Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226821
Fariba Ansari, William Durrer, S. Nazarian, V. Kreinovich
One of the most efficient non-destructive techniques for finding hidden faults in a plate is the use of ultrasonic Lamb waves. These waves are reflected by faults, and from this reflection, we can locate the faults. For that, we need to know how the Lamb waves propagate. Their propagation is determined by the dynamic elastic constants C'/sub pq/, so we must find these constants. These constants cannot be measured directly; instead, we measure the dependence of the speed of frequency c(f), and we must reconstruct C'/sub pq/ from the measured values of c(f). In this paper, we show how this can be done in the presence of probabilistic, interval, and fuzzy uncertainty.
{"title":"Determination of properties of composite materials from the Lamb wave propagation: probabilistic, interval, and fuzzy approaches","authors":"Fariba Ansari, William Durrer, S. Nazarian, V. Kreinovich","doi":"10.1109/NAFIPS.2003.1226821","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226821","url":null,"abstract":"One of the most efficient non-destructive techniques for finding hidden faults in a plate is the use of ultrasonic Lamb waves. These waves are reflected by faults, and from this reflection, we can locate the faults. For that, we need to know how the Lamb waves propagate. Their propagation is determined by the dynamic elastic constants C'/sub pq/, so we must find these constants. These constants cannot be measured directly; instead, we measure the dependence of the speed of frequency c(f), and we must reconstruct C'/sub pq/ from the measured values of c(f). In this paper, we show how this can be done in the presence of probabilistic, interval, and fuzzy uncertainty.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"12 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":"132410275","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.2003.1226790
J. Paetz
In a classical arrangement we have on the one hand discrete symbolic and on the other hand continous numerical data attributes. The following evident questions arise: How can fuzzy sets be integrated in this classical schema? Are fuzzy sets discrete and/or continous? Can we measure how discrete, or continous, respectively, an attribute is? We will present the idea that fuzzy sets are continous and discrete sets with a certain degree by using a visualization technique. We measure continuity of a fuzzy set M by an area q(M)/spl isin/[0,1], that will be defined. If q(M)=0, then M is discrete. If q(M)=1, then it is continous. If q(M) is in (0,1), then M is defined as fuzzy-continous. Thus, a non-degenerated fuzzy set is a fuzzy-continous set. The value q(M) is a natural measure for fuzzy-continuity and 1-q(M) for fuzzy discreteness. Additionally to our theoretical consideration we will give some visualized examples.
{"title":"Fuzzy sets are fuzzy-continous","authors":"J. Paetz","doi":"10.1109/NAFIPS.2003.1226790","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226790","url":null,"abstract":"In a classical arrangement we have on the one hand discrete symbolic and on the other hand continous numerical data attributes. The following evident questions arise: How can fuzzy sets be integrated in this classical schema? Are fuzzy sets discrete and/or continous? Can we measure how discrete, or continous, respectively, an attribute is? We will present the idea that fuzzy sets are continous and discrete sets with a certain degree by using a visualization technique. We measure continuity of a fuzzy set M by an area q(M)/spl isin/[0,1], that will be defined. If q(M)=0, then M is discrete. If q(M)=1, then it is continous. If q(M) is in (0,1), then M is defined as fuzzy-continous. Thus, a non-degenerated fuzzy set is a fuzzy-continous set. The value q(M) is a natural measure for fuzzy-continuity and 1-q(M) for fuzzy discreteness. Additionally to our theoretical consideration we will give some visualized examples.","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":"132440126","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.2003.1226747
L. Meyyappan, M. José, C. Dagli, P. Silva, H. Pottinger
Many civil and mechanical systems are in continuous use despite aging and associated potential risk for damage accumulation. Hence, the ability to monitor the structural health of these systems on a real-time basis is becoming very important. This paper describes a practical real-time structural health monitoring system using soft computing tools and its application to the structural health monitoring of a steel bridge located in Missouri. Vibration data collected from this bridge was processed and fed to the fuzzy logic decision system. The fuzzy logic decision system makes use of fuzzy clustering to determine the possible presence of damage in the bridge. A neural network prediction system which makes use of backpropagation algorithm predicts the amount of actual damage in the members which were predicted damaged by the fuzzy logic.
