the foot (FGSR) is used and abstract features are generated by unsupervised feature learning using 1D-convolutional auto-encoder (CAE). Then, the features are sent to four different base classifiers, namely k-nearest neighbor (KNN), gradient boosting decision tree (GBDT),support vector machine (SVM) and random forest (RF).Finally, the outputs of different base classifiers are integrated with voting method to improve the stability and accuracy of driving stress detection. The proposed model is validated on the MIT-drivedb data set, which shows that the feature extracted from GSR using convolutional auto-encoder has good representational ability for the driving stress and the ensemble of different base classifiers can improve the accuracy of final recognition results.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200603001
Abstract:
The inaccurate nozzle-exit conditions due to difficulties in measurements make the numerical simulation insufficient to predict the jet development of the laboratory nozzle. Moreover, there is a greater discrepancy for the full-scale turbulent jet. Therefore, the inflow conditions effects have been of great interest again in jet noise recently. Large eddy simulations (LES) of a round jet at Ma=0.75 and ReD=8.7×105 is carried out so as to study the inflow forcing effects on the predicted flow and noise results. A modified multi-mode linear instability forcing method for the inflow is proposed and is employed so as to trigger the turbulence. For comparison, calculations both without inflow forcing and with a vortex-ring forcing are also carried out. Numerical results show that both the vortex-ring forcing and the multi-mode liner instability forcing methods are efficient to damage the azimuthal stability of the large scale ring vortices appeared near nozzle exit. This leads to an accelerated flow transition to turbulent shear-layer, which is the exact state of the flow in realistic nozzles. The predicted spectra of the axial and radial velocity fluctuations are compared. It is found that the peak related to flow transition is less distinct for jets with forcing. Besides, the initial turbulent fluctuations are at higher levels with the proposed forcing than vortex-ring forcing method, which result in a faster flow transition. Comparison of the predicted sound far fields showed that with the inflow forcing methods, the low frequency noises caused by vortex pairing during transition are suppressed. As a result, better agreements with the experimental data for turbulent jets are obtained.
The inaccurate nozzle-exit conditions due to difficulties in measurements make the numerical simulation insufficient to predict the jet development of the laboratory nozzle. Moreover, there is a greater discrepancy for the full-scale turbulent jet. Therefore, the inflow conditions effects have been of great interest again in jet noise recently. Large eddy simulations (LES) of a round jet at Ma=0.75 and ReD=8.7×105 is carried out so as to study the inflow forcing effects on the predicted flow and noise results. A modified multi-mode linear instability forcing method for the inflow is proposed and is employed so as to trigger the turbulence. For comparison, calculations both without inflow forcing and with a vortex-ring forcing are also carried out. Numerical results show that both the vortex-ring forcing and the multi-mode liner instability forcing methods are efficient to damage the azimuthal stability of the large scale ring vortices appeared near nozzle exit. This leads to an accelerated flow transition to turbulent shear-layer, which is the exact state of the flow in realistic nozzles. The predicted spectra of the axial and radial velocity fluctuations are compared. It is found that the peak related to flow transition is less distinct for jets with forcing. Besides, the initial turbulent fluctuations are at higher levels with the proposed forcing than vortex-ring forcing method, which result in a faster flow transition. Comparison of the predicted sound far fields showed that with the inflow forcing methods, the low frequency noises caused by vortex pairing during transition are suppressed. As a result, better agreements with the experimental data for turbulent jets are obtained.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200623003
Abstract:
The CRISPR/Cas9 mediated gene editing was used in a cephalosporin C (CPC) industrial producer Acremonium chrysogenum 1-D1 to improve the production and quality of CPC by reducing the accumulation of by-product deacetylcephalosporin C (DCPC). Firstly, we used the CRISPR/Cas9 system to knock out the CPC acetylhydrolase gene cahB to reduce the accumulation of impurities DCPC, and a strain Ac-ΔcahB with 2 bp deletions in cahB was obtained. Secondly, we inserted donor DNA, which contained a cefG gene that encodes DCPC acetyltransferase into the cahB locus by using the HDR and CRISPR, thus the cahB deletion strain Ac-ΔcahB::cefG combined with cefG overexpression capability, was obtained. It was found that the CPC production of strain Ac-ΔcahB was only increased slightly, while strain Ac-ΔcahB::cefG produced 6072 μg/ml of CPC after 168 h cultivation, which was increased by 32.6% compared to the original strain (1-D1). Moreover, the ratio of the peak area of DCPC to CPC was employed to determine the DCPC content. The results showed that the DCPC content of strain Ac-ΔcahB was slightly decreased from 12.56% of original strain to 11.33%. However, the DCPC content in the strain Ac-ΔcahB::cefG was dramatically dropped to 6.81%, which decreased significantly (P=0.001797) comparing to the original strain. In addition, the expression levels of the acetyltransferase gene cefG in both functional strains were increased in 72 h and 96 h, and the transcription level of Ac-ΔcahB::cefG strain was increased by 5 times at 96 h. These results suggested that the overexpression of cefG gene by the strong promoter gpdA greatly increased the transcription of acetyltransferase gene, and promoted the conversion of DCPC to CPC. Thus, the accumulation of DCPC was significantly reduced and the production of CPC was improved. And the CRISPR/Cas9 system combined with Donor DNA is a more efficient gene editing method to be applied to industrial A. chrysogenum.
The CRISPR/Cas9 mediated gene editing was used in a cephalosporin C (CPC) industrial producer Acremonium chrysogenum 1-D1 to improve the production and quality of CPC by reducing the accumulation of by-product deacetylcephalosporin C (DCPC). Firstly, we used the CRISPR/Cas9 system to knock out the CPC acetylhydrolase gene cahB to reduce the accumulation of impurities DCPC, and a strain Ac-ΔcahB with 2 bp deletions in cahB was obtained. Secondly, we inserted donor DNA, which contained a cefG gene that encodes DCPC acetyltransferase into the cahB locus by using the HDR and CRISPR, thus the cahB deletion strain Ac-ΔcahB::cefG combined with cefG overexpression capability, was obtained. It was found that the CPC production of strain Ac-ΔcahB was only increased slightly, while strain Ac-ΔcahB::cefG produced 6072 μg/ml of CPC after 168 h cultivation, which was increased by 32.6% compared to the original strain (1-D1). Moreover, the ratio of the peak area of DCPC to CPC was employed to determine the DCPC content. The results showed that the DCPC content of strain Ac-ΔcahB was slightly decreased from 12.56% of original strain to 11.33%. However, the DCPC content in the strain Ac-ΔcahB::cefG was dramatically dropped to 6.81%, which decreased significantly (P=0.001797) comparing to the original strain. In addition, the expression levels of the acetyltransferase gene cefG in both functional strains were increased in 72 h and 96 h, and the transcription level of Ac-ΔcahB::cefG strain was increased by 5 times at 96 h. These results suggested that the overexpression of cefG gene by the strong promoter gpdA greatly increased the transcription of acetyltransferase gene, and promoted the conversion of DCPC to CPC. Thus, the accumulation of DCPC was significantly reduced and the production of CPC was improved. And the CRISPR/Cas9 system combined with Donor DNA is a more efficient gene editing method to be applied to industrial A. chrysogenum.
, Available online
, doi: 10.14133/j.cnki.1006-3080.20200326001
Abstract:
At present, neural networks have been widely used, and have achieved some success in many fields. However, there is not much theoretical analysis about neural networks. This paper analyzed the convergence of the back-propagation algorithm with momentum for the three-layer feed-forward neural networks. In our model, the learning rate is set to be a constant, and the momentum coefficient is set as an adaptive variable to accelerate and stabilize the training procedure of network parameters. The corresponding convergence results and detailed proofs are given. Compared with the existing results, our results are more general.
At present, neural networks have been widely used, and have achieved some success in many fields. However, there is not much theoretical analysis about neural networks. This paper analyzed the convergence of the back-propagation algorithm with momentum for the three-layer feed-forward neural networks. In our model, the learning rate is set to be a constant, and the momentum coefficient is set as an adaptive variable to accelerate and stabilize the training procedure of network parameters. The corresponding convergence results and detailed proofs are given. Compared with the existing results, our results are more general.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200713005
Abstract:
As the combat missions and environments of unmanned aerial vehicles(UAV) are becoming increasingly complicated, a UAV may not have sufficient resources to complete the assigned tasks. In this sense, it is necessary that the unmanned aerial vehicles should form a coalition so that they can accomplish the complex tasks more efficiently and improve the success rate as well as effectiveness of tasks in large measure. This paper has provided insight into multi-UAV task allocation algorithm amid military environment. The main research work is as follows: First and foremost, based on the various tasks that are required by the UAV resources, a resource model is established. Besides, quantitative calculation of various resources required by the UAV Alliance was carried out. At the same time, according to the difficulty of the task, this paper proposes a task allocation(TA) algorithm, and also calculates the reward, costs, success rate and effectiveness of each coalition to perform each task. There is no denying that these endeavors will make for selecting the best alliance with the most effectiveness as a way to carry out the task. These efforts has improved the robustness and versatility of the algorithm significantly. In order to verify the effectiveness of the algorithm, simulation experiments were carried out in matlab. In addition, in a bid to make human-computer interaction more economical and safer, the above mentioned task allocation algorithm, the auction algorithm, leader-follower algorithm and the Hungarian algorithm are compared in success rate and effectiveness. At last, the experimental data shows that the UAV coalition can significantly improve the success rate and effectiveness of task execution.
As the combat missions and environments of unmanned aerial vehicles(UAV) are becoming increasingly complicated, a UAV may not have sufficient resources to complete the assigned tasks. In this sense, it is necessary that the unmanned aerial vehicles should form a coalition so that they can accomplish the complex tasks more efficiently and improve the success rate as well as effectiveness of tasks in large measure. This paper has provided insight into multi-UAV task allocation algorithm amid military environment. The main research work is as follows: First and foremost, based on the various tasks that are required by the UAV resources, a resource model is established. Besides, quantitative calculation of various resources required by the UAV Alliance was carried out. At the same time, according to the difficulty of the task, this paper proposes a task allocation(TA) algorithm, and also calculates the reward, costs, success rate and effectiveness of each coalition to perform each task. There is no denying that these endeavors will make for selecting the best alliance with the most effectiveness as a way to carry out the task. These efforts has improved the robustness and versatility of the algorithm significantly. In order to verify the effectiveness of the algorithm, simulation experiments were carried out in matlab. In addition, in a bid to make human-computer interaction more economical and safer, the above mentioned task allocation algorithm, the auction algorithm, leader-follower algorithm and the Hungarian algorithm are compared in success rate and effectiveness. At last, the experimental data shows that the UAV coalition can significantly improve the success rate and effectiveness of task execution.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200802001
Abstract:
When the cyber-physical system (Cyber-Physical System, CPS) performs remote state estimation, it is easy for an attacker to attack the system by tampering with wirelessly transmitted measurement data, etc., thereby causing loss of system performance. In order to better defend against attacks, we need to fully understand the attacker's attack strategy. According to the attacker's understanding of the system knowledge, we divide the research into two situations: one is that the attacker has limited ability and cannot directly obtain the transmission data, but can use additional sensors to get a measurement; the other one is that the attacker has a good understanding of the system, and can either directly obtain the transmission data or use its own additional sensors to measure the data. We analyze estimation performance the attacker's optimal linear deception attack strategy under the KL divergence detector for these two situations, transforms the optimal attack problem into a convex optimization problem. Finally, we give a closed-form expression of the optimal linear deception attack in a one dimensional situation. We compare the estimation error covariance caused by the optimal attack in the two cases, and conclude that the more the attacker understands the system knowledge, the greater the impact of the attack on system performance. At the same time, it is also compared with the existing literature in terms of estimation performance and optimal attack, and use numerical simulation to verify the effectiveness of the proposed results.
When the cyber-physical system (Cyber-Physical System, CPS) performs remote state estimation, it is easy for an attacker to attack the system by tampering with wirelessly transmitted measurement data, etc., thereby causing loss of system performance. In order to better defend against attacks, we need to fully understand the attacker's attack strategy. According to the attacker's understanding of the system knowledge, we divide the research into two situations: one is that the attacker has limited ability and cannot directly obtain the transmission data, but can use additional sensors to get a measurement; the other one is that the attacker has a good understanding of the system, and can either directly obtain the transmission data or use its own additional sensors to measure the data. We analyze estimation performance the attacker's optimal linear deception attack strategy under the KL divergence detector for these two situations, transforms the optimal attack problem into a convex optimization problem. Finally, we give a closed-form expression of the optimal linear deception attack in a one dimensional situation. We compare the estimation error covariance caused by the optimal attack in the two cases, and conclude that the more the attacker understands the system knowledge, the greater the impact of the attack on system performance. At the same time, it is also compared with the existing literature in terms of estimation performance and optimal attack, and use numerical simulation to verify the effectiveness of the proposed results.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200911002
Abstract:
In order to improve the performance of multi-phase motor torque control strategy, the symmetrical six-phase permanent magnet synchronous motor was taken as the research object. According to its structure and winding distribution characteristics, a mathematical model in natural coordinate system was established. In view of the complexity of calculation under multiphase electromagnetic coupling, the vector transformation model under the rotating coordinate system was derived. Aiming at the large starting torque fluctuation of multiphase motors, a new direct torque control (DTC) strategy based on SVPWM technology was proposed. In the MATLAB/Simulink environment, the mathematical model under the vector decoupling transformation of the motor was simulated. The results show that the proposed control algorithm has better anti-disturbance and regulation performance on the motor electromagnetic torque, flux linkage and other parameters than the traditional direct torque control. The experimental results verify the effectiveness and feasibility of the developed control algorithm.
