Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515988
Chao Zhang, D. Shen
This paper studies the zero-error tacking problem of finite-level quantized iterative learning control using an encoding-decoding method, where both measurement and actuator side quantization and transmission are considered. In particular, the system output is encoded, quantized, transmitted and decoded in sequence for input updating of the next iteration. Then the generated input is transmitted through networks following the same procedure as the output transmission for plant input updating. The zero-error convergence of the proposed scheme is strictly proved and a numerical simulation is provided to demonstrate the effectiveness of the proposed scheme.
{"title":"Finite-level Quantized Iterative Learning Control by Encoding-Decoding Mechanisms","authors":"Chao Zhang, D. Shen","doi":"10.1109/DDCLS.2018.8515988","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515988","url":null,"abstract":"This paper studies the zero-error tacking problem of finite-level quantized iterative learning control using an encoding-decoding method, where both measurement and actuator side quantization and transmission are considered. In particular, the system output is encoded, quantized, transmitted and decoded in sequence for input updating of the next iteration. Then the generated input is transmitted through networks following the same procedure as the output transmission for plant input updating. The zero-error convergence of the proposed scheme is strictly proved and a numerical simulation is provided to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"97 1","pages":"289-294"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86513919","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515992
Junde Wang, Jing Wang, Jinglin Zhou
On-line active fault detection (AFD) and its optimization problems are proposed based on the set-membership ellipsoid technique in order to solve the problem of on-line fault detection. The design of auxiliary input signal should satisfy two conditions: the signal amplitude is small enough without obvious impact on the system, and it simultaneously separates the system output in the normal and fault operation. Here we describe the output set as an ellipsoid under the framework of set-membership. The system model of moving window is established based on the parity space, and the equivalent optimization design of auxiliary input signal is solved based on this model. The proposed method can significantly reduce the complexity of the optimization calculation and conveniently obtain the auxiliary input signal on-line. The system fault is detected more intuitively by comparing the degree of separation between the output ellipsoid of the actual system and that of the identification normal (or fault) model. The simulation results on a general example verify the effectiveness of the proposed method.
{"title":"On-line Active Fault Detection Based on Set-membership Ellipsoid and Moving Window","authors":"Junde Wang, Jing Wang, Jinglin Zhou","doi":"10.1109/DDCLS.2018.8515992","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515992","url":null,"abstract":"On-line active fault detection (AFD) and its optimization problems are proposed based on the set-membership ellipsoid technique in order to solve the problem of on-line fault detection. The design of auxiliary input signal should satisfy two conditions: the signal amplitude is small enough without obvious impact on the system, and it simultaneously separates the system output in the normal and fault operation. Here we describe the output set as an ellipsoid under the framework of set-membership. The system model of moving window is established based on the parity space, and the equivalent optimization design of auxiliary input signal is solved based on this model. The proposed method can significantly reduce the complexity of the optimization calculation and conveniently obtain the auxiliary input signal on-line. The system fault is detected more intuitively by comparing the degree of separation between the output ellipsoid of the actual system and that of the identification normal (or fault) model. The simulation results on a general example verify the effectiveness of the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"44 1","pages":"420-425"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87062699","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516078
Weiwei Kong, Weiqiang Feng, Yi Zheng, Tianjiang Hu
Unmanned Surface Vehicle(USV) in today’s military and commercial application is growing exponentially. Benefiting from the autonomous capability, this unmanned platform can execute various tasks without human directly control. So evaluation of their autonomy and other capabilities are critical to realize the autonomous operation ability of unmanned systems. We present the quantitative indices, typical scenes and a practical framework to test and evaluate the performance of an USV. Then a test and evaluation (T&E) framework was established for data collection. By setting up a simulation environment, it can be seen that the proposed framework gives quantified results with different testing assignments.
