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http://dx.doi.org/10.5207/JIEIE.2010.24.1.063

A Study on the Structure of Neural Network for Predicting Defect Size of Steam Generator Tube in Nuclear Power Plant  

Jo, Nam-Hoon (숭실대학교 전기공학부)
Publication Information
Journal of the Korean Institute of Illuminating and Electrical Installation Engineers / v.24, no.1, 2010 , pp. 63-70 More about this Journal
Abstract
In this paper, we study the structure of neural network for predicting defect size of steam generator tube. After extracting the features from the eddy current testing (ECT) signals, multi-layer neural networks are used to predict the defect size. In order to maximize the prediction performance for the defect size, we should carefully choose the structure of neural networks, especially the number of neurons in the hidden layer. In this paper, it is shown that, for the prediction of defect size, the number of neurons in the hidden layer can be efficiently determined by using cross-validation.
Keywords
Steam Generator Tube; Eddy Current Testing; Neural Network; Cross-Validation;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 C. Schaffer, “Selecting a Classification Method by Cross-Validation,” Machine Learning, Vol. 13, pp. 135-143, 1993.   DOI
2 G. Chen, A. Yamaguchi, K. Miya, “A novel signal processing technique for eddy-current testing of steam generator tubes,” IEEE Trans. Magnetics, Vol. 34, No. 3, pp. 642-648, 1998.   DOI
3 P. Xiang, S. Ramakrishnan, X. Cai, P. Ramuhalli, R. Polikar, S.S. Udpa, L. Udpa, “Automated analysis of rotating probe multi-frequency eddy current data from steam generator tubes,” International Journal of Applied Electromagnetics and Mechanics, Vol. 12, pp. 151-164, 2000.
4 M. Das, H. Shekhar, X. Liu, R. Polikar, P. Ramuhalli, L. Udpa, S. Udpa, “A generalized likelihood ratio technique for automated analysis of bobbin coil eddy current data,” NDT & E International, Vol. 35, pp. 329-336, 2002.   DOI
5 H. Haoyu, T. Takagi, “Inverse analyses for natural and multicracks using signals from a differential transmitreceive ECT probe,” IEEE Trans. Magnetics, Vol. 38, No. 2, pp. 1009-1012, 2002.   DOI
6 S.J. Song and Y.K. Shin, “Eddy current Flaw characterization in tubes by neural networks and finite element modeling,” NDT & E International, Vol. 33, pp. 233-243, 2000.   DOI
7 H. Haoyu, and T. Takagi, “Inverse analyses for natural and multicracks using signals from a differential transmitreceive ECT probe,” IEEE Trans. Magnetics, Vol. 38, No. 2, part 1, pp. 1009-1012, 2002.   DOI
8 M. Rebican, N. Yusa, Z. Chen, K. Miya, T. Uchimoto, and T. Takagi, “Reconstruction of multiple cracks in an ECT round-robin test,” International Journal of Applied Electromagnetics and Mechanics, Vol. 19, No. 1-4, pp. 399-404, 2004.
9 조남훈, 이향범, 한기원, 송성진 “신경회로망을 이용한 원전SG 세관 결함패턴 분류성능 향상기법,” 전기학회 논문지 , Vol. 56, No. 7, pp. 1224 - 1230, July, 2007.   과학기술학회마을
10 S. Haykin, Neural Networks, New Jersey: Prentice -Hall, 1999.