Journal of the Korean Institute of Illuminating and Electrical Installation Engineers (조명전기설비학회논문지)

The Korean Institute of IIIuminating and Electrical Installation Engineers (한국조명전기설비학회)
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A Study on the Structure of Neural Network for Predicting Defect Size of Steam Generator Tube in Nuclear Power Plant

원전SG 세관 결함크기 예측을 위한 신경회로망 구조에 관한 연구

  • 조남훈 (숭실대학교 전기공학부)
  • Published : 2010.01.31
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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.

본 논문에서는 원자력발전소 증기세관 크기 예측을 위한 신경회로망 구조에 대해서 연구한다. 와류탐상 시험(ECT) 신호로부터 특징을 추출한 후, 결함크기 예측을 위해서 다층퍼셉트론 신경회로망을 이용한다. 결함크기 예측성능을 최대화하기 위해서는 신경회로망의 구조, 특히 은닉층 내의 뉴런의 개수를 신중히 결정하여야 한다. 본 논문에서는, 결함크기 예측을 위한 은닉층 내의 뉴런의 개수를 교차검증을 이용하여 매우 효과적으로 결정할 수 있음을 보인다.

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References

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