Bagging 방법을 이용한 원전SG 세관 결함패턴 분류성능 향상기법
- Authors
- 이준표; 조남훈
- Issue Date
- 2009
- Publisher
- 대한전기학회
- Keywords
- Eddy Current Testing (ECT); Steam Generator (SG); Neural Network; Bagging
- Citation
- 전기학회논문지ABCD, v.58, no.12, pp.2532 - 2537
- Journal Title
- 전기학회논문지ABCD
- Volume
- 58
- Number
- 12
- Start Page
- 2532
- End Page
- 2537
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/16577
- ISSN
- 1229-2443
- 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.
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