Pub Date : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280201
G. Zhang
When radar traces the target in low altitude, because of multi-path effect, radar echo wave gets interfered and phenomenon of false alarm or false dismissal occurs, which even can lead to losing target. This paper proposes a separating algorithm that utilizes time-frequency clustering characteristic of Fractional Fourier Transform combing features of multi-path signal. The parameter estimation algorithm based on semi-blind signal processing is also adopted. The paper models the process of blind signal which fits low altitude tracking measurement, solves optimal estimation with simulated annealing algorithm and separates direct wave and reflected wave among multi-path signal to decline multi-path error and increase accuracy of tracking measurements.
{"title":"Simulation of restrain of radar's multi-path effect","authors":"G. Zhang","doi":"10.1109/ICEDIF.2015.7280201","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280201","url":null,"abstract":"When radar traces the target in low altitude, because of multi-path effect, radar echo wave gets interfered and phenomenon of false alarm or false dismissal occurs, which even can lead to losing target. This paper proposes a separating algorithm that utilizes time-frequency clustering characteristic of Fractional Fourier Transform combing features of multi-path signal. The parameter estimation algorithm based on semi-blind signal processing is also adopted. The paper models the process of blind signal which fits low altitude tracking measurement, solves optimal estimation with simulated annealing algorithm and separates direct wave and reflected wave among multi-path signal to decline multi-path error and increase accuracy of tracking measurements.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121318785","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280147
D. Sampath, G. Wimalarathne
A visually impaired person comes across objects with different attributes in his navigational path, and all of those objects can't be considered as obstacles. One person's obstacle could become a landmark for another. Therefore, getting some insights about the features of the obstacles that come across will provide a significant impact on improving the navigational process of the visually impaired individuals. In this study, a wearable obstacle classification system which extends the currently available obstacle detection approaches using sonar echolocation has been developed. In order to increase the detection range and to improve user-friendliness, an innovative approach based on integration of electronics onto textile environment has been studied. Optimum architecture to embed electronic equipment's and sensors to the textile has been realized. Finally, a smart garment prototype including ultrasonic sensors, coin vibration motors, power supplies and a micro controller has been developed.
{"title":"Obstacle classification through acoustic echolocation","authors":"D. Sampath, G. Wimalarathne","doi":"10.1109/ICEDIF.2015.7280147","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280147","url":null,"abstract":"A visually impaired person comes across objects with different attributes in his navigational path, and all of those objects can't be considered as obstacles. One person's obstacle could become a landmark for another. Therefore, getting some insights about the features of the obstacles that come across will provide a significant impact on improving the navigational process of the visually impaired individuals. In this study, a wearable obstacle classification system which extends the currently available obstacle detection approaches using sonar echolocation has been developed. In order to increase the detection range and to improve user-friendliness, an innovative approach based on integration of electronics onto textile environment has been studied. Optimum architecture to embed electronic equipment's and sensors to the textile has been realized. Finally, a smart garment prototype including ultrasonic sensors, coin vibration motors, power supplies and a micro controller has been developed.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751734","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280153
Wenqing Ma
In order to achieve a reasonable evaluation of direct trust, this paper proposes a trust evaluation algorithm based on the domain, using the technique of constructing a hierarchical tree of trust evaluation subjectively. The algorithm adopts the rules of series and parallel operations in the D-S theory, acquires the results of the recommended trust problem of a single path by quadrature methods and implements the integration of multiple paths by the weighted algorithm which takes the cooperative roles and industry roles as factors. The algorithm can effectively avoid the phenomenon of a single node's weight being too heavy and unfair treatment on recommended resource nodes and realize the trust value computation.