In order to improve the performance of multi-phase motor torque control strategy, the symmetrical six-phase permanent magnet synchronous motor was taken as the research object. According to its structure and winding distribution characteristics, a mathematical model in natural coordinate system was established. In view of the complexity of calculation under multiphase electromagnetic coupling, the vector transformation model under the rotating coordinate system was derived. Aiming at the large starting torque fluctuation of multiphase motors, a new direct torque control (DTC) strategy based on SVPWM technology was proposed. In the MATLAB/Simulink environment, the mathematical model under the vector decoupling transformation of the motor was simulated. The results show that the proposed control algorithm has better anti-disturbance and regulation performance on the motor electromagnetic torque, flux linkage and other parameters than the traditional direct torque control. The experimental results verify the effectiveness and feasibility of the developed control algorithm.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200828001
Abstract:
Based on the screening of tumor cytotoxicity, fractions and compounds from the South China Sea sponge Pseduoceratina sp. were isolated and purified by silica gel column and HPLC. The structure were identified by ESI-MS and NMR spectroscopy. The anti-proliferation of fractions and compounds against A549, HepG2 and HeLa cell lines had been investigated by SRB method. The targets in the cell were calculated and predicted by Chemmapper Server. The results showed that two bromotyrosine alkaloids were obtained and identified as hemifistularin-3(1)and 11, 19- dideoxyfistularin 3(2). Their IC50 values were greater than 91.76 μmol·L−1 against three tumor cells. While the combination of the two compounds with certain ratio had good cytotoxicity, the IC50 value against A549 cells was as low as 8.71 μmol·L−1. It was indicated that they had significant synergistic effect on three tested tumor cells. Their targets might focus on Hsf1 protein and Vanilloid Receptor 1 and the synergistic effect might be associated with multi-target of compounds.
Based on the screening of tumor cytotoxicity, fractions and compounds from the South China Sea sponge Pseduoceratina sp. were isolated and purified by silica gel column and HPLC. The structure were identified by ESI-MS and NMR spectroscopy. The anti-proliferation of fractions and compounds against A549, HepG2 and HeLa cell lines had been investigated by SRB method. The targets in the cell were calculated and predicted by Chemmapper Server. The results showed that two bromotyrosine alkaloids were obtained and identified as hemifistularin-3(1)and 11, 19- dideoxyfistularin 3(2). Their IC50 values were greater than 91.76 μmol·L−1 against three tumor cells. While the combination of the two compounds with certain ratio had good cytotoxicity, the IC50 value against A549 cells was as low as 8.71 μmol·L−1. It was indicated that they had significant synergistic effect on three tested tumor cells. Their targets might focus on Hsf1 protein and Vanilloid Receptor 1 and the synergistic effect might be associated with multi-target of compounds.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200831001
Abstract:
In view of the complexity and variety of production modes in the actual production system, the paper studies hybrid flowshop lot-streaming scheduling problem with batch processing. Considering the capacity of batch machine and the processing ability of unrelated machines, a variable batching method is proposed. We establish a scheduling model to minimize the completion time, and propose a dynamic continuous processing strategy to optimize it. At the same time, a discrete water wave optimization (DWWO) is proposed to solve the model. According to the characteristic of batching and optimization objectives, four decoding methods are designed to optimize the machine selection and processing sequence of jobs; the operation operators are improved by block optimal insertion, cross operation and multi neighborhood search to enhance the local search ability; an operation of replace inferior solution is proposed to improve the convergence ability of the algorithm. Finally, the experimental design method is used to calibrate the parameters of DWWO, and different scale examples are designed to evaluate the performance of DWWO. The experimental results show that DWWO algorithm can effectively solve hybrid flowshop lot-streaming scheduling problem with batch processing.
In view of the complexity and variety of production modes in the actual production system, the paper studies hybrid flowshop lot-streaming scheduling problem with batch processing. Considering the capacity of batch machine and the processing ability of unrelated machines, a variable batching method is proposed. We establish a scheduling model to minimize the completion time, and propose a dynamic continuous processing strategy to optimize it. At the same time, a discrete water wave optimization (DWWO) is proposed to solve the model. According to the characteristic of batching and optimization objectives, four decoding methods are designed to optimize the machine selection and processing sequence of jobs; the operation operators are improved by block optimal insertion, cross operation and multi neighborhood search to enhance the local search ability; an operation of replace inferior solution is proposed to improve the convergence ability of the algorithm. Finally, the experimental design method is used to calibrate the parameters of DWWO, and different scale examples are designed to evaluate the performance of DWWO. The experimental results show that DWWO algorithm can effectively solve hybrid flowshop lot-streaming scheduling problem with batch processing.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200903001
Abstract:
The goal of music source separation is to separate a piece of music into its individual sounds. As a specific case, the Singing Voice Separation (SVS) is to separate the music into vocals and accompaniment. Due to its potential applications in music melody extraction, music genre classification, singing voice detection, and singer identification, etc, SVS has become a hot topic in the music information retrieval field in the past few years. It was recently reported that a variety of convolutional neural network architectures based on U-Net were successfully employed for the SVS task and achieved great performance, which was originally used for medical image semantic segmentation. Then, Wave-U-Net was proposed to achieve the end-to-end SVS by analyzing the music waveform, directly. The performance of the SVS approaches that work in the time-domain relies heavily on the quality of the feature extraction procedure. In this paper, the conventional Wave-U-Net based SVS scheme is modified as follows to enhance its performance. First, at the encoding and decoding blocks, a residual unit is designed and adopted to replace the plain neural unit to solve the degradation problem to some extent. Second, at the skip connection, an attention gate mechanism is introduced to reduce the semantic gap between the output of the previous layer in the decoding block and that of the corresponding layer in the encoding block. To verify the effectiveness of the proposed scheme, called RA-WaveUNet, in the SVS task, its performances are compared with those of state-of-the-art schemes on the maximum open dataset MUSDB18. Experimental results demonstrate that: i) The proposed scheme achieves better performances than Wave-U-Net based ones and other SVS schemes. ii) Both the above modifications contribute to the performance enhancement.
The goal of music source separation is to separate a piece of music into its individual sounds. As a specific case, the Singing Voice Separation (SVS) is to separate the music into vocals and accompaniment. Due to its potential applications in music melody extraction, music genre classification, singing voice detection, and singer identification, etc, SVS has become a hot topic in the music information retrieval field in the past few years. It was recently reported that a variety of convolutional neural network architectures based on U-Net were successfully employed for the SVS task and achieved great performance, which was originally used for medical image semantic segmentation. Then, Wave-U-Net was proposed to achieve the end-to-end SVS by analyzing the music waveform, directly. The performance of the SVS approaches that work in the time-domain relies heavily on the quality of the feature extraction procedure. In this paper, the conventional Wave-U-Net based SVS scheme is modified as follows to enhance its performance. First, at the encoding and decoding blocks, a residual unit is designed and adopted to replace the plain neural unit to solve the degradation problem to some extent. Second, at the skip connection, an attention gate mechanism is introduced to reduce the semantic gap between the output of the previous layer in the decoding block and that of the corresponding layer in the encoding block. To verify the effectiveness of the proposed scheme, called RA-WaveUNet, in the SVS task, its performances are compared with those of state-of-the-art schemes on the maximum open dataset MUSDB18. Experimental results demonstrate that: i) The proposed scheme achieves better performances than Wave-U-Net based ones and other SVS schemes. ii) Both the above modifications contribute to the performance enhancement.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201010001
Abstract:
Smart nano-based pesticides are designed to efficiently delivery sufficient amounts of active ingredients in response to different stimuli, employing targeted and controlled release mechanisms. In this paper, enzyme-responsive carrier for controlled release of lambda-cyhalothrin(LC) was prepared through loading LC into aminated hollow mesoporous silica (HMSN) nanoparticles by physical adsorption, and then grafting carboxymethyl-β-cyclodextrin (CM-β-CD) on the surface of HMSN using amidation reaction as the blocking molecule. A series of physicochemical characterization further demonstrated that CM-β-CD/LC/HMSN has been successfully constructed. In vitro release test showed that the LC release amount in CM-β-CD /LC/HMSN was up to 55% in the presence of α-amylase, while only about 20% in the absence of enzyme, indicating that the pesticide formulation was highly enzyme-dependent. The biological activity survey confirmed that the pesticide formulation had good insecticidal effect on Mythimna separata larvae. According to the results of different experimental methods, it could be speculated that the nanoparticles would release LC after uncapping cyclodextrin enzymatically in vivo, leading to the death of Mythimna separata larvae. This study may provide a basic theoretical basis for the controlled release of pesticides.
Smart nano-based pesticides are designed to efficiently delivery sufficient amounts of active ingredients in response to different stimuli, employing targeted and controlled release mechanisms. In this paper, enzyme-responsive carrier for controlled release of lambda-cyhalothrin(LC) was prepared through loading LC into aminated hollow mesoporous silica (HMSN) nanoparticles by physical adsorption, and then grafting carboxymethyl-β-cyclodextrin (CM-β-CD) on the surface of HMSN using amidation reaction as the blocking molecule. A series of physicochemical characterization further demonstrated that CM-β-CD/LC/HMSN has been successfully constructed. In vitro release test showed that the LC release amount in CM-β-CD /LC/HMSN was up to 55% in the presence of α-amylase, while only about 20% in the absence of enzyme, indicating that the pesticide formulation was highly enzyme-dependent. The biological activity survey confirmed that the pesticide formulation had good insecticidal effect on Mythimna separata larvae. According to the results of different experimental methods, it could be speculated that the nanoparticles would release LC after uncapping cyclodextrin enzymatically in vivo, leading to the death of Mythimna separata larvae. This study may provide a basic theoretical basis for the controlled release of pesticides.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200824002
Abstract:
Aero-engine is a complex system with interdisciplinary and multi-component coupling, and its working conditions are complex. It can have catastrophic consequence once the failure of aero-engine occurs. Therefore, the failure analysis of aero-engine system can provide important reference for its design, maintenance and safe operation. In this work, co-word analysis method is adopted, and three multivariate analysis methods of factor analysis, cluster analysis and multidimensional scale analysis are used to classify the keywords in the field of aero-engine system failure, and the main failure forms are summarized and the results are visualized.It is found that clustering analysis with autocorrelation is better. On the whole, compared with traditional statistical analysis or theoretical analysis, the analysis results of typical failure forms are basically consistent, but co-word analysis is more specific, detailed and accurate for the study of different failure mechanisms. The results of traditional statistical analysis only stay at the level of general quantitative analysis, and can only indicate the general failure direction for practical engineering application. Multi-dimensional scale analysis realizes visual similarity visualization of failure keywords. The closer to the origin of coordinates, the more core the failure forms are, the higher the frequency of occurrence is. For these common faults in the service process, combining cluster analysis results and multidimensional scale analysis charts, it is convenient for technicians to find out the failure reasons of faulty equipment more quickly, grasp the key points of work more accurately, and determine the protection scheme more effectively, effectively avoid the recurrence of similar events and prolong the service life of the engine.
Aero-engine is a complex system with interdisciplinary and multi-component coupling, and its working conditions are complex. It can have catastrophic consequence once the failure of aero-engine occurs. Therefore, the failure analysis of aero-engine system can provide important reference for its design, maintenance and safe operation. In this work, co-word analysis method is adopted, and three multivariate analysis methods of factor analysis, cluster analysis and multidimensional scale analysis are used to classify the keywords in the field of aero-engine system failure, and the main failure forms are summarized and the results are visualized.It is found that clustering analysis with autocorrelation is better. On the whole, compared with traditional statistical analysis or theoretical analysis, the analysis results of typical failure forms are basically consistent, but co-word analysis is more specific, detailed and accurate for the study of different failure mechanisms. The results of traditional statistical analysis only stay at the level of general quantitative analysis, and can only indicate the general failure direction for practical engineering application. Multi-dimensional scale analysis realizes visual similarity visualization of failure keywords. The closer to the origin of coordinates, the more core the failure forms are, the higher the frequency of occurrence is. For these common faults in the service process, combining cluster analysis results and multidimensional scale analysis charts, it is convenient for technicians to find out the failure reasons of faulty equipment more quickly, grasp the key points of work more accurately, and determine the protection scheme more effectively, effectively avoid the recurrence of similar events and prolong the service life of the engine.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200929001
Abstract:
Great progress has been made in the fundamental theories and applications of chemical engineering. Combined with the urgent requirements and development trends in modern agricultural, chemical engineering and agricultural technology generate the upstart interdisciplinary filed named agrochemical engineering. In this article, the connotation of agrochemical engineering and the development of preparation and application of nano-agrochemicals in this filed were reviewed. Especially in the novel nano-pesticide preparation technologies represented by flash nano-precipitation technology, the development of the intelligent responsive controlled release pesticides, migration of nano-pesticide, and the promotion of novel fertilizers and pesticides to the integral control of water and agrochemicals. Finally, the future opportunities of agrochemical engineering are prospected.
