This paper proposes a computational scheme for fuzzy similarity analysis and classification of images that uses first an information granulation procedure followed by a subsequent fuzzy decision procedure. A special new version of the growing unsupervised learning algorithm is introduced in the paper for information granulation. It reduces the original ldquoraw datardquo (the RGB pixels) of the image to a considerably smaller number of information granules (neurons). After that two features are extracted from each image, as follows: the center-of-gravity and the weighted average size of the image. These features are further used as inputs of a special fuzzy inference procedure that computes numerically the similarity degree for a given pair if images. Finally, a sorting procedure with a predefined threshold is used to obtain the classification results for all available images. The proposed similarity and classification scheme is illustrated on the example of 18 images of flowers. It is also discussed in the paper that the appropriate tuning of the parameters of the fuzzy inference procedure is quite important for obtaining plausible, humanlike results Therefore a simple empirical process for selection of these parameters is also suggested in the paper.
{"title":"Similarity analysis of images based on information granulation and fuzzy decision","authors":"G. Vachkov","doi":"10.1109/IS.2008.4670491","DOIUrl":"https://doi.org/10.1109/IS.2008.4670491","url":null,"abstract":"This paper proposes a computational scheme for fuzzy similarity analysis and classification of images that uses first an information granulation procedure followed by a subsequent fuzzy decision procedure. A special new version of the growing unsupervised learning algorithm is introduced in the paper for information granulation. It reduces the original ldquoraw datardquo (the RGB pixels) of the image to a considerably smaller number of information granules (neurons). After that two features are extracted from each image, as follows: the center-of-gravity and the weighted average size of the image. These features are further used as inputs of a special fuzzy inference procedure that computes numerically the similarity degree for a given pair if images. Finally, a sorting procedure with a predefined threshold is used to obtain the classification results for all available images. The proposed similarity and classification scheme is illustrated on the example of 18 images of flowers. It is also discussed in the paper that the appropriate tuning of the parameters of the fuzzy inference procedure is quite important for obtaining plausible, humanlike results Therefore a simple empirical process for selection of these parameters is also suggested in the paper.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302606","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}
G. Shahgholian, S. Eshtehardiha, H. Mahdavi-Nasab, M. Yousefi
Static synchronous compensator (STATCOM) is a shunt-connected converter, which can affect rapid control of reactive flow in the transmission line by controlling the generated AC voltage. This article presents a new STATCOM voltage controller design for power system damping. The method of multiplicative uncertainty has been employed to model the variations of the operating points. The controller was tested for a number the step increase in torque reference. Voltage of capacitor STATCOM can be controlled by method of linear quadratic regulator (LQR). In this article, matrixes coefficients and dominant poles of closed loop transfer function are selected based on genetic algorithm method. The results show an improvement in voltage control response in a short response time.
{"title":"A novel approach in automatic control based on the genetic algorithm in STATCOM for improvement power system transient stability","authors":"G. Shahgholian, S. Eshtehardiha, H. Mahdavi-Nasab, M. Yousefi","doi":"10.1109/IS.2008.4670419","DOIUrl":"https://doi.org/10.1109/IS.2008.4670419","url":null,"abstract":"Static synchronous compensator (STATCOM) is a shunt-connected converter, which can affect rapid control of reactive flow in the transmission line by controlling the generated AC voltage. This article presents a new STATCOM voltage controller design for power system damping. The method of multiplicative uncertainty has been employed to model the variations of the operating points. The controller was tested for a number the step increase in torque reference. Voltage of capacitor STATCOM can be controlled by method of linear quadratic regulator (LQR). In this article, matrixes coefficients and dominant poles of closed loop transfer function are selected based on genetic algorithm method. The results show an improvement in voltage control response in a short response time.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133592332","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}
Revlin Abbi, E. El-Darzi, C. Vasilakis, P. Millard
The expectation maximisation (EM) algorithm is an iterative maximum likelihood procedure often used for estimating the parameters of a mixture model. Theoretically, increases in the likelihood function are guaranteed as the algorithm iteratively improves upon previously derived parameter estimates. The algorithm is considered to converge when all parameter estimates become stable and no further improvements can be made to the likelihood value. However, to reduce computational time, it is often common practice for the algorithm to be stopped before complete convergence using heuristic approaches. In this paper, we consider various stopping criteria and evaluate their effect on fitting Gaussian mixture models (GMMs) to patient length of stay (LOS) data. Although the GMM can be successfully fitted to positively skewed data such as LOS, the fitting procedure often requires many iterations of the EM algorithm. To our knowledge, no previous study has evaluated the effect of different stopping criteria on fitting GMMs to skewed distributions. Hence, the aim of this paper is to evaluate the effect of various stopping criteria in order to select and justify their use within a patient spell classification methodology. Results illustrate that criteria based on the difference in the likelihood value and on the GMM parameters may not always be a good indicator for stopping the algorithm. In fact we show that the values of the difference in the variance parameters should be used instead, as these parameters are the last to stabilise. In addition, we also specify threshold values for the other stopping criteria.
