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Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete...
Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop...
The research on reliability of electric power communication network originates from the research on equipment fault in it, which is fully significant. In this paper, the equipment fault cases are classified by K-means clustering method, the neural network is trained for fault cases classification based on radial basis function (RBF), the network inputs dimension is reduced by rough set method before...
A new technology of fault prediction was presented based on the neural network and Fisher discriminance in statistics. First, many enough character of running situation of decision-making were extracted from the real-time observation data. Secondly, the FP software systems were designed and the algorithm of FP of decision-making systems was presented. Finally, a simply example indicated that the algorithm...
The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to...
Motor systems are highly important and are critical components in industrial processes. Up to 60% of the electricity produced in the U.S. converts into other forms of energy to provide power to equipment through motor [1]. Machinery reliability and performance can be improved with early fault diagnosis and condition monitoring; therefore, the fault diagnosis system for motor has been highlighted for...
The objective of the present paper is to present a multi-parametric approach based on artificial neural networks for identification and classification purposes of high-impedance faults in distribution systems. More specifically, the proposed methodology uses artificial neural networks integrated with other several statistical techniques that have also been used these problem types. Besides providing...
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