Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206640
Jin Peng, K. Song
The credibility measure of fuzzy events is a relatively new concept related to fuzzy variable. This paper aims at demonstrating how this concept can be used for managing fuzzy scheduling on flow-shop problems. Three types of fuzzy flow-shop scheduling models are presented. A hybrid intelligent algorithm is then designed to solve the proposed fuzzy flow-shop scheduling models. Computation experiments are provided to illustrate its effectiveness.
{"title":"Fuzzy flow-shop scheduling models based on credibility measure","authors":"Jin Peng, K. Song","doi":"10.1109/FUZZ.2003.1206640","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206640","url":null,"abstract":"The credibility measure of fuzzy events is a relatively new concept related to fuzzy variable. This paper aims at demonstrating how this concept can be used for managing fuzzy scheduling on flow-shop problems. Three types of fuzzy flow-shop scheduling models are presented. A hybrid intelligent algorithm is then designed to solve the proposed fuzzy flow-shop scheduling models. Computation experiments are provided to illustrate its effectiveness.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"66 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113941161","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209421
Rong-Jyue Wang, Wen-June Wang
In this paper, the perturbed continuous-time large-scale system with time-delay is represented by an equivalent Takagi-Sugeno type fuzzy model. The state feedback decentralized fuzzy controller is considered in this paper. Based on Lyapunov criterion and Razumikhin theorem, some sufficient conditions are derived to stabilize the whole perturbed fuzzy time-delay system asymptotically. Moreover, if all the interconnection matrices A/sub ij//sup l/ and time-delays /spl tau//sub ij//sup l/(t) of each subsystem are the same for all rules, we shall propose a simpler and less conservative criterion. These criteria do not need the solution of a Lyapunov equation or Riccati equation. The so-called "matching condition" for the interconnection matrices and perturbations are not needed.
{"title":"Fuzzy control design for perturbed fuzzy time-delay large-scale systems","authors":"Rong-Jyue Wang, Wen-June Wang","doi":"10.1109/FUZZ.2003.1209421","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209421","url":null,"abstract":"In this paper, the perturbed continuous-time large-scale system with time-delay is represented by an equivalent Takagi-Sugeno type fuzzy model. The state feedback decentralized fuzzy controller is considered in this paper. Based on Lyapunov criterion and Razumikhin theorem, some sufficient conditions are derived to stabilize the whole perturbed fuzzy time-delay system asymptotically. Moreover, if all the interconnection matrices A/sub ij//sup l/ and time-delays /spl tau//sub ij//sup l/(t) of each subsystem are the same for all rules, we shall propose a simpler and less conservative criterion. These criteria do not need the solution of a Lyapunov equation or Riccati equation. The so-called \"matching condition\" for the interconnection matrices and perturbations are not needed.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126518292","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209416
Yung-Yue Chen, Bor‐Sen Chen, C. Tseng
An adaptive fuzzy mixed H/sub 2//H/sub /spl infin// lateral control of nonlinear missile systems with uncertain disturbances considered is proposed to achieve H/sub 2/ quadratic tracking with H/sub /spl infin// disturbance rejection. This approach can be applied to control the lateral directional dynamics of the missiles at high angle of attack or generate moments on missile operating in flight regimes where the effectiveness of conventional aerodynamic surfaces is reduced. Using an adaptive fuzzy approximation method, the uncertain nonlinear model of the missile system is estimated. Then, by a mixed H/sub 2//H/sub /spl infin// control design, the effects of external disturbance and fuzzy approximation error as well as consumed energy of the controller is minimized. By the skew symmetric property of the missile system and adequate choice of state variable transformation, this problem can be reduced to solve two algebraic Riccati-like equations. Furthermore, a closed-form solution to these two algebraic equations can be obtained with very simple form for the preceding control design.
