Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295545
P. J. Escamilla-Ambrosio, N. Lieven
The Autotaxi system is a safety critical sensor system developed to perform the sensing required for an autonomous vehicle to drive safely along a dedicated paved guideway network. The host vehicle is equipped with a set of sensors used to detect and track any object of interest in the field of view. In this work a multiple-sensor obstacle identification and fusion approach for the Autotaxi system is proposed. Based on the knowledge about the vehicles, the obstacles to be detected, and the guideway network system, two obstacle classifier systems are designed using the principles of fuzzy logic. In Classifier 1 the classification process is carried out based on the obstacle's width and kind of road in which the host vehicle is navigating. In Classifier 2 the classification process is carried out based on the obstacle's width and height together with the kind of road in which the host vehicle is navigating. Furthermore, as different declarations of identity can be performed by using information from different sensors, a method to fuse these identity declarations is proposed. The viability of the proposed approach is demonstrated through a simulated example. Promising results are reported.
{"title":"Fuzzy Logic Obstacle Identity Declaration and Fusion in the Autotaxi System","authors":"P. J. Escamilla-Ambrosio, N. Lieven","doi":"10.1109/FUZZY.2007.4295545","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295545","url":null,"abstract":"The Autotaxi system is a safety critical sensor system developed to perform the sensing required for an autonomous vehicle to drive safely along a dedicated paved guideway network. The host vehicle is equipped with a set of sensors used to detect and track any object of interest in the field of view. In this work a multiple-sensor obstacle identification and fusion approach for the Autotaxi system is proposed. Based on the knowledge about the vehicles, the obstacles to be detected, and the guideway network system, two obstacle classifier systems are designed using the principles of fuzzy logic. In Classifier 1 the classification process is carried out based on the obstacle's width and kind of road in which the host vehicle is navigating. In Classifier 2 the classification process is carried out based on the obstacle's width and height together with the kind of road in which the host vehicle is navigating. Furthermore, as different declarations of identity can be performed by using information from different sensors, a method to fuse these identity declarations is proposed. The viability of the proposed approach is demonstrated through a simulated example. Promising results are reported.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"31 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":"114749212","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.2007.4295625
H. Frigui, R. Mahdi
We propose a Semi-Supervised Clustering and Attribute Discrimination (S-SCAD) algorithm that performs fuzzy clustering and coarse feature weighting simultaneously. The supervision information in S-SCAD consists of a small set of constraints on which instances should or should not reside in the same cluster. The feature set is divided into logical subsets of features, and a degree of relevance is dynamically assigned to each subset based on its partial degree of dissimilarity. These weights have two advantages. First, they help in partitioning the data set into more meaningful clusters. Second, they can be used as part of a more complex learning system to enhance its learning behavior. We show that the partial supervision can guide the algorithm in learning the prototype parameters and the feature relevance weights, and thus, improve the final partition. The performance of the proposed algorithm is illustrated by using it to categorize a collection of color images. We use four feature subsets that encode color, structure, and texture information. The results are compared to other similar algorithms.
