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Classification Performance Improvement of Steam Generator Tube Defects in Nuclear Power Plant Using Bagging Method  

Lee, Jun-Po (숭실대 공대 전기공학부)
Jo, Nam-Hoon (숭실대 공대 전기공학부)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.58, no.12, 2009 , pp. 2532-2537 More about this Journal
Abstract
For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.
Keywords
Eddy Current Testing (ECT); Steam Generator (SG); Neural Network; Bagging;
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