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Transformer fault diagnosis based on relevance vector machine (RVM) is proposed. The advantages of the RVM over the support vector machine (SVM) are probabilistic predictions, automatic estimations of parameters, and the possibility of choosing arbitrary kernel functions. Most importantly, RVM is capable of comparable classification accuracy to SVM, but with fewer relevance vectors (RVs) and higher...
This paper proposes a novel and simple method that uses the randomness of random matrix and SVM ensemble learning to discriminate eight types of heartbeats. We use random matrices to generate 15 groups of random features. Then we construct one SVM classifier on each group of random features along with a RR interval. The type of heartbeat is determined using majority voting strategy to combine 15 SVM...
Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes. The prediction of interesting proteins types by computational methods can provide new clues in functional studies of uncharacterized proteins without performing extensive experiments. Support vector machine (SVM) is a new kind of approach to supervised pattern classification...
Power transformer is one of the most expensive component of electrical power plants and the failures of such transformer can result in serious power system issues, so fault forecasting for power transformer is very important to insure the whole power system runs normally. In this paper, a novel fault prediction approach for power transformer based on Support Vector Machine (SVM) is presented using...
A multi-level fault diagnosis model for power transformer fault diagnosis based on Statistical theory is presented The fault information within Dissolved Gas Analysis (DGA) is used to build fault diagnosis model and the fault diagnosis is accomplished according to the concentration distribution of typical fault gases in higher dimensional space. The proposed approach is constructing the most accuracy...
A new method based on Euclidean clustering and support vector machines is presented and constructed in the paper. According to the Euclidean distances between the transformer's state sorts, build the multi-classification model of support vector machines. The diagnosing experiments of different transformer testing scenarios show this method can avoid the blindness when building the multi-classifier,...
A test case consists of a set of inputs and a list of expected outputs. To automatically generate the expected outputs for the test case is rather difficult. An approach based on wavelet support vector machines (WSVM) is proposed to overcome it. After training, WSVM is used to automatically generate the expected outputs, which approximate the correct outputs. Actual outputs of the application under...
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