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Abstract: This paper presents a core vector machine (CVM)-based algorithm for on-line voltage security assessment of power systems. To classify the system security status, a CVM has been trained for each contingency. The proposed CVM-based security assessment has very small training time and space in comparison with support vector machines (SVM) and artificial neural networks (ANNs)-based algorithms. The proposed algorithm produces less support vectors (SV) and therefore is faster than existing algorithms. In this paper, a new decision tree (DT)-based feature selection technique has been presented, too. The proposed CVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line voltage security assessment procedure of large-scale power system. [Copyright &y& Elsevier]

Abstract: Automatic communication signal recognition plays an important role for many novel computer and communication technologies. Most of the proposed techniques can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal to noise ratio (SNR). In this paper, we investigate twofold. First, we propose an efficient system that uses a combination set of spectral characteristics and higher order moments up to eighth and higher order cumulants up to eighth as the effective features. As the classifier we used a multi-layer perceptron (MLP) neural network. In this stage we investigate different learning algorithms of MLP neural networks that some of them, such as quick prop (QP) learning algorithm, extended delta-bar-delta (EDBD), super self adaptive back propagation (SuperSAB) and conjugate gradient (CG) are proposed for the first time in the area of communication signals recognition. Experimental results show that proposed system discriminates a lot of digital communication signals with high accuracy even at very low SNRs. But a lot of features are used for this recognition. Then at the second fold, in order to reduce the complexity of the recognizer, we have proposed a novel hybrid intelligent technique. In this technique we have optimized the classifier design by Bees Algorithm (BA) for selection of the best features that are fed to the classifier. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA. [ABSTRACT FROM AUTHOR]