Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226819
Jianzhong Zhang, D. Berleant
A cumulative distribution function (CDF) states the probability that a sample of a random variable will be no greater than a value x, where x is a real value. Closed form expressions for important CDFs have parameters, such as mean and variance. If these parameters are not point values but rather intervals, sharp or fuzzy, then a single CDF is not specified. Instead, a family of CDFs is specified. Sharp intervals lead to sharp boundaries ("envelopes") around the family, while fuzzy intervals lead to fuzzy boundaries. Algorithms exist that compute the family of CDFs possible for some function g(v) where v is a vector of distributions or bounded families of distribution. We investigate the bounds on families of CDFs implied by interval values for their parameters. These bounds can then be used as inputs to algorithms that manipulate distributions and bounded spaces defining families of distributions (sometimes called probability boxes or p-boxes). For example, problems defining inputs this way may be found in. In this paper, we present the bounds for the families of a few common CDFs when parameters to those CDFs are intervals.
{"title":"Envelopes around cumulative distribution functions from interval parameters of standard continuous distributions","authors":"Jianzhong Zhang, D. Berleant","doi":"10.1109/NAFIPS.2003.1226819","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226819","url":null,"abstract":"A cumulative distribution function (CDF) states the probability that a sample of a random variable will be no greater than a value x, where x is a real value. Closed form expressions for important CDFs have parameters, such as mean and variance. If these parameters are not point values but rather intervals, sharp or fuzzy, then a single CDF is not specified. Instead, a family of CDFs is specified. Sharp intervals lead to sharp boundaries (\"envelopes\") around the family, while fuzzy intervals lead to fuzzy boundaries. Algorithms exist that compute the family of CDFs possible for some function g(v) where v is a vector of distributions or bounded families of distribution. We investigate the bounds on families of CDFs implied by interval values for their parameters. These bounds can then be used as inputs to algorithms that manipulate distributions and bounded spaces defining families of distributions (sometimes called probability boxes or p-boxes). For example, problems defining inputs this way may be found in. In this paper, we present the bounds for the families of a few common CDFs when parameters to those CDFs are intervals.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076413","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226834
P. Bonissone
We describe the life cycle of a fuzzy knowledge-based classifier with special emphasis on one of its most neglected steps: the maintenance of its knowledge base. First, we analyze the process of underwriting Insurance applications, which is the classification problem used to illustrate the life cycle of a classifier. After discussing some design tradeoffs that must be addressed for the on-line and off-line use of a classifier, we describe the design and implementation of a fuzzy rule-based (FRB) and a fuzzy case-based (FCB) classifier. We establish a standard reference dataset (SRD), consisting of 3,000 insurance applications with their corresponding decisions. The SRD exemplifies the results achieved by an ideal, optimal classifier, and represents the target for our design. We apply evolutionary algorithms to perform an off-line optimization of the design parameters of each classifier, modifying their behavior to approximate this target. The SRD is also used as a reference for testing and performing a five-fold cross-validation of the classifiers. Finally, we focus on the monitoring and maintenance of the FRB classifier. We describe a fusion architecture that supports an off-line quality assurance process of the on-line FRB classifier. The fusion module takes the outputs of multiple classifiers, determines their degree of consensus, and compares their overall agreement with that of the FRB classifier. From this analysis, we can identify the most suitable cases to update the SRD, to audit, or to be reviewed by senior underwriters.
