Pub Date : 2019-06-06DOI: 10.1109/YAC.2019.8787695
Kang Nie, Qi Qiao, Jiuqiang Deng, Wei Ren, Xi Zhou, Yao Mao
In this paper, a control strategy with a model-assisted linear extended state observer (MLESO) is proposed to enhance the disturbance suppression performance for Opto-Electronic stabilized platform. First, we incorporate known model information which can be identified from the open loop frequency response of controlled plant in the framework of the presented linear extended state observer (LESO), for the degree and high order gain of the controlled plant are enough. The tuning parameters of observer gain and controller gain are reduced to two: observer bandwidth and controller bandwidth. Then, constructing a MLESO can estimate and compensate the generalized disturbance to stabilize line of sight (LOS). Simulation results indicate that system with MLESO shows a stronger disturbance rejection ability in low and medium frequency by a simple linear PD control law, compared with traditional single position closed-loop control system.
{"title":"Model-assisted Linear Extended State Observer for Opto-Electronic Stabilized Platform","authors":"Kang Nie, Qi Qiao, Jiuqiang Deng, Wei Ren, Xi Zhou, Yao Mao","doi":"10.1109/YAC.2019.8787695","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787695","url":null,"abstract":"In this paper, a control strategy with a model-assisted linear extended state observer (MLESO) is proposed to enhance the disturbance suppression performance for Opto-Electronic stabilized platform. First, we incorporate known model information which can be identified from the open loop frequency response of controlled plant in the framework of the presented linear extended state observer (LESO), for the degree and high order gain of the controlled plant are enough. The tuning parameters of observer gain and controller gain are reduced to two: observer bandwidth and controller bandwidth. Then, constructing a MLESO can estimate and compensate the generalized disturbance to stabilize line of sight (LOS). Simulation results indicate that system with MLESO shows a stronger disturbance rejection ability in low and medium frequency by a simple linear PD control law, compared with traditional single position closed-loop control system.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"13 1","pages":"370-374"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82978426","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787671
Wenhao Li
This paper proposes a modified artificial potential field method based on chaos theory. In this algorithm, the search algorithm of chaos theory is introduced into the potential field function of artificial potential field method, which changes the repulsion coefficients of obstacles and the gravitational coefficients of target points. This method resolves the defects of the traditional artificial potential field method, such as the local optimum problem, the inability to find the path between the close obstacles, the oscillation in front of the obstacles, and the oscillation in the narrow channel. Simulation experiments show that this algorithm can not only effectively solve the problems of the unmanned aerial vehicle (UAV) in the route planning, such as easily falling into the minimum and wandering around the end point, but also realize the route planning in complex situations, reduce the flight cost, and improve the speed and accuracy of the UAV route planning.
{"title":"An Improved Artificial Potential Field Method Based on Chaos Theory for UAV Route Planning","authors":"Wenhao Li","doi":"10.1109/YAC.2019.8787671","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787671","url":null,"abstract":"This paper proposes a modified artificial potential field method based on chaos theory. In this algorithm, the search algorithm of chaos theory is introduced into the potential field function of artificial potential field method, which changes the repulsion coefficients of obstacles and the gravitational coefficients of target points. This method resolves the defects of the traditional artificial potential field method, such as the local optimum problem, the inability to find the path between the close obstacles, the oscillation in front of the obstacles, and the oscillation in the narrow channel. Simulation experiments show that this algorithm can not only effectively solve the problems of the unmanned aerial vehicle (UAV) in the route planning, such as easily falling into the minimum and wandering around the end point, but also realize the route planning in complex situations, reduce the flight cost, and improve the speed and accuracy of the UAV route planning.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"8 1","pages":"47-51"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84771559","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787685
Hanyue Dou
These years, with artificial intelligence and machine learning becoming the hotspot of research, several applications have emerged in each of these areas. It exists not only as a kind of academic frontier but also something close to our life. In this trend, the combination of medical care and machine learning becomes more and more tighter. The proposal of its main idea also greatly alleviated the existing situation of unbalanced medical distribution and resources strain. This paper summarizes some application of machine learning and auxiliary tumor treatment in the process of medical resource allocation, and puts forward some new methods of application to realize it closer to human life in the era of artificial intelligence and the explores a good situation of mutual combination of medical industry and computer industry, which is benefit both.
