The Experts below are selected from a list of 94503 Experts worldwide ranked by ideXlab platform
Muhsin Tunay Gencoglu - One of the best experts on this subject based on the ideXlab platform.
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an expert system based on s transform and neural network for Automatic Classification of power quality disturbances
Expert Systems With Applications, 2009Co-Authors: Murat Uyar, Selcuk Yildirim, Muhsin Tunay GencogluAbstract:In this paper, an S-transform-based neural network structure is presented for Automatic Classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of Classification. Features extracted by using the S-transform are applied as input to NN for Automatic Classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the Classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.
M Manteiga - One of the best experts on this subject based on the ideXlab platform.
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an artificial neural network approach to Automatic Classification of stellar spectra
International Work-Conference on Artificial and Natural Neural Networks, 2009Co-Authors: Alejandra Rodriguez, Carlos Dafonte, B Arcay, M ManteigaAbstract:This paper presents the design and implementation of several models of artificial neural networks for the Automatic Classification of low-resolution spectra of stars. In previous works, we have developed knowledge-based systems for the analysis of spectra. We shall now use these analysis methods to extract the most important spectral features, training the proposed neural networks with this numeric characterization. Although there are published works about neural networks applied to the Classification problem, our final purpose is the integration of several artificial techniques in a unique hybrid system. In the development of such a system we have combined signal processing techniques, knowledge- based systems, fuzzy logic and artificial neural networks, integrating them by means of a relational database which allow us to structure the collected astronomical data and also contrast the results achieved with the different Classification methods.
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Automatic Classification of stellar spectra
LNEA, 2004Co-Authors: Iciar Carricajo, M Manteiga, Alejandra Rodriguez, Carlos DafonteAbstract:We propose and discuss the application of Artificial Intelligence techniques to the Classification of stellar spectra. Two types of systems are considered, knowledge-based systems (Expert Systems) and different classes of neural networks. After analysing and comparing the performance of both systems in the Classification of stellar spectra, we reach the conclusion that neural networks are more adequate to determine the spectral types and luminosity of stars, whereas knowledge-based systems are more performative in determining global temperatures. In order to determine the best approach to the Classification of each spectrum type, we describe and analyse the performance and results of various neural networks models. Backpropagation networks, self-organising maps and RBF networks in particular were designed and tested, through the implementation of different topologies, to obtain the global Classification, spectral type and luminosity of stars. The best networks reached a success rate of approximately 97% for a sample of 100 testing spectra. The morphological analysis algorithms that were developed in the knowledgebased systems are used to extract and measure spectral features, and to obtain the input patterns of the neural networks. Some networks were trained with this parameterisation, others with flux values of specific spectral zones; it was the first strategy that resulted in a better performance. Our approach is focused on the integration of several techniques in a unique hybrid system. In particular, signal processing, expert systems, fuzzy logic and artificial neural networks are integrated by means of a relational database, which allows us to structure the collected astronomical data and to contrast the results of the different Classification methods. In addition, we designed several models of artificial neural networks that were trained with synthetic spectra, and included them as an alternative Classification method.
Murat Uyar - One of the best experts on this subject based on the ideXlab platform.
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an expert system based on s transform and neural network for Automatic Classification of power quality disturbances
Expert Systems With Applications, 2009Co-Authors: Murat Uyar, Selcuk Yildirim, Muhsin Tunay GencogluAbstract:In this paper, an S-transform-based neural network structure is presented for Automatic Classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of Classification. Features extracted by using the S-transform are applied as input to NN for Automatic Classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the Classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.
Yakup Demir - One of the best experts on this subject based on the ideXlab platform.
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A new algorithm for Automatic Classification of power quality events based on wavelet transform and SVM
Expert Systems with Applications, 2010Co-Authors: Huseyin Eristi, Yakup DemirAbstract:This paper presents a new approach for Automatic Classification of power quality events, which is based on the wavelet transform and support vector machines. In the proposed approach, an effective single feature vector representing three phase event signals is extracted after signals are applied normalization and segmentation process. The kernel and penalty parameters of the support vector machine (SVM) are determined by cross-validation. The parameter set that gives the smallest misClassification error is retained. ATP/EMTP model for six types of power system events, namely phase-to-ground fault, phase-to-phase fault, three-phase fault, load switching, capacitor switching and transformer energizing, are constructed. Both the noisy and noiseless event signals are applied to the proposed algorithm. Obtained results indicate that the proposed Automatic event Classification algorithm is robust and has ability to distinguish different power quality event classes easily.
Alejandra Rodriguez - One of the best experts on this subject based on the ideXlab platform.
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an artificial neural network approach to Automatic Classification of stellar spectra
International Work-Conference on Artificial and Natural Neural Networks, 2009Co-Authors: Alejandra Rodriguez, Carlos Dafonte, B Arcay, M ManteigaAbstract:This paper presents the design and implementation of several models of artificial neural networks for the Automatic Classification of low-resolution spectra of stars. In previous works, we have developed knowledge-based systems for the analysis of spectra. We shall now use these analysis methods to extract the most important spectral features, training the proposed neural networks with this numeric characterization. Although there are published works about neural networks applied to the Classification problem, our final purpose is the integration of several artificial techniques in a unique hybrid system. In the development of such a system we have combined signal processing techniques, knowledge- based systems, fuzzy logic and artificial neural networks, integrating them by means of a relational database which allow us to structure the collected astronomical data and also contrast the results achieved with the different Classification methods.
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Automatic Classification of stellar spectra
LNEA, 2004Co-Authors: Iciar Carricajo, M Manteiga, Alejandra Rodriguez, Carlos DafonteAbstract:We propose and discuss the application of Artificial Intelligence techniques to the Classification of stellar spectra. Two types of systems are considered, knowledge-based systems (Expert Systems) and different classes of neural networks. After analysing and comparing the performance of both systems in the Classification of stellar spectra, we reach the conclusion that neural networks are more adequate to determine the spectral types and luminosity of stars, whereas knowledge-based systems are more performative in determining global temperatures. In order to determine the best approach to the Classification of each spectrum type, we describe and analyse the performance and results of various neural networks models. Backpropagation networks, self-organising maps and RBF networks in particular were designed and tested, through the implementation of different topologies, to obtain the global Classification, spectral type and luminosity of stars. The best networks reached a success rate of approximately 97% for a sample of 100 testing spectra. The morphological analysis algorithms that were developed in the knowledgebased systems are used to extract and measure spectral features, and to obtain the input patterns of the neural networks. Some networks were trained with this parameterisation, others with flux values of specific spectral zones; it was the first strategy that resulted in a better performance. Our approach is focused on the integration of several techniques in a unique hybrid system. In particular, signal processing, expert systems, fuzzy logic and artificial neural networks are integrated by means of a relational database, which allows us to structure the collected astronomical data and to contrast the results of the different Classification methods. In addition, we designed several models of artificial neural networks that were trained with synthetic spectra, and included them as an alternative Classification method.
