Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209373
L. Kiss, A. Várkonyi-Kóczy, P. Baranyi
This paper proposes a method for the navigation of an autonomous robot used inside a building. The main problem to be solved in this issue is the need to use both a priori information on the environment and momentary sensor data in an intelligent way, and thus make the robot capable of finding its way around while also avoiding the obstacles, both static and dynamic. In the paper, a hybrid navigation method is proposed, using two techniques that deal with a priori information and sensory data separately. An algorithm for finding the optimal route to the goal using a priori information is suggested. The properties of the complete system are verified by computer simulation.
{"title":"Autonomous navigation in a known dynamic environment","authors":"L. Kiss, A. Várkonyi-Kóczy, P. Baranyi","doi":"10.1109/FUZZ.2003.1209373","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209373","url":null,"abstract":"This paper proposes a method for the navigation of an autonomous robot used inside a building. The main problem to be solved in this issue is the need to use both a priori information on the environment and momentary sensor data in an intelligent way, and thus make the robot capable of finding its way around while also avoiding the obstacles, both static and dynamic. In the paper, a hybrid navigation method is proposed, using two techniques that deal with a priori information and sensory data separately. An algorithm for finding the optimal route to the goal using a priori information is suggested. The properties of the complete system are verified by computer simulation.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128317148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206563
Yo-Ping Huang, Tsun-Wei Chang
We present a novel method to segment objects in images based on the similarity measurement of fuzzy gray level technique in this paper. In our model, we classify the processing steps into three stages. First, we utilize the attributes of luminance and chromaticity components of HLS color coordinate system to form a fuzzy gray level. These attributes can describe the relationship between different frequent colors and the image can be transferred to smooth gray level, which can capture the objects in images. Second, we reduce the gray levels of image pixels to lower gray levels to speed up computation. Third, we label each root pixel based on a similarity measurement. We perform a sliding window to move from one block to the next one. The similarity of the two root pixels blocked by the sliding window depends on their neighboring pixels. Via the similarity computation, we assign a label number to the root pixels. We generate objects from grouping different labels. The image data are classified by fuzzy gray level technique and the objects are segmented from images. According to the simulation results, our model shows the efficiency and effectiveness for image segmentation.
{"title":"A fuzzy inference model for image segmentation","authors":"Yo-Ping Huang, Tsun-Wei Chang","doi":"10.1109/FUZZ.2003.1206563","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206563","url":null,"abstract":"We present a novel method to segment objects in images based on the similarity measurement of fuzzy gray level technique in this paper. In our model, we classify the processing steps into three stages. First, we utilize the attributes of luminance and chromaticity components of HLS color coordinate system to form a fuzzy gray level. These attributes can describe the relationship between different frequent colors and the image can be transferred to smooth gray level, which can capture the objects in images. Second, we reduce the gray levels of image pixels to lower gray levels to speed up computation. Third, we label each root pixel based on a similarity measurement. We perform a sliding window to move from one block to the next one. The similarity of the two root pixels blocked by the sliding window depends on their neighboring pixels. Via the similarity computation, we assign a label number to the root pixels. We generate objects from grouping different labels. The image data are classified by fuzzy gray level technique and the objects are segmented from images. According to the simulation results, our model shows the efficiency and effectiveness for image segmentation.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115020949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206572
L. Reznik, V. Kreinovich, S. Starks
The paper considers the problem of measurement information fusion from different sources, when one of the sources is an information about approximate values of the measured variables or their combinations. The information is given with fuzzy models and is used in combination with the measurement results. The properties of the modified estimates are studied in comparison with the conventional ones. The conditions when an expert's information application can give a high gain are derived, the gain value is estimated, the recommendations to an expert on making predictions are given. The possible gain in measurement result efficiency in geophysical applications is analyzed.
