Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295534
Hirosato Seki, H. Ishii, M. Mizumoto
Yubazaki et al. proposed single input rule modules connected type fuzzy reasoning method (SIRMs method) whose final output is obtained by summarizing the product of the importance degree and the inference result from single input fuzzy rule module. This paper clarifies the relationship between the simplified reasoning method and SIRMs method, and shows that SIRMs method can be transformed into simplified reasoning method, but not vice versa.
{"title":"On the Property of Single Input Rule Modules Connected Type Fuzzy Reasoning Method","authors":"Hirosato Seki, H. Ishii, M. Mizumoto","doi":"10.1109/FUZZY.2007.4295534","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295534","url":null,"abstract":"Yubazaki et al. proposed single input rule modules connected type fuzzy reasoning method (SIRMs method) whose final output is obtained by summarizing the product of the importance degree and the inference result from single input fuzzy rule module. This paper clarifies the relationship between the simplified reasoning method and SIRMs method, and shows that SIRMs method can be transformed into simplified reasoning method, but not vice versa.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129817489","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295591
R. A. Assi, S. Kaci
Preferences, which control our decisions in the daily life, have been widely studied and analyzed in computer science. In artificial intelligence, preferences are used in many domains such as decision theory, learning, etc. Several representations and reasoning techniques of preferences were proposed. One of these representations is the non-monotonic logic of preferences characterized by the ability to express several interpretations of preferences simultaneously. In relational databases, preferences are used for the personalization of queries to reduce the volume of data presented to the user by offering only the information that interests him. There, preferences are typically specified using binary preference relations among tuples. Binary preference relations are defined by preference formulas which can be embedded into classical relational queries. This paper is intended to discuss the encoding of relational database preference queries in the framework of the non-monotonic logic of preferences. We show that this framework allows the representation of binary preference relations that are asymmetric orders. In addition, it provides several mechanisms to manipulate preference queries efficiently.
{"title":"A Compact Representation of Preference Queries","authors":"R. A. Assi, S. Kaci","doi":"10.1109/FUZZY.2007.4295591","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295591","url":null,"abstract":"Preferences, which control our decisions in the daily life, have been widely studied and analyzed in computer science. In artificial intelligence, preferences are used in many domains such as decision theory, learning, etc. Several representations and reasoning techniques of preferences were proposed. One of these representations is the non-monotonic logic of preferences characterized by the ability to express several interpretations of preferences simultaneously. In relational databases, preferences are used for the personalization of queries to reduce the volume of data presented to the user by offering only the information that interests him. There, preferences are typically specified using binary preference relations among tuples. Binary preference relations are defined by preference formulas which can be embedded into classical relational queries. This paper is intended to discuss the encoding of relational database preference queries in the framework of the non-monotonic logic of preferences. We show that this framework allows the representation of binary preference relations that are asymmetric orders. In addition, it provides several mechanisms to manipulate preference queries efficiently.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131015114","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295593
J. Marín-Blázquez, G. Pérez, M. Pérez
Data-driven construction of fuzzy systems has followed two different approaches. One approach is termed precise (or approximative) fuzzy modelling, that aims at numerical approximation of functions by rules, but that pays little attention to the interpretability of the resulting rule base. On the other side is linguistic (or descriptive) fuzzy modelling, that aims at automatic rule extraction but that uses fixed human provided and linguistically labelled fuzzy sets. This work follows the linguistic fuzzy modelling approach. It uses an extended Classifier System (XCS) as mechanism to extract linguistic fuzzy rules. XCS is one of the most successful accuracy-based learning classifier systems. It provides several mechanisms for rule generalization and also allows for online training if necessary. It can be used in sequential and non-sequential tasks. Although originally applied in discrete domains it has been extended to continuous and fuzzy environments. The proposed Linguistic Fuzzy XCS has been applied to several well-known classification problems and the results compared with both, precise and linguistic fuzzy models.
{"title":"A Linguistic Fuzzy-XCS classifier system","authors":"J. Marín-Blázquez, G. Pérez, M. Pérez","doi":"10.1109/FUZZY.2007.4295593","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295593","url":null,"abstract":"Data-driven construction of fuzzy systems has followed two different approaches. One approach is termed precise (or approximative) fuzzy modelling, that aims at numerical approximation of functions by rules, but that pays little attention to the interpretability of the resulting rule base. On the other side is linguistic (or descriptive) fuzzy modelling, that aims at automatic rule extraction but that uses fixed human provided and linguistically labelled fuzzy sets. This work follows the linguistic fuzzy modelling approach. It uses an extended Classifier System (XCS) as mechanism to extract linguistic fuzzy rules. XCS is one of the most successful accuracy-based learning classifier systems. It provides several mechanisms for rule generalization and also allows for online training if necessary. It can be used in sequential and non-sequential tasks. Although originally applied in discrete domains it has been extended to continuous and fuzzy environments. The proposed Linguistic Fuzzy XCS has been applied to several well-known classification problems and the results compared with both, precise and linguistic fuzzy models.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125011726","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295476
Minyar Sassi Hidri, A. Touzi, Habib Ounelli
The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.
