Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10054978
Ge Yang, Shen Zhang, Xin Liu, Shan Li, Kaina Zhang, Richeng Guo
This paper studies the tracking control problem of airbreathing hypersonic vehicles subjected to complicated and uncertain property of the aerodynamic characteristics. An ESO-based backstepping control scheme with acceleration feedback is proposed. The control scheme includes inner and outer loops. The outer loop is designed by backstepping method, which is incorporated with acceleration feedback. And the extended state observer is involved to compensate the compound disturbance. The inner loop is designed by dynamic inversion method to track the virtual control signal of the outer loop. Lastly, numerical simulations are conducted to verify the performance and effectiveness of the control scheme.
{"title":"ESO-based backstepping control for air-breathing hypersonic vehicle with acceleration feedback","authors":"Ge Yang, Shen Zhang, Xin Liu, Shan Li, Kaina Zhang, Richeng Guo","doi":"10.1109/CAC57257.2022.10054978","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054978","url":null,"abstract":"This paper studies the tracking control problem of airbreathing hypersonic vehicles subjected to complicated and uncertain property of the aerodynamic characteristics. An ESO-based backstepping control scheme with acceleration feedback is proposed. The control scheme includes inner and outer loops. The outer loop is designed by backstepping method, which is incorporated with acceleration feedback. And the extended state observer is involved to compensate the compound disturbance. The inner loop is designed by dynamic inversion method to track the virtual control signal of the outer loop. Lastly, numerical simulations are conducted to verify the performance and effectiveness of the control scheme.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121159179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055962
Qian Xu, Chutian Yu, Xiang Yuan, Zao Fu, Hongzhe Liu
In this paper, a distributed Q-learning algorithm is studied to solve the economic dispatch (ED) problem in smart grid. To tackle the ED problem in the presence of unknown cost functions, most of the existing methods are designed based on the global information of generation units, which would suffer from potential network attack. To conquer such limitation, the distributed Q-leaning algorithm consisting of distributed communication and reinforcement learning (RL) is proposed, where no global information is allowed to used, but information exchange among neighboring generation units can be available. In distributed Q-learning, each generation unit learns the local action-value function and collaborates to optimize the ED problem. Finally, the convergence and optimality of the proposed algorithm are proven, and the numerical simulation results demonstrate the effectiveness of the algorithm.
{"title":"Distributed Q-learning Algorithm for Economic Dispatch of Smart Grid with Unknown Cost Functions","authors":"Qian Xu, Chutian Yu, Xiang Yuan, Zao Fu, Hongzhe Liu","doi":"10.1109/CAC57257.2022.10055962","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055962","url":null,"abstract":"In this paper, a distributed Q-learning algorithm is studied to solve the economic dispatch (ED) problem in smart grid. To tackle the ED problem in the presence of unknown cost functions, most of the existing methods are designed based on the global information of generation units, which would suffer from potential network attack. To conquer such limitation, the distributed Q-leaning algorithm consisting of distributed communication and reinforcement learning (RL) is proposed, where no global information is allowed to used, but information exchange among neighboring generation units can be available. In distributed Q-learning, each generation unit learns the local action-value function and collaborates to optimize the ED problem. Finally, the convergence and optimality of the proposed algorithm are proven, and the numerical simulation results demonstrate the effectiveness of the algorithm.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121190749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The robust prescribed-time algorithms for nonlinear high-order systems and leader-follower systems are investigated with the help of backstepping approach. Firstly, for nonlinear high-order systems, the states can converge to zero within a prescribed time by designing virtual input in each step. Secondly, for leader-follower systems, new error function is defined to achieved consensus among followers and to ensure that follower’s states are identical to leader’s states within the prescribed time that has been arbitrarily set. In addition, the proposed algorithms all have disturbance rejection ability. To show that the suggested algorithms are effective, two numerical simulations are utilised in the end.
