| Title |
Classification Performance Improvement of Steam Generator Tube Defects in Nuclear Power Plant Using Bagging Method |
| Authors |
이준표(Lee, Jun-Po) ; 조남훈(Jo, Nam-Hoon) |
| Keywords |
Eddy Current Testing (ECT) ; Steam Generator (SG) ; Neural Network ; Bagging |
| 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. |