{"title":"A Test and Evaluation Framework for Unmanned Surface Vehicle","authors":"Weiwei Kong, Weiqiang Feng, Yi Zheng, Tianjiang Hu","doi":"10.1109/DDCLS.2018.8516078","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516078","url":null,"abstract":"Unmanned Surface Vehicle(USV) in today’s military and commercial application is growing exponentially. Benefiting from the autonomous capability, this unmanned platform can execute various tasks without human directly control. So evaluation of their autonomy and other capabilities are critical to realize the autonomous operation ability of unmanned systems. We present the quantitative indices, typical scenes and a practical framework to test and evaluate the performance of an USV. Then a test and evaluation (T&E) framework was established for data collection. By setting up a simulation environment, it can be seen that the proposed framework gives quantified results with different testing assignments.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"42 1","pages":"1134-1139"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86985074","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516003
Jian Hou, Aihua Zhang, Lv Chengcong, E. Xu
Recently, a clustering algorithm is proposed by treating local density peaks as cluster centers. This algorithm proposes to describe the data to be clustered with local density and the distance of one data to the nearest data of larger local density. This description highlights the uniqueness of cluster centers and is utilized to determine cluster centers. With the assumption that one data and the nearest data of larger local density are in the same cluster, the non-center data are assigned labels efficiently. By studying the clustering process of this algorithm in depth, we find that the local density is not very effective in highlighting the uniqueness of cluster centers. As a result, this algorithm is dependent on the parameters in local density calculation. We discuss this problem and find that it is the role of density peaks, but not the absolute local density, that highlights the uniqueness of cluster centers. Based on this observation, we introduce the concept of subordinate and use the amount of subordinates to replace the local density in cluster center identification. Together with a new density kernel, this new criterion is shown to be effective in experiments and comparisons.
{"title":"Subordinate based Cluster Center Identification in Density Peak Clustering","authors":"Jian Hou, Aihua Zhang, Lv Chengcong, E. Xu","doi":"10.1109/DDCLS.2018.8516003","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516003","url":null,"abstract":"Recently, a clustering algorithm is proposed by treating local density peaks as cluster centers. This algorithm proposes to describe the data to be clustered with local density and the distance of one data to the nearest data of larger local density. This description highlights the uniqueness of cluster centers and is utilized to determine cluster centers. With the assumption that one data and the nearest data of larger local density are in the same cluster, the non-center data are assigned labels efficiently. By studying the clustering process of this algorithm in depth, we find that the local density is not very effective in highlighting the uniqueness of cluster centers. As a result, this algorithm is dependent on the parameters in local density calculation. We discuss this problem and find that it is the role of density peaks, but not the absolute local density, that highlights the uniqueness of cluster centers. Based on this observation, we introduce the concept of subordinate and use the amount of subordinates to replace the local density in cluster center identification. Together with a new density kernel, this new criterion is shown to be effective in experiments and comparisons.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"106 1","pages":"551-554"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88028363","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515964
Dawei Hou, J. Na, Guanbin Gao, Guang Li
In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
{"title":"Data-Driven Adaptive Optimal Tracking Control for Completely Unknown Systems","authors":"Dawei Hou, J. Na, Guanbin Gao, Guang Li","doi":"10.1109/DDCLS.2018.8515964","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515964","url":null,"abstract":"In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"1039-1044"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90052256","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515936
Zhi-wen Chen, S. Ding, Kai Zhang, Chunhua Yang, Tao Peng
Canonical correlation analysis (CCA) is a well-established multivariate analysis method for finding the relationship between two data sets, which has been explored for fault detection recently. In this paper, we revisit the generalized canonical correlation analysis (CCA) form and discuss its applications for fault detection and estimation. The motivation of using CCA for fault detection is to reduce process uncertainty by taking the correlation coefficients into account. Then, the fault detectability in terms of fault detection rate is increased. Finally, the generalized CCA-based fault detection method is validated on the benchmark, which is a simulation of high-speed trains traction drive control system. The achieved results show that the proposed method is able to successfully detect the faults.