Great progress has been made in the fundamental theories and applications of chemical engineering. Combined with the urgent requirements and development trends in modern agricultural, chemical engineering and agricultural technology generate the upstart interdisciplinary filed named agrochemical engineering. In this article, the connotation of agrochemical engineering and the development of preparation and application of nano-agrochemicals in this filed were reviewed. Especially in the novel nano-pesticide preparation technologies represented by flash nano-precipitation technology, the development of the intelligent responsive controlled release pesticides, migration of nano-pesticide, and the promotion of novel fertilizers and pesticides to the integral control of water and agrochemicals. Finally, the future opportunities of agrochemical engineering are prospected.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200911004
Abstract:
Metal silver nanoparticles (AgNPs) are widely used in flexible electronic products for their superior physical and chemical properties. However, the thin films formed by sintering single-sized silver nanoparticles are faced with many challenges due to their defects. The films formed by single small-sized AgNPs have high porosity, small grain size and many defects, while the ones formed by single large-sized AgNPs have larger grain size and less defects, but its sintering temperature and porosity are high. Based on this, the mechanical properties of the films mixed with 10 nm and 50 nm silver particles were investigated by finite element simulation to enhance the mechanical stabilities, service reliabilities and electrical conductivities of the sintered structure of AgNPs, in which the 10 nm AgNPs serve as the “filler” to increase the initial stacking density and weld the large AgNPs together, while the 50 nm AgNPs play as the framework to decrease the initial crystallographic defects and stabilize the sintered structures. In the process of simulation, the filling spacing between large nanoparticles is taken as a parameter to characterize the mixing ratio of large and small nanoparticles. Simulation results show that when the spacing is short, the small particles are subjected to uneven stress in the filling area, and cracks are easy to occur. When the spacing is too long, the film strength decreases due to the increased number of pores. The results show that the mechanical properties are superior when the spacing is 50 nm in mixed mode.
Metal silver nanoparticles (AgNPs) are widely used in flexible electronic products for their superior physical and chemical properties. However, the thin films formed by sintering single-sized silver nanoparticles are faced with many challenges due to their defects. The films formed by single small-sized AgNPs have high porosity, small grain size and many defects, while the ones formed by single large-sized AgNPs have larger grain size and less defects, but its sintering temperature and porosity are high. Based on this, the mechanical properties of the films mixed with 10 nm and 50 nm silver particles were investigated by finite element simulation to enhance the mechanical stabilities, service reliabilities and electrical conductivities of the sintered structure of AgNPs, in which the 10 nm AgNPs serve as the “filler” to increase the initial stacking density and weld the large AgNPs together, while the 50 nm AgNPs play as the framework to decrease the initial crystallographic defects and stabilize the sintered structures. In the process of simulation, the filling spacing between large nanoparticles is taken as a parameter to characterize the mixing ratio of large and small nanoparticles. Simulation results show that when the spacing is short, the small particles are subjected to uneven stress in the filling area, and cracks are easy to occur. When the spacing is too long, the film strength decreases due to the increased number of pores. The results show that the mechanical properties are superior when the spacing is 50 nm in mixed mode.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200719001
Abstract:
Glycerol-3-phosphate acyltransferase (GPAT) is a key enzyme in the synthesis of triacylglycerols (TAGs). It catalyzes the formation of lysophosphatidic acid (LPA) from glycerol-3-phosphate and fatty acyl-CoA, which is the initial reaction of the synthesis of TAG and glycerol-phospholipids. However, previous research works shows that there is no definite GO annotations of GPAT gene in the complete genome sequence of Corynebacterium glutamicum ATCC13032. Aided by bioinformatics and literature mining, several candidate genes encoding GPAT in C.glutamicum ATCC 13032 have been predicted out. cg2777, which is a putative membrane protein gene, has been selected for subsequent analysis and verification. The cg2777 gene has typical GPAT functional domain and four typical transmembrane helix regions. Compared with the GPAT gene sequences derived from a variety of oil-producing bacteria, there are several deletion and insertion regions inside the amino acid sequence. By constructing knockout strain and overexpression strain of the gene cg2777, fermentation verification analysis implied that cg2777 performs partial GPAT function with low activity. As lipid synthesis is a complex bio-catalytic process involving multiple enzymes, single overexpression of gene cg2777 cannot significantly increase the intracellular lipid content.
Glycerol-3-phosphate acyltransferase (GPAT) is a key enzyme in the synthesis of triacylglycerols (TAGs). It catalyzes the formation of lysophosphatidic acid (LPA) from glycerol-3-phosphate and fatty acyl-CoA, which is the initial reaction of the synthesis of TAG and glycerol-phospholipids. However, previous research works shows that there is no definite GO annotations of GPAT gene in the complete genome sequence of Corynebacterium glutamicum ATCC13032. Aided by bioinformatics and literature mining, several candidate genes encoding GPAT in C.glutamicum ATCC 13032 have been predicted out. cg2777, which is a putative membrane protein gene, has been selected for subsequent analysis and verification. The cg2777 gene has typical GPAT functional domain and four typical transmembrane helix regions. Compared with the GPAT gene sequences derived from a variety of oil-producing bacteria, there are several deletion and insertion regions inside the amino acid sequence. By constructing knockout strain and overexpression strain of the gene cg2777, fermentation verification analysis implied that cg2777 performs partial GPAT function with low activity. As lipid synthesis is a complex bio-catalytic process involving multiple enzymes, single overexpression of gene cg2777 cannot significantly increase the intracellular lipid content.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200824003
Abstract:
As the core technology of the robot controller, the motion control algorithm is under a significant influence on the robot motion performance including stability, reliability, and rapidity. However, the application of motion control algorithms to motion controllers usually faces many problems such as poor versatility, algorithm embedded in complex, and long design cycles. For this problem, this study combines the complex flexible S-shaped acceleration/deceleration (ACC/DEC) motion control algorithm and proposes an efficient design method of model-based hardware-software collaboration, which can shorten the design cycle of the robot motion control system and improve the development efficiency. By modeling the flexible motion control algorithm and establishing a set of the parameter list, the algorithm will adaptively change the motion speed planning according to the parameters in the list, which improves the flexibility of application. Based on analysis, design software and hardware models for flexible motion control algorithms on the Simulink platform, and perform system-level interconnection and simulation to verify the algorithms. Then, use the MathWorks toolbox to automatically generate embedded C code and programmable logic IP cores for the software and hardware models. At last, deploy the generated code to a motion controller based on Zynq-7000, and set up an experimental platform to implement algorithm functions. The simulation results prove that the design model achieves the effect of the S-shaped ACC/DEC algorithm, and the speed profile has great flexibility characteristics. The experimental results show that the ACC/DEC algorithm deployed on the Zynq-7000 is in agreement with the simulation results. Simulation and experiment jointly verify the feasibility and effectiveness of the model-based software and hardware co-design method, which has important application value in the field of personalized robots.
As the core technology of the robot controller, the motion control algorithm is under a significant influence on the robot motion performance including stability, reliability, and rapidity. However, the application of motion control algorithms to motion controllers usually faces many problems such as poor versatility, algorithm embedded in complex, and long design cycles. For this problem, this study combines the complex flexible S-shaped acceleration/deceleration (ACC/DEC) motion control algorithm and proposes an efficient design method of model-based hardware-software collaboration, which can shorten the design cycle of the robot motion control system and improve the development efficiency. By modeling the flexible motion control algorithm and establishing a set of the parameter list, the algorithm will adaptively change the motion speed planning according to the parameters in the list, which improves the flexibility of application. Based on analysis, design software and hardware models for flexible motion control algorithms on the Simulink platform, and perform system-level interconnection and simulation to verify the algorithms. Then, use the MathWorks toolbox to automatically generate embedded C code and programmable logic IP cores for the software and hardware models. At last, deploy the generated code to a motion controller based on Zynq-7000, and set up an experimental platform to implement algorithm functions. The simulation results prove that the design model achieves the effect of the S-shaped ACC/DEC algorithm, and the speed profile has great flexibility characteristics. The experimental results show that the ACC/DEC algorithm deployed on the Zynq-7000 is in agreement with the simulation results. Simulation and experiment jointly verify the feasibility and effectiveness of the model-based software and hardware co-design method, which has important application value in the field of personalized robots.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200411005
Abstract:
Waviness of rolling bearing has greatly influence on the vibration and acoustic performances of bearings. This paper takes the deep groove ball bearing as the research subject. With the autocorrelation function, 3D random surface waviness is simulated. Through mechanical analysis, the calculation method for trajectory of inner ring and the center of each ball is established. Combined with the theory of acoustics, the noise generated by bearing can be calculated quantitatively. Through a specific example, the acoustic characteristics of inner ring and balls have been studied. The research shows that noise generated by bearing varied in terms of different directions. Through the study of sound pressure signal and frequency spectrum, the result shows that peaks of the sound pressure spectrum observed close to the frequency of ball passing frequency of inner ring and outer ring. The influences of different waviness amplitude on each noise source of bearing have been studied. The result shows that the peak-to-peak and RMS values of sound pressure signal increase with the increase of waviness amplitude.
Waviness of rolling bearing has greatly influence on the vibration and acoustic performances of bearings. This paper takes the deep groove ball bearing as the research subject. With the autocorrelation function, 3D random surface waviness is simulated. Through mechanical analysis, the calculation method for trajectory of inner ring and the center of each ball is established. Combined with the theory of acoustics, the noise generated by bearing can be calculated quantitatively. Through a specific example, the acoustic characteristics of inner ring and balls have been studied. The research shows that noise generated by bearing varied in terms of different directions. Through the study of sound pressure signal and frequency spectrum, the result shows that peaks of the sound pressure spectrum observed close to the frequency of ball passing frequency of inner ring and outer ring. The influences of different waviness amplitude on each noise source of bearing have been studied. The result shows that the peak-to-peak and RMS values of sound pressure signal increase with the increase of waviness amplitude.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200824001
Abstract:
Ranpirnase, as a multi-functional protein drug, is one of many drugs that being studied all over the world. it was first isolated from the oocytes and early embryos of Northern Leopard Frog, and belongs to the ribonuclease A (RNase A) superfamily. Ranpirnase can not only inhibit protein biosynthesis pathway, but also independently induce tumor cell apoptosis. it not only has anti-tumor effect, but also has antiviral activity. In this study, In order to improve the expression level of ranpirnase in E. coli. Plackett-Burman (PB) experimental design was applied to investigate the effect of various factors on onconase expression in recombinant E. coli and optimize recombinant E. coli fermentation medium and inducing conditions. On the basis of these results, the onconase expression in recombinant E. coli was carried out in 7 L fermentor with the combined exponential and pH-Stat feeding strategy. Finally, using the optimized fermentation medium, the protein expression at flask level was increased from 9% to 36%, OD600 value was from 4.8 up to 5.47. Expression of oncanase at 7 L fermentor level was reached to 55%, OD600 value was increased to 35 after the optimization.The use of optimized medium for high-density fermentation of ranpirnase further saved the production cost and accelerated the marketization of ranpirnase.
Ranpirnase, as a multi-functional protein drug, is one of many drugs that being studied all over the world. it was first isolated from the oocytes and early embryos of Northern Leopard Frog, and belongs to the ribonuclease A (RNase A) superfamily. Ranpirnase can not only inhibit protein biosynthesis pathway, but also independently induce tumor cell apoptosis. it not only has anti-tumor effect, but also has antiviral activity. In this study, In order to improve the expression level of ranpirnase in E. coli. Plackett-Burman (PB) experimental design was applied to investigate the effect of various factors on onconase expression in recombinant E. coli and optimize recombinant E. coli fermentation medium and inducing conditions. On the basis of these results, the onconase expression in recombinant E. coli was carried out in 7 L fermentor with the combined exponential and pH-Stat feeding strategy. Finally, using the optimized fermentation medium, the protein expression at flask level was increased from 9% to 36%, OD600 value was from 4.8 up to 5.47. Expression of oncanase at 7 L fermentor level was reached to 55%, OD600 value was increased to 35 after the optimization.The use of optimized medium for high-density fermentation of ranpirnase further saved the production cost and accelerated the marketization of ranpirnase.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200911005
Abstract:
Modified activated carbon was prepared by stepwise composite modification with oxidant and nucleophilic addition reagent. The specific surface area and pore structure of activated carbon were measured by N2 adsorption-desorption (BET). Formaldehyde was used as a model pollutant to test the purification performance of modified activated carbon. The results showed that the stepwise composite modification, first with the oxidant then with the nucleophilic addition reagent, could significantly improve the fast purification efficiency of activated carbon for formaldehyde. While the stepwise composite modification, first with the nucleophilic addition reagent then with the oxidant, showed excellent long-term purification effect. After the activated carbon was impregnated first with 2-imidazolidinone and then with sodium hypochlorite in a stepwise composite modification, the long-term purification efficiency of formaldehyde with coconut shell and coal-based activated carbon reached 94.2% and 96.2%, respectively.
