Revising and updating beliefs and knowledge bases has been an important problem in knowledge representation and reasoning. While various proposals in Answer Set Programming updates have come up, in particular one of them presents and interesting persistence situation that others do not manage well for foundation reasons. In a need of a general semantics capable of dealing both with general properties and most exceptional unforeseen situations, this paper presents an extension to one of the latest semantics for updates that does not contravene that situation. Besides the formalism of properties that this approach inherits from its predecessors as a strong framework for an update semantics, this proposal is also supported by a solver as an important component of logic programming for new experiments and for further potential more complex (agent) applications that manage knowledge bases.
{"title":"Maintaining Knowledge Bases at the Object Level","authors":"J. Guadarrama","doi":"10.1109/MICAI.2007.35","DOIUrl":"https://doi.org/10.1109/MICAI.2007.35","url":null,"abstract":"Revising and updating beliefs and knowledge bases has been an important problem in knowledge representation and reasoning. While various proposals in Answer Set Programming updates have come up, in particular one of them presents and interesting persistence situation that others do not manage well for foundation reasons. In a need of a general semantics capable of dealing both with general properties and most exceptional unforeseen situations, this paper presents an extension to one of the latest semantics for updates that does not contravene that situation. Besides the formalism of properties that this approach inherits from its predecessors as a strong framework for an update semantics, this proposal is also supported by a solver as an important component of logic programming for new experiments and for further potential more complex (agent) applications that manage knowledge bases.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121197267","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}
This paper proposed a self-organization genetic algorithm with cyclic mutation (SOGACM) and used it to optimize PID controller parameters. A dominant selection operator and a cyclic mutation strategy were given firstly. The former enhances the action of the dominant individuals in the evolutionary process. And the later changes mutation probability periodically in accordance with evolution generation and the period. Moreover mutation probability keeps smaller and crossover operator plays a dominant role in a relatively long period of time. At certain particular time, the probability of mutation increases quickly. The SOGACM was then constructed based on the two operators mentioned above. The analysis of algorithm performance shows the self-organization genetic algorithm with cyclic mutation possesses self-organization property, and has a good global search performance. The simulation results of PID controller optimization experiment indicate that a suitable set of PID parameters could be calculated by SOGACM optimization method.
{"title":"PID Controller Optimization Based on the Self-Organization Genetic Algorithm with Cyclic Mutation","authors":"Z. Jinhua, Zhuang Jian, Duan Haifeng, Wang Sun-an","doi":"10.1109/MICAI.2007.23","DOIUrl":"https://doi.org/10.1109/MICAI.2007.23","url":null,"abstract":"This paper proposed a self-organization genetic algorithm with cyclic mutation (SOGACM) and used it to optimize PID controller parameters. A dominant selection operator and a cyclic mutation strategy were given firstly. The former enhances the action of the dominant individuals in the evolutionary process. And the later changes mutation probability periodically in accordance with evolution generation and the period. Moreover mutation probability keeps smaller and crossover operator plays a dominant role in a relatively long period of time. At certain particular time, the probability of mutation increases quickly. The SOGACM was then constructed based on the two operators mentioned above. The analysis of algorithm performance shows the self-organization genetic algorithm with cyclic mutation possesses self-organization property, and has a good global search performance. The simulation results of PID controller optimization experiment indicate that a suitable set of PID parameters could be calculated by SOGACM optimization method.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122469958","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}
This research was developed in a greenhouse located in Mexico, in which there are big variations in temperature and relative humidity, generating production losses. Consequently a good greenhouse control tool was necessary to keep these variables inside of the optimal levels. Black box models have been applied in this greenhouse to predict temperature and relative humidity, however they fail in relative humidity predictions because of non linear relationships in the variables. Therefore an Artificial Neural Network (ANN) was implemented because it excel at uncovering patterns or relationships in data and it is also a powerful non-linear estimator. A total number of 14,490 data patterns were available 50% for training, 25% for verification, and 25% for testing. The ANN developed demonstrates a highly accurate estimation for both variables which can be used to forecast the conditions inside of the greenhouse and consequently take actions ahead of time, avoiding economical losses.