{"title":"Generalized CCA with Applications for Fault Detection and Estimation","authors":"Zhi-wen Chen, S. Ding, Kai Zhang, Chunhua Yang, Tao Peng","doi":"10.1109/DDCLS.2018.8515936","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515936","url":null,"abstract":"Canonical correlation analysis (CCA) is a well-established multivariate analysis method for finding the relationship between two data sets, which has been explored for fault detection recently. In this paper, we revisit the generalized canonical correlation analysis (CCA) form and discuss its applications for fault detection and estimation. The motivation of using CCA for fault detection is to reduce process uncertainty by taking the correlation coefficients into account. Then, the fault detectability in terms of fault detection rate is increased. Finally, the generalized CCA-based fault detection method is validated on the benchmark, which is a simulation of high-speed trains traction drive control system. The achieved results show that the proposed method is able to successfully detect the faults.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"31 1","pages":"545-550"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90244017","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515942
Ruikun Zhang, R. Chi
In this paper, reliable control strategy is studied for nonlinear system with input saturation by adaptive iterative learning control. The system dynamic function is described by a class of nonlinearly parameterized functions with input saturation and actuator faults. In order to address nonlinearity of system, input saturation and the actuator fault term, we design the adaptive iterative learning reliable controller (AILRC), which is a feedback P-type ILC controller. Based on the constructed composite energy function (CEF) and some necessary assumptions, the convergence analysis is given, which shows that the system tracking error converges to zero when the iteration number tends to infinity. Finally, simulation is given to illustrate the correctness of the proposed AILRC.
{"title":"Reliable Control of Nonlinear System with Input Saturation by Adaptive Iterative Learning Control","authors":"Ruikun Zhang, R. Chi","doi":"10.1109/DDCLS.2018.8515942","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515942","url":null,"abstract":"In this paper, reliable control strategy is studied for nonlinear system with input saturation by adaptive iterative learning control. The system dynamic function is described by a class of nonlinearly parameterized functions with input saturation and actuator faults. In order to address nonlinearity of system, input saturation and the actuator fault term, we design the adaptive iterative learning reliable controller (AILRC), which is a feedback P-type ILC controller. Based on the constructed composite energy function (CEF) and some necessary assumptions, the convergence analysis is given, which shows that the system tracking error converges to zero when the iteration number tends to infinity. Finally, simulation is given to illustrate the correctness of the proposed AILRC.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"11 1","pages":"1001-1005"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90357563","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515973
Zhiqiang Geng, Ju Bai, Qunxiong Zhu, Yuan Xu, Yangming Han
Data envelopment analysis (DEA) has been commonly used in the energy saving of enterprise plants. Nevertheless, when the traditional DEA model analyzes the effectiveness of decision-making units (DMUs), over 1/3 of the DMUs’ efficiency values are 1, so the traditional DEA model cannot distinguish the cons and pros of the DMUs. And although the DEA cross-model(DEACM) is able to differentiate the cons as well as pros of the effective DMUs, it can’t obtain the improvement direction of the ineffective DMUs. Therefore, an energy saving and management method based on an improved DEACM, which can use the higher efficiency distinction to identify the efficiency state of the DMUs, is proposed in this paper. Meanwhile, the improvement direction of the ineffective DMU can be found by the self-evaluation of the improved DEACM. Finally, the improved DEACM is utilized to save and manage the energy configuration of the PTA solvent system in the industrial process. The experimental results reveal that the practicality and effectiveness of the proposed method are verified, and in addition, the efficiency discrimination is well. Moreover, the proposed model can find the direction of the quantitative targets of energy saving to improve the energy efficiency of PTA production.