{"title":"Use of fuzzy expert's information in measurement and what we can gain from its application in geophysics","authors":"L. Reznik, V. Kreinovich, S. Starks","doi":"10.1109/FUZZ.2003.1206572","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206572","url":null,"abstract":"The paper considers the problem of measurement information fusion from different sources, when one of the sources is an information about approximate values of the measured variables or their combinations. The information is given with fuzzy models and is used in combination with the measurement results. The properties of the modified estimates are studied in comparison with the conventional ones. The conditions when an expert's information application can give a high gain are derived, the gain value is estimated, the recommendations to an expert on making predictions are given. The possible gain in measurement result efficiency in geophysical applications is analyzed.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206608
A. Orfila, J. Rubiera, A. Ribagorda
Nowadays one of the main problems of Intrusion Detection Systems (IDS) is the high rate of false positives that they show. The number of alerts that an IDS launches are clearly higher than the number of real attacks. This paper tries to introduce a measure of the IDS prediction skill in close relationship with these false positives. So the prediction skill of an IDS is then computed according to the false positives produced. The problem faced is how to make an accurate prediction from the results of different IDS. The fraction of IDS over the total number of them that predicts a given event will determine whether such event is predicted or not. The performance obtained from the application of fuzzy thresholds over such fraction is compared with the corresponding crisp thresholds. The results of these comparisons allow us to conclude a relevant improvement when fuzzy thresholds are involved.
{"title":"Fuzzy logic on decision model for IDS","authors":"A. Orfila, J. Rubiera, A. Ribagorda","doi":"10.1109/FUZZ.2003.1206608","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206608","url":null,"abstract":"Nowadays one of the main problems of Intrusion Detection Systems (IDS) is the high rate of false positives that they show. The number of alerts that an IDS launches are clearly higher than the number of real attacks. This paper tries to introduce a measure of the IDS prediction skill in close relationship with these false positives. So the prediction skill of an IDS is then computed according to the false positives produced. The problem faced is how to make an accurate prediction from the results of different IDS. The fraction of IDS over the total number of them that predicts a given event will determine whether such event is predicted or not. The performance obtained from the application of fuzzy thresholds over such fraction is compared with the corresponding crisp thresholds. The results of these comparisons allow us to conclude a relevant improvement when fuzzy thresholds are involved.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133574756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209387
Kosuke Yamamoto, T. Yoshikawa, T. Furuhashi
Fuzzy modeling is known as one of the effective methods to identify unknown non-linear input-output relationships. In gathering information from constructed models or constructing models from known information, the model's understandability becomes essential. This paper defines new axes by fitting distributed data in input space and proposes a fuzzy modeling method considering data structure. This paper calls these axes, "fusion axes". The effectiveness of the proposed method is shown through some numerical experiments.
{"title":"A proposal of fuzzy modeling on fusion axes considering the data structure","authors":"Kosuke Yamamoto, T. Yoshikawa, T. Furuhashi","doi":"10.1109/FUZZ.2003.1209387","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209387","url":null,"abstract":"Fuzzy modeling is known as one of the effective methods to identify unknown non-linear input-output relationships. In gathering information from constructed models or constructing models from known information, the model's understandability becomes essential. This paper defines new axes by fitting distributed data in input space and proposes a fuzzy modeling method considering data structure. This paper calls these axes, \"fusion axes\". The effectiveness of the proposed method is shown through some numerical experiments.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133078476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206585
Chang-Lan Tsai, Bor‐Sen Chen
Direct-sequence Code-division multiple-access (DS-CDMA) has merged as proper format for wireless communication systems. As a result of multiple-access interference (MAI), inter-symbol interference (ISI), narrowband interference (NBI) and nonlinearities in channels, DS-CDMA systems can suffer deterioration in performance. In practical application, it is difficult to design optimal equalizer for nonlinear DS-CDMA systems. Here, we use fuzzy models as the nonlinear interpolation of several linear channels. Based on fuzzy linear interpolation channel, a robust suboptimal fuzzy equalizer is proposed to efficiently compensate the nonlinear channel, MAI, ISI and channel noise to reconstruct the transmitted signals for each user. The robust suboptimal equalization design problem is transformed to a linear matrix inequality problem, which can be efficiently solved convex optimization technique.