{"title":"Semi-Supervised Clustering and Feature Discrimination with Instance-Level Constraints","authors":"H. Frigui, R. Mahdi","doi":"10.1109/FUZZY.2007.4295625","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295625","url":null,"abstract":"We propose a Semi-Supervised Clustering and Attribute Discrimination (S-SCAD) algorithm that performs fuzzy clustering and coarse feature weighting simultaneously. The supervision information in S-SCAD consists of a small set of constraints on which instances should or should not reside in the same cluster. The feature set is divided into logical subsets of features, and a degree of relevance is dynamically assigned to each subset based on its partial degree of dissimilarity. These weights have two advantages. First, they help in partitioning the data set into more meaningful clusters. Second, they can be used as part of a more complex learning system to enhance its learning behavior. We show that the partial supervision can guide the algorithm in learning the prototype parameters and the feature relevance weights, and thus, improve the final partition. The performance of the proposed algorithm is illustrated by using it to categorize a collection of color images. We use four feature subsets that encode color, structure, and texture information. The results are compared to other similar algorithms.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"54 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":"124496676","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.2007.4295434
M. Chiang, Lian-Wang Lee, Hsien-Hsush Liu
The variable displacement hydraulic servo system performs specific characteristics on non-linearity and time-varying. An exact model-based controller is difficult to be realized. In this study, the design method and experimental implementation of an adaptive fuzzy sliding-mode controller (AFSMC) are presented, which has on-line learning ability for dealing with the system time-varying and non-linear uncertainty behaviors for adjusting the control rule parameters. The tuning algorithms are derived in the sense of the Lyapunov stability theorem; thus, the stability of the system can be guaranteed. The experimental results show that the AFSMC can perform excellent position control and path control for the variable displacement hydraulic servo system.
{"title":"Adaptive Fuzzy Sliding-Mode Control for Variable Displacement Hydraulic Servo System","authors":"M. Chiang, Lian-Wang Lee, Hsien-Hsush Liu","doi":"10.1109/FUZZY.2007.4295434","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295434","url":null,"abstract":"The variable displacement hydraulic servo system performs specific characteristics on non-linearity and time-varying. An exact model-based controller is difficult to be realized. In this study, the design method and experimental implementation of an adaptive fuzzy sliding-mode controller (AFSMC) are presented, which has on-line learning ability for dealing with the system time-varying and non-linear uncertainty behaviors for adjusting the control rule parameters. The tuning algorithms are derived in the sense of the Lyapunov stability theorem; thus, the stability of the system can be guaranteed. The experimental results show that the AFSMC can perform excellent position control and path control for the variable displacement hydraulic servo system.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"72 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":"126506752","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.2007.4295477
Shang-Ming Zhou, R. John, F. Chiclana, J. Garibaldi
In this paper, we propose two novel indices for type-2 fuzzy rule ranking to identify the most influential fuzzy rules in designing type-2 fuzzy logic systems, and name them as R-values and c-values of fuzzy rules separately. The R-values of type-2 fuzzy rules are obtained by applying QR decomposition in which there is no need to estimate a rank as required in the SVD-QR with column pivoting algorithm. The c-values of type-2 fuzzy rules are suggested to rank rules based on the effects of rule consequents. Experimental results on a signal recovery problem have shown that by using the proposed indices the most influential type-2 fuzzy rules can be effectively selected to construct parsimonious type-2 fuzzy models while the system performances are kept at a satisfied level.
{"title":"New Type-2 Rule Ranking Indices for Designing Parsimonious Interval Type-2 Fuzzy Logic Systems","authors":"Shang-Ming Zhou, R. John, F. Chiclana, J. Garibaldi","doi":"10.1109/FUZZY.2007.4295477","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295477","url":null,"abstract":"In this paper, we propose two novel indices for type-2 fuzzy rule ranking to identify the most influential fuzzy rules in designing type-2 fuzzy logic systems, and name them as R-values and c-values of fuzzy rules separately. The R-values of type-2 fuzzy rules are obtained by applying QR decomposition in which there is no need to estimate a rank as required in the SVD-QR with column pivoting algorithm. The c-values of type-2 fuzzy rules are suggested to rank rules based on the effects of rule consequents. Experimental results on a signal recovery problem have shown that by using the proposed indices the most influential type-2 fuzzy rules can be effectively selected to construct parsimonious type-2 fuzzy models while the system performances are kept at a satisfied level.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"31 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":"125668056","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.2007.4295475
T. Entani, Hideo Tanaka
Interval probabilities have been proposed as one of non-additive measures. The frame of interval probabilities is similar to evidence theory proposed by Dempster and Shafer and they can be regarded as evidences on a finite set. The interval probability is suitable to represent ignorance on the given phenomenon so that it can be used as a kind of subjective probability. We show how to obtain the evidence by a pairwise comparison matrix on a finite set. The pariwise comparisons are usually inconsistent each other since they are given based on human judgements. The interval probabilities from them are determined so as to include such inconsistency. In case of two evidences whose prior and conditional probabilities are obtained as intervals, the marginal and posterior probabilities are also calculated as interval probabilities from the view of possibility. The illustrative numerical example is given in this paper.