{"title":"Applications of Machine Learning in The Field of Medical Care","authors":"Hanyue Dou","doi":"10.1109/YAC.2019.8787685","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787685","url":null,"abstract":"These years, with artificial intelligence and machine learning becoming the hotspot of research, several applications have emerged in each of these areas. It exists not only as a kind of academic frontier but also something close to our life. In this trend, the combination of medical care and machine learning becomes more and more tighter. The proposal of its main idea also greatly alleviated the existing situation of unbalanced medical distribution and resources strain. This paper summarizes some application of machine learning and auxiliary tumor treatment in the process of medical resource allocation, and puts forward some new methods of application to realize it closer to human life in the era of artificial intelligence and the explores a good situation of mutual combination of medical industry and computer industry, which is benefit both.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"12 1","pages":"176-179"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87345246","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787690
Shuai Yang, F. Zhou, Weibo Liu, Zhiqiang Zhang, Danmin Chen
The sampling frequency of different sensor used to collect data may be different, which will result in a structure incomplete sample at a particular sampling point. It is a kind of data missing problem. Deep learning based fault diagnosis model may be inaccurate because there are fewer well-structured samples that can be used to train the DNN based fault diagnosis model. In this paper, the potential cross-correlation between missing variables and existing variables is used to obtain additional well-structured samples by establishing an interpolation model based on BP neural network. Using the new well-structured samples, an online update mechanism of the DNN fault diagnosis model is designed to update the parameters of DNN. It is effective to get more accurate fault diagnosis result since more structure incomplete samples is used in the training process. The experimental results show that the method proposed in this paper can effectively improve the accuracy of fault diagnosis in the case of missing data.
{"title":"Deep Learning Fault Diagnosis Based on Model Updation in Case of Missing data","authors":"Shuai Yang, F. Zhou, Weibo Liu, Zhiqiang Zhang, Danmin Chen","doi":"10.1109/YAC.2019.8787690","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787690","url":null,"abstract":"The sampling frequency of different sensor used to collect data may be different, which will result in a structure incomplete sample at a particular sampling point. It is a kind of data missing problem. Deep learning based fault diagnosis model may be inaccurate because there are fewer well-structured samples that can be used to train the DNN based fault diagnosis model. In this paper, the potential cross-correlation between missing variables and existing variables is used to obtain additional well-structured samples by establishing an interpolation model based on BP neural network. Using the new well-structured samples, an online update mechanism of the DNN fault diagnosis model is designed to update the parameters of DNN. It is effective to get more accurate fault diagnosis result since more structure incomplete samples is used in the training process. The experimental results show that the method proposed in this paper can effectively improve the accuracy of fault diagnosis in the case of missing data.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"1 1","pages":"170-175"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89786414","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787726
S. Yin, J. Xin
Path planning of Automated Guided Vehicles(AGVs) is critical for the material handling in manufacturing and warehouses. For collision-free path planning of AGVs, this paper proposes a new time-space network model which combines an minimal time objective with the constraints of time and space. First of all, the paper gives an optimal mathematical model to plan the shortest time path to complete a number of tasks for AGVs. The space constraints are added to resolve vehicle collision on the basis of the shortest path. Time constraints are added to make the AGV correspond to its space and time states when moving. In this way, the state of the AGV can be obtained to plan the optimal path. In order to verify the validity of the proposed method, collision avoidances of the proposed planning method are demonstrated with an example of three AGVs working at the same time. The results show that this method could be used to plan non-conflicting paths for AGVs when working simultaneously and to achieve the shortest time path. It can be found that the total time can be optimized by changing the running time of the AGV.