Modified activated carbon was prepared by stepwise composite modification with oxidant and nucleophilic addition reagent. The specific surface area and pore structure of activated carbon were measured by N2 adsorption-desorption (BET). Formaldehyde was used as a model pollutant to test the purification performance of modified activated carbon. The results showed that the stepwise composite modification, first with the oxidant then with the nucleophilic addition reagent, could significantly improve the fast purification efficiency of activated carbon for formaldehyde. While the stepwise composite modification, first with the nucleophilic addition reagent then with the oxidant, showed excellent long-term purification effect. After the activated carbon was impregnated first with 2-imidazolidinone and then with sodium hypochlorite in a stepwise composite modification, the long-term purification efficiency of formaldehyde with coconut shell and coal-based activated carbon reached 94.2% and 96.2%, respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200724001
Abstract:
A nanopesticide has been increasingly favored due to its small particle size, high biological efficacy, and good dispersibility. However, a nanopesticide typically has a high price since of its costly production, which limits its applications in many cost-sensitive agricultural areas. Aligned with the national policy of the dosage reduction and efficacy enhancement of agrochemicals, a nanopesicide has very promising and demanded applications. This review introduces a new technology, capable of highly effective production of an aqueous nanodispersion with an ultra-high load of a pesticide, the flash nanoformation (FNF) technology, which is expected to be able to realize massive production of nanopesticides in a fast and economical way.
A nanopesticide has been increasingly favored due to its small particle size, high biological efficacy, and good dispersibility. However, a nanopesticide typically has a high price since of its costly production, which limits its applications in many cost-sensitive agricultural areas. Aligned with the national policy of the dosage reduction and efficacy enhancement of agrochemicals, a nanopesicide has very promising and demanded applications. This review introduces a new technology, capable of highly effective production of an aqueous nanodispersion with an ultra-high load of a pesticide, the flash nanoformation (FNF) technology, which is expected to be able to realize massive production of nanopesticides in a fast and economical way.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200731002
Abstract:
Water-soluble fluorescent probes have been developed and applied in physiological environments for biomedical science applications. Such probe molecules require good water-solubility, and the introduction of water-soluble substituents on conventional organic fluorophoreshas brought challenge about the preparation, separation, and purification processes. In this paper, probe molecules (3、6 and 9) based on naphthalene anhydride derived on rigid spiro-bifluorene skeleton were designed and synthesized. By converting commonly used naphthalenedimide functionality to anhydride groups, we were able to hydrolyzethe of acid anhydride groups under alkaline conditions, into a charged carboxylate structure to improve the water solubility of the probe molecule. At the same time, the solubility of the acid anhydride group in common organic solvents facilitates the separation and purification of the compound. UV-vis absorption spectra of a probe molecule in water upon increasing pH value, displayed hyposchromic-shift in absorption and increased solubility, implying the naphthalic anhydride group of the probe has been converted to carboxylate, which makes the probe good water-soluble to facilitate imaging. Probes 3、6、9 were successfully stained in cell imaging, effectively eliminating background signal interference. This result suggested that our strategy is promising for developing water-soluble fluorescent probes.
Water-soluble fluorescent probes have been developed and applied in physiological environments for biomedical science applications. Such probe molecules require good water-solubility, and the introduction of water-soluble substituents on conventional organic fluorophoreshas brought challenge about the preparation, separation, and purification processes. In this paper, probe molecules (3、6 and 9) based on naphthalene anhydride derived on rigid spiro-bifluorene skeleton were designed and synthesized. By converting commonly used naphthalenedimide functionality to anhydride groups, we were able to hydrolyzethe of acid anhydride groups under alkaline conditions, into a charged carboxylate structure to improve the water solubility of the probe molecule. At the same time, the solubility of the acid anhydride group in common organic solvents facilitates the separation and purification of the compound. UV-vis absorption spectra of a probe molecule in water upon increasing pH value, displayed hyposchromic-shift in absorption and increased solubility, implying the naphthalic anhydride group of the probe has been converted to carboxylate, which makes the probe good water-soluble to facilitate imaging. Probes 3、6、9 were successfully stained in cell imaging, effectively eliminating background signal interference. This result suggested that our strategy is promising for developing water-soluble fluorescent probes.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200629003
Abstract:
Oligo-1,6-glucosidase (EC 3.2.1.10) showed a good activity for the utilization of isomaltose in the residual sugar of lactic acid fermentation broth. Based on the previous research work and approved by BRENDA database search, five possible heat-resistant and acid-resistant oligo-1,6-glucosidase enzyme genes (Bacillus coagulans ATCC 7050: malL BF29_2011(BC1) and malL BF29_2004(BC2); Bacillus subtilis 168: yvdL BSU34560(BS1) and yugT BSU31290(BS2); Bacillus thermoglucosidasius KP 1006: malL(KP)) were selected for the research of recombinant expression in E. coli, purification and enzymatic properties of these oligo-1,6-glucosidases. The optimal pH of BC1, BC2 and KP was 5.0, and that of BS1 and BS2 was 6.0. The optimal temperature of these five oligo-1,6-glucosidases from high to low was KP (60 ℃) > BC1 (56 ℃) = BC2 (56 ℃) > BS1 (43 ℃) = BS2 (43 ℃). In addition, Mg2+ and\begin{document}${\rm{NH}}_4^ + $\end{document} ![]()
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Oligo-1,6-glucosidase (EC 3.2.1.10) showed a good activity for the utilization of isomaltose in the residual sugar of lactic acid fermentation broth. Based on the previous research work and approved by BRENDA database search, five possible heat-resistant and acid-resistant oligo-1,6-glucosidase enzyme genes (Bacillus coagulans ATCC 7050: malL BF29_2011(BC1) and malL BF29_2004(BC2); Bacillus subtilis 168: yvdL BSU34560(BS1) and yugT BSU31290(BS2); Bacillus thermoglucosidasius KP 1006: malL(KP)) were selected for the research of recombinant expression in E. coli, purification and enzymatic properties of these oligo-1,6-glucosidases. The optimal pH of BC1, BC2 and KP was 5.0, and that of BS1 and BS2 was 6.0. The optimal temperature of these five oligo-1,6-glucosidases from high to low was KP (60 ℃) > BC1 (56 ℃) = BC2 (56 ℃) > BS1 (43 ℃) = BS2 (43 ℃). In addition, Mg2+ and
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200817001
Abstract:
With the increase in the number of electric vehicles connected to the grid and the continuous increase in battery capacity, the uncoordinated charging has been bringing tremendous pressure to the grid, and even affect the stability of the operation of the grid. On the contrary, reasonable dispatch of electric vehicles can bring additional benefits to the grid. This paper proposes a sliding window variable speed optimization charging method to achieve the real-time V2G scheduling of grid-connected electric vehicles. The real-time electricity prices are combined to minimize economic costs, the network loss is quickly solved through offline network loss sensitivity, and then, the battery aging cost is quantified via charging power fluctuation method. By constructing a multi-objective optimization problem composed of battery aging cost, charging cost, and grid loss cost minimization, the V2G real-time scheduling strategy is obtained via a convex optimization algorithm. Finally, the comparative experiments are made about the average distribution scheme, natural charging scheme, and the proposed sliding window variable speed optimized charging scheme on the improved IEEE33 node distribution network, from which it is shown that the proposed scheme can slow down battery aging and effectively reduce charging costs and network losses, and balance the loads.
With the increase in the number of electric vehicles connected to the grid and the continuous increase in battery capacity, the uncoordinated charging has been bringing tremendous pressure to the grid, and even affect the stability of the operation of the grid. On the contrary, reasonable dispatch of electric vehicles can bring additional benefits to the grid. This paper proposes a sliding window variable speed optimization charging method to achieve the real-time V2G scheduling of grid-connected electric vehicles. The real-time electricity prices are combined to minimize economic costs, the network loss is quickly solved through offline network loss sensitivity, and then, the battery aging cost is quantified via charging power fluctuation method. By constructing a multi-objective optimization problem composed of battery aging cost, charging cost, and grid loss cost minimization, the V2G real-time scheduling strategy is obtained via a convex optimization algorithm. Finally, the comparative experiments are made about the average distribution scheme, natural charging scheme, and the proposed sliding window variable speed optimized charging scheme on the improved IEEE33 node distribution network, from which it is shown that the proposed scheme can slow down battery aging and effectively reduce charging costs and network losses, and balance the loads.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200610001
Abstract:
Nowadays, with the increasing data dimension of detection and storage in industrial process, the traditional detection methods have some problems such as slow processing speed and difficult feature extraction. Therefore, the research on fault detection technology based on high-dimensional data is very necessary. In this paper, a fault detection method based on adaptive sparse representation and locality preserving projects(ASRLPP) is proposed. Firstly, the sparse dictionary learning algorithm is used to construct the residual space for feature extraction, which makes the global feature of the data more obvious. Then, the locality preserving projections (LPP) algorithm is used to reduce the dimension of the data in the residual space. LPP can effectively preserve the local features of data. Finally, T2 statistics is used to calculate the control limit for monitoring. In the process of monitoring, the adaptive updating rules are introduced to update the initial training data. It can improve the efficiency and accuracy of fault detection by dynamically updating the control limits. Through numerical example test and Tennessee-Eastman(TE) process simulation, it is proved that ASRLPP is superior to LPP and Sparse Residual Distance(SRD), ASRLPP has better fault detection ability, numerical examples and TE process simulation results verify the effectiveness and superiority of the proposed algorithm in fault detection of industrial process.
Nowadays, with the increasing data dimension of detection and storage in industrial process, the traditional detection methods have some problems such as slow processing speed and difficult feature extraction. Therefore, the research on fault detection technology based on high-dimensional data is very necessary. In this paper, a fault detection method based on adaptive sparse representation and locality preserving projects(ASRLPP) is proposed. Firstly, the sparse dictionary learning algorithm is used to construct the residual space for feature extraction, which makes the global feature of the data more obvious. Then, the locality preserving projections (LPP) algorithm is used to reduce the dimension of the data in the residual space. LPP can effectively preserve the local features of data. Finally, T2 statistics is used to calculate the control limit for monitoring. In the process of monitoring, the adaptive updating rules are introduced to update the initial training data. It can improve the efficiency and accuracy of fault detection by dynamically updating the control limits. Through numerical example test and Tennessee-Eastman(TE) process simulation, it is proved that ASRLPP is superior to LPP and Sparse Residual Distance(SRD), ASRLPP has better fault detection ability, numerical examples and TE process simulation results verify the effectiveness and superiority of the proposed algorithm in fault detection of industrial process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200316001
Abstract:
α-Al2O3 is widely used in the fields of machinery, chemical industry, electronics and other fields due to its excellent properties such as high hardness, ultra-high wear resistance, and chemical resistance. At present, most of the methods for preparing α-Al2O3 powders use different precursors calcinated at certain temperature. Many researchers have done a lot of research on the phase transition of alumina. However, there are few comparative studies on the phase transition process of different precursor materials. In this study, α-Al2O3 was synthesized by using γ-AlOOH and γ-Al2O3 as precursor materials and MgO as a doped additive. The phase transition process was characterized by means of SEM, XRD and particle size distribution. The results show that both γ-Al2O3 and γ-AlOOH undergo α phase transition process of γ-Al2O3→ θ-Al2O3→α-Al2O3 during calcinations and pure α-Al2O3 phases could be obtained at 1200°C and 1100°C respectively. The temperature for pure α-Al2O3 phase is affected by the particle size distribution of γ-Al2O3 formed during its calcination. And MgO doping additive can promote the α-Al2O3 phase transition process. The promotion effect is most obvious when the doping amount is appropriate and it would be weakened when the doping amount is too high or too low. When the MgO doping amount is 0.5wt%, the temperature at which γ-Al2O3 completes the α phase transformation is 1050°C, which is 150°C lower than that in the case without MgO. When γ-AlOOH is 0.3wt% and 0.5wt% of MgO doped, the temperature for complete transition to α-Al2O3 is 1075°C, which is 25°C lower than the case without MgO. The promotion effect of MgO on the phase transition of different precursors is affected by the size of the primary particles of the powder.