{"title":"Prescribed-Time Backstepping Algorithms for Leader-Follower Multi-Agent Systems","authors":"Yingjiang Zhou, Yong Ding, Zicheng Yu, Guoping Jiang","doi":"10.1109/CAC57257.2022.10054783","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054783","url":null,"abstract":"The robust prescribed-time algorithms for nonlinear high-order systems and leader-follower systems are investigated with the help of backstepping approach. Firstly, for nonlinear high-order systems, the states can converge to zero within a prescribed time by designing virtual input in each step. Secondly, for leader-follower systems, new error function is defined to achieved consensus among followers and to ensure that follower’s states are identical to leader’s states within the prescribed time that has been arbitrarily set. In addition, the proposed algorithms all have disturbance rejection ability. To show that the suggested algorithms are effective, two numerical simulations are utilised in the end.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"439 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113987669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055453
Yifan Hu, Wenhui Liu
The article inquiries an event-trigger-based adaptive fuzzy state feedback control for indeterminate nonlinear systems comprising input dead-zone. We estimate unknown nonlinear terms containing the uncertain parameter by utilizing fuzzy logic systems (FLSs) . An adaptive controller and an auxiliary system are co-construct to get rid of the impact of dead-zone. The event-triggered control (ETC) algorithm is raised to alleviate the corresponding encumbrance. Furthermore, a variable interval technology is adopted to handle the algebraic ring issue generated by nonstrict-feedback nonlinear systems. The presented control framework can ensure that all closed ring signs are limited. Finally, a practical emulation consequence of an underwater vehicle is developed to certify the availability of the given control project.
{"title":"Fuzzy adaptive event-triggered control for a class of uncertain nonlinear systems subject to actuator dead-zone","authors":"Yifan Hu, Wenhui Liu","doi":"10.1109/CAC57257.2022.10055453","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055453","url":null,"abstract":"The article inquiries an event-trigger-based adaptive fuzzy state feedback control for indeterminate nonlinear systems comprising input dead-zone. We estimate unknown nonlinear terms containing the uncertain parameter by utilizing fuzzy logic systems (FLSs) . An adaptive controller and an auxiliary system are co-construct to get rid of the impact of dead-zone. The event-triggered control (ETC) algorithm is raised to alleviate the corresponding encumbrance. Furthermore, a variable interval technology is adopted to handle the algebraic ring issue generated by nonstrict-feedback nonlinear systems. The presented control framework can ensure that all closed ring signs are limited. Finally, a practical emulation consequence of an underwater vehicle is developed to certify the availability of the given control project.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"59 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055974
Qingpei Fan, Jun Meng
Motion planning is an important decision-making part for robots to perform motion tasks, and it is indispensable for UAV to perform autonomous flight tasks. First, this article introduces the relevant theories of motion planning and a variety of current representative motion planning strategies. Then point out the problems of UAVs in motion planning tasks, and the application of motion planning in a series of UAVs decision and control problems is introduced. Finally, the development direction of motion planning in the field of UAVs is prospected to provide reference for future research.
{"title":"Application of Motion Planning in UAVs: A Review","authors":"Qingpei Fan, Jun Meng","doi":"10.1109/CAC57257.2022.10055974","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055974","url":null,"abstract":"Motion planning is an important decision-making part for robots to perform motion tasks, and it is indispensable for UAV to perform autonomous flight tasks. First, this article introduces the relevant theories of motion planning and a variety of current representative motion planning strategies. Then point out the problems of UAVs in motion planning tasks, and the application of motion planning in a series of UAVs decision and control problems is introduced. Finally, the development direction of motion planning in the field of UAVs is prospected to provide reference for future research.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127592847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blast furnace iron-making process is one of the most crucial parts in iron and steel industry. Due to many interference factors and a series of complex physical and chemical reactions, abnormal furnace conditions often occur. The nonlinear characteristics hidden in the blast furnace data and the existence of large noise and outliers make it difficult to establish an effective monitoring model. In this paper, a novel robust broad nonlinear representation canonical variate analysis (RBNCVA) method is proposed to overcome the above problems. First, a feature extraction strategy is developed to extract robust broad nonlinear features based on stacked denoising autoencoder (SDAE). The robust broad nonlinear features can assist the model to cope with the complex nonlinearity and resist the interference of noise and outliers. Then canonical variate analysis (CVA) method is used to analyze the relationship between past and future feature vectors. Subsequently, control limits are computed through probability density functions defined by kernel density estimation. Finally, the practical blast furnace data is adopted to validate the effectiveness and robustness of the proposed method.
