Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277088
M. Umano, M. Okamura, Kazuhisa Seta
We have various kinds of time series such as stock prices. We understand them via their linguistic expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a linguistic expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).
{"title":"Improved method for linguistic expression of time series with global trend and local features","authors":"M. Umano, M. Okamura, Kazuhisa Seta","doi":"10.1109/FUZZY.2009.5277088","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277088","url":null,"abstract":"We have various kinds of time series such as stock prices. We understand them via their linguistic expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a linguistic expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552345","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277360
Balaji Parasumanna Gokulan, D. Srinivasan
Rapid advances made in vehicle technology and increased level of urbanization have caused an exponential increase in road traffic congestion levels. This has necessitated the implementation of intelligent traffic responsive signal controllers capable of maintaining the saturation levels in each link thereby reducing congestion and increasing utilization of existing infrastructure. This paper presents one such distributed multi-agent architecture based on weighted type-2 fuzzy inference engine for the urban traffic signal control. Agents have been programmed in PARAMICS microscopic traffic simulator and tested on a simulated section of Central Business District in Singapore with twenty five interconnected intersections. A comparative analysis of the proposed architecture with the existing traffic signal controller HMS - Hierarchical multi-agent system, was performed for two different traffic scenarios. The results clearly indicates better performance of the proposed agent architecture over the benchmark controller and offers scope for improvement in the future.
{"title":"Distributed multi-agent type-2 fuzzy architecture for urban traffic signal control","authors":"Balaji Parasumanna Gokulan, D. Srinivasan","doi":"10.1109/FUZZY.2009.5277360","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277360","url":null,"abstract":"Rapid advances made in vehicle technology and increased level of urbanization have caused an exponential increase in road traffic congestion levels. This has necessitated the implementation of intelligent traffic responsive signal controllers capable of maintaining the saturation levels in each link thereby reducing congestion and increasing utilization of existing infrastructure. This paper presents one such distributed multi-agent architecture based on weighted type-2 fuzzy inference engine for the urban traffic signal control. Agents have been programmed in PARAMICS microscopic traffic simulator and tested on a simulated section of Central Business District in Singapore with twenty five interconnected intersections. A comparative analysis of the proposed architecture with the existing traffic signal controller HMS - Hierarchical multi-agent system, was performed for two different traffic scenarios. The results clearly indicates better performance of the proposed agent architecture over the benchmark controller and offers scope for improvement in the future.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"688 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134127343","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5276885
S. Khanmohammadi, S. M. Bakhshmand, Hadi Seyedarabi
Automatic finding exact location of facial salient points under translation, rotation and changing lightning illumination is a considerable task in face image processing. This paper presents a multistage procedure for finding landmark points on a rigid object like human face. Gabor filter jets make EBGM, very effective but computationally expensive. In proposed method, searching landmark points using Gabor filter jets is optimized by using particle swarm optimization (PSO) and similarity between model jet and extracted jet as cost function. After locating first landmark, the location of next landmark is estimated and then is refined by local search criteria (FLS) until localizing of all desired 5 landmarks. Model jets are used for accounting pixels and can be extracted manually from landmark points of same identity for more robustness and accuracy. Results based on the proposed approach are included to prove the accuracy and low computational cost of proposed method comparing the exhaustive search.
{"title":"High precision PSO and FLS integrated method for facial landmark localization","authors":"S. Khanmohammadi, S. M. Bakhshmand, Hadi Seyedarabi","doi":"10.1109/FUZZY.2009.5276885","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5276885","url":null,"abstract":"Automatic finding exact location of facial salient points under translation, rotation and changing lightning illumination is a considerable task in face image processing. This paper presents a multistage procedure for finding landmark points on a rigid object like human face. Gabor filter jets make EBGM, very effective but computationally expensive. In proposed method, searching landmark points using Gabor filter jets is optimized by using particle swarm optimization (PSO) and similarity between model jet and extracted jet as cost function. After locating first landmark, the location of next landmark is estimated and then is refined by local search criteria (FLS) until localizing of all desired 5 landmarks. Model jets are used for accounting pixels and can be extracted manually from landmark points of same identity for more robustness and accuracy. Results based on the proposed approach are included to prove the accuracy and low computational cost of proposed method comparing the exhaustive search.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133965126","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277077
Xiao-Ying Wang, J. Garibaldi, Shang-Ming Zhou, R. John
Recommending appropriate follow-up treatment options to patients after diagnosis and primary (usually surgical) treatment of breast cancer is a complex decision making problem. Often, the decision is reached by consensus from a multi-disciplinary team of oncologists, radiologists, surgeons and pathologists. Non-stationary fuzzy sets have been proposed as a mechanism to represent and reason with the knowledge of such multiple experts. In this paper, we briefly describe the creation of a non-stationary fuzzy inference system to provide decision support in this context, and examine a number of alternative methods for interpreting the output of such a non-stationary inference system. The alternative interpretation methodologies and the experiments carried out to compare these methods are detailed. Results are presented which shown that using majority voting ensemble decision making from a non-stationary fuzzy system improves accuracy of the decision making. We conclude that non-stationary systems coupled with ensemble interpretation methods are worthy of further exploration.