{"title":"Analysis of stopping criteria for the EM algorithm in the context of patient grouping according to length of stay","authors":"Revlin Abbi, E. El-Darzi, C. Vasilakis, P. Millard","doi":"10.1109/IS.2008.4670413","DOIUrl":"https://doi.org/10.1109/IS.2008.4670413","url":null,"abstract":"The expectation maximisation (EM) algorithm is an iterative maximum likelihood procedure often used for estimating the parameters of a mixture model. Theoretically, increases in the likelihood function are guaranteed as the algorithm iteratively improves upon previously derived parameter estimates. The algorithm is considered to converge when all parameter estimates become stable and no further improvements can be made to the likelihood value. However, to reduce computational time, it is often common practice for the algorithm to be stopped before complete convergence using heuristic approaches. In this paper, we consider various stopping criteria and evaluate their effect on fitting Gaussian mixture models (GMMs) to patient length of stay (LOS) data. Although the GMM can be successfully fitted to positively skewed data such as LOS, the fitting procedure often requires many iterations of the EM algorithm. To our knowledge, no previous study has evaluated the effect of different stopping criteria on fitting GMMs to skewed distributions. Hence, the aim of this paper is to evaluate the effect of various stopping criteria in order to select and justify their use within a patient spell classification methodology. Results illustrate that criteria based on the difference in the likelihood value and on the GMM parameters may not always be a good indicator for stopping the algorithm. In fact we show that the values of the difference in the variance parameters should be used instead, as these parameters are the last to stabilise. In addition, we also specify threshold values for the other stopping criteria.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132784346","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}
Elena Baralis, M. Cabutto, T. Cerquitelli, A. Garofalo, P. Garza
Traditional database management systems have been designed to assist in maintaining large collections of data. However, their performance is not adequate for instant communication applications with tight real-time constraints. The Italian research laboratory Telecom Italia Lab has designed and implemented SOFTRAM DB, a real-time distributed system which stores user profile data in main memory. Different real-time applications (e.g., presence services) concurrently connect to the SOFTRAM DB to store and query user profile data (e.g., nicknames, user identifiers, moods). To improve query and update performance, accessory data structures (e.g., indices, materialized views) are usually exploited in relational DBMSs. This paper presents a soft real-time view management for user profile data in SOFTRAM DB. Different main memory views (i.e., user view and application view) and structures are presented, highlighting their advantages and disadvantages. Preliminary experimental results, performed on large synthetic datasets, show the adaptability and the effectiveness of the proposed approach.