{"title":"Robust suboptimal fuzzy equalizer in nonlinear DS-CDMA systems","authors":"Chang-Lan Tsai, Bor‐Sen Chen","doi":"10.1109/FUZZ.2003.1206585","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206585","url":null,"abstract":"Direct-sequence Code-division multiple-access (DS-CDMA) has merged as proper format for wireless communication systems. As a result of multiple-access interference (MAI), inter-symbol interference (ISI), narrowband interference (NBI) and nonlinearities in channels, DS-CDMA systems can suffer deterioration in performance. In practical application, it is difficult to design optimal equalizer for nonlinear DS-CDMA systems. Here, we use fuzzy models as the nonlinear interpolation of several linear channels. Based on fuzzy linear interpolation channel, a robust suboptimal fuzzy equalizer is proposed to efficiently compensate the nonlinear channel, MAI, ISI and channel noise to reconstruct the transmitted signals for each user. The robust suboptimal equalization design problem is transformed to a linear matrix inequality problem, which can be efficiently solved convex optimization technique.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130084064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206540
Hyo-Jin Suh, J. Keller, C. Carson
To identify the source of Escherichia coli (E.coli) fecal bacterial contamination, we propose a fuzzy dissimilarity measure to calculate the similarity between the E.coli DNA patterns. The fuzzy dissimilarity measure preserves the dimension of the DNA patterns and at the same time allows variation among same host patterns. The fuzzy dissimilarity measure produces a dissimilarity matrix, a form of relational data. For classification of this type of data representation we present a weighted k-nearest neighbor algorithm. The weighted k.nearest neighbor technique uses the classical k-nearest neighbor rule but solves the problem of 'tie' between multi-classes. In addition, we suggest an ensemble data set method for sample sets with a large range of class sizes. The proposed system showed potential as a stable system in detecting fecal bacterial hosts and as a base for future studies in interpreting DNA patterns.
{"title":"An analysis of a fuzzy dissimilarity measure to perform Escherichia coli source tracking","authors":"Hyo-Jin Suh, J. Keller, C. Carson","doi":"10.1109/FUZZ.2003.1206540","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206540","url":null,"abstract":"To identify the source of Escherichia coli (E.coli) fecal bacterial contamination, we propose a fuzzy dissimilarity measure to calculate the similarity between the E.coli DNA patterns. The fuzzy dissimilarity measure preserves the dimension of the DNA patterns and at the same time allows variation among same host patterns. The fuzzy dissimilarity measure produces a dissimilarity matrix, a form of relational data. For classification of this type of data representation we present a weighted k-nearest neighbor algorithm. The weighted k.nearest neighbor technique uses the classical k-nearest neighbor rule but solves the problem of 'tie' between multi-classes. In addition, we suggest an ensemble data set method for sample sets with a large range of class sizes. The proposed system showed potential as a stable system in detecting fecal bacterial hosts and as a base for future studies in interpreting DNA patterns.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114612919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206637
Á. Orille, S. Bogarra, M. Grau, J. Iglesias
As lightning surges are considered to be the most dangerous events in power distribution systems, the more we know about them the better we can select and coordinate protection devices. Moreover, a better knowledge of lightning surges gives rise to the accurate positioning of device protection, the reduction of insulation costs at installations and allows operation with well-known risks of failure. The development of a computer application based on fuzzy logic techniques, which allow the determination of the accurate position of the surge arrester in power systems, controls the risk of failure, thus permitting the selection of appropriate protection schemes for each network. As a consequence, protection costs are reduced in accordance with the costs of the elements actually protected and the continuity of service to be achieved.