{"title":"The life cycle of a fuzzy knowledge-based classifier","authors":"P. Bonissone","doi":"10.1109/NAFIPS.2003.1226834","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226834","url":null,"abstract":"We describe the life cycle of a fuzzy knowledge-based classifier with special emphasis on one of its most neglected steps: the maintenance of its knowledge base. First, we analyze the process of underwriting Insurance applications, which is the classification problem used to illustrate the life cycle of a classifier. After discussing some design tradeoffs that must be addressed for the on-line and off-line use of a classifier, we describe the design and implementation of a fuzzy rule-based (FRB) and a fuzzy case-based (FCB) classifier. We establish a standard reference dataset (SRD), consisting of 3,000 insurance applications with their corresponding decisions. The SRD exemplifies the results achieved by an ideal, optimal classifier, and represents the target for our design. We apply evolutionary algorithms to perform an off-line optimization of the design parameters of each classifier, modifying their behavior to approximate this target. The SRD is also used as a reference for testing and performing a five-fold cross-validation of the classifiers. Finally, we focus on the monitoring and maintenance of the FRB classifier. We describe a fusion architecture that supports an off-line quality assurance process of the on-line FRB classifier. The fusion module takes the outputs of multiple classifiers, determines their degree of consensus, and compares their overall agreement with that of the FRB classifier. From this analysis, we can identify the most suitable cases to update the SRD, to audit, or to be reviewed by senior underwriters.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123775336","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226759
H. Uesu, E. Tsuda, H. Yamashita
We often represent the inexact phenomena regarding mental process and cognition as fuzzy graphs. If we investigate the cluster and the order of the nodes in the fuzzy graph, we have a lot of interesting results. For this purpose we define the similarity Index and the connectivity Index. In this paper, we would discuss the definition of the indices and its properties.
{"title":"Mathematical analysis of similarity index and connectivity index in fuzzy graph","authors":"H. Uesu, E. Tsuda, H. Yamashita","doi":"10.1109/NAFIPS.2003.1226759","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226759","url":null,"abstract":"We often represent the inexact phenomena regarding mental process and cognition as fuzzy graphs. If we investigate the cluster and the order of the nodes in the fuzzy graph, we have a lot of interesting results. For this purpose we define the similarity Index and the connectivity Index. In this paper, we would discuss the definition of the indices and its properties.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123450046","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226822
P. Melin, E. Zamudio, O. Castillo
In this paper is proposed a proposed control system for optimising the transmission power in cellular phones. As the mobile station moves toward the edge of a cell, the cell's base station notes that the signal strength is diminishing. Meanwhile, the base station in the cell the mobile station is moving toward (which is listening and measuring signal strength on all frequencies, not just its own one) sees the phone's signal strength increasing. The two base stations coordinate with each other, and at some point, the phone gets a signal on a control channel telling it to change frequencies. This hand off switches the phone to a different cell, which receives the signal with a bigger intensity, so the next decrement of the transmission power will be the lower possible without risking the quality of the transmission. Nowadays the central cellular controls the transmission power on the mobile station, on intervals of 4 dbs to increase or decrease it, so the final power always is above or under the required power.
{"title":"Intelligent control of the transmission power in cellular phones using fuzzy logic","authors":"P. Melin, E. Zamudio, O. Castillo","doi":"10.1109/NAFIPS.2003.1226822","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226822","url":null,"abstract":"In this paper is proposed a proposed control system for optimising the transmission power in cellular phones. As the mobile station moves toward the edge of a cell, the cell's base station notes that the signal strength is diminishing. Meanwhile, the base station in the cell the mobile station is moving toward (which is listening and measuring signal strength on all frequencies, not just its own one) sees the phone's signal strength increasing. The two base stations coordinate with each other, and at some point, the phone gets a signal on a control channel telling it to change frequencies. This hand off switches the phone to a different cell, which receives the signal with a bigger intensity, so the next decrement of the transmission power will be the lower possible without risking the quality of the transmission. Nowadays the central cellular controls the transmission power on the mobile station, on intervals of 4 dbs to increase or decrease it, so the final power always is above or under the required power.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978511","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226812
C. Campos, G. R. Keller, V. Kreinovich, L. Longpré, François Modave, S. Starks, R. Torres
Geospatial databases generally consist of measurements related to points (or pixels in the case of raster data), lines, and polygons. In recent years, the size and complexity of these databases have increased significantly and they often contain duplicate records, i.e., two or more close records representing the same measurement result. In this paper, we use fuzzy measures to address the problem of detecting duplicates in a database consisting of point measurements. As a test case, we use a database of measurements of anomalies in the Earth's gravity field that we have compiled. We show that a natural duplicate deletion algorithm requires (in the worst case) quadratic time, and we propose a new asymptotically optimal O(n/spl middot/log(n)) algorithm. These algorithms have been successfully applied to gravity databases. We believe that they will prove to be useful when dealing with many other types of point data.
