Pub Date : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167256
Gaofeng Deng, Shan Liu
A visual servo algorithm based on Siamese Convolution Neural Network is proposed for the manipulator to avoid the requirement of feature extraction and feature matching in the traditional image-based visual servo (IBVS). The algorithm feeds the current image and the desired image into the network at the same time, and outputs the relative pose difference between the two images. A closed-loop control system is constructed through the pose difference, and control the end-effector of the manipulator to reach the desired position to grasp the target workpiece. Meanwhile, in order to meet the large amount of data needed in training the neural network, an algorithm to automatically generate the data set is proposed, which can avoid manual collection and labeling of the data set and greatly save the cost. The simulations show the effectiveness and accuracy of the proposed method by comparing with the traditional feature point based IBVS, and the grasping experiment shows the feasibility of the proposed method in actual practice.
{"title":"Siamese Convolutional Neural Network Based Visual Servo for Manipulator","authors":"Gaofeng Deng, Shan Liu","doi":"10.1109/DDCLS58216.2023.10167256","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167256","url":null,"abstract":"A visual servo algorithm based on Siamese Convolution Neural Network is proposed for the manipulator to avoid the requirement of feature extraction and feature matching in the traditional image-based visual servo (IBVS). The algorithm feeds the current image and the desired image into the network at the same time, and outputs the relative pose difference between the two images. A closed-loop control system is constructed through the pose difference, and control the end-effector of the manipulator to reach the desired position to grasp the target workpiece. Meanwhile, in order to meet the large amount of data needed in training the neural network, an algorithm to automatically generate the data set is proposed, which can avoid manual collection and labeling of the data set and greatly save the cost. The simulations show the effectiveness and accuracy of the proposed method by comparing with the traditional feature point based IBVS, and the grasping experiment shows the feasibility of the proposed method in actual practice.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122238859","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166594
Weiming Zhang, Dezhi Xu, Weilin Yang, Jianxing Liu, Fei Hua
In this paper, a dual observer based model-free adaptive control strategy is designed for multiple input multiple output (MIMO) nonlinear systems with disturbances and input/output (I/O) constraints. The dual observers consists of an adaptive observer and a discrete extended state observer, in which the former is designed to realize the dynamic reconfiguration of the system and devise the Lyapunov stability criterion-based estimation algorithm for time-varying parameters, and the latter is explored for composite disturbance estimation. Based on the information from dual observers, a dynamic anti-windup compensator along with an improved prescribed performance control method are proposed in the sliding mode controller to solve the I/O constraint problem. Finally, the stability analysis and simulation are supplied for performance verification.
{"title":"Dual Observer-Based Model-Free Adaptive I/O Constrained Control for MIMO Nonlinear Systems","authors":"Weiming Zhang, Dezhi Xu, Weilin Yang, Jianxing Liu, Fei Hua","doi":"10.1109/DDCLS58216.2023.10166594","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166594","url":null,"abstract":"In this paper, a dual observer based model-free adaptive control strategy is designed for multiple input multiple output (MIMO) nonlinear systems with disturbances and input/output (I/O) constraints. The dual observers consists of an adaptive observer and a discrete extended state observer, in which the former is designed to realize the dynamic reconfiguration of the system and devise the Lyapunov stability criterion-based estimation algorithm for time-varying parameters, and the latter is explored for composite disturbance estimation. Based on the information from dual observers, a dynamic anti-windup compensator along with an improved prescribed performance control method are proposed in the sliding mode controller to solve the I/O constraint problem. Finally, the stability analysis and simulation are supplied for performance verification.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"59 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114126626","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10167230
Zhong Fan, Shihua Li, Rongjie Liu
This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. System dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.
{"title":"Reinforcement Learning based Data-driven Optimal Control Strategy for Systems with Disturbance","authors":"Zhong Fan, Shihua Li, Rongjie Liu","doi":"10.1109/DDCLS58216.2023.10167230","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167230","url":null,"abstract":"This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. System dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115259144","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166153
Jiali Dang, Jiacheng Ding, N. Zhang, Shubo Wang
This paper proposes an adaptive tracking and synchronization control scheme for dual-motor driving servo system with nonlinear dead-zone. To achieve the tracking performance, the neural network is used to approximate the unknown dynamics, and the approximation is incorporated into the control design to compensate the unknown dynamics. Then, adaptive dynamic surface controller is designed to improve the tracking performance. Moreover, a robust controller is presented based on the mean deviation coupling strategy to guarantee the synchronous operation of dual motors. Simulation results illustrate the performance of the proposed control strategy.
