Pub Date : 2022-11-25DOI: 10.1109/CAC57257.2022.10055256
Y. Qi, Jintao Liu, Jiuhong Li
Railway operational accidents are caused by a variety of related hazards’ interactions, which can be shown in the form of a complex network. By analyzing the topological characteristics of such a network, we can understand the causes of the accidents more deeply and make effective countermeasures. In this paper, a new analysis approach to understanding railway operational accidents based on a complex heterogeneous network is proposed. Its originality is to analyze accidents through topological indicators which are more suitable for heterogeneous networks and to design the targeted analysis process based on these indicators. Besides, the causality between other hazards or accidents directly related to one hazard in the network is quantified, which can describe the causal relationship in the network more accurately. This analysis approach provides a decision-making basis for ensuring the safety of railway operations. This paper takes the British railway operational accidents as a case study. The results show that this approach can effectively determine the key factors affecting the accidents and give a feasible decision-making basis.
{"title":"A Complex Heterogeneous Network-based Analysis Approach for Exploring Railway Operational Accidents","authors":"Y. Qi, Jintao Liu, Jiuhong Li","doi":"10.1109/CAC57257.2022.10055256","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055256","url":null,"abstract":"Railway operational accidents are caused by a variety of related hazards’ interactions, which can be shown in the form of a complex network. By analyzing the topological characteristics of such a network, we can understand the causes of the accidents more deeply and make effective countermeasures. In this paper, a new analysis approach to understanding railway operational accidents based on a complex heterogeneous network is proposed. Its originality is to analyze accidents through topological indicators which are more suitable for heterogeneous networks and to design the targeted analysis process based on these indicators. Besides, the causality between other hazards or accidents directly related to one hazard in the network is quantified, which can describe the causal relationship in the network more accurately. This analysis approach provides a decision-making basis for ensuring the safety of railway operations. This paper takes the British railway operational accidents as a case study. The results show that this approach can effectively determine the key factors affecting the accidents and give a feasible decision-making basis.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"96 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":"127082964","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/CAC57257.2022.10055131
Baolin Gong, Wenxing Zhu
In this paper, we propose an adaptive traffic signal control model combining time period division and deep reinforcement learning to improve the efficiency of traffic by dynamically changing the traffic phase duration according to the real-time situation. In our model, a day-time period is divided into two overlap period parts representing the morning situation and the evening situation, then the deep reinforcement learning algorithm-TD3 is selected to train the corresponding agent in each part, and finally a fuzzy method is used to coordinate these two agents at different time. In order to get better performance, we make some improvements in TD3. We improve the algorithm’s experience-replay mechanism and use some tricks in training. Simulation results shows that our model can effectively reduce vehicles’ accumulative waiting time, queue length and alleviate CO2 emission.
{"title":"Adaptive Traffic Signal Control Through Time Period Division and Deep Reinforcement Learning","authors":"Baolin Gong, Wenxing Zhu","doi":"10.1109/CAC57257.2022.10055131","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055131","url":null,"abstract":"In this paper, we propose an adaptive traffic signal control model combining time period division and deep reinforcement learning to improve the efficiency of traffic by dynamically changing the traffic phase duration according to the real-time situation. In our model, a day-time period is divided into two overlap period parts representing the morning situation and the evening situation, then the deep reinforcement learning algorithm-TD3 is selected to train the corresponding agent in each part, and finally a fuzzy method is used to coordinate these two agents at different time. In order to get better performance, we make some improvements in TD3. We improve the algorithm’s experience-replay mechanism and use some tricks in training. Simulation results shows that our model can effectively reduce vehicles’ accumulative waiting time, queue length and alleviate CO2 emission.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"18 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":"127098013","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/CAC57257.2022.10055372
Xiaoxuan Zhao, Haoyu Wang, Xiujuan Wang, U. Lewlomphaisarl, Dong Li, Jing Hua, Mengzhen Kang
The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.