{"title":"Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer","authors":"Xiao-Ying Wang, J. Garibaldi, Shang-Ming Zhou, R. John","doi":"10.1109/FUZZY.2009.5277077","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277077","url":null,"abstract":"Recommending appropriate follow-up treatment options to patients after diagnosis and primary (usually surgical) treatment of breast cancer is a complex decision making problem. Often, the decision is reached by consensus from a multi-disciplinary team of oncologists, radiologists, surgeons and pathologists. Non-stationary fuzzy sets have been proposed as a mechanism to represent and reason with the knowledge of such multiple experts. In this paper, we briefly describe the creation of a non-stationary fuzzy inference system to provide decision support in this context, and examine a number of alternative methods for interpreting the output of such a non-stationary inference system. The alternative interpretation methodologies and the experiments carried out to compare these methods are detailed. Results are presented which shown that using majority voting ensemble decision making from a non-stationary fuzzy system improves accuracy of the decision making. We conclude that non-stationary systems coupled with ensemble interpretation methods are worthy of further exploration.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133586612","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277251
Katsuhiro Honda, A. Notsu, H. Ichihashi
This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized recommendation is performed by finding user-item neighborhoods (co-clusters) from a rectangular relational data matrix, in which users and items have mutually positive relations. In the proposed approach, user-item clusters are extracted one by one in a sequential manner via a structural balancing technique, used in conjunction with the sequential fuzzy cluster extraction method.
{"title":"Collaborative filtering by sequential extraction of user-item clusters based on structural balancing approach","authors":"Katsuhiro Honda, A. Notsu, H. Ichihashi","doi":"10.1109/FUZZY.2009.5277251","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277251","url":null,"abstract":"This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized recommendation is performed by finding user-item neighborhoods (co-clusters) from a rectangular relational data matrix, in which users and items have mutually positive relations. In the proposed approach, user-item clusters are extracted one by one in a sequential manner via a structural balancing technique, used in conjunction with the sequential fuzzy cluster extraction method.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117168282","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5276879
Kwang-Baek Kim, Y. Woo, D. Song
Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM (Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM (Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.
{"title":"Improved SIM algorithm for effective image retrieval","authors":"Kwang-Baek Kim, Y. Woo, D. Song","doi":"10.1109/FUZZY.2009.5276879","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5276879","url":null,"abstract":"Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM (Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM (Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116097578","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277356
Tzu-Chao Lin
A novel decision-based fuzzy averaging filter consisting of a new Dempster-Shafer (D-S) noise detector and a two-pass noise filtering mechanism is proposed. Bodies of evidence are extracted, and the basic belief assignment is developed, avoiding the counter-intuitive problem of Dempster's combination rule. The combination belief value can be the decision rule for the D-S noise detector. A fuzzy averaging method where the weights are constructed using a predefined fuzzy set is developed to achieve noise cancellation. Besides that, a simple second-pass filter is also employed to improve the final filtering performance. Experimental results have confirmed the proposed filter outperforms other decision-based filters in terms of both noise suppression and detail preservation.
{"title":"Fuzzy image restoration for noise reduction based on dempster-shafer theory","authors":"Tzu-Chao Lin","doi":"10.1109/FUZZY.2009.5277356","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277356","url":null,"abstract":"A novel decision-based fuzzy averaging filter consisting of a new Dempster-Shafer (D-S) noise detector and a two-pass noise filtering mechanism is proposed. Bodies of evidence are extracted, and the basic belief assignment is developed, avoiding the counter-intuitive problem of Dempster's combination rule. The combination belief value can be the decision rule for the D-S noise detector. A fuzzy averaging method where the weights are constructed using a predefined fuzzy set is developed to achieve noise cancellation. Besides that, a simple second-pass filter is also employed to improve the final filtering performance. Experimental results have confirmed the proposed filter outperforms other decision-based filters in terms of both noise suppression and detail preservation.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114882349","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277313
A. Aziz
Fuzzy systems have been proven very successfully in many important applications and are rapidly growing to become a powerful technique for multisenosr-multitarget data fusion. The functional paradigm for fuzzy multisenosr-multitarget data fusion consists of fuzzification, fuzzy knowledge-base, fuzzy inference mechanism, and defuzzification. In fuzzy system design, users start with some fuzzy rules, which are chosen heuristically based on their experience, and membership functions, which in many cases are chosen subjectively based on understanding the problem, and they use the developed system to tune these rules and membership functions. Constructing membership function is the most important step in the fuzzy system design. This paper addresses the problem of constructing the optimal membership functions from input data in a multisenosr-multitarget environment. This analysis has been applied to clustering of multisensor information in a two-dimensional multisenosr-multitarget data fusion system. Clustering performance using optimal membership functions is compared to that of clustering using non-optimal membership functions. The results show that there is a significant performance improvement when using optimal membership functions.
