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, 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 60.0%-methane conversion rate is only 57%, while the exergy efficiency of the process with 80.0%—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 60.0%-methane conversion rate is only 57%, while the exergy efficiency of the process with 80.0%—99.3% methane conversion rate can be as high as 71%—74%.
, 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.1006-3080.20200802001
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 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 of the attacker's optimal linear deception attack strategy under the KL divergence detector for these two situations, and transform 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 and 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 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 of the attacker's optimal linear deception attack strategy under the KL divergence detector for these two situations, and transform 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 and 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.1006-3080.20200911004
Abstract:
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 undergo 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. In this context, the mechanical properties of the films mixed with 10 nm and 50 nm AgNPs 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 simulation, the filling spacing between large nanoparticles is selected as a parameter to characterize the mixing ratio of large and small nanoparticles. Simulation results showed that when the spacing was short, the small particles were subjected to uneven stress in the filling area, and cracks were easy to occur. When the spacing was too long, the film strength decreased due to the increased number of pores. The results showed that the mechanical properties were superior when the spacing was 50 nm in mixed mode.
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 undergo 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. In this context, the mechanical properties of the films mixed with 10 nm and 50 nm AgNPs 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 simulation, the filling spacing between large nanoparticles is selected as a parameter to characterize the mixing ratio of large and small nanoparticles. Simulation results showed that when the spacing was short, the small particles were subjected to uneven stress in the filling area, and cracks were easy to occur. When the spacing was too long, the film strength decreased due to the increased number of pores. The results showed that the mechanical properties were superior when the spacing was 50 nm in mixed mode.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200824003
Abstract:
As the core technology of the robot controller, the motion control algorithm has important influence on the robot motion performance including stability, reliability, and rapidity. However, the application of motion control algorithms usually faces the problems of poor generality, complex embedded algorithm, and long design cycles. Aiming at these problems, this study combines the complex flexible S-shaped acceleration/deceleration (ACC/DEC) motion control algorithm and proposes a model-based hardware-software collaboration design method, which can greatly 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 interface parameter list easily calculated, the proposed algorithm can adaptively change the motion speed planning according to the parameters in the list such that the flexibility of application can be improved. The model design and simulation test for flexible motion control algorithms are completed in Simulink platform. The Math Works toolbox is utilized to automatically generate the embedded C code and programmable logic IP cores for the software and hardware models. Finally, the proposed algorithm function is realized in the motion controller based on zynq-7000. It is verified via the simulation results that the designed model can achieve the effect of the S-shaped ACC/DEC algorithm and the speed profile has great flexible characteristics. Moreover, the ACC/DEC algorithm deployed on the Zynq-7000 is in agreement with the simulation results. Hence, the model-based software and hardware co-design method has important application value in the field of personalized robots.
As the core technology of the robot controller, the motion control algorithm has important influence on the robot motion performance including stability, reliability, and rapidity. However, the application of motion control algorithms usually faces the problems of poor generality, complex embedded algorithm, and long design cycles. Aiming at these problems, this study combines the complex flexible S-shaped acceleration/deceleration (ACC/DEC) motion control algorithm and proposes a model-based hardware-software collaboration design method, which can greatly 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 interface parameter list easily calculated, the proposed algorithm can adaptively change the motion speed planning according to the parameters in the list such that the flexibility of application can be improved. The model design and simulation test for flexible motion control algorithms are completed in Simulink platform. The Math Works toolbox is utilized to automatically generate the embedded C code and programmable logic IP cores for the software and hardware models. Finally, the proposed algorithm function is realized in the motion controller based on zynq-7000. It is verified via the simulation results that the designed model can achieve the effect of the S-shaped ACC/DEC algorithm and the speed profile has great flexible characteristics. Moreover, the ACC/DEC algorithm deployed on the Zynq-7000 is in agreement with the simulation results. Hence, the model-based software and hardware co-design method has important application value in the field of personalized robots.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200911005
Abstract:
Modified activated carbon was prepared by stepwise composite modification in the presence of oxidant and nucleophilic addition reagent. The specific surface area and pore structure of the activated carbon were measured by N2 adsorption-desorption (BET). Formaldehyde was used as a model pollutant to test the purification performance of the modified activated carbon. The results showed that the stepwise composite modification, first with the oxidant, and then the nucleophilic addition reagent, could significantly improved the fast purification efficiency of activated carbon for formaldehyde. In contrast, the stepwise composite modification, first with the nucleophilic addition reagent, and then with the oxidant, showed an excellent long-term purification effect. After the activated carbon was impregnated first with 2-imidazolidinone followed by treatment with sodium hypochlorite in a stepwise manner, 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 in the presence of oxidant and nucleophilic addition reagent. The specific surface area and pore structure of the activated carbon were measured by N2 adsorption-desorption (BET). Formaldehyde was used as a model pollutant to test the purification performance of the modified activated carbon. The results showed that the stepwise composite modification, first with the oxidant, and then the nucleophilic addition reagent, could significantly improved the fast purification efficiency of activated carbon for formaldehyde. In contrast, the stepwise composite modification, first with the nucleophilic addition reagent, and then with the oxidant, showed an excellent long-term purification effect. After the activated carbon was impregnated first with 2-imidazolidinone followed by treatment with sodium hypochlorite in a stepwise manner, 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.1006-3080.20200903030
Abstract:
A computational fluid dynamics model for simulating gas-liquid two-phase flow in a shake flask was proposed, named the centrifugal acceleration model. The model was successfully applied to investigate effects of baffles, rotation speed and filling volume on the mass transfer and shearing environment in the shake flask flow field. The results show the presence of the baffle increases the gas-liquid oxygen transfer capacity inside the flask and provids a larger shearing environment than the unbaffled flask; Higher rotation speed 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 flow field simulation results were 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.