α-Al2O3 is widely used in the fields of machinery, chemical industry, electronics and other fields due to its excellent properties such as high hardness, ultra-high wear resistance, and chemical resistance. At present, most of the methods for preparing α-Al2O3 powders use different precursors calcinated at certain temperature. Many researchers have done a lot of research on the phase transition of alumina. However, there are few comparative studies on the phase transition process of different precursor materials. In this study, α-Al2O3 was synthesized by using γ-AlOOH and γ-Al2O3 as precursor materials and MgO as a doped additive. The phase transition process was characterized by means of SEM, XRD and particle size distribution. The results show that both γ-Al2O3 and γ-AlOOH undergo α phase transition process of γ-Al2O3→ θ-Al2O3→α-Al2O3 during calcinations and pure α-Al2O3 phases could be obtained at 1200°C and 1100°C respectively. The temperature for pure α-Al2O3 phase is affected by the particle size distribution of γ-Al2O3 formed during its calcination. And MgO doping additive can promote the α-Al2O3 phase transition process. The promotion effect is most obvious when the doping amount is appropriate and it would be weakened when the doping amount is too high or too low. When the MgO doping amount is 0.5wt%, the temperature at which γ-Al2O3 completes the α phase transformation is 1050°C, which is 150°C lower than that in the case without MgO. When γ-AlOOH is 0.3wt% and 0.5wt% of MgO doped, the temperature for complete transition to α-Al2O3 is 1075°C, which is 25°C lower than the case without MgO. The promotion effect of MgO on the phase transition of different precursors is affected by the size of the primary particles of the powder.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200328001
Abstract:
The contact angles of five types of fine ash with different particle size distributions from opposed multi-burner coal water slurry gasification were measured by Washburn riser, and the reference solution for measuring the effective radius is n-hexane. X-ray diffraction and fourier infrared spectrometer were used to determine the XRD pattern and infrared spectrum of the fine ash. The surface groups, crystal mass fraction and crystallinity of inorganic minerals were analyzed in order to find out the influence of physical and chemical properties on its wettability. The results show that the contact angle of the opposed multi-burner coal water slurry gasification fine ash decreases as the particle size increases, but it does not change monotonously with the particle size. In addition to the particle size, the contact angle is still affected by other physical and chemical properties. Surface groups mainly composed of Si−O−Si and −OH and inorganic minerals mainly composed of SiO2 are the main physical and chemical properties that affect the wetting characteristics of particles in addition to particle size. All three factors contribute to the enhancement of particle wettability. There is a good linear relationship between the peak areas of the surface groups of Si−O−Si and −OH in the infrared spectrum and the contact angle of fine coal gasification ash. As the peak area of the group increases, the wetting contact angle decreases. It indicates thatt the surface group peak area represented by Si−O−Si and −OH can be used as a quantitative index to characterize the wettability of the same coal gasification fine ash.
The contact angles of five types of fine ash with different particle size distributions from opposed multi-burner coal water slurry gasification were measured by Washburn riser, and the reference solution for measuring the effective radius is n-hexane. X-ray diffraction and fourier infrared spectrometer were used to determine the XRD pattern and infrared spectrum of the fine ash. The surface groups, crystal mass fraction and crystallinity of inorganic minerals were analyzed in order to find out the influence of physical and chemical properties on its wettability. The results show that the contact angle of the opposed multi-burner coal water slurry gasification fine ash decreases as the particle size increases, but it does not change monotonously with the particle size. In addition to the particle size, the contact angle is still affected by other physical and chemical properties. Surface groups mainly composed of Si−O−Si and −OH and inorganic minerals mainly composed of SiO2 are the main physical and chemical properties that affect the wetting characteristics of particles in addition to particle size. All three factors contribute to the enhancement of particle wettability. There is a good linear relationship between the peak areas of the surface groups of Si−O−Si and −OH in the infrared spectrum and the contact angle of fine coal gasification ash. As the peak area of the group increases, the wetting contact angle decreases. It indicates thatt the surface group peak area represented by Si−O−Si and −OH can be used as a quantitative index to characterize the wettability of the same coal gasification fine ash.
, Available online
Abstract:
The effects of bubbles in the liquid jet on the air-blast atomization of viscous liquid were investigated using the Malvern laser particle size analyzer. The glycerol-water solution (Oh=3.46×10-2~0.407) is used as the liquid phase, and air as the gas phase. Experiments with various coaxial gas velocities (ug=94.8~142 m/s) and liquid jet velocities (ul,0=0.29~1.21 m/s) were conducted to study influences of inner bubbles on the droplets size. It was found that at low Oh, the droplet size decreases with the volumetric flow rate of aerating gas for the low liquid jet velocity, and the droplet size increase with the volumetric flow rate of aerating gas for the high liquid jet velocity; the droplet size increases with the volumetric flow rate of aerating gas at high Oh. A model is deduced to delineate the variation trend of the droplet size. We compared the predictions of this model with experimental results and found the relative deviations are less than ±5%.
The effects of bubbles in the liquid jet on the air-blast atomization of viscous liquid were investigated using the Malvern laser particle size analyzer. The glycerol-water solution (Oh=3.46×10-2~0.407) is used as the liquid phase, and air as the gas phase. Experiments with various coaxial gas velocities (ug=94.8~142 m/s) and liquid jet velocities (ul,0=0.29~1.21 m/s) were conducted to study influences of inner bubbles on the droplets size. It was found that at low Oh, the droplet size decreases with the volumetric flow rate of aerating gas for the low liquid jet velocity, and the droplet size increase with the volumetric flow rate of aerating gas for the high liquid jet velocity; the droplet size increases with the volumetric flow rate of aerating gas at high Oh. A model is deduced to delineate the variation trend of the droplet size. We compared the predictions of this model with experimental results and found the relative deviations are less than ±5%.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200331002
Abstract:
The gasification characteristics of petroleum coke, fine slag and Shenfu coal char were comparatively studied using a high temperature stage microscope (HTSM) system. Petroleum coke mixed with Shenfu slag were prepared for comparison. The thermogravimetric analyzer (TGA) was used to compare the gasification reactivities of the different samples. It was found that the gasification reactivity of fine slag was obviously better than that of petroleum coke and slightly worse than that of Shenfu coal char at 1200 ℃. The addition of Shenfu slag accelerated the reaction rate of petroleum coke, indicating that the ash contained in the fine slag promoted the gasification reaction. The images of micro structure of petroleum coke and fine slag sample obtained from a scanning electron microscope showed that the surface of petroleum coke was very dense with few pores that can be clearly observed. The structure of fine slag particles was relatively loose. Micron-sized pores could be observed. The results of specific surface area and pore structure analysis showed that compared with petroleum coke, the pore structure of fine slag was developed after the gasification process that the specific surface area of which was about 15 times than petroleum coke. There were more 2-10 nm pores in the fine slag sample. The irregular pore structure of fine slag provided more surface for the gasification reaction and accelerated the gasification reaction rate. In addition, according to the results of the micro-Raman analysis, fine slag was relatively low in graphitization tendency compared with petroleum coke, which reflected the good gasification reactivity of fine slag.
The gasification characteristics of petroleum coke, fine slag and Shenfu coal char were comparatively studied using a high temperature stage microscope (HTSM) system. Petroleum coke mixed with Shenfu slag were prepared for comparison. The thermogravimetric analyzer (TGA) was used to compare the gasification reactivities of the different samples. It was found that the gasification reactivity of fine slag was obviously better than that of petroleum coke and slightly worse than that of Shenfu coal char at 1200 ℃. The addition of Shenfu slag accelerated the reaction rate of petroleum coke, indicating that the ash contained in the fine slag promoted the gasification reaction. The images of micro structure of petroleum coke and fine slag sample obtained from a scanning electron microscope showed that the surface of petroleum coke was very dense with few pores that can be clearly observed. The structure of fine slag particles was relatively loose. Micron-sized pores could be observed. The results of specific surface area and pore structure analysis showed that compared with petroleum coke, the pore structure of fine slag was developed after the gasification process that the specific surface area of which was about 15 times than petroleum coke. There were more 2-10 nm pores in the fine slag sample. The irregular pore structure of fine slag provided more surface for the gasification reaction and accelerated the gasification reaction rate. In addition, according to the results of the micro-Raman analysis, fine slag was relatively low in graphitization tendency compared with petroleum coke, which reflected the good gasification reactivity of fine slag.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200331001
Abstract:
For multi-object tracking in complex scenes, there are problems of high object identification switching rate and high object trajectory false alarm rate. This paper proposes a multi-object tracking algorithm based on pedestrian re-identification network and CNN-GRU (Convolutional Neural Networks-Gated Recurrent Unit) metric network. By constructing a deep metric model combining CNN and dual GRU network, the time characteristics of appearance and motion features of the tracking object trajectory boxes are predicted simultaneously, so that the object has more discriminative features and the ID switch rate of object is reduced. Based on the CNN-GRU network, the correct matching probability of historical object trajectory is automatically learned. Different attentions are assigned to different track trajectory boxes of the same object, so as to suppress the influence of misdetected object boxes in the object trajectory on the overall features of the object and effectively aggregate the features of the object trajectory box. The algorithm combines the similarity of detection boxes and trajectory boxes calculated by the features of pedestrian re-identification network, and the similarity CNN-GRU network output as the matching cost of data association part. The experimental evaluation results on a standard multi-object tracking dataset show the effectiveness of the proposed algorithm.
For multi-object tracking in complex scenes, there are problems of high object identification switching rate and high object trajectory false alarm rate. This paper proposes a multi-object tracking algorithm based on pedestrian re-identification network and CNN-GRU (Convolutional Neural Networks-Gated Recurrent Unit) metric network. By constructing a deep metric model combining CNN and dual GRU network, the time characteristics of appearance and motion features of the tracking object trajectory boxes are predicted simultaneously, so that the object has more discriminative features and the ID switch rate of object is reduced. Based on the CNN-GRU network, the correct matching probability of historical object trajectory is automatically learned. Different attentions are assigned to different track trajectory boxes of the same object, so as to suppress the influence of misdetected object boxes in the object trajectory on the overall features of the object and effectively aggregate the features of the object trajectory box. The algorithm combines the similarity of detection boxes and trajectory boxes calculated by the features of pedestrian re-identification network, and the similarity CNN-GRU network output as the matching cost of data association part. The experimental evaluation results on a standard multi-object tracking dataset show the effectiveness of the proposed algorithm.
, Available online
Abstract:
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2021, 47(3): 255-261.
doi: 10.14135/j.cnki.1006-3080.20200308002
Abstract:
Cyclohexanone, a widely used organic chemical raw material, plays a very important role in industrial production. Hydrogenation of phenol to cyclohexanone has the advantages of mild reaction conditions, few by-products, and environmental friendliness. Pd/C catalyst was used for the hydrogenation of phenol to cyclohexanone. After comparing the dichloromethane, cyclohexane, ethanol, and benzene, benzene was selected as the solvent. At the reaction temperature 160—250 ℃, the reaction pressure 0.1—2.0 MPa, the weight hourly space velocity of phenol 0.2—1.0 h−1, the molar ratio of hydrogen to phenol 2—12, the influence of reaction conditions on the hydrogenation of phenol was investigated. A fixed bed tubular reactor charged with 80—100 mesh(150—180 μm）Pd/C catalyst was developed. Eliminating the influence of internal and external diffusion, 25 groups of experimental data were measured by changing the temperature, pressure and molar ratio of hydrogen to phenol. An intrinsic kinetic model was established based on the ideal adsorption model. The optimal reaction parameters were determined using 25 sets of orthogonal experimental data, and the dynamics model was also examined. The results show that the suitable reaction conditions are as follows: reaction temperature 175—205 ℃, reaction pressure 0.1 MPa, the molar ratio of hydrogen to phenol about 4, and the weight hourly space velocity of phenol 0.2—0.4 h−1. After statistical test and residual analysis, the established intrinsic kinetic model is applicable and has good reliability.
Cyclohexanone, a widely used organic chemical raw material, plays a very important role in industrial production. Hydrogenation of phenol to cyclohexanone has the advantages of mild reaction conditions, few by-products, and environmental friendliness. Pd/C catalyst was used for the hydrogenation of phenol to cyclohexanone. After comparing the dichloromethane, cyclohexane, ethanol, and benzene, benzene was selected as the solvent. At the reaction temperature 160—250 ℃, the reaction pressure 0.1—2.0 MPa, the weight hourly space velocity of phenol 0.2—1.0 h−1, the molar ratio of hydrogen to phenol 2—12, the influence of reaction conditions on the hydrogenation of phenol was investigated. A fixed bed tubular reactor charged with 80—100 mesh(150—180 μm）Pd/C catalyst was developed. Eliminating the influence of internal and external diffusion, 25 groups of experimental data were measured by changing the temperature, pressure and molar ratio of hydrogen to phenol. An intrinsic kinetic model was established based on the ideal adsorption model. The optimal reaction parameters were determined using 25 sets of orthogonal experimental data, and the dynamics model was also examined. The results show that the suitable reaction conditions are as follows: reaction temperature 175—205 ℃, reaction pressure 0.1 MPa, the molar ratio of hydrogen to phenol about 4, and the weight hourly space velocity of phenol 0.2—0.4 h−1. After statistical test and residual analysis, the established intrinsic kinetic model is applicable and has good reliability.