{"title":"Fuzzy-neuro system for bridge health monitoring","authors":"L. Meyyappan, M. José, C. Dagli, P. Silva, H. Pottinger","doi":"10.1109/NAFIPS.2003.1226747","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226747","url":null,"abstract":"Many civil and mechanical systems are in continuous use despite aging and associated potential risk for damage accumulation. Hence, the ability to monitor the structural health of these systems on a real-time basis is becoming very important. This paper describes a practical real-time structural health monitoring system using soft computing tools and its application to the structural health monitoring of a steel bridge located in Missouri. Vibration data collected from this bridge was processed and fed to the fuzzy logic decision system. The fuzzy logic decision system makes use of fuzzy clustering to determine the possible presence of damage in the bridge. A neural network prediction system which makes use of backpropagation algorithm predicts the amount of actual damage in the members which were predicted damaged by the fuzzy logic.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"157 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":"115593260","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.2003.1226802
Yongyi Chen, Hanzhong Feng
Fuzzy systems modeling has been an active research topic for almost twenty years. In general, two approaches have been proposed in the literature: 1) translate experts' prior knowledge into fuzzy rules; 2) learn a set of fuzzy rules from given training data. The first approach applies to the case that prior knowledge can be easily obtained and training data are not sufficient. However, in many applications, the amount of training data is usually large. In that case, the second approach may provide more objective rules than the first approach. In this paper, we show that a class of fuzzy systems is in essence kernel machines. Therefore, the support vector machine (SVM) method can be used to construct fuzzy systems. This method has been tested on real weather forecast data. Experimental results demonstrate the effectiveness of the method.
{"title":"A kernel method for fuzzy systems modeling and approximate reasoning","authors":"Yongyi Chen, Hanzhong Feng","doi":"10.1109/NAFIPS.2003.1226802","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226802","url":null,"abstract":"Fuzzy systems modeling has been an active research topic for almost twenty years. In general, two approaches have been proposed in the literature: 1) translate experts' prior knowledge into fuzzy rules; 2) learn a set of fuzzy rules from given training data. The first approach applies to the case that prior knowledge can be easily obtained and training data are not sufficient. However, in many applications, the amount of training data is usually large. In that case, the second approach may provide more objective rules than the first approach. In this paper, we show that a class of fuzzy systems is in essence kernel machines. Therefore, the support vector machine (SVM) method can be used to construct fuzzy systems. This method has been tested on real weather forecast data. Experimental results demonstrate the effectiveness of the method.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"24 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":"122979136","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.2003.1226752
P. Melin, O. Castillo
We describe in this paper the application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, we developed an intelligent system for automated quality control in sound speaker manufacturing. The intelligent system has a type-2 fuzzy rule base containing the knowledge of human experts in quality control. The parameters of the fuzzy system are tuned by applying neural networks using, as training data, a real time series of measured sounds as given by good sound speakers. We also use the fractal dimension as a measure of the complexity of the sound signal.
