방사성폐기물학회지 (Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT))
- 제2권1호
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- Pages.35-40
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- 2004
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- 1738-1894(pISSN)
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- 2288-5471(eISSN)
인공신경망 이론을 이용한 척도인자 결정방법의 향상방안에 관한 연구
A Study on the Improvement of Scaling Factor Determination Using Artificial Neural Network
- Sang-Chul Lee (Korea Advanced Institute of Science and Technology) ;
- Ki-Ha Hwang (Korea Advanced Institute of Science and Technolog) ;
- Sang-Hee Kang (Korea Advanced Institute of Science and Technolog) ;
- Kun-Jai Lee (Korea Advanced Institute of Science and Technology)
- 발행 : 2004.03.01
초록
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 the Difficult-to-Measure (DTM) nuclide is estimated 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 by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14