{"title":"A Neural Network Model to Control Greenhouse Environment","authors":"R. Salazar, I. Lopez, A. Rojano","doi":"10.1109/MICAI.2007.33","DOIUrl":"https://doi.org/10.1109/MICAI.2007.33","url":null,"abstract":"This research was developed in a greenhouse located in Mexico, in which there are big variations in temperature and relative humidity, generating production losses. Consequently a good greenhouse control tool was necessary to keep these variables inside of the optimal levels. Black box models have been applied in this greenhouse to predict temperature and relative humidity, however they fail in relative humidity predictions because of non linear relationships in the variables. Therefore an Artificial Neural Network (ANN) was implemented because it excel at uncovering patterns or relationships in data and it is also a powerful non-linear estimator. A total number of 14,490 data patterns were available 50% for training, 25% for verification, and 25% for testing. The ANN developed demonstrates a highly accurate estimation for both variables which can be used to forecast the conditions inside of the greenhouse and consequently take actions ahead of time, avoiding economical losses.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"53 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197995","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}
An algorithm for tracking time-varying references for a nonlinear second order uncertain servo is proposed. Uncertainties in nonlinear functions associated to the state and uncertainties on the servo gain are counteracted using a desired adaptive fuzzy compensator plus a linear proportional derivative plus feedforward compensation. A depart from existing approaches is the fact that the proposed algorithm is not based on switching terms and as consequence chattering is avoided. Stability is proved using the second Lyapunov method and performance is evaluated through experiments in a laboratory prototype.
{"title":"Nonlinear Servo Adaptive Fuzzy Tracking","authors":"Rubén Garrido, D. Calderon, A. Soria","doi":"10.1109/MICAI.2007.29","DOIUrl":"https://doi.org/10.1109/MICAI.2007.29","url":null,"abstract":"An algorithm for tracking time-varying references for a nonlinear second order uncertain servo is proposed. Uncertainties in nonlinear functions associated to the state and uncertainties on the servo gain are counteracted using a desired adaptive fuzzy compensator plus a linear proportional derivative plus feedforward compensation. A depart from existing approaches is the fact that the proposed algorithm is not based on switching terms and as consequence chattering is avoided. Stability is proved using the second Lyapunov method and performance is evaluated through experiments in a laboratory prototype.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131099795","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 a new input representation of the data of the time series and a new learning approach is presented. The input data representation is based on the information obtained by the division of image axis of the time series into boxes. Then, this new information is implemented in a new learning technique which through probabilistic mechanism this learning could be applied to the interesting forecasting problem. The results indicate that using the methodology proposed in this article it is possible to obtain forecasting results with good enough accuracy.
{"title":"Machine Learning Tools to Time Series Forecasting","authors":"K. Ramirez-Amaro, J. C. Chimal-Eguía","doi":"10.1109/MICAI.2007.42","DOIUrl":"https://doi.org/10.1109/MICAI.2007.42","url":null,"abstract":"In this paper a new input representation of the data of the time series and a new learning approach is presented. The input data representation is based on the information obtained by the division of image axis of the time series into boxes. Then, this new information is implemented in a new learning technique which through probabilistic mechanism this learning could be applied to the interesting forecasting problem. The results indicate that using the methodology proposed in this article it is possible to obtain forecasting results with good enough accuracy.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114883181","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}
J.L. Varela-Benitez, F. Gallegos-Funes, V. Ponomaryov, J.M. de la Rosa Vazquez
In this paper we present the capability features of the RM L-filter in the wavelet domain for the removal of impulsive and multiplicative noise in image processing applications. The proposed filter uses the robust RM-estimator in the filtering scheme of L-filter. Extensive simulation results have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and detail preservation.
{"title":"RM L-Filters in Wavelet Domain for Image Processing Applications","authors":"J.L. Varela-Benitez, F. Gallegos-Funes, V. Ponomaryov, J.M. de la Rosa Vazquez","doi":"10.1109/MICAI.2007.22","DOIUrl":"https://doi.org/10.1109/MICAI.2007.22","url":null,"abstract":"In this paper we present the capability features of the RM L-filter in the wavelet domain for the removal of impulsive and multiplicative noise in image processing applications. The proposed filter uses the robust RM-estimator in the filtering scheme of L-filter. Extensive simulation results have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and detail preservation.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130677569","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}
Few years old children lift and manipulate unfamiliar objects more dexterously than todaypsilas robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the friction coefficient of the objectpsilas material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to estimate the friction coefficient using artificial neural networks that receive as input simulated human afferent responses. This method is strongly inspired on neurophysiological studies of the afferent responses during the human dexterous manipulation of objects. Finite element analysis was used to model a finger and an object, and simulated experiments using the proposed method were done. To the best of our knowledge, this is the first time that simulated human afferent signals are combined with finite element analysis and artificial neural networks, to estimate the friction coefficient.
