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The smart grid initiative requires self-healing distribution systems with more accurate fault detection and classification techniques. A multi-sensor feeder-level fault detection and classification algorithm is presented in this work, based on the techniques of the support vector machine and the principal components. An IEEE 34-bus feeder model with dynamic loading conditions is used to evaluate the...
A power quality disturbance classification method based on modified S-transform (MST) and multi-classification support vector machine (SVM) is proposed in this paper. Firstly, the MST, which introduces two regulatory factors into traditional S-transform and obtains proper time and frequency resolution, is detailed. Then, the time-frequency matrix model is obtained through MST time-frequency analysis...
In this paper, we consider the parametric version of Wiener systems where both the linear and nonlinear parts are identified with clipped observations in the presence of internal and external noises. Also the static functions are allowed noninvertible. We propose a classification based support vector machine (SVM) and formulate the identification problem as a convex optimization. The solution to the...
Medicine is one of the major fields where the application of artificial intelligence primarily deals with construction of programs that perform diagnosis and make therapy recommendations. In digital mammography, data mining techniques are used to detect and characterize abnormalities in images and clinical reports. In the existing approaches, the mammogram image classification is done in either clinical...
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean...
A new method for detecting and classifying loudspeaker faults is presented in this paper. Total response of high-order harmonics groups is measured and used as defect features of loudspeaker. Based on support vector machine (SVM), we built a classification system combined with one-class SVM and Directed Acyclic Graphic SVM (DAGSVM). Comparing with K-nearest neighbor (k-NN) classifier, the accuracy...
The problem of classification on highly imbalanced datasets has been studied extensively in the literature. Most classifiers show significant deterioration in performance when dealing with skewed datasets. In this paper, we first examine the underlying reasons for SVM's deterioration on imbalanced datasets. We then propose two modifications for the soft margin SVM, where we change or add constraints...
When building a classifier from clean training data for a particular test environment, knowledge about the environmental noise and channel should be taken into account. We propose training a support vector machine (SVM) classifier using a modified kernel that is the expected kernel with respect to a probability distribution over channels and noise that might affect the test signal. We compare the...
Most practical signal processing problems have to deal with uncertainties, e. g., due to noisy input data. Usual strategies to do this are based on estimating these uncertainties by statistical methods in advance. For some systems with multi-staged signal processing it is possible to identify these estimates at runtime and to relate a degree of certainty to them. If such degrees of certainty are known...
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