A computational fluid dynamics model for simulating gas-liquid two-phase flow in a shake flask was proposed, named the centrifugal acceleration model. The model was successfully applied to investigate effects of baffles, rotation speed and filling volume on the mass transfer and shearing environment in the shake flask flow field. The results show the presence of the baffle increases the gas-liquid oxygen transfer capacity inside the flask and provids a larger shearing environment than the unbaffled flask; Higher rotation speed 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 flow field simulation results were 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.1006-3080.20200927001
Abstract:
In the process of using reverse osmosis membrane method to treat wastewater, the surface of membrane is easily scaled, which needs adding the corresponding membrane antiscalant, the quality of membrane antiscalant directly affects the effectiveness and efficiency of wastewater treatment. Nano-sized spherical polyelectrolyte brush with a core-shell structure synthesized by emulsion polymerization. Under the influence of static electricity and Donnan effect, it 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 wastewater treatment process as antiscalant. The scale inhibition performance of nano-sized spherical polyelectrolyte brush on CaCO3, CaSO4, Al3+ was evaluated through dynamic evaluation experiments. The experimental results show that compared with the imported scale inhibitor ASD-200, the scale inhibition performance of nano-sized spherical polyelectrolyte brush on CaCO3 and CaSO4 is improved by 100% and 30%, respectively. Besides, it has excellent resistance performance on Al3+.
In the process of using reverse osmosis membrane method to treat wastewater, the surface of membrane is easily scaled, which needs adding the corresponding membrane antiscalant, the quality of membrane antiscalant directly affects the effectiveness and efficiency of wastewater treatment. Nano-sized spherical polyelectrolyte brush with a core-shell structure synthesized by emulsion polymerization. Under the influence of static electricity and Donnan effect, it 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 wastewater treatment process as antiscalant. The scale inhibition performance of nano-sized spherical polyelectrolyte brush on CaCO3, CaSO4, Al3+ was evaluated through dynamic evaluation experiments. The experimental results show that compared with the imported scale inhibitor ASD-200, the scale inhibition performance of nano-sized spherical polyelectrolyte brush on CaCO3 and CaSO4 is improved by 100% and 30%, respectively. Besides, it has excellent resistance performance on Al3+.
, 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 can also systematically control the microstructures of nanoparticle, such as morphology, internal structure and surface structure, which can 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 can also systematically control the microstructures of nanoparticle, such as morphology, internal structure and surface structure, which can provide help for further improving the efficiency and low-toxic utilization of pesticide nanoparticle.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200731002
Abstract:
The development of water-soluble fluorescent probes has become a topical research area for environmental and biological applications. Water solubility is desired for fluorescent probes to function in aqueous media. The introduction of water-soluble groups to conventional organic fluorophores is challenging in terms of preparation and purification. In this paper, fluorescent probes 3 , 6 and 9 based on spirobilfuorene skeleton were designed and synthesized, featuring naphthalene anhydride functionality. These compounds were prepared by hydrolysis of corresponding molecules bearing naphthalenedimide, which is a common electron acceptor often used in fluorophores. The anhydride group not only allows these compounds to be separated and purified in common organic solvents, such as chloroform and tetrahydrofuran, but also imparts enough water solubility. The anhydride group undergoes hydrolysis upon exposing to alkaline conditions in aqueous media, evidenced by a hypochromic-shift in UV-vis absorption spectra with increasing pH. For example, the ratio of absorption at 365 nm over absorption at 472 nm of compound 3 in water, elevated from 2.5 at pH = 2 continuously to 3.4 at pH = 11. The conversion from charge neutral anhydride form to negatively charged carboxylate form switches the solubility of such probes to more polar media. These probes displayed fluorescence emission in the visible range, at 519, 440, and 404 nm, respectively. After incubation Hela cells with these probes at 10 μmol/L for 30 min, confocal microscopy displays clear cell contours with minimal noise, demonstrating efficiency of our strategy.
The development of water-soluble fluorescent probes has become a topical research area for environmental and biological applications. Water solubility is desired for fluorescent probes to function in aqueous media. The introduction of water-soluble groups to conventional organic fluorophores is challenging in terms of preparation and purification. In this paper, fluorescent probes 3 , 6 and 9 based on spirobilfuorene skeleton were designed and synthesized, featuring naphthalene anhydride functionality. These compounds were prepared by hydrolysis of corresponding molecules bearing naphthalenedimide, which is a common electron acceptor often used in fluorophores. The anhydride group not only allows these compounds to be separated and purified in common organic solvents, such as chloroform and tetrahydrofuran, but also imparts enough water solubility. The anhydride group undergoes hydrolysis upon exposing to alkaline conditions in aqueous media, evidenced by a hypochromic-shift in UV-vis absorption spectra with increasing pH. For example, the ratio of absorption at 365 nm over absorption at 472 nm of compound 3 in water, elevated from 2.5 at pH = 2 continuously to 3.4 at pH = 11. The conversion from charge neutral anhydride form to negatively charged carboxylate form switches the solubility of such probes to more polar media. These probes displayed fluorescence emission in the visible range, at 519, 440, and 404 nm, respectively. After incubation Hela cells with these probes at 10 μmol/L for 30 min, confocal microscopy displays clear cell contours with minimal noise, demonstrating efficiency of our strategy.
