(A) study on the improvement of scaling factor determination method using artificial neural network = 인공신경망이론을 이용한 척도인자 결정방법의 향상 방안

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Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of a Difficult-to-Measure (DTM) nuclide are determined using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined using the log mean average method (LMA) and the regression method. These methods are adequate to apply most corrosion product nuclides. But in case of fission product nuclides and some corrosion product nuclides, the predicted values are not well matched with the measured values. In this study, the ANN method is compared with the conventional SF determination method - the LMA and the regression method - for the improved SF determination. Before these comparisons, the sensitivity analysis for each ANN model is performed to determine the optimum size of hidden layers of ANN models. Moreover, the ensemble model, which combines the ANN model with the regression model, is compared with the original models to evaluate the applicability of the ensemble model in SF determination. It is concluded that the ANN method is superior to the conventional SF determination method in some nuclides and the ensemble model can be used as the supplement of the original models.
Advisors
Lee, Kun-Jairesearcher이건재researcher
Description
한국과학기술원 : 원자력및양자공학과,
Publisher
한국과학기술원
Issue Date
2004
Identifier
238256/325007  / 020023421
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 원자력및양자공학과, 2004.2, [ vii, 43 p. ]

Keywords

Regression; Log Mean Average; Artificial Neural Network; Scaling Factor; Ensemble; 결합모델; 회귀모델; 기하평균; 인공신경망; 척도인자

URI
http://hdl.handle.net/10203/49476
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=238256&flag=dissertation
Appears in Collection
NE-Theses_Master(석사논문)
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