{"title":"Path Planning of Multiple AGVs Using a Time-space Network Model","authors":"S. Yin, J. Xin","doi":"10.1109/YAC.2019.8787726","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787726","url":null,"abstract":"Path planning of Automated Guided Vehicles(AGVs) is critical for the material handling in manufacturing and warehouses. For collision-free path planning of AGVs, this paper proposes a new time-space network model which combines an minimal time objective with the constraints of time and space. First of all, the paper gives an optimal mathematical model to plan the shortest time path to complete a number of tasks for AGVs. The space constraints are added to resolve vehicle collision on the basis of the shortest path. Time constraints are added to make the AGV correspond to its space and time states when moving. In this way, the state of the AGV can be obtained to plan the optimal path. In order to verify the validity of the proposed method, collision avoidances of the proposed planning method are demonstrated with an example of three AGVs working at the same time. The results show that this method could be used to plan non-conflicting paths for AGVs when working simultaneously and to achieve the shortest time path. It can be found that the total time can be optimized by changing the running time of the AGV.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"3 1","pages":"73-78"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76643299","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787670
Yuchen Qiao, Yunlu Li, Xiaotian Lv
In order to solve the problem of low efficiency of K-Means algorithm in processing the data mining prediction problem of big data with more attributes, an annual income prediction method of residents based on improved K-Means algorithm is proposed. The improved K-Means algorithm combines the principal component analysis method with the traditional K-Means algorithm. After reducing the dimensionality of various data attributes, the data are classified with K-Means algorithm. The research makes use of 1994 U.S. census database and conducts a contrastive analysis of the two algorithms. The results show that the prediction accuracy has been significantly improved by 13.3313%, from 53.1016% to 66.4329%. It is clear the improved algorithm can effectively improve the accuracy of clustering and annual income prediction.
{"title":"The Application of Big Data Mining Prediction Based on Improved K-Means Algorithm","authors":"Yuchen Qiao, Yunlu Li, Xiaotian Lv","doi":"10.1109/YAC.2019.8787670","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787670","url":null,"abstract":"In order to solve the problem of low efficiency of K-Means algorithm in processing the data mining prediction problem of big data with more attributes, an annual income prediction method of residents based on improved K-Means algorithm is proposed. The improved K-Means algorithm combines the principal component analysis method with the traditional K-Means algorithm. After reducing the dimensionality of various data attributes, the data are classified with K-Means algorithm. The research makes use of 1994 U.S. census database and conducts a contrastive analysis of the two algorithms. The results show that the prediction accuracy has been significantly improved by 13.3313%, from 53.1016% to 66.4329%. It is clear the improved algorithm can effectively improve the accuracy of clustering and annual income prediction.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"14 1","pages":"348-351"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87704929","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787682
Fengpeng Guo, Hongcheng Huang, Liangren Shi, Yanbo Liu, Han Zhang
This paper describes how to use online learning platform for traffic light identification, as well as including lane-line identification. First, the traditional traffic light and lane-line identification method were explained; then explaining the concept of neural network and its application in driverless car field; finally, the paper explains how to use the learning platform on the line to train, which can get outputs the model. Using the model, we will get the corresponding results. Based on the continuous optimization of previous studies, this paper makes full use of the advantages of online learning platforms to improve learning methods, to some extent, which enables students to broaden their minds and understand the important position of deep learning in the field of unmanned driving.
{"title":"The driverless car based on the online learning platform realizes the red light recognition and lane line recognition","authors":"Fengpeng Guo, Hongcheng Huang, Liangren Shi, Yanbo Liu, Han Zhang","doi":"10.1109/YAC.2019.8787682","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787682","url":null,"abstract":"This paper describes how to use online learning platform for traffic light identification, as well as including lane-line identification. First, the traditional traffic light and lane-line identification method were explained; then explaining the concept of neural network and its application in driverless car field; finally, the paper explains how to use the learning platform on the line to train, which can get outputs the model. Using the model, we will get the corresponding results. Based on the continuous optimization of previous studies, this paper makes full use of the advantages of online learning platforms to improve learning methods, to some extent, which enables students to broaden their minds and understand the important position of deep learning in the field of unmanned driving.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"54 1","pages":"58-61"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75700580","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787660
Jiuqiang Deng, Kang Nie, Qi Qiao, Wei Ren, Xi Zhou, Yao Mao
The low sample-rate of the sensors limit the control performance of the control system. The traditional control methods such as PI control method can provide little extra effect to improve the system's control property. Thus, the input multi-rate digital control system with PI controller is proposed and analyzed in this paper to improve the system's property, which adopts the low sample-rate sensors. The controlled object is discretized as an input multi-rate digital control system. We propose the design method of the multi-rate PI controller, which can be used in the input multi-rate digital control system. The Lyapunov stability criterion is used to guarantee the stabilization of the closed-loop input multi-rate digital control system. The Schur complement is utilized to solve the Linear Matrix Inequalities and to calculate the parameters of the proposed multi-rate PI controller. In the end, the simulations proved the validity of the proposed PI controller in the input multi-rate digital control system, which can help the system have a faster response speed.