{"title":"Learning Greenhouse Climate Control Policy from Monitored Data","authors":"Xiaoxuan Zhao, Haoyu Wang, Xiujuan Wang, U. Lewlomphaisarl, Dong Li, Jing Hua, Mengzhen Kang","doi":"10.1109/CAC57257.2022.10055372","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055372","url":null,"abstract":"The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"72 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":"127377013","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/CAC57257.2022.10055118
Zhenbo Liu, Changwei Cheng, Zhenhui Yi
In order to decrease computational complexity of dense mapping in large-scale environment based on Euclidean Signed Distance Function(ESDF) submap model, a registration algorithm based on octree sampling and sliding window structure is designed. By introducing the octree structure, the surface points on ESDF model with larger weights are evenly selected uniformly to reduce the number of residuals. The sliding window structure keeps the number of optimization variables constant, keeping the optimization time within the controllable range. We integrate these algorithms into the Voxgraph framework to build a new fast mapping system. The experimental results show that the octree sampling algorithm can decrease registration speed by 50% and the sliding window structure can decrease mapping speed by 33%.
{"title":"Fast Dense Mapping Based on Signed Distance Function Submaps","authors":"Zhenbo Liu, Changwei Cheng, Zhenhui Yi","doi":"10.1109/CAC57257.2022.10055118","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055118","url":null,"abstract":"In order to decrease computational complexity of dense mapping in large-scale environment based on Euclidean Signed Distance Function(ESDF) submap model, a registration algorithm based on octree sampling and sliding window structure is designed. By introducing the octree structure, the surface points on ESDF model with larger weights are evenly selected uniformly to reduce the number of residuals. The sliding window structure keeps the number of optimization variables constant, keeping the optimization time within the controllable range. We integrate these algorithms into the Voxgraph framework to build a new fast mapping system. The experimental results show that the octree sampling algorithm can decrease registration speed by 50% and the sliding window structure can decrease mapping speed by 33%.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"131 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":"127506759","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/CAC57257.2022.10055000
Boyu Wen, Xin Chen, Yipu Sun
The feedback linearization method can accurately linearize the nonlinear system. However, feedback linearization needs the exact dynamic of nonlinear systems, it is difficult to apply to unknown nonlinear systems. To be capable to perform the feedback linearization of the continuous-time nonlinear system containing uncertain parameters, a model reference adaptive control (MRAC) scheme is introduced in this paper. First, we construct a state feedback controller by the knowledge of the system model structure and form it into an adjustable system together with the nonlinear object. The reference model is decided upon to be a standard linear system. Second, based on the output errors of reference model and adjustable system, we adaptively modify the state feedback controller’s unknown parameters using the gradient descent approach. Finally, the simulations and real-world experiments on a first-order inverted pendulum system are carried out to assess the effectiveness of given methods.
{"title":"MRAC-Based Adaptive Feedback Linearization Control Method for Continuous-Time Nonlinear Systems with Uncertain Parameters","authors":"Boyu Wen, Xin Chen, Yipu Sun","doi":"10.1109/CAC57257.2022.10055000","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055000","url":null,"abstract":"The feedback linearization method can accurately linearize the nonlinear system. However, feedback linearization needs the exact dynamic of nonlinear systems, it is difficult to apply to unknown nonlinear systems. To be capable to perform the feedback linearization of the continuous-time nonlinear system containing uncertain parameters, a model reference adaptive control (MRAC) scheme is introduced in this paper. First, we construct a state feedback controller by the knowledge of the system model structure and form it into an adjustable system together with the nonlinear object. The reference model is decided upon to be a standard linear system. Second, based on the output errors of reference model and adjustable system, we adaptively modify the state feedback controller’s unknown parameters using the gradient descent approach. Finally, the simulations and real-world experiments on a first-order inverted pendulum system are carried out to assess the effectiveness of given methods.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"44 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":"124927196","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}
This paper proposes a feature extraction method based on whale optimization algorithm and variational mode decomposition (WOA-VMD) to overcome the low feature extraction accuracy of generator early inter-turn short circuit fault. WOA-VMD process the current signal, and the sample entropy is taken as the fitness function of WOA to optimize the VMD parameter combination of modal components' number K and penalty parament α. Then, the optimized VMD decomposes current signals into K intrinsic mode functions (IMFs). IMFs with higher kurtosis values are selected to extract energy entropy as the feature vectors. Finally, the whale optimization algorithm and support vector machine (WOA-SVM) pattern recognition model is used to classify the feature vectors and diagnose generator inter-turn short circuit degree. The experiments show that the proposed method extracts the weak fault features in the early inter-turn short circuit signal and improves the fault diagnosis accuracy, reaching 97.75%.