{"title":"Effects of fuzzy membership function shapes on clustering performance in multisensor-multitarget data fusion systems","authors":"A. Aziz","doi":"10.1109/FUZZY.2009.5277313","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277313","url":null,"abstract":"Fuzzy systems have been proven very successfully in many important applications and are rapidly growing to become a powerful technique for multisenosr-multitarget data fusion. The functional paradigm for fuzzy multisenosr-multitarget data fusion consists of fuzzification, fuzzy knowledge-base, fuzzy inference mechanism, and defuzzification. In fuzzy system design, users start with some fuzzy rules, which are chosen heuristically based on their experience, and membership functions, which in many cases are chosen subjectively based on understanding the problem, and they use the developed system to tune these rules and membership functions. Constructing membership function is the most important step in the fuzzy system design. This paper addresses the problem of constructing the optimal membership functions from input data in a multisenosr-multitarget environment. This analysis has been applied to clustering of multisensor information in a two-dimensional multisenosr-multitarget data fusion system. Clustering performance using optimal membership functions is compared to that of clustering using non-optimal membership functions. The results show that there is a significant performance improvement when using optimal membership functions.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072925","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277182
Katsuhiro Honda, Tomonari Nomaguchi, A. Notsu, H. Ichihashi
Cluster validation is an important issue in cluster analysis. In this paper, a comparative study on validity criteria is performed with linear fuzzy clustering that can be identified with a local PCA technique. Besides the standard fuzzification approach, the entropy regularization approach is responsible for fuzzification of data partition and the approach implies a close relation between FCM-type linear fuzzy clustering and probabilistic PCA models. This comparative study reveals mutual differences between two fuzzification approaches from the view point of cluster validation using several cluster validity criteria. Additional characteristics are shown in a pareto analysis, in which the effect of noise sensitivity is also discussed.
{"title":"A comparative study on cluster validity criteria in linear fuzzy clustering and pareto optimality analysis","authors":"Katsuhiro Honda, Tomonari Nomaguchi, A. Notsu, H. Ichihashi","doi":"10.1109/FUZZY.2009.5277182","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277182","url":null,"abstract":"Cluster validation is an important issue in cluster analysis. In this paper, a comparative study on validity criteria is performed with linear fuzzy clustering that can be identified with a local PCA technique. Besides the standard fuzzification approach, the entropy regularization approach is responsible for fuzzification of data partition and the approach implies a close relation between FCM-type linear fuzzy clustering and probabilistic PCA models. This comparative study reveals mutual differences between two fuzzification approaches from the view point of cluster validation using several cluster validity criteria. Additional characteristics are shown in a pareto analysis, in which the effect of noise sensitivity is also discussed.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122049210","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277057
Hong-Gi Lee, J. Hong, Hoon Kang, K. Sim
Even though the genetic algorithm is known to be a very effective method to solve the global minimization problem, it needs much time (a large population size and a large number of generations) for a reliable answer and thus it seems to be inadequate for on-line performance. We propose a population feedback GA scheme. we show the effectiveness of our scheme by finding an observer for the discrete-time nonlinear autonomous systems with simulations.
{"title":"A genetic algorithms for on-line calculation with application to system theory","authors":"Hong-Gi Lee, J. Hong, Hoon Kang, K. Sim","doi":"10.1109/FUZZY.2009.5277057","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277057","url":null,"abstract":"Even though the genetic algorithm is known to be a very effective method to solve the global minimization problem, it needs much time (a large population size and a large number of generations) for a reliable answer and thus it seems to be inadequate for on-line performance. We propose a population feedback GA scheme. we show the effectiveness of our scheme by finding an observer for the discrete-time nonlinear autonomous systems with simulations.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241680","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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