, 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. Generally, 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 avoiding the recurrence of similar events and prolonging 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. Generally, 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 avoiding the recurrence of similar events and prolonging the service life of the engine.
, 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 higher than 91.76 μmol/L against three tumor cells. While the combination of the two compounds at a certain ratio showed good cytotoxicity, the IC50 value against A549 cells was as low as 8.71 μmol/L. 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 higher than 91.76 μmol/L against three tumor cells. While the combination of the two compounds at a certain ratio showed good cytotoxicity, the IC50 value against A549 cells was as low as 8.71 μmol/L. 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.1006-3080.20201209001
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 verification performance. In most of the speaker recognition application scenarios, it is usually impossible to obtain enough samples to retrain the speaker recognition model. At the same time, the samples that is 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 (e.g., the changes in emotion, language, vocal effect, speaking style, and aging, etc. or extrinsic ones (e.g., 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 for enhancing its domain adaptation ability. First, the X-Vector scheme is trained on the source dataset (AISHELL1). Then, the domain adaptation strategy is applied to the obtained X-Vector scheme for enabling 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 via the target dataset, from which it is demonstrated that the proposed 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 verification performance. In most of the speaker recognition application scenarios, it is usually impossible to obtain enough samples to retrain the speaker recognition model. At the same time, the samples that is 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 (e.g., the changes in emotion, language, vocal effect, speaking style, and aging, etc. or extrinsic ones (e.g., 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 for enhancing its domain adaptation ability. First, the X-Vector scheme is trained on the source dataset (AISHELL1). Then, the domain adaptation strategy is applied to the obtained X-Vector scheme for enabling 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 via the target dataset, from which it is demonstrated that the proposed 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.1006-3080.20201128002
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 has been developing and making continuous breakthroughs. In the field of target detection, it is difficult to solve the problem of target detection with few sample categories, and detect small targets with high accuracy via the training with few sample categories. By means of the model-agnostic meta-learning (MAML) algorithm in meta learning, this paper improves the information transmission form of the backbone network in YOLOv3 to make Darknet-53 achieve two stages of parameter internal update and external update in gradient descent. By multi-step gradient adjustment on the initial parameters, the trained weights can focus more on the feature information of the target. Even only via a small number of sample categories, it can also maintain the sensitivity to the target in the new task. It is shown via the experiment results that the mean average precision(mAP) value of YOLOv3 model attains 74.81%, and the mAP value of YOLOv3 model based on MAML algorithm can reach 80.05%, which improves the accuracy by about 5%. The network structure and training mechanism of the modified YOLOv3 via MAML can improve the accuracy of detection in training and prediction, and the trained weights can make the model have 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 has been developing and making continuous breakthroughs. In the field of target detection, it is difficult to solve the problem of target detection with few sample categories, and detect small targets with high accuracy via the training with few sample categories. By means of the model-agnostic meta-learning (MAML) algorithm in meta learning, this paper improves the information transmission form of the backbone network in YOLOv3 to make Darknet-53 achieve two stages of parameter internal update and external update in gradient descent. By multi-step gradient adjustment on the initial parameters, the trained weights can focus more on the feature information of the target. Even only via a small number of sample categories, it can also maintain the sensitivity to the target in the new task. It is shown via the experiment results that the mean average precision(mAP) value of YOLOv3 model attains 74.81%, and the mAP value of YOLOv3 model based on MAML algorithm can reach 80.05%, which improves the accuracy by about 5%. The network structure and training mechanism of the modified YOLOv3 via MAML can improve the accuracy of detection in training and prediction, and the trained weights can make the model have high detection accuracy and high generalization.
, 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 will result in certain blindness in the initial stage. Meanwhile, the single updating mechanism of VLPSO and the information isolation among subpopulations also affect the classification performance. In order to cope with the defect of VLPSO, this paper proposes a co-evolutionary feature selection method based on variable-length particle and multi-behavior interaction(M-CVLPSO). 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 then, 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 integrated into the fitness function to further enhance the performance of the algorithm on some datasets. The convergence of the proposed algorithm is proved theoretically. Finally, the experimental analysis is carried out on the classification accuracy, dimension reduction and calculation time based on 11 large-scale feature selection data sets, which 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 will result in certain blindness in the initial stage. Meanwhile, the single updating mechanism of VLPSO and the information isolation among subpopulations also affect the classification performance. In order to cope with the defect of VLPSO, this paper proposes a co-evolutionary feature selection method based on variable-length particle and multi-behavior interaction(M-CVLPSO). 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 then, 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 integrated into the fitness function to further enhance the performance of the algorithm on some datasets. The convergence of the proposed algorithm is proved theoretically. Finally, the experimental analysis is carried out on the classification accuracy, dimension reduction and calculation time based on 11 large-scale feature selection data sets, which show that the proposed model has better comprehensive performance than the four comparison algorithms.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201201005
Abstract:
It is of great significance to study the flow shop scheduling problem with no-idle constrains, since No-idle production Scheduling exists widely in modern industry. This paper proposes a multi-objective discrete sine optimization algorithm (MDSOA) to solve the mixed no-idle permutation flow shop scheduling problem (MNPFSP), whose goal is to minimize 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, whose key feature is suitable for discrete scheduling problems. Besides, both the fast non-dominate sorting method and the crowding distance are utilized to screen the population for retaining the elite solutions and ensuring the diversity and distribution of solutions. Finally, simulation experiments are made via 11 instances with different scales in Taillard Benchmark, which, together with the comparisons with NSGA-II and NSGA-III, demonstrate the effectiveness of the proposed MDSOA algorithm for solving MNPFSP.