{"title":"Application of WOA-VMD-SVM in Fault Diagnosis of Generator Inter-turn Short Circuit","authors":"Jing Huang, Ruping Lin, Zhiguo He, Huishu Song, Xiaosheng Huang, Binyi Chen","doi":"10.1109/cac57257.2022.10055106","DOIUrl":"https://doi.org/10.1109/cac57257.2022.10055106","url":null,"abstract":"This paper proposes a feature extraction method based on whale optimization algorithm and variational mode decomposition (WOA-VMD) to overcome the low feature extraction accuracy of generator early inter-turn short circuit fault. WOA-VMD process the current signal, and the sample entropy is taken as the fitness function of WOA to optimize the VMD parameter combination of modal components' number K and penalty parament α. Then, the optimized VMD decomposes current signals into K intrinsic mode functions (IMFs). IMFs with higher kurtosis values are selected to extract energy entropy as the feature vectors. Finally, the whale optimization algorithm and support vector machine (WOA-SVM) pattern recognition model is used to classify the feature vectors and diagnose generator inter-turn short circuit degree. The experiments show that the proposed method extracts the weak fault features in the early inter-turn short circuit signal and improves the fault diagnosis accuracy, reaching 97.75%.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"37 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":"124956458","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/CAC57257.2022.10054747
Yu Zha, W. Shangguan
On account of occlusion and limited visual range, the independent perception of the single vehicle is restricted, which cannot meet the requirements of high-level autonomous driving. In view of the characteristics of information interaction in connected environment, a vehicle-vehicle based beyond-line-of-sight fusion perception framework is proposed. Effective data fusion of multi-source heterogeneous sensor is realized based on D-S evidence theory. Precise object detection and recognition is achieved based on lightweight object detection Faster R-CNN algorithm with backbone used MobileNetV2. Finally, the beyond-line-of-sight perception enhancement method in typical scenes is verified and analyzed on Prescan. Results show that the presented method helps autonomous vehicles make full use of sensory data effectively, expand perception scope, avoid blind fields, which plays a supporting role in the safe and efficient operation of autonomous vehicles.
{"title":"Beyond-line-of-sight Perception Enhancement via Information Interaction in Connected Autonomous Driving Environment","authors":"Yu Zha, W. Shangguan","doi":"10.1109/CAC57257.2022.10054747","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10054747","url":null,"abstract":"On account of occlusion and limited visual range, the independent perception of the single vehicle is restricted, which cannot meet the requirements of high-level autonomous driving. In view of the characteristics of information interaction in connected environment, a vehicle-vehicle based beyond-line-of-sight fusion perception framework is proposed. Effective data fusion of multi-source heterogeneous sensor is realized based on D-S evidence theory. Precise object detection and recognition is achieved based on lightweight object detection Faster R-CNN algorithm with backbone used MobileNetV2. Finally, the beyond-line-of-sight perception enhancement method in typical scenes is verified and analyzed on Prescan. Results show that the presented method helps autonomous vehicles make full use of sensory data effectively, expand perception scope, avoid blind fields, which plays a supporting role in the safe and efficient operation of autonomous vehicles.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"191 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":"123261188","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/CAC57257.2022.10055307
Xueqing Liu, Maoyin Chen, Li Sheng, Donghua Zhou
This paper develops a stochastic high-order fully-actuated systems model that complements the existing highorder fully-actuated system methodology. Different from the deterministic model, stochastic signals can be considered in the proposed version. By employing a high-order operator, the equivalent control and stabilization control laws are obtained to guarantee the global asymptotical stability in probability of the closed-loop system. Finally, the simulation results show the effectiveness of the proposed control schemes.