{"title":"Trust value calculation in domains based on grid environment","authors":"Wenqing Ma","doi":"10.1109/ICEDIF.2015.7280153","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280153","url":null,"abstract":"In order to achieve a reasonable evaluation of direct trust, this paper proposes a trust evaluation algorithm based on the domain, using the technique of constructing a hierarchical tree of trust evaluation subjectively. The algorithm adopts the rules of series and parallel operations in the D-S theory, acquires the results of the recommended trust problem of a single path by quadrature methods and implements the integration of multiple paths by the weighted algorithm which takes the cooperative roles and industry roles as factors. The algorithm can effectively avoid the phenomenon of a single node's weight being too heavy and unfair treatment on recommended resource nodes and realize the trust value computation.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114155305","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280164
Luyan Chen, Fuqiao Hu
This paper proposes a detection approach for localizing the object of specific category in images. Based on the ensemble of exemplars, a per-exemplar classifier for each exemplar is learnt, which is simple but powerful to perform well in detecting visually similar objects. Meanwhile, considering the fact that the number of negatives is always considerably larger than that of positives, the method of hard-negatives mining is employed. In addition, using multiple instance learning, we also learn the per-category classifier with inputting the detections produced by per-exemplar classifier in the validation set. The better performance on object detection is boosted further with the contribution of co-occurrence matrices that encodes the relationship of each per-exemplar classifier. In the experiment section, we evaluate the performance of our approach on PASCAL VOC 2007 dataset and compare it to Tomasz's Exemplar-SVM model directly. The experimental result demonstrates that our approach outperforms the Exemplar-SVM model in object detection with higher average precision.
{"title":"Object detection based on ensemble of exemplars","authors":"Luyan Chen, Fuqiao Hu","doi":"10.1109/ICEDIF.2015.7280164","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280164","url":null,"abstract":"This paper proposes a detection approach for localizing the object of specific category in images. Based on the ensemble of exemplars, a per-exemplar classifier for each exemplar is learnt, which is simple but powerful to perform well in detecting visually similar objects. Meanwhile, considering the fact that the number of negatives is always considerably larger than that of positives, the method of hard-negatives mining is employed. In addition, using multiple instance learning, we also learn the per-category classifier with inputting the detections produced by per-exemplar classifier in the validation set. The better performance on object detection is boosted further with the contribution of co-occurrence matrices that encodes the relationship of each per-exemplar classifier. In the experiment section, we evaluate the performance of our approach on PASCAL VOC 2007 dataset and compare it to Tomasz's Exemplar-SVM model directly. The experimental result demonstrates that our approach outperforms the Exemplar-SVM model in object detection with higher average precision.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122220518","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280169
Chunshan Yang, Z. Deng
For the multisensor time-invariant uncertain system with uncertainties of both parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, the uncertain system can be converted into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle, and weighted least squares method, a robust weighted measurement fusion Kalman predictor is presented based on the worst-case conservative system with the conservative upper bounds of noise variances. The robustness and robust accuracy relation prove by Lyapunov equation approach. It is prove that it is equivalent to the robust centralized fusion Kalman predictor, and its robust accuracy is higher than that of each local robust Kalman predictor. A Monte-Carlo simulation example shows its correctness and effectiveness.
{"title":"Robust measurement fusion steady-state Kalman predictor for multisensor uncertain system","authors":"Chunshan Yang, Z. Deng","doi":"10.1109/ICEDIF.2015.7280169","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280169","url":null,"abstract":"For the multisensor time-invariant uncertain system with uncertainties of both parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, the uncertain system can be converted into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle, and weighted least squares method, a robust weighted measurement fusion Kalman predictor is presented based on the worst-case conservative system with the conservative upper bounds of noise variances. The robustness and robust accuracy relation prove by Lyapunov equation approach. It is prove that it is equivalent to the robust centralized fusion Kalman predictor, and its robust accuracy is higher than that of each local robust Kalman predictor. A Monte-Carlo simulation example shows its correctness and effectiveness.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116012780","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280172
X. Z. Wang, X. Bi, Z. Ge, L. Li
This paper presents a deep data fusion model for risk perception and coordinated control in a regional power system control center. A knowledge learning data fusion approach has been used to find an efficient state representation based on prior knowledge from cross-domain energy management systems. In particular, a kernel principal components analysis technique is presented for nonlinear dimensionality reduction of knowledge learning. The control strategy we study is based on cross-domain global optimization approach, which regards the contingencies and control actions of mutual backup systems as constraints. The objective function is defined as the product of cross-domain assessment and control factors. The method for obtaining optimal solution is given by interior point code. To show the applicability, different machine learning method has been studied. The experimental results show that the proposed knowledge learning approach consistently outperforms the traditional machine learning method. In addition, the proposed coordinated control approach is verified effective on large-scale smart grid decision support system for East China project.