{"title":"A Bio-inspired Method for Friction Estimation","authors":"R. M. Herrera","doi":"10.1109/MICAI.2007.39","DOIUrl":"https://doi.org/10.1109/MICAI.2007.39","url":null,"abstract":"Few years old children lift and manipulate unfamiliar objects more dexterously than todaypsilas robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the friction coefficient of the objectpsilas material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to estimate the friction coefficient using artificial neural networks that receive as input simulated human afferent responses. This method is strongly inspired on neurophysiological studies of the afferent responses during the human dexterous manipulation of objects. Finite element analysis was used to model a finger and an object, and simulated experiments using the proposed method were done. To the best of our knowledge, this is the first time that simulated human afferent signals are combined with finite element analysis and artificial neural networks, to estimate the friction coefficient.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130928532","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}
Despite of good theoretic foundations and high classification accuracy of support vector machines (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel SVM classification approach for large data sets by considering models of classes distribution (MCD). A first stage uses SVM classification in order to gets a sketch of classes distribution. Then the algorithm obtain the support vectors (SVs) most close between each class and construct a ball using minimum enclosing ball from each pair of SVs with different label. The data points included in the balls constitute the MCD, which is the framework in the boundary of each class and represents the most important data points, these data points are used as training data for a posterior SVM classification. Experimental results show that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers.
{"title":"SVM Classification for Large Data Sets by Considering Models of Classes Distribution","authors":"Jair Cervantes, Xiaoou Li, Wen Yu","doi":"10.1109/MICAI.2007.27","DOIUrl":"https://doi.org/10.1109/MICAI.2007.27","url":null,"abstract":"Despite of good theoretic foundations and high classification accuracy of support vector machines (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel SVM classification approach for large data sets by considering models of classes distribution (MCD). A first stage uses SVM classification in order to gets a sketch of classes distribution. Then the algorithm obtain the support vectors (SVs) most close between each class and construct a ball using minimum enclosing ball from each pair of SVs with different label. The data points included in the balls constitute the MCD, which is the framework in the boundary of each class and represents the most important data points, these data points are used as training data for a posterior SVM classification. Experimental results show that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127372661","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}
This paper proposes the use of a recurrent neural network model (RNNM) for decentralized and centralized identification of an aerobic digestion process, carried out in a fixed bed and a recirculation tank anaerobic wastewater treatment system. The analytical model of the digestion bioprocess represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points. The proposed decentralized RNNM consists of four independently working recurrent neural networks (RNN), so to approximate the process dynamics in three different measurement points plus the recirculation tank. The RNN learning algorithm is the dynamic Backpropagation one. The comparative graphical simulation results of the digestion wastewater treatment system approximation, obtained via decentralized and centralized RNNM learning, exhibited a good convergence, and precise plant variables tracking.
{"title":"Anaerobic Digestion Process Identification Using Recurrent Neural Network Model","authors":"R. Galván-Guerra, I. Baruch","doi":"10.1109/MICAI.2007.10","DOIUrl":"https://doi.org/10.1109/MICAI.2007.10","url":null,"abstract":"This paper proposes the use of a recurrent neural network model (RNNM) for decentralized and centralized identification of an aerobic digestion process, carried out in a fixed bed and a recirculation tank anaerobic wastewater treatment system. The analytical model of the digestion bioprocess represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points. The proposed decentralized RNNM consists of four independently working recurrent neural networks (RNN), so to approximate the process dynamics in three different measurement points plus the recirculation tank. The RNN learning algorithm is the dynamic Backpropagation one. The comparative graphical simulation results of the digestion wastewater treatment system approximation, obtained via decentralized and centralized RNNM learning, exhibited a good convergence, and precise plant variables tracking.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130994994","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}
This paper presents a novel approach that recognizing heart rhythm with the combination of adaptive Hermite decomposition and support vector machines (SVM) classification. The novelty lies in two aspects. In the first aspect, for the goal of feature extraction, the orthogonal transformation based on Hermite basis functions is proposed to characterize the morphological features of ECG data. In the other aspect, as to the multi-class electrocardiogram (ECG) classification, the one-against-all strategy is applied to a cluster of binary SVMs. Finally, in terms of numerical experiments, the major types of heart rhythms in the MIT-BIH arrhythmia database are taken into account. The results confirm its reliability and accuracy of the proposed ECG interpreter.
{"title":"An Application of Morphological Feature Extraction and Support Vector Machines in Computerized ECG Interpretation","authors":"W. Lei, Bing-Nan Li, M. Dong, Binbin Fu","doi":"10.1109/MICAI.2007.32","DOIUrl":"https://doi.org/10.1109/MICAI.2007.32","url":null,"abstract":"This paper presents a novel approach that recognizing heart rhythm with the combination of adaptive Hermite decomposition and support vector machines (SVM) classification. The novelty lies in two aspects. In the first aspect, for the goal of feature extraction, the orthogonal transformation based on Hermite basis functions is proposed to characterize the morphological features of ECG data. In the other aspect, as to the multi-class electrocardiogram (ECG) classification, the one-against-all strategy is applied to a cluster of binary SVMs. Finally, in terms of numerical experiments, the major types of heart rhythms in the MIT-BIH arrhythmia database are taken into account. The results confirm its reliability and accuracy of the proposed ECG interpreter.","PeriodicalId":296192,"journal":{"name":"2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)","volume":"31 13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131628340","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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