{"title":"Stochastic High-order Fully-actuated Systems: Model, Equivalence and Stabilization","authors":"Xueqing Liu, Maoyin Chen, Li Sheng, Donghua Zhou","doi":"10.1109/CAC57257.2022.10055307","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055307","url":null,"abstract":"This paper develops a stochastic high-order fully-actuated systems model that complements the existing highorder fully-actuated system methodology. Different from the deterministic model, stochastic signals can be considered in the proposed version. By employing a high-order operator, the equivalent control and stabilization control laws are obtained to guarantee the global asymptotical stability in probability of the closed-loop system. Finally, the simulation results show the effectiveness of the proposed control schemes.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"788 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":"123286039","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/CAC57257.2022.10056106
Ruitian Yang, Lizhang Peng, Yongqing Yang, Lei Wang
Practical fixed-time bipartite consensus problem for multi-agent systems on undirected graphs is studied in this article. A new event-triggered control protocol is constructed, which incorporates fully distributed way and time base generator(TBG). The application of fully distributed control mechanism makes the controller based on local information, rather than global information. The settling time can be determined in advance due to the application of TBG, which is not affected by initial states. Some conditions are proposed to guarantee the achievement of practical fixed-time bipartite consensus and the avoidance of Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the control algorithm.
{"title":"Fixed-Time Event-Based Cooperative Control Algorithm Design for Multi-Agent Systems based on Time Base Generator","authors":"Ruitian Yang, Lizhang Peng, Yongqing Yang, Lei Wang","doi":"10.1109/CAC57257.2022.10056106","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10056106","url":null,"abstract":"Practical fixed-time bipartite consensus problem for multi-agent systems on undirected graphs is studied in this article. A new event-triggered control protocol is constructed, which incorporates fully distributed way and time base generator(TBG). The application of fully distributed control mechanism makes the controller based on local information, rather than global information. The settling time can be determined in advance due to the application of TBG, which is not affected by initial states. Some conditions are proposed to guarantee the achievement of practical fixed-time bipartite consensus and the avoidance of Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the control algorithm.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"9 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":"123327025","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/CAC57257.2022.10056028
Hepei Zhang, Guohai Liu, Duo Zhang, Yue Shen, Zijie Wang, Yan Xu
In this research, the path tracking control and the anti-roll control are constructed based on the kinematic model of the sprayer, respectively. The upper layer uses model predictive control (MPC) to output the desired steering angle and speed of the sprayer based on the current state of the sprayer and the desired path to achieve path tracking control. The lower layer controller uses lateral load transfer rate (LTR) as a measure of rollover to judge whether the vehicle state produces rollover, and compensates for steering angle by introducing a fuzzy controller, which in turn controls the LTR to stabilize at a certain threshold value. So that the sprayer has both better control accuracy and better safety in the process of path tracking, and ensure that the sprayer does not roll. The combined simulation results of ADAMS/MATLAB show that the LTR can be controlled within 0.5 when the sprayer adopts anti-roll control under complex road conditions, which ensures the safety of the sprayer, and the lateral deviation reaches 0.13m, which has a high path tracking accuracy.
{"title":"Path Tracking Control of Four-wheel Independent Driving High Ground Clearance Sprayer Considering Rollover","authors":"Hepei Zhang, Guohai Liu, Duo Zhang, Yue Shen, Zijie Wang, Yan Xu","doi":"10.1109/CAC57257.2022.10056028","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10056028","url":null,"abstract":"In this research, the path tracking control and the anti-roll control are constructed based on the kinematic model of the sprayer, respectively. The upper layer uses model predictive control (MPC) to output the desired steering angle and speed of the sprayer based on the current state of the sprayer and the desired path to achieve path tracking control. The lower layer controller uses lateral load transfer rate (LTR) as a measure of rollover to judge whether the vehicle state produces rollover, and compensates for steering angle by introducing a fuzzy controller, which in turn controls the LTR to stabilize at a certain threshold value. So that the sprayer has both better control accuracy and better safety in the process of path tracking, and ensure that the sprayer does not roll. The combined simulation results of ADAMS/MATLAB show that the LTR can be controlled within 0.5 when the sprayer adopts anti-roll control under complex road conditions, which ensures the safety of the sprayer, and the lateral deviation reaches 0.13m, which has a high path tracking accuracy.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"24 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":"123441596","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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