{"title":"Deep data fusion model for risk perception and coordinated control of smart grid","authors":"X. Z. Wang, X. Bi, Z. Ge, L. Li","doi":"10.1109/ICEDIF.2015.7280172","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280172","url":null,"abstract":"This paper presents a deep data fusion model for risk perception and coordinated control in a regional power system control center. A knowledge learning data fusion approach has been used to find an efficient state representation based on prior knowledge from cross-domain energy management systems. In particular, a kernel principal components analysis technique is presented for nonlinear dimensionality reduction of knowledge learning. The control strategy we study is based on cross-domain global optimization approach, which regards the contingencies and control actions of mutual backup systems as constraints. The objective function is defined as the product of cross-domain assessment and control factors. The method for obtaining optimal solution is given by interior point code. To show the applicability, different machine learning method has been studied. The experimental results show that the proposed knowledge learning approach consistently outperforms the traditional machine learning method. In addition, the proposed coordinated control approach is verified effective on large-scale smart grid decision support system for East China project.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128576961","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280160
Xiaoming Huang, Tao Shen, R. Wang, Chenqiang Gao
Text detection and recognition in natural scene images plays an important role in content analysis of images. In this paper, based on the characteristics of scene text, we propose a robust text detection and recognition method using Maximally Stable Extremal Regions (MSER) and Support Vector Machine (SVM). Different from the end to end text recognition, we split the recognition problem into detection and recognition procedure. Firstly, in the detection stage, in order to extract potential text as much as possible, we use MSER and color clustering to extract connected component. Then, for the obtained candidate connected component, we use visual saliency and some prior information to filter non-text regions. Finally, we can obtain word image by text line generation. In the recognition stage, we use vertical projection to segment word images, then recognize character in SVM based framework. The experiment results evaluated on standard dataset show that with a small amount of prior information and simple segment strategy, the proposed method has a better performance compared to conventional text detection and recognition method.
{"title":"Text detection and recognition in natural scene images","authors":"Xiaoming Huang, Tao Shen, R. Wang, Chenqiang Gao","doi":"10.1109/ICEDIF.2015.7280160","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280160","url":null,"abstract":"Text detection and recognition in natural scene images plays an important role in content analysis of images. In this paper, based on the characteristics of scene text, we propose a robust text detection and recognition method using Maximally Stable Extremal Regions (MSER) and Support Vector Machine (SVM). Different from the end to end text recognition, we split the recognition problem into detection and recognition procedure. Firstly, in the detection stage, in order to extract potential text as much as possible, we use MSER and color clustering to extract connected component. Then, for the obtained candidate connected component, we use visual saliency and some prior information to filter non-text regions. Finally, we can obtain word image by text line generation. In the recognition stage, we use vertical projection to segment word images, then recognize character in SVM based framework. The experiment results evaluated on standard dataset show that with a small amount of prior information and simple segment strategy, the proposed method has a better performance compared to conventional text detection and recognition method.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129234532","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280224
Xicao Xie, Chao Sun
In geosteering drilling technology, the transmission of information measured by near-bit sensor has always been a problem. Downhole drilling tool is a very complex medium for sonic transmission. This paper takes geosteering drilling tool as the study object, and puts forward a method of short distance transmission, which is based on sonic wave transmission theory. The experiment testing the drilling pipes sonic transmission characteristics in the laboratory is done. This paper analyzes the sensitivity of sonic wave to some parameters when it acts as carrier. These parameters include the position of the excitation signal, the frequency of the excitation signal, the amplitude of the excitation signal and the position of the response signal. The strength of the propagated sonic signal would be weakened or strengthened because of reflection wave and multipath, but the data transmission is feasible by sonic wave underground short distance.