{"title":"Soft real-time view management","authors":"Elena Baralis, M. Cabutto, T. Cerquitelli, A. Garofalo, P. Garza","doi":"10.1109/IS.2008.4670549","DOIUrl":"https://doi.org/10.1109/IS.2008.4670549","url":null,"abstract":"Traditional database management systems have been designed to assist in maintaining large collections of data. However, their performance is not adequate for instant communication applications with tight real-time constraints. The Italian research laboratory Telecom Italia Lab has designed and implemented SOFTRAM DB, a real-time distributed system which stores user profile data in main memory. Different real-time applications (e.g., presence services) concurrently connect to the SOFTRAM DB to store and query user profile data (e.g., nicknames, user identifiers, moods). To improve query and update performance, accessory data structures (e.g., indices, materialized views) are usually exploited in relational DBMSs. This paper presents a soft real-time view management for user profile data in SOFTRAM DB. Different main memory views (i.e., user view and application view) and structures are presented, highlighting their advantages and disadvantages. Preliminary experimental results, performed on large synthetic datasets, show the adaptability and the effectiveness of the proposed approach.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134742","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}
The model we suggest makes the data quality an intrinsic feature of an intuitionistic fuzzy relational database. The quality of the data is no more determined by the level of user complaints or ad hoc sql queries prior to the data load but it is stored explicitly in relational tables and could be monitored and measured regularly. The quality is stored on an attribute level basis in supplementary tables to the base user ones. The quality is measured along preferred quality dimensions and is represented by intuitionistic fuzzy degrees. To consider the preferences of the user with respect to the different quality dimensions and table attributes we create additional tables that contain the weight values. The user base tables are not intuitionistic fuzzy but we have to use an intuitionistic fuzzy RDBMS to represent and manipulate data quality measures.
{"title":"An extension of the relational model to intuitionistic fuzzy data quality attribute model","authors":"D. Boyadzhieva, B. Kolev","doi":"10.1109/IS.2008.4670520","DOIUrl":"https://doi.org/10.1109/IS.2008.4670520","url":null,"abstract":"The model we suggest makes the data quality an intrinsic feature of an intuitionistic fuzzy relational database. The quality of the data is no more determined by the level of user complaints or ad hoc sql queries prior to the data load but it is stored explicitly in relational tables and could be monitored and measured regularly. The quality is stored on an attribute level basis in supplementary tables to the base user ones. The quality is measured along preferred quality dimensions and is represented by intuitionistic fuzzy degrees. To consider the preferences of the user with respect to the different quality dimensions and table attributes we create additional tables that contain the weight values. The user base tables are not intuitionistic fuzzy but we have to use an intuitionistic fuzzy RDBMS to represent and manipulate data quality measures.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"13 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131055339","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}
The paper explains fuzzy critical analysis for the electric generator (EG) protection system. Power system electric generator (EG) is protected to various types of faults and abnormal workings. The protection system (PS) is composed of waiting subsystems, which must properly respond to each kind of dangerous events. An original fuzzy logic- system enables us to analyze the qualitative evaluation of the event-tree, modeling PS behavior. Fuzzy - set logic is used to account for imprecision and uncertainty in data while employing event-tree analysis. The fuzzy event-tree logic allows the use of verbal statement for the probabilities and consequences, such as very high, moderate and low probability.
{"title":"Fuzzy critical analysis for an electric generator protection system","authors":"M. Dumitrescu","doi":"10.1109/IS.2008.4670403","DOIUrl":"https://doi.org/10.1109/IS.2008.4670403","url":null,"abstract":"The paper explains fuzzy critical analysis for the electric generator (EG) protection system. Power system electric generator (EG) is protected to various types of faults and abnormal workings. The protection system (PS) is composed of waiting subsystems, which must properly respond to each kind of dangerous events. An original fuzzy logic- system enables us to analyze the qualitative evaluation of the event-tree, modeling PS behavior. Fuzzy - set logic is used to account for imprecision and uncertainty in data while employing event-tree analysis. The fuzzy event-tree logic allows the use of verbal statement for the probabilities and consequences, such as very high, moderate and low probability.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127512276","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}
In this paper we propose an approach to build a decision support system that can help emergency planners and responders to detect and manage emergency situations. The internal mechanism of the system is independent from the treated application. Therefore, we think the system may be used or adapted easily to different case studies. We focus here on a first step in the decision-support process which concerns the modeling of information issued from the perceived environment and their representation dynamically using a multiagent system. This modeling was applied on the RoboCupRescue simulation system. An implementation and some results are presented here.