It is of great significance to study the flow shop scheduling problem with no-idle constrains, since No-idle production Scheduling exists widely in modern industry. This paper proposes a multi-objective discrete sine optimization algorithm (MDSOA) to solve the mixed no-idle permutation flow shop scheduling problem (MNPFSP), whose goal is to minimize 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, whose key feature is suitable for discrete scheduling problems. Besides, both the fast non-dominate sorting method and the crowding distance are utilized to screen the population for retaining the elite solutions and ensuring the diversity and distribution of solutions. Finally, simulation experiments are made via 11 instances with different scales in Taillard Benchmark, which, together with the 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.1006-3080.20200903001
Abstract:
The music source separation is to separate a piece of music into its individual sounds. As a specific case, the Singing Voice Separation (SVS) separates 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 been becoming a hot topic in the music information retrieval field in recent years. It is recently reported that a variety of convolutional neural network architectures based on U-Net has been successfully employed for the SVS task and the better performance can be achieved. Besides, Wave-U-Net is proposed to achieve the end-to-end SVS by analyzing the music waveform directly. However, the performance of the SVS approaches 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 for enhancing its performance. Firstly, 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. Secondly, 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 the one of the corresponding layer in the encoding block. To verify the effectiveness of the proposed scheme, termed as RA-WaveUNet, in the SVS task, its performances are compared with those of state-of-the-art schemes on the maximum open dataset MUSDB18. It is demonstrated from experimental results that the proposed scheme can achieve better performances than Wave-U-Net based ones and other SVS schemes. Moreover, both the above modifications contribute to the performance enhancement.
The music source separation is to separate a piece of music into its individual sounds. As a specific case, the Singing Voice Separation (SVS) separates 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 been becoming a hot topic in the music information retrieval field in recent years. It is recently reported that a variety of convolutional neural network architectures based on U-Net has been successfully employed for the SVS task and the better performance can be achieved. Besides, Wave-U-Net is proposed to achieve the end-to-end SVS by analyzing the music waveform directly. However, the performance of the SVS approaches 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 for enhancing its performance. Firstly, 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. Secondly, 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 the one of the corresponding layer in the encoding block. To verify the effectiveness of the proposed scheme, termed as RA-WaveUNet, in the SVS task, its performances are compared with those of state-of-the-art schemes on the maximum open dataset MUSDB18. It is demonstrated from experimental results that the proposed scheme can achieve better performances than Wave-U-Net based ones and other SVS schemes. Moreover, both the above modifications contribute to the performance enhancement.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200831001
Abstract:
Aiming at the complexity and variety of production modes in the actual production system, this paper investigates the hybrid flowshop lot-streaming scheduling problem with batch processing. A variable batching method is proposed by considering the capacity of batch machine and the processing ability of unrelated machines. A scheduling model is established to minimize the completion time via dynamic continuous processing strategy. At the same time, a discrete water wave optimization (DWWO) is proposed to solve the scheduling 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. By the block optimal insertion, cross operation, and multi neighborhood search, the operation operators is improved for enhancing the local search ability. Moreover, an operation of replacing inferior solution is proposed to improve the convergence ability of the proposed 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. It is shown via the experimental results that the proposed DWWO algorithm can effectively deal with the hybrid flowshop lot-streaming scheduling problem with batch processing.
Aiming at the complexity and variety of production modes in the actual production system, this paper investigates the hybrid flowshop lot-streaming scheduling problem with batch processing. A variable batching method is proposed by considering the capacity of batch machine and the processing ability of unrelated machines. A scheduling model is established to minimize the completion time via dynamic continuous processing strategy. At the same time, a discrete water wave optimization (DWWO) is proposed to solve the scheduling 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. By the block optimal insertion, cross operation, and multi neighborhood search, the operation operators is improved for enhancing the local search ability. Moreover, an operation of replacing inferior solution is proposed to improve the convergence ability of the proposed 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. It is shown via the experimental results that the proposed DWWO algorithm can effectively deal with the hybrid flowshop lot-streaming scheduling problem with batch processing.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200824001
Abstract:
Ranpirnase is a multi-functional protein drug widely studied by researchers all over the world. It was first isolated from the oocytes and early embryos of the Northern Leopard Frog, and belonged to the ribonuclease A (RNase A) superfamily. Ranpirnase not only inhibits protein biosynthetic pathway, but independently induces tumor cell apoptosis. It also has antiviral activity. In this study, in order to improve the expression level of ranpirnase in E. coli. , a Plackett-Burman (PB) experimental design was applied to examine the effect of various factors on ranpirnase expression in recombinant E. coli . We also optimized the recombinant E. coli fermentation medium and inducing conditions. On the basis of these results, the ranpirnase expression in recombinant E. coli was carried out in a 7 L fermentor with the combined exponential and pH-Stat feeding strategy. With the optimized fermentation medium, the protein expression at the flask level was increased from 9% to 36%, and the OD600 value increased from 4.8 to 5.47. The expression level of ranpirnase at the 7 L fermentor level reached 55%, and the OD600 value was increased to 35 after the optimization. The use of the optimized medium for high-density fermentation of ranpirnase leads to a lowered production cost, facilitating its commercialization.