{"title":"Interpreting Fuzzy Clustering Results based on Fuzzy Formal Concept Analysis","authors":"Minyar Sassi Hidri, A. Touzi, Habib Ounelli","doi":"10.1109/FUZZY.2007.4295476","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295476","url":null,"abstract":"The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128490350","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295485
J. M. Alonso, O. Cordón, S. Guillaume, L. Magdalena
This work shows how to achieve a good interpretability-accuracy trade-off through keeping the strong fuzzy partition property along the whole fuzzy modeling process. First, a small compact knowledge base is built. It is highly interpretable and reasonably accurate. Second, an optimization procedure, which only affects the fuzzy partitions defining the system variables, is carried out. It improves the system accuracy while preserving the system interpretability. Two optimization strategies are compared: Solis-Wetts, a local search based strategy; and Genetic Tuning, a global search based strategy. Results obtained in a well-known benchmark medical classification problem, related to breast cancer diagnosis, show that our methodology is able to achieve knowledge bases with high interpretability and accuracy comparable to that obtained by other methodologies.
{"title":"Highly Interpretable Linguistic Knowledge Bases Optimization: Genetic Tuning versus Solis-Wetts. Looking for a good interpretability-accuracy trade-off","authors":"J. M. Alonso, O. Cordón, S. Guillaume, L. Magdalena","doi":"10.1109/FUZZY.2007.4295485","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295485","url":null,"abstract":"This work shows how to achieve a good interpretability-accuracy trade-off through keeping the strong fuzzy partition property along the whole fuzzy modeling process. First, a small compact knowledge base is built. It is highly interpretable and reasonably accurate. Second, an optimization procedure, which only affects the fuzzy partitions defining the system variables, is carried out. It improves the system accuracy while preserving the system interpretability. Two optimization strategies are compared: Solis-Wetts, a local search based strategy; and Genetic Tuning, a global search based strategy. Results obtained in a well-known benchmark medical classification problem, related to breast cancer diagnosis, show that our methodology is able to achieve knowledge bases with high interpretability and accuracy comparable to that obtained by other methodologies.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125765045","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295453
Luciano Stefanini
The generalized differentiability of a fuzzy-number-valued function of a real variable, as recently introduced by Bede and Gal (Fuzzy Sets and Systems, vol. 151, 2005), can be expressed by first defining a generalized Hukuhara difference and using it for the differentiability; to do so, the basic elements are the lower and upper functions which characterize the level-cuts of the fuzzy quantities i.e. functions that are monotonic over [0,1]. Using this fact, we present a (parametric) representation of fuzzy numbers and its application to the solution of fuzzy differential (initial value) equations (FDE). The representation uses a finite decomposition of the membership interval [0,1] and models the level-cuts of fuzzy numbers and fuzzy functions to obtain the formulation of a fuzzy differential equation y'=f(x,y) in terms of a set of ordinary (non fuzzy) differential equations, defined by the lower and upper components of the fuzzy-valued function f(x,y). From a computational view, the resulting ODE's can be analyzed and solved by standard methods of numerical analysis.
Bede和Gal (Fuzzy Sets and Systems, vol. 151, 2005)最近引入了实变量的模糊数值函数的广义可微性，可以通过首先定义广义Hukuhara差分并将其用于可微性来表示;要做到这一点，基本元素是表征模糊量的水平切割的下函数和上函数，即在[0,1]上单调的函数。利用这一事实，我们给出了模糊数的参数表示及其在模糊微分初值方程(FDE)求解中的应用。该表示使用隶属度区间[0,1]的有限分解，并对模糊数和模糊函数的水平切进行建模，得到由模糊值函数f(x,y)的上下分量定义的一组普通(非模糊)微分方程的模糊微分方程y'=f(x,y)的表达式。从计算的角度来看，所得的ODE可以用数值分析的标准方法进行分析和求解。
{"title":"On the generalized LU-fuzzy derivative and fuzzy differential equations","authors":"Luciano Stefanini","doi":"10.1109/FUZZY.2007.4295453","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295453","url":null,"abstract":"The generalized differentiability of a fuzzy-number-valued function of a real variable, as recently introduced by Bede and Gal (Fuzzy Sets and Systems, vol. 151, 2005), can be expressed by first defining a generalized Hukuhara difference and using it for the differentiability; to do so, the basic elements are the lower and upper functions which characterize the level-cuts of the fuzzy quantities i.e. functions that are monotonic over [0,1]. Using this fact, we present a (parametric) representation of fuzzy numbers and its application to the solution of fuzzy differential (initial value) equations (FDE). The representation uses a finite decomposition of the membership interval [0,1] and models the level-cuts of fuzzy numbers and fuzzy functions to obtain the formulation of a fuzzy differential equation y'=f(x,y) in terms of a set of ordinary (non fuzzy) differential equations, defined by the lower and upper components of the fuzzy-valued function f(x,y). From a computational view, the resulting ODE's can be analyzed and solved by standard methods of numerical analysis.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176448","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295362
A. Chatterjee, M. Dutta, A. Rakshit
A sophisticated impedance measurement technique, using an automatic digital ac bridge, is developed which is capable of providing fast and accurate real life measurement. The measurement technique employs LMS algorithm to achieve fast balance in real time. The present paper proposes to employ an intelligent neuro-fuzzy based accuracy improvement module for the LMS bridge. The objective of the neuro-fuzzy system is to add a synthetic phase offset to improve accuracy of the phase measurement in real life. The neuro-fuzzy system is successfully trained by employing particle swarm optimization (PSO), a relatively new combinatorial metaheuristic technique. The success of the proposed technique is effectively demonstrated by employing the bridge in real life for a variety of unknown impedances under measurement.
