Pub Date : 2018-06-01DOI: 10.1109/CCDC.2018.8407936
Ming Gao, Jun Hu, Hongxu Zhang
This paper studies the event-triggered resilient filtering problem for a class of nonlinear systems with randomly occurring nonlinearity and missing measurements. Both the phenomena of the randomly occurring nonlinearity and the missing measurements are described by Bernoulli distributed random variables, where the occurrence probabilities could be uncertain. The event-triggered communication mechanism is introduced to save the network bandwidth during the data transmissions through the network. Additionally, the filter gain perturbations are characterized by employing the norm bounded uncertainties. The aim of the paper is to develop a robust event-triggered resilient filtering algorithm against the randomly occurring nonlinearity and missing measurements. Note that the analytical expressions of the filtering error covariance cannot be computed directly. Consequently, we derive its upper bound as an alternative way and subsequently minimize such an upper bound by properly designing the filter gain at each time step. Finally, an illustrative example is presented to show the effectiveness of the provided filtering algorithm.
{"title":"Event-triggered Resilient Filtering for Time-varying Systems with Randomly Occurring Nonlinearity and Missing Measurements","authors":"Ming Gao, Jun Hu, Hongxu Zhang","doi":"10.1109/CCDC.2018.8407936","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8407936","url":null,"abstract":"This paper studies the event-triggered resilient filtering problem for a class of nonlinear systems with randomly occurring nonlinearity and missing measurements. Both the phenomena of the randomly occurring nonlinearity and the missing measurements are described by Bernoulli distributed random variables, where the occurrence probabilities could be uncertain. The event-triggered communication mechanism is introduced to save the network bandwidth during the data transmissions through the network. Additionally, the filter gain perturbations are characterized by employing the norm bounded uncertainties. The aim of the paper is to develop a robust event-triggered resilient filtering algorithm against the randomly occurring nonlinearity and missing measurements. Note that the analytical expressions of the filtering error covariance cannot be computed directly. Consequently, we derive its upper bound as an alternative way and subsequently minimize such an upper bound by properly designing the filter gain at each time step. Finally, an illustrative example is presented to show the effectiveness of the provided filtering algorithm.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131992204","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8408104
Xielian Hou, Caikou Chen, Shengwei Zhou, Jingshan Li
Due to occlusion or camouflage existing in the current face images, previous face recognition algorithms such as sparse representation classification algorithm do not take face damage into consideration during the training period, and therefore their testing performance will be degraded. In this paper, we propose a novel non-negative sparse discriminative low-rank representation algorithm (NSDLRR). First, we seek a sparse, low-rank and non-negative matrix in training samples. Then, we add a structural inconsistency constraint on this basis, make different kinds of samples as independent as possible, thereby increasing the extra recognition ability. Finally, the test samples are classified by sparse linear representation. Experimental results on different face database show that the algorithm has better performance.
{"title":"Robust face recognition based on non-negative sparse discriminative low-rank representation","authors":"Xielian Hou, Caikou Chen, Shengwei Zhou, Jingshan Li","doi":"10.1109/CCDC.2018.8408104","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8408104","url":null,"abstract":"Due to occlusion or camouflage existing in the current face images, previous face recognition algorithms such as sparse representation classification algorithm do not take face damage into consideration during the training period, and therefore their testing performance will be degraded. In this paper, we propose a novel non-negative sparse discriminative low-rank representation algorithm (NSDLRR). First, we seek a sparse, low-rank and non-negative matrix in training samples. Then, we add a structural inconsistency constraint on this basis, make different kinds of samples as independent as possible, thereby increasing the extra recognition ability. Finally, the test samples are classified by sparse linear representation. Experimental results on different face database show that the algorithm has better performance.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132215633","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8407595
Zhai Kun, L. Feng, Du Wen-Xia, Shao Meng-Ya, Huang Zhan-ping
Aiming at noise interference problem in detecting stator current of induction motor, denoising method based on principal component analysis (PCA) Kalman filtering is proposed in this paper. Firstly, the state equation is obtained by building five order mathematical model, in order to reduce the correlation of data, data space is mapped to low dimensional subspaces via orthogonal transformations. Then, the Kalman prediction value is taken as the center of the observation signal which is processed by adding window processing, and the prediction value is gotten based on the PCA in the window. There are good stability and simple calculation in the algorithm. Simulation results demonstrate that the mean square error based on PCA) Kalman filtering is lower than that based on the traditional Kalman filtering algorithm, and the filtering effect is obviously improved.