Ranpirnase is a multi-functional protein drug widely studied by researchers all over the world. It was first isolated from the oocytes and early embryos of the Northern Leopard Frog, and belonged to the ribonuclease A (RNase A) superfamily. Ranpirnase not only inhibits protein biosynthetic pathway, but independently induces tumor cell apoptosis. It also has antiviral activity. In this study, in order to improve the expression level of ranpirnase in E. coli. , a Plackett-Burman (PB) experimental design was applied to examine the effect of various factors on ranpirnase expression in recombinant E. coli . We also optimized the recombinant E. coli fermentation medium and inducing conditions. On the basis of these results, the ranpirnase expression in recombinant E. coli was carried out in a 7 L fermentor with the combined exponential and pH-Stat feeding strategy. With the optimized fermentation medium, the protein expression at the flask level was increased from 9% to 36%, and the OD600 value increased from 4.8 to 5.47. The expression level of ranpirnase at the 7 L fermentor level reached 55%, and the OD600 value was increased to 35 after the optimization. The use of the optimized medium for high-density fermentation of ranpirnase leads to a lowered production cost, facilitating its commercialization.
, 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. On the basis of 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. In consideration of 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 showed that the proposed control algorithm presented better anti-disturbance and regulation performance on the motor electromagnetic torque, flux linkage and other parameters than the traditional direct torque control. The study demonstrates 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. On the basis of 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. In consideration of 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 showed that the proposed control algorithm presented better anti-disturbance and regulation performance on the motor electromagnetic torque, flux linkage and other parameters than the traditional direct torque control. The study demonstrates verify the effectiveness and feasibility of the developed control algorithm.
, 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 was 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 temperature of these two mutants were the same as those of the wild-type Kex2. Their optimal pH and temperature were pH 9.0 and 37 ℃, respectively. 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 the temperature range of 4 ℃ to 37 ℃. Enzymatic reaction kinetics studies showed that the Kcat/Km values of mutant Kex2-K291H and Kex2-K291L were 1.8 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 was 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 temperature of these two mutants were the same as those of the wild-type Kex2. Their optimal pH and temperature were pH 9.0 and 37 ℃, respectively. 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 the temperature range of 4 ℃ to 37 ℃. Enzymatic reaction kinetics studies showed that the Kcat/Km values of mutant Kex2-K291H and Kex2-K291L were 1.8 and 2.0 fold higher than that of the wild-type Kex2 protease, respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201010001
Abstract:
Smart nano-pesticides have been prepared to efficiently deliver a sufficient amount of active ingredients in response to different stimuli, employing targeted and controlled release mechanisms. In this paper, an 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 carboxymethyl-β-cyclodextrin (CM-β-CD) was coated onto the surface of HMSN using amidation reaction as the blocking molecule. A series of physicochemical characterization showed the successful construction of the CM-β-CD/LC/HMSN material. An in vitro release test showed that the LC release amount from CM-β-CD /LC/HMSN reached up to 55% in the presence of α-amylase, while only about 20% could be released in the absence of the enzyme, suggesting that the pesticide release was dependent on enzymatic activity. Biological tests showed that the pesticide formulation had good insecticidal effect on Mythimna separate larvae. Based on the results obtained by different experimental methods, we believe that the nanoparticles would be useful for releasing LC after degradation of cyclodextrin enzymatically in vivo , suppressing the survival of Mythimna separate larvae.
Smart nano-pesticides have been prepared to efficiently deliver a sufficient amount of active ingredients in response to different stimuli, employing targeted and controlled release mechanisms. In this paper, an 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 carboxymethyl-β-cyclodextrin (CM-β-CD) was coated onto the surface of HMSN using amidation reaction as the blocking molecule. A series of physicochemical characterization showed the successful construction of the CM-β-CD/LC/HMSN material. An in vitro release test showed that the LC release amount from CM-β-CD /LC/HMSN reached up to 55% in the presence of α-amylase, while only about 20% could be released in the absence of the enzyme, suggesting that the pesticide release was dependent on enzymatic activity. Biological tests showed that the pesticide formulation had good insecticidal effect on Mythimna separate larvae. Based on the results obtained by different experimental methods, we believe that the nanoparticles would be useful for releasing LC after degradation of cyclodextrin enzymatically in vivo , suppressing the survival of Mythimna separate larvae.
