Nuclear Engineering and Technology

  • 제53권10호
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  • Pages.3431-3437
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  • 2021
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Comparison of theoretical and machine learning models to estimate gamma ray source positions using plastic scintillating optical fiber detector

  • Kim, Jinhong (School of Energy Systems Engineering, Chung-Ang University) ;
  • Kim, Seunghyeon (School of Energy Systems Engineering, Chung-Ang University) ;
  • Song, Siwon (School of Energy Systems Engineering, Chung-Ang University) ;
  • Park, Jae Hyung (School of Energy Systems Engineering, Chung-Ang University) ;
  • Kim, Jin Ho (School of Energy Systems Engineering, Chung-Ang University) ;
  • Lim, Taeseob (School of Energy Systems Engineering, Chung-Ang University) ;
  • Pyeon, Cheol Ho (Research Center for Safe Nuclear System, Institute for Integrated Radiation and Nuclear Science, Kyoto University) ;
  • Lee, Bongsoo (School of Energy Systems Engineering, Chung-Ang University)
  • 투고 : 2021.02.23
  • 심사 : 2021.04.15
  • 발행 : 2021.10.25
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초록

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

키워드

과제정보

This research was supported by the Chung-Ang University Graduate Research Scholarship in 2019 and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020M2D2A2062457).

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