• Journal of Radiation Protection and Research
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• v.29 no.1
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• pp.57-63
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• 2004

As a part of In-service Inspection works in a nuclear power plant, Eddy Current Testing through all the outage of nuclear power plants has been controlled by the radiation management. From the case study about the periodical ECT work, the exposed dose rate of worker has announced over the organized dose rate before the radiation work, it affects the personnel exposed dose management and radiation work permit issue. It is not easy to get some information about ECT related working hours, scope of work and how many workers to forecast the radiation working and the predict dose exposure. It should be need the data accumulation about ECT related radiation work to prepare the ALARA achievement and the radiation work plan for dose mitigation. We can discuss a few information about ECT related radiation working issue for the application of predict dose exposure on this paper.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.13-19
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• 2010

In this paper, we study a new estimation system for the prediction of steam generator tube defects. In the previous research works, defect size estimators were independently designed for each defect types in order to estimate the defect size. As a result, the structure of estimation system is rather complex and the estimation performance gets worse if the classification performance is degraded for some reason. This paper studies a new estimation system that does not require the classification of defect types. Although the previous works are expected to achieve much better estimation performance than the proposed system since it uses the estimator specialized in each defect, the performance difference is not so large. Therefore, it is expected that the proposed estimator can be effectively used for the case where the defect type classification is imperfect.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.