{"title":"Adaptive Tracking and Synchronization Control of Dual-motor Driving Servo System","authors":"Jiali Dang, Jiacheng Ding, N. Zhang, Shubo Wang","doi":"10.1109/DDCLS58216.2023.10166153","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166153","url":null,"abstract":"This paper proposes an adaptive tracking and synchronization control scheme for dual-motor driving servo system with nonlinear dead-zone. To achieve the tracking performance, the neural network is used to approximate the unknown dynamics, and the approximation is incorporated into the control design to compensate the unknown dynamics. Then, adaptive dynamic surface controller is designed to improve the tracking performance. Moreover, a robust controller is presented based on the mean deviation coupling strategy to guarantee the synchronous operation of dual motors. Simulation results illustrate the performance of the proposed control strategy.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123885651","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10165814
Minghua Liu, X. Li, Kang Wang
The effective control of converter inlet temperature in the process of acid production with flue gas is an effective means to improve the conversion rate of sulfur dioxide and reduce environmental pollution. According to the characteristics of the process of acid production with flue gas, the control process of converter inlet temperature is studied in this paper. Firstly, the CARIMA (Controlled auto-regressive integrated moving average, CARIMA) model of converter inlet temperature is established. Then, a generalized predictive controller based on CARIMA model is designed. Finally, the proposed method is verified by experiment and compared with PID controller. Experimental results show that the proposed method has a better tracking effect and smaller error. The effectiveness of the proposed method is verified.
{"title":"Generalized Predictive Control of Converter Inlet Temperature in the Process of Acid Production with Flue Gas","authors":"Minghua Liu, X. Li, Kang Wang","doi":"10.1109/DDCLS58216.2023.10165814","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10165814","url":null,"abstract":"The effective control of converter inlet temperature in the process of acid production with flue gas is an effective means to improve the conversion rate of sulfur dioxide and reduce environmental pollution. According to the characteristics of the process of acid production with flue gas, the control process of converter inlet temperature is studied in this paper. Firstly, the CARIMA (Controlled auto-regressive integrated moving average, CARIMA) model of converter inlet temperature is established. Then, a generalized predictive controller based on CARIMA model is designed. Finally, the proposed method is verified by experiment and compared with PID controller. Experimental results show that the proposed method has a better tracking effect and smaller error. The effectiveness of the proposed method is verified.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124887780","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166899
Bowei Liu, Jingliang Sun, Teng Long, Dawei Liu, Yan Cao
To cope with the dynamic mission decision-making issue in complex environments for UAV swarm, a hybrid variable structure-based dynamic Bayesian network (HVSDBN) inference decision-making method is proposed. Firstly, the UAV swarm mission decision-making model is established to assess the UAV swarm state and threat state accurately. To further improve the accuracy of decision-making, the threat assessment model and swarm state assessment model are built by using mixed continuous and discrete variables, respectively. Furthermore, a dynamic HVSDBN decision-making algorithm based on hybrid performance-capability parameters is proposed, which can adjust the structure of the decision model according to the priori information and observation data to improve the adaptability of the solution strategy. Simulation results demonstrate that, the HVSDBN method can im-prove the variance of decision results by 25.03% compared with traditional method, which effectively improves the accuracy of UAV swarm mission decision-making under complex dynamic environment.
{"title":"Hybrid Variable Structure DBN Mission Decision-Making Method for UAV Swarm","authors":"Bowei Liu, Jingliang Sun, Teng Long, Dawei Liu, Yan Cao","doi":"10.1109/DDCLS58216.2023.10166899","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166899","url":null,"abstract":"To cope with the dynamic mission decision-making issue in complex environments for UAV swarm, a hybrid variable structure-based dynamic Bayesian network (HVSDBN) inference decision-making method is proposed. Firstly, the UAV swarm mission decision-making model is established to assess the UAV swarm state and threat state accurately. To further improve the accuracy of decision-making, the threat assessment model and swarm state assessment model are built by using mixed continuous and discrete variables, respectively. Furthermore, a dynamic HVSDBN decision-making algorithm based on hybrid performance-capability parameters is proposed, which can adjust the structure of the decision model according to the priori information and observation data to improve the adaptability of the solution strategy. Simulation results demonstrate that, the HVSDBN method can im-prove the variance of decision results by 25.03% compared with traditional method, which effectively improves the accuracy of UAV swarm mission decision-making under complex dynamic environment.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116779103","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166143
Ting Wu, Hui Ye, Z. Xiang, Xiaofei Yang
In this paper, a deep reinforcement learning-based speed and heading control method is proposed for an unmanned surface vehicle (USV). A deep deterministic policy gradient (DDPG) algorithm which combines with an actor-critic reinforcement learning mechanism, is adopted to provide continuous control variables by interacting with the environment. Moreover, two types of reward functions are created for speed and heading control of the USV. The control policy is trained by trial and error so that the USV can be guided to achieve the desired speed and heading angle steadily and rapidly. Simulation results verify the feasibility and effectiveness of the proposed approach by comparisons with classical PID control and S plane control.