{"title":"The use of fuzzy measures as a data fusion tool in geographic information systems: case study","authors":"C. Campos, G. R. Keller, V. Kreinovich, L. Longpré, François Modave, S. Starks, R. Torres","doi":"10.1109/NAFIPS.2003.1226812","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226812","url":null,"abstract":"Geospatial databases generally consist of measurements related to points (or pixels in the case of raster data), lines, and polygons. In recent years, the size and complexity of these databases have increased significantly and they often contain duplicate records, i.e., two or more close records representing the same measurement result. In this paper, we use fuzzy measures to address the problem of detecting duplicates in a database consisting of point measurements. As a test case, we use a database of measurements of anomalies in the Earth's gravity field that we have compiled. We show that a natural duplicate deletion algorithm requires (in the worst case) quadratic time, and we propose a new asymptotically optimal O(n/spl middot/log(n)) algorithm. These algorithms have been successfully applied to gravity databases. We believe that they will prove to be useful when dealing with many other types of point data.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656748","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226823
K. Balasubramanian, K. Rattan
A linearizing control scheme for a highly nonlinear pneumatic muscle (PM) system is presented in this paper. Linearizing controllers have been widely used in the control of robotic systems. Since PM is a highly nonlinear system, the concept of linearizing control can be extended to the control of these muscles. Pneumatic muscle has air pressure as its input and the output is a displacement of the muscle. The system is considered to be a mass-spring-damper system with a nonlinear damper and a spring. This nonlinearity makes the design of a mathematical controller more difficult. The scheme presented in this paper uses fuzzy logic to implement the controller. The controller has a model-based portion and a servo-based portion. The model-based portion cancels all the nonlinearities caused by the nonlinear damper and spring. Therefore, the system as seen by the servo-based portion is linear, which can then be controlled using a linear PID controller. The controller is conceptually simple but exhibited superior tracking capability.
{"title":"Fuzzy logic control of a pneumatic muscle system using a linearing control scheme","authors":"K. Balasubramanian, K. Rattan","doi":"10.1109/NAFIPS.2003.1226823","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226823","url":null,"abstract":"A linearizing control scheme for a highly nonlinear pneumatic muscle (PM) system is presented in this paper. Linearizing controllers have been widely used in the control of robotic systems. Since PM is a highly nonlinear system, the concept of linearizing control can be extended to the control of these muscles. Pneumatic muscle has air pressure as its input and the output is a displacement of the muscle. The system is considered to be a mass-spring-damper system with a nonlinear damper and a spring. This nonlinearity makes the design of a mathematical controller more difficult. The scheme presented in this paper uses fuzzy logic to implement the controller. The controller has a model-based portion and a servo-based portion. The model-based portion cancels all the nonlinearities caused by the nonlinear damper and spring. Therefore, the system as seen by the servo-based portion is linear, which can then be controlled using a linear PID controller. The controller is conceptually simple but exhibited superior tracking capability.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129589947","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226767
S. Lancaster, M. J. Wierman
The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge. Conventional FLCs use Center of Gravity or Mean of Maxima defuzzification methods, though other methods have been studied. This paper compares the efficiency of many different models of the defuzzification process. The goal is to examine the accuracy of the output data and the amount of processing time required. A simple controller that backs a truck up to a gate is used in the study. All of the variables are granulated with trapezoidal fuzzy numbers. Some of the defuzzification methods examined are Fast Center of Gravity, Mean of Maxima, True Center of Gravity and various new methods that have shown promise in application.