{"title":"An Intelligent Method of Impedance Measurement Employing PSO-Aided Neuro-Fuzzy System with LMS Algorithm","authors":"A. Chatterjee, M. Dutta, A. Rakshit","doi":"10.1109/FUZZY.2007.4295362","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295362","url":null,"abstract":"A sophisticated impedance measurement technique, using an automatic digital ac bridge, is developed which is capable of providing fast and accurate real life measurement. The measurement technique employs LMS algorithm to achieve fast balance in real time. The present paper proposes to employ an intelligent neuro-fuzzy based accuracy improvement module for the LMS bridge. The objective of the neuro-fuzzy system is to add a synthetic phase offset to improve accuracy of the phase measurement in real life. The neuro-fuzzy system is successfully trained by employing particle swarm optimization (PSO), a relatively new combinatorial metaheuristic technique. The success of the proposed technique is effectively demonstrated by employing the bridge in real life for a variety of unknown impedances under measurement.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126244205","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295373
Jérica Urías, P. Melin, O. Castillo
We describe in this paper a new method for response integration in modular neural networks using type-2 fuzzy logic. The modular neural networks were used in human person recognition. Biometric authentication is used to achieve person recognition. Three biometric characteristics of the person are used: face, fingerprint, and voice. A modular neural network of three modules is used. Each module is a local expert on person recognition based on each of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper the results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.
{"title":"A Method for Response Integration in Modular Neural Networks using Interval Type-2 Fuzzy Logic","authors":"Jérica Urías, P. Melin, O. Castillo","doi":"10.1109/FUZZY.2007.4295373","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295373","url":null,"abstract":"We describe in this paper a new method for response integration in modular neural networks using type-2 fuzzy logic. The modular neural networks were used in human person recognition. Biometric authentication is used to achieve person recognition. Three biometric characteristics of the person are used: face, fingerprint, and voice. A modular neural network of three modules is used. Each module is a local expert on person recognition based on each of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper the results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127408755","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295332
P. Mastorocostas
A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled Dynamic Block-Diagonal Fuzzy Neural Network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark problem, where a dynamic system is to be identified. A comparative analysis with a series of recurrent fuzzy and neural models is conducted, highlighting the modeling characteristics of DBD-FNN.
{"title":"A Block-Diagonal Recurrent Fuzzy Neural Network for Dynamic System Identification","authors":"P. Mastorocostas","doi":"10.1109/FUZZY.2007.4295332","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295332","url":null,"abstract":"A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled Dynamic Block-Diagonal Fuzzy Neural Network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark problem, where a dynamic system is to be identified. A comparative analysis with a series of recurrent fuzzy and neural models is conducted, highlighting the modeling characteristics of DBD-FNN.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114453506","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295513
C. Fayad, J. Garibaldi, D. Ouelhadj
This paper considers the problem of grid scheduling in which different jobs are assigned to different processors, and a scheduling algorithm is devised, using tabu search, to find optimal solutions in order to maximize the number of scheduled jobs. However, inherent in the nature of the application, the processing times of jobs are not precise but are estimates that vary between minimal values, in case of premature failure of jobs, to maximal values as specified 'a priori' by well-experienced users. Fuzzy methodology becomes instrumental in this application as it allows the use of fuzzy sets to represent the processing times of jobs, modelling their uncertainty. This work presents the implementation of a tabu search algorithm to create good schedules and explores the robustness of the schedule when processing times do vary by assessing its performance in both fuzzy and crisp modes. Finally, the impact of changing the shapes of fuzzy completion times and the average job length on the schedule performance is discussed.
{"title":"Fuzzy Grid Scheduling Using Tabu Search","authors":"C. Fayad, J. Garibaldi, D. Ouelhadj","doi":"10.1109/FUZZY.2007.4295513","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295513","url":null,"abstract":"This paper considers the problem of grid scheduling in which different jobs are assigned to different processors, and a scheduling algorithm is devised, using tabu search, to find optimal solutions in order to maximize the number of scheduled jobs. However, inherent in the nature of the application, the processing times of jobs are not precise but are estimates that vary between minimal values, in case of premature failure of jobs, to maximal values as specified 'a priori' by well-experienced users. Fuzzy methodology becomes instrumental in this application as it allows the use of fuzzy sets to represent the processing times of jobs, modelling their uncertainty. This work presents the implementation of a tabu search algorithm to create good schedules and explores the robustness of the schedule when processing times do vary by assessing its performance in both fuzzy and crisp modes. Finally, the impact of changing the shapes of fuzzy completion times and the average job length on the schedule performance is discussed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122302904","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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