, 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 because 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 nanopesticide 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 because 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 nanopesticide 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.1006-3080.20200929001
Abstract:
Great progress has been made in the fundamental theories and applications of chemical engineering. With the advancement of modern sciences, there emerges an interdisciplinary field termed agrochemical engineering that incorporates the knowledge and techniques of both chemical engineering and agricultural science. In this review, the conceptual advancement of agrochemical engineering and the recent progress in the preparation and application of nano-agrochemicals are described. In particular, the newly emerged preparation methods for nano-pesticides such as the flash nano-precipitation technology, the development of smart pesticides for controlled drug release, the migration trend of nano-pesticides, and the extension of new fertilizers and pesticides to the integral control of water and agrochemicals are discussed in details. Finally, a perspective is given in regard to the future development of agrochemical engineering.
Great progress has been made in the fundamental theories and applications of chemical engineering. With the advancement of modern sciences, there emerges an interdisciplinary field termed agrochemical engineering that incorporates the knowledge and techniques of both chemical engineering and agricultural science. In this review, the conceptual advancement of agrochemical engineering and the recent progress in the preparation and application of nano-agrochemicals are described. In particular, the newly emerged preparation methods for nano-pesticides such as the flash nano-precipitation technology, the development of smart pesticides for controlled drug release, the migration trend of nano-pesticides, and the extension of new fertilizers and pesticides to the integral control of water and agrochemicals are discussed in details. Finally, a perspective is given in regard to the future development of agrochemical engineering.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201126003
Abstract:
To screen out the potential targets and molecular mechanisms of Epigallocatechin gallate (EGCG) in the treatment of triple negative breast cancer (MDA-MB-231). Databases 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 analyses; Finally, the prediction results were verified through in vitro experiment. 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 results of the enrichment analyses showed that the active targets were involved in 20 core GO biological processes and 17 KEGG signaling pathways, such as cancer signaling pathways, toxic tolerance pathways, pancreatic cancer pathways, rectal cancer pathways, small cell lung cancer pathways. 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. EGCG inhibited cell viability through multiple targets and multiple pathways, among them, it has been basically confirmed that EGCG can affect the HGF / β-catenin pathway, providing a theoretical and practical basis for further mechanism exploration.
To screen out the potential targets and molecular mechanisms of Epigallocatechin gallate (EGCG) in the treatment of triple negative breast cancer (MDA-MB-231). Databases 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 analyses; Finally, the prediction results were verified through in vitro experiment. 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 results of the enrichment analyses showed that the active targets were involved in 20 core GO biological processes and 17 KEGG signaling pathways, such as cancer signaling pathways, toxic tolerance pathways, pancreatic cancer pathways, rectal cancer pathways, small cell lung cancer pathways. 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. EGCG inhibited cell viability through multiple targets and multiple pathways, among them, it has been basically 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.1006-3080.20200326001
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.1006-3080.20201124001
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 the linear frequency analysis-based handcrafted feature and the deep learning-based classification model method. However, the linear frequency analysis-based feature extraction method cannot mimic the nonlinear frequency selectivity of the human basilar membrane, which results in lower feature resolution. On the other hand, the existing classification model cannot solve the low classification accuracy caused by complex sound sources and highly overlapping of sound events. To deal with these problems, this paper proposes an ASC model based on cochleagram multi-instance analysis. The equivalent rectangular bandwidth cosine filter bank is adopted to analyze the signal spectrum and simulate the acoustic perception property of human beings. Meanwhile, multi-instance learning strategy is introduced to characterize the entire data structure of acoustic scenes for improving classification accuracy. In addition, in order to enhance the robustness to frequency shift of sound events, the average pooling method is adopted in the classification prediction integrator of the multi-instance learning classification model. Finally, it is shown via the experimental results on the DCASE 2018 and DCASE 2019 Challenge Task1a dataset that the proposed model in this work can achieve 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. Moreover, it is also verified that the average pooling is better than the maximum pooling.
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 the linear frequency analysis-based handcrafted feature and the deep learning-based classification model method. However, the linear frequency analysis-based feature extraction method cannot mimic the nonlinear frequency selectivity of the human basilar membrane, which results in lower feature resolution. On the other hand, the existing classification model cannot solve the low classification accuracy caused by complex sound sources and highly overlapping of sound events. To deal with these problems, this paper proposes an ASC model based on cochleagram multi-instance analysis. The equivalent rectangular bandwidth cosine filter bank is adopted to analyze the signal spectrum and simulate the acoustic perception property of human beings. Meanwhile, multi-instance learning strategy is introduced to characterize the entire data structure of acoustic scenes for improving classification accuracy. In addition, in order to enhance the robustness to frequency shift of sound events, the average pooling method is adopted in the classification prediction integrator of the multi-instance learning classification model. Finally, it is shown via the experimental results on the DCASE 2018 and DCASE 2019 Challenge Task1a dataset that the proposed model in this work can achieve 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. Moreover, it is also verified that the average pooling is better than the maximum pooling.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201126004
Abstract:
With the explosive growth of video data, video intelligent analysis has been becoming the academic and industrial research hotspot. The objective of video action detection is to obtain the location and time information of actions based on action recognition. By combining the single shot multi-box detector (SSD) with the RGB space flow and optical flow, this paper proposes a region spatiotemporal two-in-one action detection network. To improve the 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 performed only on the selected key motion regions in the spatial flow. The proposed module can get long-range dependence of actions effectively and reduce the computational cost of the nonlocal module and the interference of video background noise. Finally, the proposed network is tested on the benchmark dataset UCF101-24, and attain better detection performance.