{"title":"A new approach for quality control of sound speakers combining type-2 fuzzy logic and the fractal dimension","authors":"P. Melin, O. Castillo","doi":"10.1109/NAFIPS.2003.1226752","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226752","url":null,"abstract":"We describe in this paper the application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, we developed an intelligent system for automated quality control in sound speaker manufacturing. The intelligent system has a type-2 fuzzy rule base containing the knowledge of human experts in quality control. The parameters of the fuzzy system are tuned by applying neural networks using, as training data, a real time series of measured sounds as given by good sound speakers. We also use the fractal dimension as a measure of the complexity of the sound signal.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"42 2 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":"124939377","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.2003.1226787
P. M. Kanade, Lawrence O. Hall
We present a swarm intelligence approach to data clustering. Data is clustered without initial knowledge of the number of clusters. Ant based clustering is used to initially create raw clusters and then these clusters are refined using the Fuzzy C Means algorithm. Initially the ants move the individual objects to form heaps. The centroids of these heaps are taken as the initial cluster centers and the Fuzzy C Means algorithm is used to refine these clusters. In the second stage the objects obtained from the Fuzzy C Means algorithm are hardened according to the maximum membership criteria to form new heaps. These new heaps are then sometimes moved and merged by the ants. The final clusters formed are refined by using the Fuzzy C Means algorithm. Results from three small data sets show that the partitions produced are competitive with those obtained from FCM.
{"title":"Fuzzy ants as a clustering concept","authors":"P. M. Kanade, Lawrence O. Hall","doi":"10.1109/NAFIPS.2003.1226787","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226787","url":null,"abstract":"We present a swarm intelligence approach to data clustering. Data is clustered without initial knowledge of the number of clusters. Ant based clustering is used to initially create raw clusters and then these clusters are refined using the Fuzzy C Means algorithm. Initially the ants move the individual objects to form heaps. The centroids of these heaps are taken as the initial cluster centers and the Fuzzy C Means algorithm is used to refine these clusters. In the second stage the objects obtained from the Fuzzy C Means algorithm are hardened according to the maximum membership criteria to form new heaps. These new heaps are then sometimes moved and merged by the ants. The final clusters formed are refined by using the Fuzzy C Means algorithm. Results from three small data sets show that the partitions produced are competitive with those obtained from FCM.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"136 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":"127568578","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.2003.1226828
B. Cao
On the introduction of the definition and properties of the variables, T-fuzzy variables are drawn into a geometric programming model before a multi-objective geometric programming with the variables is built. The programming is determined on the condition that the variables are handled in a non-fuzzification way. Besides the programming is changed into an ordinary geometric programming dependent on the cone index J before a dual form is acquired corresponding to the primal posynomial geometric programming with T-fuzzy variables. Therefore lots of results concerning geometric programming can be completely transplanted. Based on this, the author first discuses a dual problem. Then he elicits the relation between the primal posynomial geometric programming with T-fuzzy variables and its dual form. Third he develops primal and dual algorithms to the programming. And final he verifies the model and algorithms through numerical examples.
{"title":"Multi-objective geometric programming with T-fuzzy variables","authors":"B. Cao","doi":"10.1109/NAFIPS.2003.1226828","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226828","url":null,"abstract":"On the introduction of the definition and properties of the variables, T-fuzzy variables are drawn into a geometric programming model before a multi-objective geometric programming with the variables is built. The programming is determined on the condition that the variables are handled in a non-fuzzification way. Besides the programming is changed into an ordinary geometric programming dependent on the cone index J before a dual form is acquired corresponding to the primal posynomial geometric programming with T-fuzzy variables. Therefore lots of results concerning geometric programming can be completely transplanted. Based on this, the author first discuses a dual problem. Then he elicits the relation between the primal posynomial geometric programming with T-fuzzy variables and its dual form. Third he develops primal and dual algorithms to the programming. And final he verifies the model and algorithms through numerical examples.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"81 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":"125752926","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.2003.1226793
S. Hirano, S. Tsumoto
This paper presents a new clustering method based on the indiscernibility of objects. It provides good partition to objects even when the proximity of objects is defined as relative proximity. The main benefit of this method is that it can be applied to proximity measures that do not satisfy the triangular inequality. Additionally, it may be used with a proximity matrix-thus it does not require direct access to the original data values.