{"title":"Management of Ignorance by Interval Probability","authors":"T. Entani, Hideo Tanaka","doi":"10.1109/FUZZY.2007.4295475","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295475","url":null,"abstract":"Interval probabilities have been proposed as one of non-additive measures. The frame of interval probabilities is similar to evidence theory proposed by Dempster and Shafer and they can be regarded as evidences on a finite set. The interval probability is suitable to represent ignorance on the given phenomenon so that it can be used as a kind of subjective probability. We show how to obtain the evidence by a pairwise comparison matrix on a finite set. The pariwise comparisons are usually inconsistent each other since they are given based on human judgements. The interval probabilities from them are determined so as to include such inconsistency. In case of two evidences whose prior and conditional probabilities are obtained as intervals, the marginal and posterior probabilities are also calculated as interval probabilities from the view of possibility. The illustrative numerical example is given in this paper.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"94 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":"132126642","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.2007.4295566
R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera
Recently, multi-objective evolutionary algorithms have been also applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is know that both requirements are usually contradictory, however, a multi-objective genetic algorithm can obtain a set of solutions with different degrees of trade-off. This contribution presents a multi-objective evolutionary algorithm to obtain linguistic models with improved accuracy and the least number of possible rules. In order to minimize the number of rules and the system error, this model performs a rule selection and a tuning of the membership functions of an initial set of candidate linguistic fuzzy rules.
{"title":"A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems","authors":"R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera","doi":"10.1109/FUZZY.2007.4295566","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295566","url":null,"abstract":"Recently, multi-objective evolutionary algorithms have been also applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is know that both requirements are usually contradictory, however, a multi-objective genetic algorithm can obtain a set of solutions with different degrees of trade-off. This contribution presents a multi-objective evolutionary algorithm to obtain linguistic models with improved accuracy and the least number of possible rules. In order to minimize the number of rules and the system error, this model performs a rule selection and a tuning of the membership functions of an initial set of candidate linguistic fuzzy rules.","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":"130089120","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.2007.4295632
T. Nakashima, Y. Yokota, G. Schaefer, H. Ishibuchi
In this paper we propose a fuzzy rule-generation method for pattern classification problems with classification priority. The assumption in this paper is that a classification priority is given a priori in relation to other classes. Our fuzzy rule-based classification system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patterns. The proposed method decides the consequent class of fuzzy if-then rules based on the number of covered training patterns for each class. In computational experiments we first show the effect of introducing classification priority for a synthetic two-dimensional problem. Then we show the effectiveness of the proposed method for several real-world pattern classification problems.
{"title":"Introducing Class-Based Classification Priority in Fuzzy Rule-Based Classification Systems","authors":"T. Nakashima, Y. Yokota, G. Schaefer, H. Ishibuchi","doi":"10.1109/FUZZY.2007.4295632","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295632","url":null,"abstract":"In this paper we propose a fuzzy rule-generation method for pattern classification problems with classification priority. The assumption in this paper is that a classification priority is given a priori in relation to other classes. Our fuzzy rule-based classification system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patterns. The proposed method decides the consequent class of fuzzy if-then rules based on the number of covered training patterns for each class. In computational experiments we first show the effect of introducing classification priority for a synthetic two-dimensional problem. Then we show the effectiveness of the proposed method for several real-world pattern classification problems.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"45 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":"130135500","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.2007.4295659
L. Sánchez, J. Otero
Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.