2021, 47(3): 262-271.
doi: 10.14135/j.cnki.1006-3080.20200316002
Abstract:
Based on the 12-lumped kinetic model established in the laboratory for residue fluid catalytic cracking (RFCC), an industrial reactor model for RFCC was established by setting unit factors and recycle calculation. An industrial simulation software of the model for unit factors calculation and product distribution prediction was developed. Based on the kinetic parameters from the laboratory model and steady operation data from an industrial unit, the unit factors were calculated by combining the fourth order RK4 (Runge-Kutta) method and the BFGS（Broyden-Fletcher-Goldforb-Shanno）optimization algorithm. It is proved that the relative errors between the calculated yields and the actual yields of the products are mostly within 5%, which indicates the reliability of the industrial reactor model established for RFCC and the unit factors. The effect of reaction temperature and mass ratio of catalyst to oil on product yields was also investigated. The prediction of product distribution trend by the software is consistent with catalytic cracking reaction rules. It is a good foundation for the further industrial application of the model.
Based on the 12-lumped kinetic model established in the laboratory for residue fluid catalytic cracking (RFCC), an industrial reactor model for RFCC was established by setting unit factors and recycle calculation. An industrial simulation software of the model for unit factors calculation and product distribution prediction was developed. Based on the kinetic parameters from the laboratory model and steady operation data from an industrial unit, the unit factors were calculated by combining the fourth order RK4 (Runge-Kutta) method and the BFGS（Broyden-Fletcher-Goldforb-Shanno）optimization algorithm. It is proved that the relative errors between the calculated yields and the actual yields of the products are mostly within 5%, which indicates the reliability of the industrial reactor model established for RFCC and the unit factors. The effect of reaction temperature and mass ratio of catalyst to oil on product yields was also investigated. The prediction of product distribution trend by the software is consistent with catalytic cracking reaction rules. It is a good foundation for the further industrial application of the model.
2021, 47(3): 272-277.
doi: 10.14135/j.cnki.1006-3080.20200101001
Abstract:
A hydrophobic coating was prepared via a simple NaCl-dissolution-assisted etching silicon template method. Epoxy resins with excellent properties were selected as the matrix. The effect of sodium chloride concentration and spraying times on the microstructure and wetting properties of the coatings were studied. The results showed that a multi-stepped rough structure was formed gradually with the increase of salt concentration and the spray times. After using saturated salt solution spraying 30 times, the water contact angle (WCA) of the surface increased from 80.2° to 130.0°, indicating its good hydrophobicity. Besides, the epoxy coating showed superior abrasion resistance and chemical stability for strong acid and alkali solutions. Even after 50 times of abrasion test, the hydrophobicity of the surface showed ignorable variations. Furthermore, the morphology of the prepared porous silicone template retained well after 20 cycles of pouring-demoulding, indicating its excellent reusable performance.
A hydrophobic coating was prepared via a simple NaCl-dissolution-assisted etching silicon template method. Epoxy resins with excellent properties were selected as the matrix. The effect of sodium chloride concentration and spraying times on the microstructure and wetting properties of the coatings were studied. The results showed that a multi-stepped rough structure was formed gradually with the increase of salt concentration and the spray times. After using saturated salt solution spraying 30 times, the water contact angle (WCA) of the surface increased from 80.2° to 130.0°, indicating its good hydrophobicity. Besides, the epoxy coating showed superior abrasion resistance and chemical stability for strong acid and alkali solutions. Even after 50 times of abrasion test, the hydrophobicity of the surface showed ignorable variations. Furthermore, the morphology of the prepared porous silicone template retained well after 20 cycles of pouring-demoulding, indicating its excellent reusable performance.
2021, 47(3): 278-283.
doi: 10.14135/j.cnki.1006-3080.20200310004
Abstract:
New drug design is a huge project, and the failure rate is very high. On the basis of the existing active compounds, the design of "me-too" drugs can increase the success rate. The lead compound can provide the same or similar spatial structure as the original active compound, and a novel skeleton. However, the skeleton of the lead compounds needn't have good physicochemical properties. Designing "me-too" drugs based on lead compounds can significantly reduce the difficulty of new drug development and improve the development efficiency and success rate. How to accurately grasp the spatial structure of the lead compounds, designing a novel structure, a reasonable valence bond, and a skeleton with the same or similar spatial structure as the lead compound are the key issues for the design of "me-too" drugs. We design and write an automated new drug skeleton design software based on the extraction of structural long chains, fragment matching and splicing: ChemCloser. The software can provide researchers with all core frameworks with reasonable structure and the same or similar spatial structure as the lead compounds. Researchers may find frameworks with novel structures, good physical properties and stable chemical properties in these core frameworks. After splicing pharmacodynamic groups on these skeletons, it can produce the same or similar biological activity as the original active compound.
New drug design is a huge project, and the failure rate is very high. On the basis of the existing active compounds, the design of "me-too" drugs can increase the success rate. The lead compound can provide the same or similar spatial structure as the original active compound, and a novel skeleton. However, the skeleton of the lead compounds needn't have good physicochemical properties. Designing "me-too" drugs based on lead compounds can significantly reduce the difficulty of new drug development and improve the development efficiency and success rate. How to accurately grasp the spatial structure of the lead compounds, designing a novel structure, a reasonable valence bond, and a skeleton with the same or similar spatial structure as the lead compound are the key issues for the design of "me-too" drugs. We design and write an automated new drug skeleton design software based on the extraction of structural long chains, fragment matching and splicing: ChemCloser. The software can provide researchers with all core frameworks with reasonable structure and the same or similar spatial structure as the lead compounds. Researchers may find frameworks with novel structures, good physical properties and stable chemical properties in these core frameworks. After splicing pharmacodynamic groups on these skeletons, it can produce the same or similar biological activity as the original active compound.
2021, 47(3): 284-291.
doi: 10.14135/j.cnki.1006-3080.20200313002
Abstract:
Cardiomyopathy is a common disease and is regarded as an important cause of premature death. The development of a gene therapy vector for cardiomyopathy is the key to gene therapy of cardiomyopathy. In order to build a gene therapy vector targeting myocardium, the eight selected AAV (AAV1−AAV4, AAV6−AAV9) capsid genes were recombined by DNA shuffling technology, and a random mutant AAV capsid library was constructed in mice direct screening. Finally the new AAV capsids with high myocardial targeting and low liver targeting, AAVH50 and AAVH59, were screened. Through the tail vein injection, the luciferase (Luc) gene carried was delivered to mice. It was found that among the eleven tissues tested, the gene expressions of AAVH50 and AAVH59 in myocardial tissue were higher than other tissues. Compared with AAV6 and AAV9, AAVH50 and AAVH59 expressed similar levels of luciferase in the myocardium, and the expression in the liver significantly decreased AAV9. After AAVH50 and AAVH59 infected primary neonatal rat cardiomyocytes, the efficiency of the selected AAVH59 in direct myocardial infection was significantly higher than that of AAV9. Therefore, this study screened to obtain a new type of AAV vector with high targeting to myocardium, and to the liver and low targeting, which is expected to provide a new type of AAV vector for gene therapy of myocardial diseases.
Cardiomyopathy is a common disease and is regarded as an important cause of premature death. The development of a gene therapy vector for cardiomyopathy is the key to gene therapy of cardiomyopathy. In order to build a gene therapy vector targeting myocardium, the eight selected AAV (AAV1−AAV4, AAV6−AAV9) capsid genes were recombined by DNA shuffling technology, and a random mutant AAV capsid library was constructed in mice direct screening. Finally the new AAV capsids with high myocardial targeting and low liver targeting, AAVH50 and AAVH59, were screened. Through the tail vein injection, the luciferase (Luc) gene carried was delivered to mice. It was found that among the eleven tissues tested, the gene expressions of AAVH50 and AAVH59 in myocardial tissue were higher than other tissues. Compared with AAV6 and AAV9, AAVH50 and AAVH59 expressed similar levels of luciferase in the myocardium, and the expression in the liver significantly decreased AAV9. After AAVH50 and AAVH59 infected primary neonatal rat cardiomyocytes, the efficiency of the selected AAVH59 in direct myocardial infection was significantly higher than that of AAV9. Therefore, this study screened to obtain a new type of AAV vector with high targeting to myocardium, and to the liver and low targeting, which is expected to provide a new type of AAV vector for gene therapy of myocardial diseases.
2021, 47(3): 292-299.
doi: 10.14135/j.cnki.1006-3080.20200323001
Abstract:
Bowman-Birk soybean trypsin inhibitor (BBTI), extracted from soybean (Glycine max L.) seeds, possesses insect resistance and anti-tumor properties. However, its specific mechanisms of action are still unknown. An efficient method to produce recombinant BBTI (rBBTI) in E. coli was reported. Some biochemical properties of rBBTI were revealed and the inhibition mechanism of BBTI was discussed. The rBBTI was successfully expressed with E. coli (BL21) expression system, and was further purified by Ni affinity chromatography and DEAE-FF column efficiently. The BBTI and rBBTI showed similar biochemical properties. The optimum conditions for inhibiting trypsin were pH 8 and 25 ℃, and the optimum conditions for inhibiting chymotrypsin were pH 9 and 16 ℃. BBTI and rBBTI were stable below 37 ℃. The inhibition kinetics assay of BBTI and rBBTI against trypsin as Lineweaver-Burk plots analysis showed an increased Michaelis constant (Km) and a decreased maximum reaction rate of enzyme (Vmax) with N-benzoyl-L-arginine ethyl ester(BAEE) as substrate. It suggested that BBTI and rBBTI were anti-competitive inhibitors interacted with trypsin. Both the inhibition kinetics assay of BBTI and rBBTI against chymotrypsin as Lineweaver-Burk plots analysis showed an unchanged Km and a decreased Vmax with N-acetyl-L-tyrosine ethyl ester(ATEE) as substrate. It suggested that BBTI and rBBTI were anti-competitive inhibitors interacted with chymotrypsin. Molecular modeling showed that LYS-16 of BBTI (trypsin active site of BBTI) interacted with residues of trypsin, forming effective hydrogen bonding interactions. However, there were hydrophobic residues at chymotrypsin activity domain of BBTI, and forming hydrophobic interactions with residues of chymotrypsin. These provide a reference for understanding the inhibition mechanism of BBTI, and the different inhibition rate of BBTI against trypsin or chymotrypsin.
Bowman-Birk soybean trypsin inhibitor (BBTI), extracted from soybean (Glycine max L.) seeds, possesses insect resistance and anti-tumor properties. However, its specific mechanisms of action are still unknown. An efficient method to produce recombinant BBTI (rBBTI) in E. coli was reported. Some biochemical properties of rBBTI were revealed and the inhibition mechanism of BBTI was discussed. The rBBTI was successfully expressed with E. coli (BL21) expression system, and was further purified by Ni affinity chromatography and DEAE-FF column efficiently. The BBTI and rBBTI showed similar biochemical properties. The optimum conditions for inhibiting trypsin were pH 8 and 25 ℃, and the optimum conditions for inhibiting chymotrypsin were pH 9 and 16 ℃. BBTI and rBBTI were stable below 37 ℃. The inhibition kinetics assay of BBTI and rBBTI against trypsin as Lineweaver-Burk plots analysis showed an increased Michaelis constant (Km) and a decreased maximum reaction rate of enzyme (Vmax) with N-benzoyl-L-arginine ethyl ester(BAEE) as substrate. It suggested that BBTI and rBBTI were anti-competitive inhibitors interacted with trypsin. Both the inhibition kinetics assay of BBTI and rBBTI against chymotrypsin as Lineweaver-Burk plots analysis showed an unchanged Km and a decreased Vmax with N-acetyl-L-tyrosine ethyl ester(ATEE) as substrate. It suggested that BBTI and rBBTI were anti-competitive inhibitors interacted with chymotrypsin. Molecular modeling showed that LYS-16 of BBTI (trypsin active site of BBTI) interacted with residues of trypsin, forming effective hydrogen bonding interactions. However, there were hydrophobic residues at chymotrypsin activity domain of BBTI, and forming hydrophobic interactions with residues of chymotrypsin. These provide a reference for understanding the inhibition mechanism of BBTI, and the different inhibition rate of BBTI against trypsin or chymotrypsin.