{"title":"Speed and heading control of an unmanned surface vehicle using deep reinforcement learning","authors":"Ting Wu, Hui Ye, Z. Xiang, Xiaofei Yang","doi":"10.1109/DDCLS58216.2023.10166143","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166143","url":null,"abstract":"In this paper, a deep reinforcement learning-based speed and heading control method is proposed for an unmanned surface vehicle (USV). A deep deterministic policy gradient (DDPG) algorithm which combines with an actor-critic reinforcement learning mechanism, is adopted to provide continuous control variables by interacting with the environment. Moreover, two types of reward functions are created for speed and heading control of the USV. The control policy is trained by trial and error so that the USV can be guided to achieve the desired speed and heading angle steadily and rapidly. Simulation results verify the feasibility and effectiveness of the proposed approach by comparisons with classical PID control and S plane control.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122646372","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166298
Lian Chen, Q. Quan
This paper proposes a reinforcement-learning additive-state-decomposition-based tracking controller for a class of non-affine nonlinear non-minimum phase systems. Because the tracking performance is not satisfied with the model-based additive-state-decomposition tracking control with an approximate ideal internal model, two reinforcement learning schemes are introduced to improve the performance under the proposed additive-state-decomposition-based control framework. One is used to generate control commands, and the other is used to generate tracking reference commands. Finally, numerical simulations show the effectiveness of the proposed controller.
{"title":"Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking Under Additive-State-Decomposition-Based Control Framework","authors":"Lian Chen, Q. Quan","doi":"10.1109/DDCLS58216.2023.10166298","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166298","url":null,"abstract":"This paper proposes a reinforcement-learning additive-state-decomposition-based tracking controller for a class of non-affine nonlinear non-minimum phase systems. Because the tracking performance is not satisfied with the model-based additive-state-decomposition tracking control with an approximate ideal internal model, two reinforcement learning schemes are introduced to improve the performance under the proposed additive-state-decomposition-based control framework. One is used to generate control commands, and the other is used to generate tracking reference commands. Finally, numerical simulations show the effectiveness of the proposed controller.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128140794","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166792
Wenze Chen, Ruizhuo Song
With the improvement of people's health awareness, people pay more attention to their health. In recent years, the intelligent health management system based on food recognition technology has become popular, which can help users maintain healthy eating habits. However, applying the current deep learning method in mobile phones and other terminal devices is difficult, mainly because the terminal devices have the low computing power and the network needs to perform many calculations during operation. In this paper, we have adopted the methods of parameter reconstruction and calculation graph fusion to reduce the network computing load so that it can run in real-time in terminal devices, and the detection speed on Snapdragon 778G SOC exceeds 7 FPS. Besides, experiments on the VIPER-FoodNet (VFN) dataset show that our model has a high mean average precision (mAP) of 9.17% compared with the current advanced model.
{"title":"A new deep learning-based food recognition system for mobile terminal","authors":"Wenze Chen, Ruizhuo Song","doi":"10.1109/DDCLS58216.2023.10166792","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166792","url":null,"abstract":"With the improvement of people's health awareness, people pay more attention to their health. In recent years, the intelligent health management system based on food recognition technology has become popular, which can help users maintain healthy eating habits. However, applying the current deep learning method in mobile phones and other terminal devices is difficult, mainly because the terminal devices have the low computing power and the network needs to perform many calculations during operation. In this paper, we have adopted the methods of parameter reconstruction and calculation graph fusion to reduce the network computing load so that it can run in real-time in terminal devices, and the detection speed on Snapdragon 778G SOC exceeds 7 FPS. Besides, experiments on the VIPER-FoodNet (VFN) dataset show that our model has a high mean average precision (mAP) of 9.17% compared with the current advanced model.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132553009","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 : 2023-05-12DOI: 10.1109/DDCLS58216.2023.10166439
Hongxuan Li, Yang Tian, Haoping Wang
In this paper, a new dynamic nonlinear gradient observer-based extremum-seeking control algorithm (DNGO-based ESC) and a dynamic Jacobian matrices estimator-based self-optimizing control algorithm (DJE-based SOC) are designed for the control of two-stage anaerobic digestion (TSAD). None of two algorithms requires priori knowledge about the system model. The proposed algorithms are compared with the classical extremum-seeking control algorithm and the Kalman Filter based Newton extremum-seeking control algorithm. The simulation results show that in the presence of disturbance both of proposed control algorithms can maintain the system at the optimal operating point and drive the hydrogen and methane yields to the extreme point. Future work is to validate the designed control algorithm in an actual two-stage anaerobic digestion process.
{"title":"Two-stage Anaerobic Digestion Process Optimal Control Study based on Extremum-seeking Control and Self-optimizing Control","authors":"Hongxuan Li, Yang Tian, Haoping Wang","doi":"10.1109/DDCLS58216.2023.10166439","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166439","url":null,"abstract":"In this paper, a new dynamic nonlinear gradient observer-based extremum-seeking control algorithm (DNGO-based ESC) and a dynamic Jacobian matrices estimator-based self-optimizing control algorithm (DJE-based SOC) are designed for the control of two-stage anaerobic digestion (TSAD). None of two algorithms requires priori knowledge about the system model. The proposed algorithms are compared with the classical extremum-seeking control algorithm and the Kalman Filter based Newton extremum-seeking control algorithm. The simulation results show that in the presence of disturbance both of proposed control algorithms can maintain the system at the optimal operating point and drive the hydrogen and methane yields to the extreme point. Future work is to validate the designed control algorithm in an actual two-stage anaerobic digestion process.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134562306","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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