{"title":"Studies on denoising method of the stator current based on PCA Kalman filter and window processing","authors":"Zhai Kun, L. Feng, Du Wen-Xia, Shao Meng-Ya, Huang Zhan-ping","doi":"10.1109/CCDC.2018.8407595","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8407595","url":null,"abstract":"Aiming at noise interference problem in detecting stator current of induction motor, denoising method based on principal component analysis (PCA) Kalman filtering is proposed in this paper. Firstly, the state equation is obtained by building five order mathematical model, in order to reduce the correlation of data, data space is mapped to low dimensional subspaces via orthogonal transformations. Then, the Kalman prediction value is taken as the center of the observation signal which is processed by adding window processing, and the prediction value is gotten based on the PCA in the window. There are good stability and simple calculation in the algorithm. Simulation results demonstrate that the mean square error based on PCA) Kalman filtering is lower than that based on the traditional Kalman filtering algorithm, and the filtering effect is obviously improved.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132319668","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8408123
Zhu Huishen, Xie Ling, Yu Huan, Wang Liujun
Loop closure detection is an essential component of many robotics applications such as SLAM (Simultaneous Localization and Mapping) and place recognition. This paper presents an appearance based loop closure detection algorithm based on bag of words method and inverse depth of feature words. Our proposed approach represent the feature points and descriptors in the images as the visual words according to the off-line generated visual directory to simplify the similarity comparison between the images. For the evaluation criteria, on the basis of tf-idf scores of visual words, the inverse depth of words are also introduced into the process of loop closure detection. Considering the temporal consistency of image sequences, the co-visibility graphs are also applied to verify the real loop from all candidates. At last, some experiment results are showed and analyzed to illustrate the feasibility and performance of our algorithm in different situations and environments.
{"title":"An improved bag of words method for appearance based visual loop closure detection","authors":"Zhu Huishen, Xie Ling, Yu Huan, Wang Liujun","doi":"10.1109/CCDC.2018.8408123","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8408123","url":null,"abstract":"Loop closure detection is an essential component of many robotics applications such as SLAM (Simultaneous Localization and Mapping) and place recognition. This paper presents an appearance based loop closure detection algorithm based on bag of words method and inverse depth of feature words. Our proposed approach represent the feature points and descriptors in the images as the visual words according to the off-line generated visual directory to simplify the similarity comparison between the images. For the evaluation criteria, on the basis of tf-idf scores of visual words, the inverse depth of words are also introduced into the process of loop closure detection. Considering the temporal consistency of image sequences, the co-visibility graphs are also applied to verify the real loop from all candidates. At last, some experiment results are showed and analyzed to illustrate the feasibility and performance of our algorithm in different situations and environments.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132359970","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8407978
Chen Xiongzi, Xie Song
An attitude scheduling approach is presented for dynamic imaging of agile earth observation satellite (AEOS) along a curve target. First, build a six-order or seven-order polynomial model of target position in the earth-fixed frame and the image time through iterative fitting. Then, based on the obtained model calculate the roll angle and pitch angle for target tracking and the yaw angle for image motion compensation of TDI-CCD camera at each imaging time point. The simulation results demonstrate that the proposed approach is capable of effectively addressing the problem of continuous dynamic imaging along curve targets, such as coastline, borderline, rivers, etc.
{"title":"Attitude Scheduling for Dynamic Imaging of Agile Earth Observation Satellite along a Curve Target","authors":"Chen Xiongzi, Xie Song","doi":"10.1109/CCDC.2018.8407978","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8407978","url":null,"abstract":"An attitude scheduling approach is presented for dynamic imaging of agile earth observation satellite (AEOS) along a curve target. First, build a six-order or seven-order polynomial model of target position in the earth-fixed frame and the image time through iterative fitting. Then, based on the obtained model calculate the roll angle and pitch angle for target tracking and the yaw angle for image motion compensation of TDI-CCD camera at each imaging time point. The simulation results demonstrate that the proposed approach is capable of effectively addressing the problem of continuous dynamic imaging along curve targets, such as coastline, borderline, rivers, etc.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130011408","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8407395
Fangchao Hu, Zhen Tian, Yinguo Li, Shuai Huang, M. Feng
3D Object Detection is important to avoid collision and path planning in field of autonomous vehicle. In this paper, we present a combined clustering and image mapping-based algorithm to segment 3D point cloud. It not only provides a dependable initial value as the seeds to cluster the class of objects, but also avoid the pre-trained classifier to detect the objects. We get an accurate 3D object detection result using our proposed algorithm. The proposed algorithm can reduce the computation complexity at the step of determining bounding area in 2D image and produce the initial center of cluster of each object at the step of segmentation in 3D point cloud. The experiment states that the proposed algorithm can improve the accuracy and feasibility of object detection.
