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A human face recognition algorithm based on greedy kernel principal component analysis (GKPCA) is presented to meet the requirement of quick face recognition on line. In the algorithm, typical human face are decomposed by fast wavelet transform(FWT), then the greedy algorithm is used to reduce training set and the features of the low frequency sub-images are extracted by kernel principal component analysis(KPCA). Consequently, the features extracted are recognized by support vector machine (SVM). Simulations of the algorithm proposed on the basis of ORL (Olivetti Research Lab) face database and NORL face databases show that the algorithm is capable of reducing training time with high recognition rate. [ABSTRACT FROM PUBLISHER]

According to the characteristics of partial discharge (PD) signals of gas insulated switchgear (GIS), a fault type recognition method based on wavelet packet transform is put forward. First the wavelet packet decomposition tree of PD signals is constructed. Then in feature extraction of PD signals, different data fusion algorithms are used in feature vector dimension reduction to enhance sensitivity of feature vector to PD fault signals characteristics. A fault recognizer based on back propagation neural network (BPNN) is designed. The results of simulation show that the wavelet transform based GIS fault signal recognition method is effective. [ABSTRACT FROM PUBLISHER]