{"title":"Sonic transmission research of geosteering drilling tools","authors":"Xicao Xie, Chao Sun","doi":"10.1109/ICEDIF.2015.7280224","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280224","url":null,"abstract":"In geosteering drilling technology, the transmission of information measured by near-bit sensor has always been a problem. Downhole drilling tool is a very complex medium for sonic transmission. This paper takes geosteering drilling tool as the study object, and puts forward a method of short distance transmission, which is based on sonic wave transmission theory. The experiment testing the drilling pipes sonic transmission characteristics in the laboratory is done. This paper analyzes the sensitivity of sonic wave to some parameters when it acts as carrier. These parameters include the position of the excitation signal, the frequency of the excitation signal, the amplitude of the excitation signal and the position of the response signal. The strength of the propagated sonic signal would be weakened or strengthened because of reflection wave and multipath, but the data transmission is feasible by sonic wave underground short distance.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123332543","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280155
Guiying Li, Y. Zhigang
The research with respect to Particle Swarm Optimization is concentrated in improving their performance on avoiding local maxima. Since standard Particle Swarm Optimization does not perform well in many cases, we propose double chaotic particle swarm optimization algorithm based on logistic map. This chaotic movement has good randomness and ergodic statistics property of chaos sequence. We propose to use chaotic sequence to initialize the particle positions, laying a solid foundation for the diversity of search particle swarm. The improved strategies in the algorithm increase the premature stagnation judgment that the new particles are added into a new region making changes in the trajectory of particles, which help algorithm escaping from local optima effectively and reduce invalid iterations. This strategies result in greatly improving the convergence of the algorithm, accuracy and speed optimization. On the other hand, the proposed algorithm requires very little number of particles and few iterations to fully meet the theory test function optimization. The results of the simulation show the good performance of the optimization algorithm.
{"title":"The double chaotic particle swarm optimization with the performance avoiding local optimum","authors":"Guiying Li, Y. Zhigang","doi":"10.1109/ICEDIF.2015.7280155","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280155","url":null,"abstract":"The research with respect to Particle Swarm Optimization is concentrated in improving their performance on avoiding local maxima. Since standard Particle Swarm Optimization does not perform well in many cases, we propose double chaotic particle swarm optimization algorithm based on logistic map. This chaotic movement has good randomness and ergodic statistics property of chaos sequence. We propose to use chaotic sequence to initialize the particle positions, laying a solid foundation for the diversity of search particle swarm. The improved strategies in the algorithm increase the premature stagnation judgment that the new particles are added into a new region making changes in the trajectory of particles, which help algorithm escaping from local optima effectively and reduce invalid iterations. This strategies result in greatly improving the convergence of the algorithm, accuracy and speed optimization. On the other hand, the proposed algorithm requires very little number of particles and few iterations to fully meet the theory test function optimization. The results of the simulation show the good performance of the optimization algorithm.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121171760","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 : 2015-10-01DOI: 10.1109/ICEDIF.2015.7280199
Zhong Zhou, Tao Sun, Lizhi Cheng
Recovering sparse signals from a few linear measurements is attracting growing attention. Bsides sparsity, the signals usually are nonnegative, nonpositive or restricted in some domain. This paper proposes an algorithm for recovering the sparse signal with some certain property on learning the sparsity. We propose this algorithm by combining the projective method with the iterative hard thresholding strategy. We prove that this algorithm is linear convergent provided the sensing matrix has suitable property. Numerical results demonstrate the efficiency of the algorithm.
{"title":"Projective iterative hard thresholding algorithm for sparse signal recovery","authors":"Zhong Zhou, Tao Sun, Lizhi Cheng","doi":"10.1109/ICEDIF.2015.7280199","DOIUrl":"https://doi.org/10.1109/ICEDIF.2015.7280199","url":null,"abstract":"Recovering sparse signals from a few linear measurements is attracting growing attention. Bsides sparsity, the signals usually are nonnegative, nonpositive or restricted in some domain. This paper proposes an algorithm for recovering the sparse signal with some certain property on learning the sparsity. We propose this algorithm by combining the projective method with the iterative hard thresholding strategy. We prove that this algorithm is linear convergent provided the sensing matrix has suitable property. Numerical results demonstrate the efficiency of the algorithm.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122923466","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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