{"title":"A combined clustering and image mapping based point cloud segmentation for 3D object detection","authors":"Fangchao Hu, Zhen Tian, Yinguo Li, Shuai Huang, M. Feng","doi":"10.1109/CCDC.2018.8407395","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8407395","url":null,"abstract":"3D Object Detection is important to avoid collision and path planning in field of autonomous vehicle. In this paper, we present a combined clustering and image mapping-based algorithm to segment 3D point cloud. It not only provides a dependable initial value as the seeds to cluster the class of objects, but also avoid the pre-trained classifier to detect the objects. We get an accurate 3D object detection result using our proposed algorithm. The proposed algorithm can reduce the computation complexity at the step of determining bounding area in 2D image and produce the initial center of cluster of each object at the step of segmentation in 3D point cloud. The experiment states that the proposed algorithm can improve the accuracy and feasibility of object detection.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130039586","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8407261
Jitong Wang, Wei Hong, Lei Gong
In order to improve the accuracy, real-time and robustness of video lane detection, in this paper, we propose a fast lane detection algorithm based on DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm and improved RANSAC (random sample consensus). Firstly, the image is preprocessed by graying and binarizing in the inverse perspective mapping image. Secondly, eliminating noise points and smoothing lane boundaries by using morphological erosion and opening operation in the binary image, then traversing the binary image and extracting the feature points. Then, the feature points are clustered by DBSCAN clustering algorithm, which are then divided into clusters. Finally, the points in every class was fitted a straight line or curve line by using improved RANSAC algorithm based on parabola. The experimental results show that the improved RANSAC algorithm accelerates the speed of lane detection. The proposed algorithm has good real-time, robustness and accuracy.
为了提高视频车道检测的准确性、实时性和鲁棒性，本文提出了一种基于DBSCAN (Density based Spatial Clustering of Applications with Noise)聚类算法和改进的RANSAC (random sample consensus)算法的快速车道检测算法。首先，对反透视映射图像进行灰度化和二值化预处理;其次，对二值图像进行形态侵蚀和开放运算，去除噪声点，平滑车道边界，然后对二值图像进行遍历，提取特征点;然后，采用DBSCAN聚类算法对特征点进行聚类，并对特征点进行聚类;最后，利用改进的基于抛物线的RANSAC算法对每一类点进行直线或曲线拟合。实验结果表明，改进的RANSAC算法提高了车道检测的速度。该算法具有较好的实时性、鲁棒性和准确性。
{"title":"Lane detection algorithm based on density clustering and RANSAC","authors":"Jitong Wang, Wei Hong, Lei Gong","doi":"10.1109/CCDC.2018.8407261","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8407261","url":null,"abstract":"In order to improve the accuracy, real-time and robustness of video lane detection, in this paper, we propose a fast lane detection algorithm based on DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm and improved RANSAC (random sample consensus). Firstly, the image is preprocessed by graying and binarizing in the inverse perspective mapping image. Secondly, eliminating noise points and smoothing lane boundaries by using morphological erosion and opening operation in the binary image, then traversing the binary image and extracting the feature points. Then, the feature points are clustered by DBSCAN clustering algorithm, which are then divided into clusters. Finally, the points in every class was fitted a straight line or curve line by using improved RANSAC algorithm based on parabola. The experimental results show that the improved RANSAC algorithm accelerates the speed of lane detection. The proposed algorithm has good real-time, robustness and accuracy.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130096893","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8407675
Zhongfeng Wang, Haifei Yu, Jun Liu, Bo Hu, M. Xue
With the rapid progress in the new power market reformation in China, electric power sales company' credit is playing as a more important role in the power market. This paper takes electric sales company without grid occupation as an example to build a credit rating system; configure out the credit index weighted by AHP and purpose an overall credit rating model. Our study will provide some basis regulations of power market and help State Grid Corp. of China and other electric buyers to rate credit risk of electric power sales companies.