{"title":"The PI Control Method in the Input Multi-rate Digital Control System with Low Sample-rate Sensors","authors":"Jiuqiang Deng, Kang Nie, Qi Qiao, Wei Ren, Xi Zhou, Yao Mao","doi":"10.1109/YAC.2019.8787660","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787660","url":null,"abstract":"The low sample-rate of the sensors limit the control performance of the control system. The traditional control methods such as PI control method can provide little extra effect to improve the system's control property. Thus, the input multi-rate digital control system with PI controller is proposed and analyzed in this paper to improve the system's property, which adopts the low sample-rate sensors. The controlled object is discretized as an input multi-rate digital control system. We propose the design method of the multi-rate PI controller, which can be used in the input multi-rate digital control system. The Lyapunov stability criterion is used to guarantee the stabilization of the closed-loop input multi-rate digital control system. The Schur complement is utilized to solve the Linear Matrix Inequalities and to calculate the parameters of the proposed multi-rate PI controller. In the end, the simulations proved the validity of the proposed PI controller in the input multi-rate digital control system, which can help the system have a faster response speed.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"78 1","pages":"365-369"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75539502","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787564
Le Cai, J. Quan, Maike Ye, Huan Quan, S. Quan
The fuel cell stack consists of hundreds of single-chip fuel cells. The detection of each battery voltage is the basis for maintaining stable operation of the battery stack. A single fuel cell detection based on battery monitoring chip LTC6803 and PIC microcontroller is designed. The system adopts a stacked structure to realize multi-chip LTC6803 chip cascading to realize voltage detection of the series battery pack which designs hardware circuit and software program and practice proves that the system realizes high-precision detection of the fuel cell stack cell voltage.
{"title":"Design of Single Fuel Cell Voltage Acquisition System Based on LTC6803-3s and PIC Microcontroller","authors":"Le Cai, J. Quan, Maike Ye, Huan Quan, S. Quan","doi":"10.1109/YAC.2019.8787564","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787564","url":null,"abstract":"The fuel cell stack consists of hundreds of single-chip fuel cells. The detection of each battery voltage is the basis for maintaining stable operation of the battery stack. A single fuel cell detection based on battery monitoring chip LTC6803 and PIC microcontroller is designed. The system adopts a stacked structure to realize multi-chip LTC6803 chip cascading to realize voltage detection of the series battery pack which designs hardware circuit and software program and practice proves that the system realizes high-precision detection of the fuel cell stack cell voltage.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"27 1","pages":"95-99"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86657152","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 : 2019-06-06DOI: 10.1109/YAC.2019.8787691
Zhaoshuai Sun, Qihong Chen, Liyan Zhang, Rong Long
Bidirectional wireless power transfer(BD-WPT) can realize the energy conversion between electric vehicles(EVs) and the power grid. It has shown broad prospects because of the development of the practical road of wireless power transfer(WPT) and the increasing market holdings of EVs. Thus, the design methodology for a bidirectional 3 kW wireless power transfer system of electric vehicles, operating at frequency 85 kHz are proposed in this paper. And the control strategy combines phase shift control and PI control to improve system performance. Using PLECS the BD-WPT topology based on LCC compensation network is optimally designed for G2V and V2G modes. Simulation results has verified the correctness and feasibility of the proposed circuit structure and control strategy.
{"title":"Research on Bidirectional Wireless Power Transfer System for Electric Vehicles","authors":"Zhaoshuai Sun, Qihong Chen, Liyan Zhang, Rong Long","doi":"10.1109/YAC.2019.8787691","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787691","url":null,"abstract":"Bidirectional wireless power transfer(BD-WPT) can realize the energy conversion between electric vehicles(EVs) and the power grid. It has shown broad prospects because of the development of the practical road of wireless power transfer(WPT) and the increasing market holdings of EVs. Thus, the design methodology for a bidirectional 3 kW wireless power transfer system of electric vehicles, operating at frequency 85 kHz are proposed in this paper. And the control strategy combines phase shift control and PI control to improve system performance. Using PLECS the BD-WPT topology based on LCC compensation network is optimally designed for G2V and V2G modes. Simulation results has verified the correctness and feasibility of the proposed circuit structure and control strategy.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"44 1","pages":"468-472"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86796562","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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