{"title":"Adaptive fuzzy mixed H/sub 2//H/sub /spl infin// lateral control of nonlinear missile systems","authors":"Yung-Yue Chen, Bor‐Sen Chen, C. Tseng","doi":"10.1109/FUZZ.2003.1209416","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209416","url":null,"abstract":"An adaptive fuzzy mixed H/sub 2//H/sub /spl infin// lateral control of nonlinear missile systems with uncertain disturbances considered is proposed to achieve H/sub 2/ quadratic tracking with H/sub /spl infin// disturbance rejection. This approach can be applied to control the lateral directional dynamics of the missiles at high angle of attack or generate moments on missile operating in flight regimes where the effectiveness of conventional aerodynamic surfaces is reduced. Using an adaptive fuzzy approximation method, the uncertain nonlinear model of the missile system is estimated. Then, by a mixed H/sub 2//H/sub /spl infin// control design, the effects of external disturbance and fuzzy approximation error as well as consumed energy of the controller is minimized. By the skew symmetric property of the missile system and adequate choice of state variable transformation, this problem can be reduced to solve two algebraic Riccati-like equations. Furthermore, a closed-form solution to these two algebraic equations can be obtained with very simple form for the preceding control design.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128177352","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206594
Yanqing Zhang, Wei Fan, Jiannong Cao
In this paper, the basic design of the fuzzy personalized wireless information agent for mobile phones is a proposed based on wireless fuzzy Web intelligence, wireless mobile computing, Internet computing, intelligent agent technology, Web databases, and personalization. The basic personalized wireless information agent's middleware is implemented by using WAP, WML, Java Servlets and intelligent information agent techniques, Oracle databases, client-server technology and personalized profiles. Typical applications such as personalized search for weather, traffic and others are demonstrated. In addition, Computational Web Intelligence (CWI) techniques can be used to design more intelligent wireless mobile agents to better serve wireless mobile users. In the future, the fuzzy wireless intelligent multi-agent system will have more applications.
{"title":"Fuzzy personalized wireless information agents","authors":"Yanqing Zhang, Wei Fan, Jiannong Cao","doi":"10.1109/FUZZ.2003.1206594","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206594","url":null,"abstract":"In this paper, the basic design of the fuzzy personalized wireless information agent for mobile phones is a proposed based on wireless fuzzy Web intelligence, wireless mobile computing, Internet computing, intelligent agent technology, Web databases, and personalization. The basic personalized wireless information agent's middleware is implemented by using WAP, WML, Java Servlets and intelligent information agent techniques, Oracle databases, client-server technology and personalized profiles. Typical applications such as personalized search for weather, traffic and others are demonstrated. In addition, Computational Web Intelligence (CWI) techniques can be used to design more intelligent wireless mobile agents to better serve wireless mobile users. In the future, the fuzzy wireless intelligent multi-agent system will have more applications.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132058636","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206642
Sameh Soliman, A. Sadeghian
As they require minimum human intervention, automated modeling approaches find preference in many areas. Fuzzy models - when obtained from system measurements - represent good example of such approaches. Because the emphasis is on its accuracy, the obtained model may exhibit unnecessary complexity which hampers its transparency and computational cost. This paper deals with the issues of transparency and accuracy of data-driven fuzzy models. We present an automated method to simplify data-driven fuzzy models. This method targets simplifying rather than reducing the model. However, model reduction may follow from its simplification if it contains high redundancy.
{"title":"Fuzzy Karnaugh maps - do they exist?","authors":"Sameh Soliman, A. Sadeghian","doi":"10.1109/FUZZ.2003.1206642","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206642","url":null,"abstract":"As they require minimum human intervention, automated modeling approaches find preference in many areas. Fuzzy models - when obtained from system measurements - represent good example of such approaches. Because the emphasis is on its accuracy, the obtained model may exhibit unnecessary complexity which hampers its transparency and computational cost. This paper deals with the issues of transparency and accuracy of data-driven fuzzy models. We present an automated method to simplify data-driven fuzzy models. This method targets simplifying rather than reducing the model. However, model reduction may follow from its simplification if it contains high redundancy.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134329009","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206634
Fun-Bin Duh, Chia-Feng Juang, Chin-Teng Lin
To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural network such as backpropagation (BP) network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural fuzzy network (SONFIN) with supervised learning algorithm. Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.
{"title":"Application of neural fuzzy network to pulse compression with binary phase code","authors":"Fun-Bin Duh, Chia-Feng Juang, Chin-Teng Lin","doi":"10.1109/FUZZ.2003.1206634","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206634","url":null,"abstract":"To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural network such as backpropagation (BP) network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural fuzzy network (SONFIN) with supervised learning algorithm. Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132841162","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206654
P. Bose
Very often, the generic term "fuzzy database" is meant for two different purposes. One is the idea of being provided with queries inside which preferences are used instead of classical Boolean conditions. Basically, such queries are addressed to regular databases and any element of the result is associated with a d e m e of satisfaction used to rank-order the answers. On the other hand, one may face a situation where some pieces of information are not known precisely (automated recognition of objects in images, data fusion, linguistic descriptions, ..., etc). This leads to store and query a new type of data with respect to that dealt with in commercial systems. When fuzzy sets (indeed possibility dis~butions) are used to model such data, one gets fuzzy databases. Obviously, flexible queries can also be used against fuzzy databases, but the two issues are fundamentally independent.