With the explosive growth of video data, video intelligent analysis has been becoming the academic and industrial research hotspot. The objective of video action detection is to obtain the location and time information of actions based on action recognition. By combining the single shot multi-box detector (SSD) with the RGB space flow and optical flow, this paper proposes a region spatiotemporal two-in-one action detection network. To improve the 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 performed only on the selected key motion regions in the spatial flow. The proposed module can get long-range dependence of actions effectively and reduce the computational cost of the nonlocal module and the interference of video background noise. Finally, the proposed network is tested on the benchmark dataset UCF101-24, and attain better detection performance.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20201127005
Abstract:
Compared with the traditional machine learning algorithms, deploying deep neural networks in embedded systems can significantly improve the performance of robot system object recognition. However, its performance is limited by the computing resources and memory capacity of the embedded platform. Hence, it is quite necessary to simplify the network structure and improve the system efficiency through model pruning and parameter quantification. Meanwhile, 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). Firstly, by quantizing the weights, the weights and activations are reduced from floating point numbers to binary values. Secondly, a constant false alarm detector (CFAR) is designed to maintain a certain false alarm rate, adaptively delete some neuron nodes, and optimize the neuron nodes involved in the calculation. Finally, on the embedded platform PYNQ-Z2, an VGG16-based optimization model is used to experimentally verify the object recognition performance of the proposed algorithm. It is shown via experimental results that compared with the classic dropout regularization method, the CFAR-Dropout regularization method can reduce the error rate by 2%, and effectively prevent the overfitting. Moreover, compared with the original network structure, the proposed algorithm can reduce the amount of the occupied memory by about 8%, and effectively prevent over-parameterization.
Compared with the traditional machine learning algorithms, deploying deep neural networks in embedded systems can significantly improve the performance of robot system object recognition. However, its performance is limited by the computing resources and memory capacity of the embedded platform. Hence, it is quite necessary to simplify the network structure and improve the system efficiency through model pruning and parameter quantification. Meanwhile, 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). Firstly, by quantizing the weights, the weights and activations are reduced from floating point numbers to binary values. Secondly, a constant false alarm detector (CFAR) is designed to maintain a certain false alarm rate, adaptively delete some neuron nodes, and optimize the neuron nodes involved in the calculation. Finally, on the embedded platform PYNQ-Z2, an VGG16-based optimization model is used to experimentally verify the object recognition performance of the proposed algorithm. It is shown via experimental results that compared with the classic dropout regularization method, the CFAR-Dropout regularization method can reduce the error rate by 2%, and effectively prevent the overfitting. Moreover, compared with the original network structure, the proposed algorithm can reduce the amount of the occupied memory by about 8%, and effectively prevent over-parameterization.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210519001
Abstract:
Two-dimensional (2D) graphene oxide (GO) exhibits superlative properties for biomedical applications such as drug delivery, biosensing and bioimaging. There are questions, however, about their biocompatibility on organs including liver, which is frequently targeted by therapeutic nanomaterials or secondary spread from other uptake sites. In this study, we systemically explored the biological impact of GO on three major liver cell types: Kupffer cells, liver sinusoidal endothelial cells (LSEC), and hepatocytes. Pristine (pGO) and reduced (rGO) oxidation states of GO flakes with large (510 nm) and small sizes (110 nm) were synthesized. These samples show differences in cellular response, depending on oxidation state as well as lateral size. While pGO induced plasma membrane damage and necrosis (large > small flakes) in Kupffer cells, minimal cytotoxicity was observed in LSEC and hepatocytes. In contrast, rGO induced apoptosis in Kupffer cells (large > small flakes) and LSEC, with negligible effects in hepatocytes. While pGO attached to the Kupffer cell membrane and induced lipid peroxidation and cytoskeleton disruption, rGO was mostly taken up in Kupffer cells. Moreover, while pGO and rGO were taken up in roughly similar amounts by LSEC, little uptake for hepatocytes. Further study of intracellular effects of the GO species at the level of lysosomes and functionally linked NLRP3 inflammasomes demonstrated that lysosomal damage by rGO in Kupffer and LSEC induced IL-1β production. Similar effects were not seen in hepatocytes or with pGO. Collectively, this study reveals that surface oxidation state and lateral size of GO differentially impact liver cells based on interactions with cell membranes, cellular uptake, and damage to lysosomes.