{"title":"A Novel Robust Broad Nonlinear Representation CVA Method for Monitoring Blast Furnace Iron-making Process","authors":"Yuelin Yang, Chunjie Yang, Bo Yang, Yu Chen, Siwei Lou, Xiong Zhu","doi":"10.1109/CAC57257.2022.10055768","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055768","url":null,"abstract":"Blast furnace iron-making process is one of the most crucial parts in iron and steel industry. Due to many interference factors and a series of complex physical and chemical reactions, abnormal furnace conditions often occur. The nonlinear characteristics hidden in the blast furnace data and the existence of large noise and outliers make it difficult to establish an effective monitoring model. In this paper, a novel robust broad nonlinear representation canonical variate analysis (RBNCVA) method is proposed to overcome the above problems. First, a feature extraction strategy is developed to extract robust broad nonlinear features based on stacked denoising autoencoder (SDAE). The robust broad nonlinear features can assist the model to cope with the complex nonlinearity and resist the interference of noise and outliers. Then canonical variate analysis (CVA) method is used to analyze the relationship between past and future feature vectors. Subsequently, control limits are computed through probability density functions defined by kernel density estimation. Finally, the practical blast furnace data is adopted to validate the effectiveness and robustness of the proposed method.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126320577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a multi-step prediction feed-forward compensation attitude control method based on the conservation of angular momentum is designed for multistage satellite with a two-dimensional scanning mechanism. Firstly, the dynamic model of multistage satellite with a scanning mechanism is established by the Newton-Euler method. Secondly, a multi-step prediction feedforward compensation method based on the conservation of angular momentum is proposed for the discrete multistage control system to solve the control period mismatching and the measurement delay. Finally, the numerical simulation shows that the control method proposed in this paper improves the attitude stability and pointing accuracy of multistage satellite by 3-4 times.
{"title":"Attitude Feedforward Compensation Control for Multistage Satellite with Scanning Mechanism","authors":"Mao Fan, Liang Tang, Xin Guan, Zixi Guo, Renjian Hao, Kebei Zhang","doi":"10.1109/CAC57257.2022.10055179","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055179","url":null,"abstract":"In this paper, a multi-step prediction feed-forward compensation attitude control method based on the conservation of angular momentum is designed for multistage satellite with a two-dimensional scanning mechanism. Firstly, the dynamic model of multistage satellite with a scanning mechanism is established by the Newton-Euler method. Secondly, a multi-step prediction feedforward compensation method based on the conservation of angular momentum is proposed for the discrete multistage control system to solve the control period mismatching and the measurement delay. Finally, the numerical simulation shows that the control method proposed in this paper improves the attitude stability and pointing accuracy of multistage satellite by 3-4 times.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128019007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055032
Shuaiyi Liu, Fan Zhou, Ying Liu, Jun Zhao
A by-product gas booster station plays an important role in the steel production process, however, due to environmental and other factors can occur jump machine failure, affecting the normal steel production. This study proposes a fault diagnosis method based on one improved domain adversarial neural network (IDANN) for the by-product gas booster station. The method considers the influence of hidden variable factors such as discharge gas pressure and resonance on fault identification and designs an improved domain-adaptive network structure. Assessing the impact of the implicit variable injection ratio on the recognition accuracy of the model, the inter-class distance is adopted to optimize the parameters of the injected implicit variable factor ratio to maximize the inter-class distance. To verify the effectiveness in this study, the operating data of a steel plant LDG booster station is selected for experiments and compared with deep neural networks (DNN) and other fault diagnosis methods. The experimental results show that the recognition rate of this paper can reach 95% for the jump machine faults and the method of this paper has good robustness and network generalization ability.