{"title":"Lightning protection of power systems using fuzzy logic techniques","authors":"Á. Orille, S. Bogarra, M. Grau, J. Iglesias","doi":"10.1109/FUZZ.2003.1206637","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206637","url":null,"abstract":"As lightning surges are considered to be the most dangerous events in power distribution systems, the more we know about them the better we can select and coordinate protection devices. Moreover, a better knowledge of lightning surges gives rise to the accurate positioning of device protection, the reduction of insulation costs at installations and allows operation with well-known risks of failure. The development of a computer application based on fuzzy logic techniques, which allow the determination of the accurate position of the surge arrester in power systems, controls the risk of failure, thus permitting the selection of appropriate protection schemes for each network. As a consequence, protection costs are reduced in accordance with the costs of the elements actually protected and the continuity of service to be achieved.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131982469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209411
D. P. Kwok, Z. Deng, C. K. Li, T. Leung, Zeng-qi Sun, J. Wong
In this paper an efficient Q-learning paradigm implemented on a fuzzy CMAC network is proposed. The fuzzy CMAC network topological architecture is described. The continuous states of the system are partitioned into a number of fuzzy boxes. With the proposed fuzzy CMAC the Q-values of agents in the fired fuzzy boxes are evaluated and the control actions with maximum Q-values can be derived. The proposed hybrid adaptive and learning type of Fuzzy Neural control system based on the Q-learning is applied to the control of a pH-neutralization process.
{"title":"Fuzzy neural control of systems with unknown dynamic using Q-learning strategies","authors":"D. P. Kwok, Z. Deng, C. K. Li, T. Leung, Zeng-qi Sun, J. Wong","doi":"10.1109/FUZZ.2003.1209411","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209411","url":null,"abstract":"In this paper an efficient Q-learning paradigm implemented on a fuzzy CMAC network is proposed. The fuzzy CMAC network topological architecture is described. The continuous states of the system are partitioned into a number of fuzzy boxes. With the proposed fuzzy CMAC the Q-values of agents in the fired fuzzy boxes are evaluated and the control actions with maximum Q-values can be derived. The proposed hybrid adaptive and learning type of Fuzzy Neural control system based on the Q-learning is applied to the control of a pH-neutralization process.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128251641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209404
Doo Jin Choi, S. S. Lee, P. Park
This paper suggests a new H/sub /spl infin// output-feedback controller for discrete-time switching fuzzy systems, which have high- and low-level weighting functions, namely, crisp switching-region weighting functions and local fuzzy weighting functions. Based on a new piecewise fuzzy weighting-dependent Lyapunov function (PFWLF) consisting of current-time states and a set of one-step-past local fuzzy weighting-dependent Lyapunov matrices, the new controller directly uses the current-time information on the high-level weighting functions as well as the current-time and the one-step-past information on the low-level weighting functions. This resulting controller is formulated in terms of parametric linear matrix inequalities (PLMIs), which are local fuzzy weighting-dependent conditions.
{"title":"Output-feedback H/sub /spl infin// control of discrete-time switching fuzzy systems","authors":"Doo Jin Choi, S. S. Lee, P. Park","doi":"10.1109/FUZZ.2003.1209404","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209404","url":null,"abstract":"This paper suggests a new H/sub /spl infin// output-feedback controller for discrete-time switching fuzzy systems, which have high- and low-level weighting functions, namely, crisp switching-region weighting functions and local fuzzy weighting functions. Based on a new piecewise fuzzy weighting-dependent Lyapunov function (PFWLF) consisting of current-time states and a set of one-step-past local fuzzy weighting-dependent Lyapunov matrices, the new controller directly uses the current-time information on the high-level weighting functions as well as the current-time and the one-step-past information on the low-level weighting functions. This resulting controller is formulated in terms of parametric linear matrix inequalities (PLMIs), which are local fuzzy weighting-dependent conditions.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134560020","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：