{"title":"Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms","authors":"L. Sánchez, J. Otero","doi":"10.1109/FUZZY.2007.4295659","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295659","url":null,"abstract":"Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"37 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":"134292618","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.2007.4295393
E. Lughofer, P. Angelov, Xiaowei Zhou
In this paper a new method for training single-model and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolving scheme for the single-model case exploits a conventional zero-order fuzzy classification model architecture with Gaussian fuzzy sets in the rules antecedents, crisp class labels in the rule consequents and rule weights standing for confidence values in the class labels. In the multi-model case FLEXFIS-Class exploits the idea of regression by an indicator matrix to evolve a Takagi-Sugeno fuzzy model for each separate class and combines the single models' predictions to a final classification statement. The paper includes a technique for increasing the prediction quality, whenever a drift in a data stream occurs. An empirical analysis will be given based on an online, adaptive image classification framework, where images showing production items should be classified into good or bad ones. This analysis will include the comparison of evolving single-and multi-model fuzzy classifiers with conventional batch modelling approaches with respect to achieved prediction accuracy on new online data. It will also be shown that multi-model architecture can outperform conventional single-model architecture ('classical' fuzzy classification models) for all data sets with respect to prediction accuracy.
{"title":"Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class","authors":"E. Lughofer, P. Angelov, Xiaowei Zhou","doi":"10.1109/FUZZY.2007.4295393","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295393","url":null,"abstract":"In this paper a new method for training single-model and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolving scheme for the single-model case exploits a conventional zero-order fuzzy classification model architecture with Gaussian fuzzy sets in the rules antecedents, crisp class labels in the rule consequents and rule weights standing for confidence values in the class labels. In the multi-model case FLEXFIS-Class exploits the idea of regression by an indicator matrix to evolve a Takagi-Sugeno fuzzy model for each separate class and combines the single models' predictions to a final classification statement. The paper includes a technique for increasing the prediction quality, whenever a drift in a data stream occurs. An empirical analysis will be given based on an online, adaptive image classification framework, where images showing production items should be classified into good or bad ones. This analysis will include the comparison of evolving single-and multi-model fuzzy classifiers with conventional batch modelling approaches with respect to achieved prediction accuracy on new online data. It will also be shown that multi-model architecture can outperform conventional single-model architecture ('classical' fuzzy classification models) for all data sets with respect to prediction accuracy.","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":"130791453","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.2007.4295624
Carlos D. Barranco, Jesús R. Campaña, J. M. Medina, O. Pons
This work deals with the need for managing large amounts of fuzzy data in the context of the Semantic Web. A schema to store ontologies with fuzzy datatypes into a database is presented as part of a framework designed to perform tasks of fuzzy information extraction and publishing. The database schema allows the storage of an ontology along with its instances preserving all information. Ontology and instances are stored in different schemas in order to improve the access to instances while retaining the capacity of reasoning over the ontology. This sets the foundations of a research opportunity on the definition of a ontology reasoner over these structures. The paper also presents a brief description of the framework on which the database is included, and the structures conforming the storage schema proposed.
{"title":"On Storing Ontologies including Fuzzy Datatypes in Relational Databases","authors":"Carlos D. Barranco, Jesús R. Campaña, J. M. Medina, O. Pons","doi":"10.1109/FUZZY.2007.4295624","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295624","url":null,"abstract":"This work deals with the need for managing large amounts of fuzzy data in the context of the Semantic Web. A schema to store ontologies with fuzzy datatypes into a database is presented as part of a framework designed to perform tasks of fuzzy information extraction and publishing. The database schema allows the storage of an ontology along with its instances preserving all information. Ontology and instances are stored in different schemas in order to improve the access to instances while retaining the capacity of reasoning over the ontology. This sets the foundations of a research opportunity on the definition of a ontology reasoner over these structures. The paper also presents a brief description of the framework on which the database is included, and the structures conforming the storage schema proposed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"63 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":"130999322","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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