Two-dimensional (2D) graphene oxide (GO) exhibits superlative properties for biomedical applications such as drug delivery, biosensing and bioimaging. There are questions, however, about their biocompatibility on organs including liver, which is frequently targeted by therapeutic nanomaterials or secondary spread from other uptake sites. In this study, we systemically explored the biological impact of GO on three major liver cell types: Kupffer cells, liver sinusoidal endothelial cells (LSEC), and hepatocytes. Pristine (pGO) and reduced (rGO) oxidation states of GO flakes with large (510 nm) and small sizes (110 nm) were synthesized. These samples show differences in cellular response, depending on oxidation state as well as lateral size. While pGO induced plasma membrane damage and necrosis (large > small flakes) in Kupffer cells, minimal cytotoxicity was observed in LSEC and hepatocytes. In contrast, rGO induced apoptosis in Kupffer cells (large > small flakes) and LSEC, with negligible effects in hepatocytes. While pGO attached to the Kupffer cell membrane and induced lipid peroxidation and cytoskeleton disruption, rGO was mostly taken up in Kupffer cells. Moreover, while pGO and rGO were taken up in roughly similar amounts by LSEC, little uptake for hepatocytes. Further study of intracellular effects of the GO species at the level of lysosomes and functionally linked NLRP3 inflammasomes demonstrated that lysosomal damage by rGO in Kupffer and LSEC induced IL-1β production. Similar effects were not seen in hepatocytes or with pGO. Collectively, this study reveals that surface oxidation state and lateral size of GO differentially impact liver cells based on interactions with cell membranes, cellular uptake, and damage to lysosomes.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200317001
Abstract:
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, 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.1006-3080.20201204003
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.1006-3080.20201127006
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.1006-3080.20210313004
Abstract:
The effects of temperature, pressure and feed ratio on the methane reformer were studied based on a kinetic model. The conversion rates of CH4, H2O and CO2 all increase with the increase of temperature at p=3.2 MPa. Compared with the steam reforming of methane, the reaction temperature required for the conversion of CH4 and CO2 is higher and the conversion of CO2 begins at 650 ℃.The effect of temperature on the reaction rate of dry reforming of methane is more significant at relatively high reaction temperature and pressure. With the increase of pressure, the conversion rates of CH4, H2O and CO2 decrease rapidly. When the pressure reaches 3.5 MPa, the conversion rates of CH4, H2O and CO2 are all less than 40%. However, the influence of pressure on n(H2)/n(CO) is not obvious. The increase of CO2 in the reaction system is beneficial to improve the conversion rate of CH4, but significantly reduces the conversion rate of H2O at p=3.2 MPa.CO2 conversion increases rapidly at first and then keeps stable with the increase of n(CO2)/n(CH4). CH4 and H2O conversion both increase with the increase of n(H2O)/n(CH4). The analysis of feed ratio and reaction temperature showed that n(H2)/n(CO) can be adjusted by adjusting the temperature and the relative concentration of H2O and CO2 in the feed gas to carry out the subsequent industrial production.
The effects of temperature, pressure and feed ratio on the methane reformer were studied based on a kinetic model. The conversion rates of CH4, H2O and CO2 all increase with the increase of temperature at p=3.2 MPa. Compared with the steam reforming of methane, the reaction temperature required for the conversion of CH4 and CO2 is higher and the conversion of CO2 begins at 650 ℃.The effect of temperature on the reaction rate of dry reforming of methane is more significant at relatively high reaction temperature and pressure. With the increase of pressure, the conversion rates of CH4, H2O and CO2 decrease rapidly. When the pressure reaches 3.5 MPa, the conversion rates of CH4, H2O and CO2 are all less than 40%. However, the influence of pressure on n(H2)/n(CO) is not obvious. The increase of CO2 in the reaction system is beneficial to improve the conversion rate of CH4, but significantly reduces the conversion rate of H2O at p=3.2 MPa.CO2 conversion increases rapidly at first and then keeps stable with the increase of n(CO2)/n(CH4). CH4 and H2O conversion both increase with the increase of n(H2O)/n(CH4). The analysis of feed ratio and reaction temperature showed that n(H2)/n(CO) can be adjusted by adjusting the temperature and the relative concentration of H2O and CO2 in the feed gas to carry out the subsequent industrial production.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210308002
Abstract:
Power-gating-aware design has been an active area of research in the last decade, aiming at reducing power dissipation while meeting a desired system throughput. In this study, an algorithm integrating both scheduling and binding processes is developed with the fine-grained functional unit (FU) power-gating technique, to achieve maximum leakage energy reduction. Firstly, the break-even points of FUs are analyzed, and the leakage energy reduction problem is formulated as an idle interval partition problem. Secondly, the idle interval length of each possible scheduling result is estimated. Finally, operations are scheduled to the control steps with maximization of the leakage energy saving. The experimental results show that our proposed algorithms can significantly reduce leakage energy while maintaining the system performance and circuit area, and therefore, provides a suit-able design solution for the circuits used in satellites.
Power-gating-aware design has been an active area of research in the last decade, aiming at reducing power dissipation while meeting a desired system throughput. In this study, an algorithm integrating both scheduling and binding processes is developed with the fine-grained functional unit (FU) power-gating technique, to achieve maximum leakage energy reduction. Firstly, the break-even points of FUs are analyzed, and the leakage energy reduction problem is formulated as an idle interval partition problem. Secondly, the idle interval length of each possible scheduling result is estimated. Finally, operations are scheduled to the control steps with maximization of the leakage energy saving. The experimental results show that our proposed algorithms can significantly reduce leakage energy while maintaining the system performance and circuit area, and therefore, provides a suit-able design solution for the circuits used in satellites.
, Available online
, 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.
, Available online
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.1006-3080.20210104002
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.1006-3080.20201222004
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.1006-3080.20201229001
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.1006-3080.20201214001
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.1006-3080.20201227001
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.1006-3080.20200614001
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.