{"title":"Energy Saving and Management of the Industrial Process Based on An Improved DEA Cross-model","authors":"Zhiqiang Geng, Ju Bai, Qunxiong Zhu, Yuan Xu, Yangming Han","doi":"10.1109/DDCLS.2018.8515973","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515973","url":null,"abstract":"Data envelopment analysis (DEA) has been commonly used in the energy saving of enterprise plants. Nevertheless, when the traditional DEA model analyzes the effectiveness of decision-making units (DMUs), over 1/3 of the DMUs’ efficiency values are 1, so the traditional DEA model cannot distinguish the cons and pros of the DMUs. And although the DEA cross-model(DEACM) is able to differentiate the cons as well as pros of the effective DMUs, it can’t obtain the improvement direction of the ineffective DMUs. Therefore, an energy saving and management method based on an improved DEACM, which can use the higher efficiency distinction to identify the efficiency state of the DMUs, is proposed in this paper. Meanwhile, the improvement direction of the ineffective DMU can be found by the self-evaluation of the improved DEACM. Finally, the improved DEACM is utilized to save and manage the energy configuration of the PTA solvent system in the industrial process. The experimental results reveal that the practicality and effectiveness of the proposed method are verified, and in addition, the efficiency discrimination is well. Moreover, the proposed model can find the direction of the quantitative targets of energy saving to improve the energy efficiency of PTA production.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"36 1","pages":"154-159"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85015178","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8516023
Zidong Ai
In this work, we conduct stability analysis for a class of multi-module impulsive control systems via an event-driven scheme. By designing some event-driven conditions and a proper event-driven impulsive control law, we establish some sufficient stability criteria for the considered systems. The proposed event-driven control scheme is advantageous to reduce the utilization of communication and computation resources. Further, we study the impulsive synchronization problem for two continuous-time dynamical systems with different initial values. Finally, an example of Chua’s circuit with simulations results are provided to illustrate the validity of the method.
{"title":"Exponential Stability for Event-Driven Impulsive Control Systems","authors":"Zidong Ai","doi":"10.1109/DDCLS.2018.8516023","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516023","url":null,"abstract":"In this work, we conduct stability analysis for a class of multi-module impulsive control systems via an event-driven scheme. By designing some event-driven conditions and a proper event-driven impulsive control law, we establish some sufficient stability criteria for the considered systems. The proposed event-driven control scheme is advantageous to reduce the utilization of communication and computation resources. Further, we study the impulsive synchronization problem for two continuous-time dynamical systems with different initial values. Finally, an example of Chua’s circuit with simulations results are provided to illustrate the validity of the method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"704-707"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83802633","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 : 2018-05-01DOI: 10.1109/DDCLS.2018.8515929
Runfeng Chen, Hong Yang
Large-scale spacecraft, such as space station, highlights the systems’ reliability and safety. Using prognostics to predict the trend of the system health state evolution can help find out the potential dangers and prevent the unexpected failure from happening. With the adoption of data-driven ideology, a system-level health state prognostics method is proposed to predict the trend information. First, the characteristics of the large-scale spacecraft and the system-level health definition are analyzed. Then the details of the solution method are described. The novelty of this method is to use the network science knowledge to extract the system-level features. The adopted predicting method is briefly introduced. Finally, a real case study with on-orbit telemetry data is presented, and relevant conclusions are drawn for reference.
{"title":"A Data-driven System-level Health State Prognostics Method for Large-scale Spacecraft Systems","authors":"Runfeng Chen, Hong Yang","doi":"10.1109/DDCLS.2018.8515929","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515929","url":null,"abstract":"Large-scale spacecraft, such as space station, highlights the systems’ reliability and safety. Using prognostics to predict the trend of the system health state evolution can help find out the potential dangers and prevent the unexpected failure from happening. With the adoption of data-driven ideology, a system-level health state prognostics method is proposed to predict the trend information. First, the characteristics of the large-scale spacecraft and the system-level health definition are analyzed. Then the details of the solution method are described. The novelty of this method is to use the network science knowledge to extract the system-level features. The adopted predicting method is briefly introduced. Finally, a real case study with on-orbit telemetry data is presented, and relevant conclusions are drawn for reference.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"44 1","pages":"565-568"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86773457","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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