{"title":"Empirical study of defuzzification","authors":"S. Lancaster, M. J. Wierman","doi":"10.1109/NAFIPS.2003.1226767","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226767","url":null,"abstract":"The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge. Conventional FLCs use Center of Gravity or Mean of Maxima defuzzification methods, though other methods have been studied. This paper compares the efficiency of many different models of the defuzzification process. The goal is to examine the accuracy of the output data and the amount of processing time required. A simple controller that backs a truck up to a gate is used in the study. All of the variables are granulated with trapezoidal fuzzy numbers. Some of the defuzzification methods examined are Fast Center of Gravity, Mean of Maxima, True Center of Gravity and various new methods that have shown promise in application.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521385","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226797
Zhou Zhuan, Ding Xiangqian, Liu Wenbin
In this paper, the concepts of fuzzy traffic network, road width and maximum road are introduced. Based on the fuzzy matrix representing traffic network the connection between the maximum road width and the power operation of the fuzzy matrix is discussed. And then the arithmetic solving method and the implemental program for the maximum road are introduced. They have practical value in analyzing traffic network such as regulating the flow of traffic in cities.
{"title":"Fuzzy matrix analysis of the maximum road in traffic network","authors":"Zhou Zhuan, Ding Xiangqian, Liu Wenbin","doi":"10.1109/NAFIPS.2003.1226797","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226797","url":null,"abstract":"In this paper, the concepts of fuzzy traffic network, road width and maximum road are introduced. Based on the fuzzy matrix representing traffic network the connection between the maximum road width and the power operation of the fuzzy matrix is discussed. And then the arithmetic solving method and the implemental program for the maximum road are introduced. They have practical value in analyzing traffic network such as regulating the flow of traffic in cities.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167663","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226781
S. Coppock, L. Mazlack
Similarity is important in knowledge discovery. Cluster analysis, classification, and granulation each involve some notion or definition of similarity. The measurement of similarity is selected based on the domain and distribution of the data. Even within a specific domain, some similarity metrics may be considered more useful than others. There is an amount of uncertainty in quantitatively measuring the similarity between records of mixed data. The uncertainty develops from the lack of scale that both nominal and ordinal data have. Rough set theory is one tool developed for handling uncertainty. Rough sets can be used in dissimilarity analysis of qualitative data. It would seem that rough sets could be applied in measuring similarity between records containing both quantitative and qualitative data for the purpose of clustering the records.
{"title":"Rough sets used in the measurement of similarity of mixed mode data","authors":"S. Coppock, L. Mazlack","doi":"10.1109/NAFIPS.2003.1226781","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226781","url":null,"abstract":"Similarity is important in knowledge discovery. Cluster analysis, classification, and granulation each involve some notion or definition of similarity. The measurement of similarity is selected based on the domain and distribution of the data. Even within a specific domain, some similarity metrics may be considered more useful than others. There is an amount of uncertainty in quantitatively measuring the similarity between records of mixed data. The uncertainty develops from the lack of scale that both nominal and ordinal data have. Rough set theory is one tool developed for handling uncertainty. Rough sets can be used in dissimilarity analysis of qualitative data. It would seem that rough sets could be applied in measuring similarity between records containing both quantitative and qualitative data for the purpose of clustering the records.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994897","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226783
S. Colak, C. Isik
Classification of systolic, mean and diastolic blood pressure profiles using the oscillometric method is a difficult process. Generally, the algorithms aim at extracting some parameters such as height, and ratios of the pulses at certain pressure levels, which are obtained from the cuff pressure. These parameters can be used to form profiles to relate to blood pressures. The effectiveness of the classification depends on many factors, such as environmental noise, white coat effect, heart rate variability and motion artifacts. In this paper, we investigate the effectiveness of a neuro-fuzzy approach to blood pressure classification. We employ feature extraction using principal component analysis, and fuzzy sets to classify pressure profiles.
{"title":"Fuzzy oscillometric blood pressure classification","authors":"S. Colak, C. Isik","doi":"10.1109/NAFIPS.2003.1226783","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226783","url":null,"abstract":"Classification of systolic, mean and diastolic blood pressure profiles using the oscillometric method is a difficult process. Generally, the algorithms aim at extracting some parameters such as height, and ratios of the pulses at certain pressure levels, which are obtained from the cuff pressure. These parameters can be used to form profiles to relate to blood pressures. The effectiveness of the classification depends on many factors, such as environmental noise, white coat effect, heart rate variability and motion artifacts. In this paper, we investigate the effectiveness of a neuro-fuzzy approach to blood pressure classification. We employ feature extraction using principal component analysis, and fuzzy sets to classify pressure profiles.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117306434","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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