{"title":"Evaluation of electric power sales company's credit under new power market reformation","authors":"Zhongfeng Wang, Haifei Yu, Jun Liu, Bo Hu, M. Xue","doi":"10.1109/CCDC.2018.8407675","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8407675","url":null,"abstract":"With the rapid progress in the new power market reformation in China, electric power sales company' credit is playing as a more important role in the power market. This paper takes electric sales company without grid occupation as an example to build a credit rating system; configure out the credit index weighted by AHP and purpose an overall credit rating model. Our study will provide some basis regulations of power market and help State Grid Corp. of China and other electric buyers to rate credit risk of electric power sales companies.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130441607","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8408116
Ya Wang
In order to improve the segmentation result of infrared images, an image segmentation method based on improved OTSU method and improved genetic algorithm is proposed. Firstly, morphological noise reduction is carried out by using morphological weighting adaptive algorithm and then global optimization of OTSU image segmentation function is carried out by using improved genetic algorithm. The method can automatically adjust the genetic control parameters according to the individual fitness and the degree of population dispersion, which can speed up the convergence while maintaining the diversity of the population. Finally, the optimal threshold for image segmentation is obtained, which overcomes the poor convergence, premature and other issues of the traditional genetic algorithm. Experiments show that the threshold range obtained by the method is more stable, the calculation time of the threshold is greatly reduced, and the requirement of real-time image processing can be satisfied.
{"title":"Improved OTSU and adaptive genetic algorithm for infrared image segmentation","authors":"Ya Wang","doi":"10.1109/CCDC.2018.8408116","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8408116","url":null,"abstract":"In order to improve the segmentation result of infrared images, an image segmentation method based on improved OTSU method and improved genetic algorithm is proposed. Firstly, morphological noise reduction is carried out by using morphological weighting adaptive algorithm and then global optimization of OTSU image segmentation function is carried out by using improved genetic algorithm. The method can automatically adjust the genetic control parameters according to the individual fitness and the degree of population dispersion, which can speed up the convergence while maintaining the diversity of the population. Finally, the optimal threshold for image segmentation is obtained, which overcomes the poor convergence, premature and other issues of the traditional genetic algorithm. Experiments show that the threshold range obtained by the method is more stable, the calculation time of the threshold is greatly reduced, and the requirement of real-time image processing can be satisfied.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134064631","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 : 2018-06-01DOI: 10.1109/CCDC.2018.8408244
Shao-zong Ma, Youmin Zhang, J. Xin, Yingmin Yi, Ding Liu, Han Liu
Traditional forest fire detection methods based on flame are subject to detection delays due to the later appearrance of flame than smoke. When the flame is detected, the fire might have already been spread and out of control. Therefore, the objective of this paper is to study new fire detection method based on smoke. First a quadrotor unmanned aerial vehicle (UAV) is adopted to capture the fire video; then the collected video sequence will be processed by a series of preprocessing including filtering and image enhancement; finally a new significance detection algorithm is investigated for early and efficient smoke detection. Experimental results show that the algorithm can detect smoke effectively.
{"title":"An early forest fire detection method based on unmanned aerial vehicle vision","authors":"Shao-zong Ma, Youmin Zhang, J. Xin, Yingmin Yi, Ding Liu, Han Liu","doi":"10.1109/CCDC.2018.8408244","DOIUrl":"https://doi.org/10.1109/CCDC.2018.8408244","url":null,"abstract":"Traditional forest fire detection methods based on flame are subject to detection delays due to the later appearrance of flame than smoke. When the flame is detected, the fire might have already been spread and out of control. Therefore, the objective of this paper is to study new fire detection method based on smoke. First a quadrotor unmanned aerial vehicle (UAV) is adopted to capture the fire video; then the collected video sequence will be processed by a series of preprocessing including filtering and image enhancement; finally a new significance detection algorithm is investigated for early and efficient smoke detection. Experimental results show that the algorithm can detect smoke effectively.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134198428","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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