{"title":"Two sides of fuzzy databases: flexible queries and imprecise information management","authors":"P. Bose","doi":"10.1109/FUZZ.2003.1206654","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206654","url":null,"abstract":"Very often, the generic term \"fuzzy database\" is meant for two different purposes. One is the idea of being provided with queries inside which preferences are used instead of classical Boolean conditions. Basically, such queries are addressed to regular databases and any element of the result is associated with a d e m e of satisfaction used to rank-order the answers. On the other hand, one may face a situation where some pieces of information are not known precisely (automated recognition of objects in images, data fusion, linguistic descriptions, ..., etc). This leads to store and query a new type of data with respect to that dealt with in commercial systems. When fuzzy sets (indeed possibility dis~butions) are used to model such data, one gets fuzzy databases. Obviously, flexible queries can also be used against fuzzy databases, but the two issues are fundamentally independent.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132630436","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209414
F. Leung, H. Lam, P. Tam, Yim-Shu Lee
This paper addresses the stable fuzzy controller design problem of nonlinear systems. The methodology is based on a fuzzy logic approach and the genetic algorithm (GA). In order to analyze the system stability, the TSK fuzzy plant model is employed to describe the dynamics of the nonlinear plant. A fuzzy controller is then developed to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the GA. An application example on stabilizing an inverted pendulum system will be given. Simulation and experimental results will be presented to verify the applicability of the proposed approach.
{"title":"Stable fuzzy controller design for uncertain nonlinear systems: genetic algorithm approach","authors":"F. Leung, H. Lam, P. Tam, Yim-Shu Lee","doi":"10.1109/FUZZ.2003.1209414","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209414","url":null,"abstract":"This paper addresses the stable fuzzy controller design problem of nonlinear systems. The methodology is based on a fuzzy logic approach and the genetic algorithm (GA). In order to analyze the system stability, the TSK fuzzy plant model is employed to describe the dynamics of the nonlinear plant. A fuzzy controller is then developed to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the GA. An application example on stabilizing an inverted pendulum system will be given. Simulation and experimental results will be presented to verify the applicability of the proposed approach.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114728056","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209342
Pobalan Govender, V. Bajic
Prediction using linear interpolation works well in systems where the destabilizing effects of long dead-times are not dominant. When the process contains a long dead-time, prediction using linear derivative control will not work because the measured signal will not contain sufficient information about any future changes that may occur in the loop. Under these conditions, the anticipatory nature of the derivative controller fails to ensure proper control of deadtime dominant processes. This paper proposes a fuzzy logic tuned, polynomial based nonlinear predictor to improve the control of plants experiencing instability due to long dead-times. The proposed predictor replaces the derivative controller in a PID controller, and contributes towards a general improvement of the control performance in systems having long dead-times.
{"title":"A fuzzy logic tuned polynomial based predictor for processes having long dead-times","authors":"Pobalan Govender, V. Bajic","doi":"10.1109/FUZZ.2003.1209342","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209342","url":null,"abstract":"Prediction using linear interpolation works well in systems where the destabilizing effects of long dead-times are not dominant. When the process contains a long dead-time, prediction using linear derivative control will not work because the measured signal will not contain sufficient information about any future changes that may occur in the loop. Under these conditions, the anticipatory nature of the derivative controller fails to ensure proper control of deadtime dominant processes. This paper proposes a fuzzy logic tuned, polynomial based nonlinear predictor to improve the control of plants experiencing instability due to long dead-times. The proposed predictor replaces the derivative controller in a PID controller, and contributes towards a general improvement of the control performance in systems having long dead-times.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121988704","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209400
P. Melin, O. Castillo
We describe in this paper adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks. First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A specific non-linear plant is used to test the hybrid approach for adaptive control. A specific plant was used as test bed in the experiments. The non-linear plant that was considered is the "Pendubot", which is a non-linear plant similar to the two-link robot arm. The results of the type-2 fuzzy logic approach for control were good, both in accuracy and efficiency.
{"title":"A new method for adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks","authors":"P. Melin, O. Castillo","doi":"10.1109/FUZZ.2003.1209400","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209400","url":null,"abstract":"We describe in this paper adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks. First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A specific non-linear plant is used to test the hybrid approach for adaptive control. A specific plant was used as test bed in the experiments. The non-linear plant that was considered is the \"Pendubot\", which is a non-linear plant similar to the two-link robot arm. The results of the type-2 fuzzy logic approach for control were good, both in accuracy and efficiency.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128386845","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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