{"title":"An Improved DANN-based Mixed Gas Booster Station Fault Diagnosis Method","authors":"Shuaiyi Liu, Fan Zhou, Ying Liu, Jun Zhao","doi":"10.1109/CAC57257.2022.10055032","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055032","url":null,"abstract":"A by-product gas booster station plays an important role in the steel production process, however, due to environmental and other factors can occur jump machine failure, affecting the normal steel production. This study proposes a fault diagnosis method based on one improved domain adversarial neural network (IDANN) for the by-product gas booster station. The method considers the influence of hidden variable factors such as discharge gas pressure and resonance on fault identification and designs an improved domain-adaptive network structure. Assessing the impact of the implicit variable injection ratio on the recognition accuracy of the model, the inter-class distance is adopted to optimize the parameters of the injected implicit variable factor ratio to maximize the inter-class distance. To verify the effectiveness in this study, the operating data of a steel plant LDG booster station is selected for experiments and compared with deep neural networks (DNN) and other fault diagnosis methods. The experimental results show that the recognition rate of this paper can reach 95% for the jump machine faults and the method of this paper has good robustness and network generalization ability.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055746
Siwei Yang, Haichen Yu, Mingyan Zhao, Tao Liu, X. Ni
For using the continuous oscillatory baffled crystallizer (COBC) to conduct cooling crystallization of β form L-glutamic acid (LGA), a prediction model of crystal product size distribution and an optimization method of process operation conditions are proposed in this paper, based on the design of experiment. The oscillation frequency and cooling rates of front two zones of the COBC are taken as the input conditions to design a 3-factor and 3-level Box-Behnken experimental scheme. A prediction model of crystal product size distribution based on double-layer basis functions is constructed by examining the chord length distribution of crystal products under different experimental conditions. Then an objective function on the target crystal size distribution and product yield is introduced to establish an optimization method on the process operating conditions, based on the above prediction model. The effectiveness and advantages of the proposed method are verified by simulation and experiments on the continuous cooling crystallization process of p form LGA.
{"title":"Process Input Optimization for Continuous Crystallization of β form L-glutamic acid","authors":"Siwei Yang, Haichen Yu, Mingyan Zhao, Tao Liu, X. Ni","doi":"10.1109/CAC57257.2022.10055746","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055746","url":null,"abstract":"For using the continuous oscillatory baffled crystallizer (COBC) to conduct cooling crystallization of β form L-glutamic acid (LGA), a prediction model of crystal product size distribution and an optimization method of process operation conditions are proposed in this paper, based on the design of experiment. The oscillation frequency and cooling rates of front two zones of the COBC are taken as the input conditions to design a 3-factor and 3-level Box-Behnken experimental scheme. A prediction model of crystal product size distribution based on double-layer basis functions is constructed by examining the chord length distribution of crystal products under different experimental conditions. Then an objective function on the target crystal size distribution and product yield is introduced to establish an optimization method on the process operating conditions, based on the above prediction model. The effectiveness and advantages of the proposed method are verified by simulation and experiments on the continuous cooling crystallization process of p form LGA.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125640485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055299
Shuyu Han, Jun Wang, Zaiyu Pan, Zhengwen Shen
Rain streaks vary in size, direction, and density, resulting in serious blurring and image quality degradation, which often directly affect the downstream visual tasks. At present, many end-to-end image removal networks have achieved good results, but image details are often lost during processing. Therefore, we propose a novel detail-recovery network to solve this problem. Unlike the existing works, we regard image rain removal and detail restoration as two different tasks simultaneously. Specifically, we use two encoder-decoder networks to extract rain streaks and detailed features and design different feature extraction blocks for two encoder-decoder networks. Due to the different receptive fields of feature layers at different scales, the information extracted at each scale is also different. The tasks of image rain removal and detail restoration are considered from the multi-scale feature level. To better respond to the image details and take full advantage of semantic information of multi-scale features, rain removal and image detail restoration are carried out at different scales. The proposed method has been validated on datasets to verify its effectiveness.
{"title":"MSDR-Net: Multi-Scale Detail-Recovery Network for Single Image Deraining","authors":"Shuyu Han, Jun Wang, Zaiyu Pan, Zhengwen Shen","doi":"10.1109/CAC57257.2022.10055299","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055299","url":null,"abstract":"Rain streaks vary in size, direction, and density, resulting in serious blurring and image quality degradation, which often directly affect the downstream visual tasks. At present, many end-to-end image removal networks have achieved good results, but image details are often lost during processing. Therefore, we propose a novel detail-recovery network to solve this problem. Unlike the existing works, we regard image rain removal and detail restoration as two different tasks simultaneously. Specifically, we use two encoder-decoder networks to extract rain streaks and detailed features and design different feature extraction blocks for two encoder-decoder networks. Due to the different receptive fields of feature layers at different scales, the information extracted at each scale is also different. The tasks of image rain removal and detail restoration are considered from the multi-scale feature level. To better respond to the image details and take full advantage of semantic information of multi-scale features, rain removal and image detail restoration are carried out at different scales. The proposed method has been validated on datasets to verify its effectiveness.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125677096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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