2021, 47(3): 300-307.
doi: 10.14135/j.cnki.1006-3080.20200213001
Abstract:
Glucose oxidase (GOD) is widely used in food, chemistry, medicine, biotechnology and other industrial applications. In this study, the gene GOD from Aspergillus niger was optimized according to the codon bias of pichia pastoris(P. pastoris), then it was used to construct the GOD secretory expression vector pPIC9K-GOD and the corresponding recombinant P. pastoris strain G/GOD. Subsequently, the higher concentration of geneticin (G418) was used to select the multicopy genomic integration strain G/GODM, and the extracellular GOD specific activity was improved to 5843.2 U/g, which was 8.2 times as high as that of the single copy strain G/GOD. On the basis of the first generation high producing strain, additional co-expression of folding factors, as well as the enhancement of central carbon metabolism, were used to construct the second generation strain. Co-expression of the protein folding factors PDI1, PDI2 and HAC1 improved the extracellular GOD activity by 32.7%, 8.9% and 54.4%, respectively. With the co-expression of the pentose phosphate pathway gene SOL3 and the tricarboxylic acid cycle gene MDH1, the extracellular GOD activity was enhanced by 6.3% and 11.6%, respectively. The best strain G/GMH1 was selected for the bioreactor fermentation to achieve 6 656.6 U/g of the extracellular GOD activity in a 50 L bioreactor, indicating its value for industrial application.
Glucose oxidase (GOD) is widely used in food, chemistry, medicine, biotechnology and other industrial applications. In this study, the gene GOD from Aspergillus niger was optimized according to the codon bias of pichia pastoris(P. pastoris), then it was used to construct the GOD secretory expression vector pPIC9K-GOD and the corresponding recombinant P. pastoris strain G/GOD. Subsequently, the higher concentration of geneticin (G418) was used to select the multicopy genomic integration strain G/GODM, and the extracellular GOD specific activity was improved to 5843.2 U/g, which was 8.2 times as high as that of the single copy strain G/GOD. On the basis of the first generation high producing strain, additional co-expression of folding factors, as well as the enhancement of central carbon metabolism, were used to construct the second generation strain. Co-expression of the protein folding factors PDI1, PDI2 and HAC1 improved the extracellular GOD activity by 32.7%, 8.9% and 54.4%, respectively. With the co-expression of the pentose phosphate pathway gene SOL3 and the tricarboxylic acid cycle gene MDH1, the extracellular GOD activity was enhanced by 6.3% and 11.6%, respectively. The best strain G/GMH1 was selected for the bioreactor fermentation to achieve 6 656.6 U/g of the extracellular GOD activity in a 50 L bioreactor, indicating its value for industrial application.
2021, 47(3): 308-315.
doi: 10.14135/j.cnki.1006-3080.20200227002
Abstract:
Coenzyme Q10 is an important hydrogen transmitter in respiratory chain, which plays an important role in the human body. It has been widely used for the treatment of cardiovascular and cerebrovascular diseases, and has been applied in cosmetics, health care products and other aspects. With the development of research, the market demand and industrial output of coenzyme Q10 are constantly expanding. Microbial fermentation is the most promising method for the production of coenzyme Q10 . This study used atmospheric and room temperature plasma (ARTP) towards Rhodobacter sphaeroides to obtain mutants. Meanwhile, an oxygen-limited model was established to screen the strains which could suffer the low oxygen concentration. The bacterial suspension was treated by ARTP for 25 s, then cultured on the plate with 0.4 g/L sodium sulfite. The strains were fermented in 2.0 mL volume for 48 h. In the primary screening 24-well plates, 6 mutants were obtained. Furthermore, these mutants were cultured in shake flask to verify genetic stability. A mutant R. sphaeroides F5D13 presented good stability and production of Coenzyme Q10. The titer of R. sphaeroides F5D13 was improved from 86.2 mg/L to 111.8 mg/L compared to the original strain. Finally, the yield of Coenzyme Q10 reached 770.06 mg/L in 5 L fermentation after 100 h, which was increased by 18.0%. According to the fermentation parameters, the mutant R. sphaeroides F5D13 showed the advantage of biomass in the cell growth phase and strong oxygen demand in product synthesis phase, which leaded to the higher oxygen uptake rate and yield than the original parent strain.
Coenzyme Q10 is an important hydrogen transmitter in respiratory chain, which plays an important role in the human body. It has been widely used for the treatment of cardiovascular and cerebrovascular diseases, and has been applied in cosmetics, health care products and other aspects. With the development of research, the market demand and industrial output of coenzyme Q10 are constantly expanding. Microbial fermentation is the most promising method for the production of coenzyme Q10 . This study used atmospheric and room temperature plasma (ARTP) towards Rhodobacter sphaeroides to obtain mutants. Meanwhile, an oxygen-limited model was established to screen the strains which could suffer the low oxygen concentration. The bacterial suspension was treated by ARTP for 25 s, then cultured on the plate with 0.4 g/L sodium sulfite. The strains were fermented in 2.0 mL volume for 48 h. In the primary screening 24-well plates, 6 mutants were obtained. Furthermore, these mutants were cultured in shake flask to verify genetic stability. A mutant R. sphaeroides F5D13 presented good stability and production of Coenzyme Q10. The titer of R. sphaeroides F5D13 was improved from 86.2 mg/L to 111.8 mg/L compared to the original strain. Finally, the yield of Coenzyme Q10 reached 770.06 mg/L in 5 L fermentation after 100 h, which was increased by 18.0%. According to the fermentation parameters, the mutant R. sphaeroides F5D13 showed the advantage of biomass in the cell growth phase and strong oxygen demand in product synthesis phase, which leaded to the higher oxygen uptake rate and yield than the original parent strain.
2021, 47(3): 316-322.
doi: 10.14135/j.cnki.1006-3080.20191230002
Abstract:
The process of traffic signal control can be taken as a typical network control system (NCSs), which has extensive application in many fields for its easy maintenance and installation. However, there are still some problems on NCSs in practical analysis and design, such as network delay, packet loss, signal quantization, and multi-packet transmission, which may decrease the performance of the control system and further lead to system instability. This paper presents an adaptive event-triggered model predictive control (MPC) strategy to reduce the communication consumption. The resulting framework is used for the stabilization of uncertain NCSs subject to quantization. The system state and control signals are transmitted via wireless networks only when the triggering conditions are satisfied, in which the adaptive triggering mechanism has more flexible and better performance. The adaptive triggering condition decides how often to transmit the current sample data. Under this mechanism, a robust MPC is designed to ensure the stability of closed-loop NCSs with quantized effects and achieve the desired control performance, meanwhile, reduce the energy consumption and improve the network congestion. A solving algorithm on the proposed control method is also given. Finally, it is shown via a simulation example that the proposed event-triggered MPC method can ensure the robust stability of the controlled system, meanwhile, the event-triggered mechanism can save more communication resources.
The process of traffic signal control can be taken as a typical network control system (NCSs), which has extensive application in many fields for its easy maintenance and installation. However, there are still some problems on NCSs in practical analysis and design, such as network delay, packet loss, signal quantization, and multi-packet transmission, which may decrease the performance of the control system and further lead to system instability. This paper presents an adaptive event-triggered model predictive control (MPC) strategy to reduce the communication consumption. The resulting framework is used for the stabilization of uncertain NCSs subject to quantization. The system state and control signals are transmitted via wireless networks only when the triggering conditions are satisfied, in which the adaptive triggering mechanism has more flexible and better performance. The adaptive triggering condition decides how often to transmit the current sample data. Under this mechanism, a robust MPC is designed to ensure the stability of closed-loop NCSs with quantized effects and achieve the desired control performance, meanwhile, reduce the energy consumption and improve the network congestion. A solving algorithm on the proposed control method is also given. Finally, it is shown via a simulation example that the proposed event-triggered MPC method can ensure the robust stability of the controlled system, meanwhile, the event-triggered mechanism can save more communication resources.
2021, 47(3): 323-330.
doi: 10.14135/j.cnki.1006-3080.20200122001
Abstract:
The airport perimeter intrusion alarm system is the first line of defense in the airport's flight zone. Traditional airport perimeter intrusion alarm system has higher false alarm rate and cannot classify different intrusion categories under the influence of severe weather. In order to solve the problem, a self-encoder long-short term memory network model is proposed in this paper. This model extracts the hidden encoder feature from input signals and constructs the feature vector matrix fused with timing information such that its complexity can be reduced. It is shown via the results of network model performance evaluation that the proposed network model has a low false alarm rate and a high accuracy of vibration states classification. Besides, this model has a low complexity that can guarantee a good practical application prospect.
The airport perimeter intrusion alarm system is the first line of defense in the airport's flight zone. Traditional airport perimeter intrusion alarm system has higher false alarm rate and cannot classify different intrusion categories under the influence of severe weather. In order to solve the problem, a self-encoder long-short term memory network model is proposed in this paper. This model extracts the hidden encoder feature from input signals and constructs the feature vector matrix fused with timing information such that its complexity can be reduced. It is shown via the results of network model performance evaluation that the proposed network model has a low false alarm rate and a high accuracy of vibration states classification. Besides, this model has a low complexity that can guarantee a good practical application prospect.
2021, 47(3): 331-339.
doi: 10.14135/j.cnki.1006-3080.20200117006
Abstract:
Aiming at the tracking failure caused by camera jitter or low-texture environment in slam, this paper proposes a hybrid slam method R-ORB slam for depth camera to achieve the task of 3D map reconstruction. A rough pose estimation method based on photometric error is used as the prior of the feature-based odometer. Under the case that ORB-SLAM2 tracking fails, the result of the method is used to participate in the pose estimation. Meanwhile, for generating dense three-dimensional point map with non-redundant points, the VoxelGrid filter is used to down sample the global point cloud map obtained from each key frame point clouds mosaic. Then, by using Poisson algorithm to reconstruct the surface of 3D point cloud map, we can obtain the 3D map model. It is shown via the experiments on two popular open datasets that the proposed method can effectively solve the problem of tracking failure and realize 3D reconstruction with high tracking accuracy and reconstruction accuracy.
Aiming at the tracking failure caused by camera jitter or low-texture environment in slam, this paper proposes a hybrid slam method R-ORB slam for depth camera to achieve the task of 3D map reconstruction. A rough pose estimation method based on photometric error is used as the prior of the feature-based odometer. Under the case that ORB-SLAM2 tracking fails, the result of the method is used to participate in the pose estimation. Meanwhile, for generating dense three-dimensional point map with non-redundant points, the VoxelGrid filter is used to down sample the global point cloud map obtained from each key frame point clouds mosaic. Then, by using Poisson algorithm to reconstruct the surface of 3D point cloud map, we can obtain the 3D map model. It is shown via the experiments on two popular open datasets that the proposed method can effectively solve the problem of tracking failure and realize 3D reconstruction with high tracking accuracy and reconstruction accuracy.
2021, 47(3): 340-347.
doi: 10.14135/j.cnki.1006-3080.20200119002
Abstract:
In the non-orthogonal multiple access (NOMA) system, the power allocation algorithm at the transmitter plays a key role in the throughput performance. However, the Full Search Power Allocation (FSPA) algorithm is difficultly applied to the practical system due to its unacceptable computational complexity, although it can achieve the optimal performance. By combining the principle of the successive interference cancellation receiver, this paper proposes a novel power allocation algorithm based on greedy policy, whose main idea comes from the principle of the local optimal discrimination in greedy algorithm. The goal of this algorithm is to maximize the total throughput performance of the system. Its detailed structure can be presented in the form of tree. Starting from the root of the tree, we begin perform the power allocation, local throughput judgment, and optimal branch reservation layer by layer. After that, the only surviving path from the tail node to the first node is the final allocation result. It is proven that the proposed greedy strategy satisfies the principle without aftereffect and the obtained final power allocation is globally optimal. As the simulation results show, under the case that the total throughout of this algorithm has less than 1.5% difference from the one of full space search, the complexity is successfully decreased from the exponential growth with the number of users to the linear growth. Moreover, compared with other suboptimal algorithms, this algorithm also shows advantages of different degrees.
In the non-orthogonal multiple access (NOMA) system, the power allocation algorithm at the transmitter plays a key role in the throughput performance. However, the Full Search Power Allocation (FSPA) algorithm is difficultly applied to the practical system due to its unacceptable computational complexity, although it can achieve the optimal performance. By combining the principle of the successive interference cancellation receiver, this paper proposes a novel power allocation algorithm based on greedy policy, whose main idea comes from the principle of the local optimal discrimination in greedy algorithm. The goal of this algorithm is to maximize the total throughput performance of the system. Its detailed structure can be presented in the form of tree. Starting from the root of the tree, we begin perform the power allocation, local throughput judgment, and optimal branch reservation layer by layer. After that, the only surviving path from the tail node to the first node is the final allocation result. It is proven that the proposed greedy strategy satisfies the principle without aftereffect and the obtained final power allocation is globally optimal. As the simulation results show, under the case that the total throughout of this algorithm has less than 1.5% difference from the one of full space search, the complexity is successfully decreased from the exponential growth with the number of users to the linear growth. Moreover, compared with other suboptimal algorithms, this algorithm also shows advantages of different degrees.