{"title":"Information modeling for a dynamic representation of an emergency situation","authors":"F. Kebair, F. Serin","doi":"10.1109/IS.2008.4670431","DOIUrl":"https://doi.org/10.1109/IS.2008.4670431","url":null,"abstract":"In this paper we propose an approach to build a decision support system that can help emergency planners and responders to detect and manage emergency situations. The internal mechanism of the system is independent from the treated application. Therefore, we think the system may be used or adapted easily to different case studies. We focus here on a first step in the decision-support process which concerns the modeling of information issued from the perceived environment and their representation dynamically using a multiagent system. This modeling was applied on the RoboCupRescue simulation system. An implementation and some results are presented here.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134584051","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}
F. Gorunescu, M. Gorunescu, E. El-Darzi, S. Gorunescu
Breast cancer is considered to be the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often more challenging task than the initial one. In this paper we investigate the potential contribution of intelligent neural networks as a useful tool to support health professionals in diagnosing such events. The neural network algorithms are applied to the breast cancer dataset obtained from Ljubljana Oncology Institute. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perception and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and finally, the classification performances of both algorithms are statistically robust.
{"title":"A statistical evaluation of neural computing approaches to predict recurrent events in breast cancer","authors":"F. Gorunescu, M. Gorunescu, E. El-Darzi, S. Gorunescu","doi":"10.1109/IS.2008.4670506","DOIUrl":"https://doi.org/10.1109/IS.2008.4670506","url":null,"abstract":"Breast cancer is considered to be the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often more challenging task than the initial one. In this paper we investigate the potential contribution of intelligent neural networks as a useful tool to support health professionals in diagnosing such events. The neural network algorithms are applied to the breast cancer dataset obtained from Ljubljana Oncology Institute. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perception and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and finally, the classification performances of both algorithms are statistically robust.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129980732","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}
During the last decade, mining companies and mobile equipment manufacturers have pursued improved efficiency, productivity, and safety in underground mining operations by automating some of the functions of underground vehicles. The work presented in this paper is the result of an effort to develop new flexible infrastructureless guidance system for autonomous tramming of center-articulated underground mining vehicles.
{"title":"Flexible infrastructure free navigation for vehicles in underground mines","authors":"J. Larsson, M. Broxvall, A. Saffiotti","doi":"10.1109/IS.2008.4670406","DOIUrl":"https://doi.org/10.1109/IS.2008.4670406","url":null,"abstract":"During the last decade, mining companies and mobile equipment manufacturers have pursued improved efficiency, productivity, and safety in underground mining operations by automating some of the functions of underground vehicles. The work presented in this paper is the result of an effort to develop new flexible infrastructureless guidance system for autonomous tramming of center-articulated underground mining vehicles.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125070334","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}
A. Boulkroune, M. M'Saad, Mohamed Tadjine, M. Farza
In this paper, a fuzzy adaptive control system is investigated for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with both unknown dead-zone and unknown sign of the control gain matrix (i.e. unknown control direction). To deal with the unknown sign of the control gain matrix, the Nussbaum-type function is used. In the designing of the fuzzy adaptive control scheme, we will fully exploit a decomposition property of the control gain matrix. To compensate the effects of the dead-zone, we require neither the knowledge of dead-zone parameters nor the construction of its inverse. Simulation results demonstrate the effectiveness of the approach.
{"title":"Adaptive fuzzy control for MIMO nonlinear systems with unknown dead-zone","authors":"A. Boulkroune, M. M'Saad, Mohamed Tadjine, M. Farza","doi":"10.1109/IS.2008.4670425","DOIUrl":"https://doi.org/10.1109/IS.2008.4670425","url":null,"abstract":"In this paper, a fuzzy adaptive control system is investigated for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with both unknown dead-zone and unknown sign of the control gain matrix (i.e. unknown control direction). To deal with the unknown sign of the control gain matrix, the Nussbaum-type function is used. In the designing of the fuzzy adaptive control scheme, we will fully exploit a decomposition property of the control gain matrix. To compensate the effects of the dead-zone, we require neither the knowledge of dead-zone parameters nor the construction of its inverse. Simulation results demonstrate the effectiveness of the approach.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132643385","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：