{"title":"Dealing with relatively proximity by rough clustering","authors":"S. Hirano, S. Tsumoto","doi":"10.1109/NAFIPS.2003.1226793","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226793","url":null,"abstract":"This paper presents a new clustering method based on the indiscernibility of objects. It provides good partition to objects even when the proximity of objects is defined as relative proximity. The main benefit of this method is that it can be applied to proximity measures that do not satisfy the triangular inequality. Additionally, it may be used with a proximity matrix-thus it does not require direct access to the original data values.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"14 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":"121861756","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.2003.1226837
Jiann-Liang Chen, Her-Song Wu, Huan-Wen Tzeng
In the modern world of wireless communications, the concept of wireless global coverage is of the utmost importance. Based on the concept, a two-tier service architecture with fuzzy-based switching scheme was proposed in an IEEE 802.11 (WLAN) and Bluetooth (WPAN) coexistence network environment. In the environment combined resources are allocated to new/handoff services according to some acceptance criterion and service facility such as motion speed and traffic characteristics. Through the fuzzification, de-fuzzification and inference procedures, a switching decision-making scheme was performed. Simulation results were shown that the performance in terms of service blocking probability, system utilization from the fuzzy-based two-tier model, which experiences a severe radio mutual interference had better than that of homogeneous networks at 26% and 3.5%, respectively. The service interworking performance in terms of end-to-end delay was also measured. The seamless roaming function can be developed to expand the scope of wireless users and densely populated wireless LAN in the proposed architecture.
{"title":"Bluetooth and IEEE 802.11 coexistence service architecture with fuzzy-based switching scheme","authors":"Jiann-Liang Chen, Her-Song Wu, Huan-Wen Tzeng","doi":"10.1109/NAFIPS.2003.1226837","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226837","url":null,"abstract":"In the modern world of wireless communications, the concept of wireless global coverage is of the utmost importance. Based on the concept, a two-tier service architecture with fuzzy-based switching scheme was proposed in an IEEE 802.11 (WLAN) and Bluetooth (WPAN) coexistence network environment. In the environment combined resources are allocated to new/handoff services according to some acceptance criterion and service facility such as motion speed and traffic characteristics. Through the fuzzification, de-fuzzification and inference procedures, a switching decision-making scheme was performed. Simulation results were shown that the performance in terms of service blocking probability, system utilization from the fuzzy-based two-tier model, which experiences a severe radio mutual interference had better than that of homogeneous networks at 26% and 3.5%, respectively. The service interworking performance in terms of end-to-end delay was also measured. The seamless roaming function can be developed to expand the scope of wireless users and densely populated wireless LAN in the proposed architecture.","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":"127017588","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.2003.1226789
J. Dickerson, Z. Cox
This paper describes part of a modeling tool, called FCModeler, for exploring metabolic networks that displays and finds structural relationships in graphs. Nodes of the map represent specific biochemicals such as proteins, RNA, and small molecules, or stimuli, such as light, heat, or nutrients. Edges of the map capture regulatory and metabolic relationships found in biological systems. These relationships are established by domain experts and the biological literature. Automated cycle analysis finds sets of connected nodes in a metabolic network. Families of interconnected cycles show how metabolic cycles interact with one another. These cycle families are formed using fuzzy measure theory.
{"title":"Using fuzzy measures to group cycles in metabolic networks","authors":"J. Dickerson, Z. Cox","doi":"10.1109/NAFIPS.2003.1226789","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226789","url":null,"abstract":"This paper describes part of a modeling tool, called FCModeler, for exploring metabolic networks that displays and finds structural relationships in graphs. Nodes of the map represent specific biochemicals such as proteins, RNA, and small molecules, or stimuli, such as light, heat, or nutrients. Edges of the map capture regulatory and metabolic relationships found in biological systems. These relationships are established by domain experts and the biological literature. Automated cycle analysis finds sets of connected nodes in a metabolic network. Families of interconnected cycles show how metabolic cycles interact with one another. These cycle families are formed using fuzzy measure theory.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"12 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":"126847980","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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