2021, 47(3): 348-353.
doi: 10.14135/j.cnki.1006-3080.20200115003
Abstract:
In order to improve the accuracy of personalized movie recommendation, this paper proposes a hybrid recommendation algorithm, termed as CAMF-CM, which combines a decision tree model with a matrix decomposition algorithm containing user context information. By means of the matrix decomposition algorithm that incorporates context preferences, we shall obtain the initial movie recommendation list TOP-N. And then, a decision tree algorithm is used to perform the feature label training on the context data set LDOS-COMODA to obtain the user's movie preferences in a given context. According to the obtained TOP-N recommendation results, the user's selection tendency in a given context is collected via the decision tree model, and the TOP-N list is filtered again to obtain the final TOP-N recommendation list. A ten-fold cross-validation method is utilized to verify the proposed CAMF-CM algorithm, in which four algorithms are compared, including the MAE mean of the collaborative filtering algorithm, the basic matrix decomposition algorithm, the Baseline prediction algorithm, and the CAMF-CM hybrid algorithm. It is shown from the size of the MAE mean that the proposed algorithm can deal with the lack problem of interpretability of the results obtained by the traditional matrix factorization algorithms, and also overcomes the shortcoming that the traditional recommendation algorithm does not consider the situation. By the comparative selection of decision tree models in the context data set LDOS-COMODA, it is verified that the proposed CAMF-CM recommendation algorithm has higher accuracy than other algorithms, including the user-based collaborative filtering algorithms, basic matrix factorization algorithms, and Baseline recommendation algorithms.
In order to improve the accuracy of personalized movie recommendation, this paper proposes a hybrid recommendation algorithm, termed as CAMF-CM, which combines a decision tree model with a matrix decomposition algorithm containing user context information. By means of the matrix decomposition algorithm that incorporates context preferences, we shall obtain the initial movie recommendation list TOP-N. And then, a decision tree algorithm is used to perform the feature label training on the context data set LDOS-COMODA to obtain the user's movie preferences in a given context. According to the obtained TOP-N recommendation results, the user's selection tendency in a given context is collected via the decision tree model, and the TOP-N list is filtered again to obtain the final TOP-N recommendation list. A ten-fold cross-validation method is utilized to verify the proposed CAMF-CM algorithm, in which four algorithms are compared, including the MAE mean of the collaborative filtering algorithm, the basic matrix decomposition algorithm, the Baseline prediction algorithm, and the CAMF-CM hybrid algorithm. It is shown from the size of the MAE mean that the proposed algorithm can deal with the lack problem of interpretability of the results obtained by the traditional matrix factorization algorithms, and also overcomes the shortcoming that the traditional recommendation algorithm does not consider the situation. By the comparative selection of decision tree models in the context data set LDOS-COMODA, it is verified that the proposed CAMF-CM recommendation algorithm has higher accuracy than other algorithms, including the user-based collaborative filtering algorithms, basic matrix factorization algorithms, and Baseline recommendation algorithms.
2021, 47(3): 354-360.
doi: 10.14135/j.cnki.1006-3080.20200212004
Abstract:
Under stretching, bending and many other mechanical loading forms, flexible electronics products would inevitably be subjected to complex deformation during working, and fatigue has become one of the most important failure modes during long-time working. In order to solve the reliability problem of flexible electronics, an in-situ fatigue testing platform is developed to study bending fatigue damage behavior of flexible electronic. In the process of bending, the relationship between the minimum radius of curvature and extrusion displacement is determined quantitatively by theoretical analysis, the correctness of the conclusion is verified by finite element simulation and experimental data, and the single and fatigue bending experiments are carried out on the samples of flexible electronics. The experimental results show that the lower concentration of silver nanoparticle ink results in silver film with higher porosity and initial electrical resistance. At the same time, the porosity as a defect makes the bending resistance worse. However, the free surface increases as the pore increases, making the vacancies formed during the deformation more easily annihilated. As a result, the fatigue damage evolution of silver thin films is effectively inhibited, which makes the bending fatigue stability higher.
Under stretching, bending and many other mechanical loading forms, flexible electronics products would inevitably be subjected to complex deformation during working, and fatigue has become one of the most important failure modes during long-time working. In order to solve the reliability problem of flexible electronics, an in-situ fatigue testing platform is developed to study bending fatigue damage behavior of flexible electronic. In the process of bending, the relationship between the minimum radius of curvature and extrusion displacement is determined quantitatively by theoretical analysis, the correctness of the conclusion is verified by finite element simulation and experimental data, and the single and fatigue bending experiments are carried out on the samples of flexible electronics. The experimental results show that the lower concentration of silver nanoparticle ink results in silver film with higher porosity and initial electrical resistance. At the same time, the porosity as a defect makes the bending resistance worse. However, the free surface increases as the pore increases, making the vacancies formed during the deformation more easily annihilated. As a result, the fatigue damage evolution of silver thin films is effectively inhibited, which makes the bending fatigue stability higher.
2021, 47(3): 361-369.
doi: 10.14135/j.cnki.1006-3080.20200318001
Abstract:
Porous carbon derived from wasted cigarette filters (WCFs) were synthesized using an atmospheric-pressure chemical vapor deposition, using KOH as an activator at 500—700 ℃. Brunauer-Emmett-Teller test showed that the surface area of porous carbon derived from WCFs increased with activation temperature. Micro- and meso-porous carbon (MMC) derived from WCFs at 700 ℃ (MMC700) exhibited a micro-pore dominant structure (pore size < 2 nm), with a surface area of 928 m2/g. MMC700 powder was made into electrodes for capacitive deionization and, with an initial NaCl concentration of 5 mmol/L, and the MMC700 electrode showed an ion adsorption capacity of 8.66 mg/g upon a bias of 1.2 V. MMC700 showed a carbon yield of 4.9% from its original WCFs. XPS showed that MMC700 contained a carbon content of 92.87%. 700 ℃ is a relative low activation temperature which provides a low vapor pressure of KOH, benefiting the environment. This work suggests a facile method of converting WCFs into porous carbon, and its application in water desalination based on capacitive deionization.
Porous carbon derived from wasted cigarette filters (WCFs) were synthesized using an atmospheric-pressure chemical vapor deposition, using KOH as an activator at 500—700 ℃. Brunauer-Emmett-Teller test showed that the surface area of porous carbon derived from WCFs increased with activation temperature. Micro- and meso-porous carbon (MMC) derived from WCFs at 700 ℃ (MMC700) exhibited a micro-pore dominant structure (pore size < 2 nm), with a surface area of 928 m2/g. MMC700 powder was made into electrodes for capacitive deionization and, with an initial NaCl concentration of 5 mmol/L, and the MMC700 electrode showed an ion adsorption capacity of 8.66 mg/g upon a bias of 1.2 V. MMC700 showed a carbon yield of 4.9% from its original WCFs. XPS showed that MMC700 contained a carbon content of 92.87%. 700 ℃ is a relative low activation temperature which provides a low vapor pressure of KOH, benefiting the environment. This work suggests a facile method of converting WCFs into porous carbon, and its application in water desalination based on capacitive deionization.
2021, 47(3): 370-377.
doi: 10.14135/j.cnki.1006-3080.20200519001
Abstract:
During decades, nonlinear sequence transformations method has been well developed in fields of mathematics and physics, and extensive simulation results have demonstrated its power of the acceleration of convergence and the summation of divergent series. The perturbation expansions for the infinite coupling limits of the quartic, sextic and octic anharmonic oscillators are strongly divergent, and renormalization techniques shall be used to slow down its rate of divergence. This paper presents the performance of Weniger’s transformation in summation of the renormalized perturbation series, and gives numerical results of infinite coupling limits. With the help of computer algebra system Maple, which has abilities of rational arithmetics, we can get rid of the bad effect of rounding errors. However, Maple consumes large amounts of memory resources to store data and calculate, as a result memory overflow occurs frequently. Aiming at the above problem, this paper proposes a method to compress the dimensions of arrays in order to reduce load of storage, and thus we can obtain more accurate approximations of infinite coupling limits than the known method.
During decades, nonlinear sequence transformations method has been well developed in fields of mathematics and physics, and extensive simulation results have demonstrated its power of the acceleration of convergence and the summation of divergent series. The perturbation expansions for the infinite coupling limits of the quartic, sextic and octic anharmonic oscillators are strongly divergent, and renormalization techniques shall be used to slow down its rate of divergence. This paper presents the performance of Weniger’s transformation in summation of the renormalized perturbation series, and gives numerical results of infinite coupling limits. With the help of computer algebra system Maple, which has abilities of rational arithmetics, we can get rid of the bad effect of rounding errors. However, Maple consumes large amounts of memory resources to store data and calculate, as a result memory overflow occurs frequently. Aiming at the above problem, this paper proposes a method to compress the dimensions of arrays in order to reduce load of storage, and thus we can obtain more accurate approximations of infinite coupling limits than the known method.
2021, 47(3): 378-386.
doi: 10.14135/j.cnki.1006-3080.20201022002
Abstract:
In recent years, the development and application of fiber-reinforced thermoplastic resin matrix composites with environmentally friendly and high toughness characteristics have received considerable attention. The fiber/matrix interface bonding properties of thermoplastic composites play an important role in load transfer. However, the water immersion environment can easily cause fiber/matrix interface damage, which seriously threatens its service safety. This article intends to introduce multi-walled carbon nanotube (MWCNT) into the fiber/matrix interface phase in order to explore the in-situ monitoring and self-healing of the interface damage of the thermoplastic composite before and after water immersion. Firstly, the preparation method of the MWCNT interface sensor with the best electrical conductivity is experimentally investigated to determine the optimal process parameters and introduce the MWCNT into the fiber/matrix interface. Secondly, the samples are immersed in water at different temperatures to investigate the water absorption behavior of the interface phase. Thirdly, the fiber bundle pull-out experiment under the quasi-static loading condition is carried out to in-situ monitor the failure process of the interface phase under the pull-out load according to the resistance change of MWCNT sensor. Finally, the interface damage formed during the pull-out process is repaired by in-situ melting using the applied voltage. The results show that the method used in this paper can obtain a multifunctional thermoplastic composite fiber/matrix interface phase that integrates water absorption resistance, in-situ monitoring, and in-situ repair functions. The proposed method provides guidance for the safety of thermoplastic composites in the service of water-immersed environment.
In recent years, the development and application of fiber-reinforced thermoplastic resin matrix composites with environmentally friendly and high toughness characteristics have received considerable attention. The fiber/matrix interface bonding properties of thermoplastic composites play an important role in load transfer. However, the water immersion environment can easily cause fiber/matrix interface damage, which seriously threatens its service safety. This article intends to introduce multi-walled carbon nanotube (MWCNT) into the fiber/matrix interface phase in order to explore the in-situ monitoring and self-healing of the interface damage of the thermoplastic composite before and after water immersion. Firstly, the preparation method of the MWCNT interface sensor with the best electrical conductivity is experimentally investigated to determine the optimal process parameters and introduce the MWCNT into the fiber/matrix interface. Secondly, the samples are immersed in water at different temperatures to investigate the water absorption behavior of the interface phase. Thirdly, the fiber bundle pull-out experiment under the quasi-static loading condition is carried out to in-situ monitor the failure process of the interface phase under the pull-out load according to the resistance change of MWCNT sensor. Finally, the interface damage formed during the pull-out process is repaired by in-situ melting using the applied voltage. The results show that the method used in this paper can obtain a multifunctional thermoplastic composite fiber/matrix interface phase that integrates water absorption resistance, in-situ monitoring, and in-situ repair functions. The proposed method provides guidance for the safety of thermoplastic composites in the service of water-immersed environment.
2010, (3).
2011, (6): 770-774.
2009, (5): 798-802.
2011, (3): 261-264.
2012, (2): 186-190.
2009, (6): 860-865.
2011, (6): 775-781.
2012, (1): 107-115.
2009, (5): 750-755.
2009, (3): 435-441.
2011, (2): 167-174.
2016, (1): 110-118.
doi: 10.14135/j.cnki.1006-3080.2016.01.018
2010, (6): 812-817.
2010, (1): 25-30.
2010, (1): 59-65.
2015, (5): 623-628.
2010, (2): 222-227.
2012, (1): 116-122.
2010, (2): 203-210.
2009, (3): 396-399.
2015, (5): 698-702.
2011, (1): 1-7.
2012, (2): 259-264.
2016, (5): 625-629.
doi: 10.14135/j.cnki.1006-3080.2016.05.006
2010, (1): 130-133.
2010, (6): 779-785.
2012, (1): 69-74.
2012, (5): 635-639.
2012, (6): 698-701.
2009, (4): 644-647.
2011, (6): 782-788.
2011, (6): 722-726.
2011, (2): 234-238.
2010, (2): 311-316.
2011, (2): 210-217.
2015, (1): 118-124.
2012, (5): 640-644.
2009, (4): 578-581.
2017, (5): 614-619.
doi: 10.14135/j.cnki.1006-3080.2017.05.003
2015, (1): 1-8.
2010, (1): 134-140.
2012, (1): 28-33.
2012, (2): 133-136.
2011, (1): 84-89.
2012, (2): 216-220.
2009, (5): 779-782.