Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system

  • Kim, Junhyeok (Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Daehee (Fuze Laboratory, Agency for Defense Development) ;
  • Kim, Jinhwan (Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Giyoon (Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Hwang, Jisung (Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Wonku (Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Cho, Gyuseong (Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2021.08.13
  • Accepted : 2021.09.26
  • Published : 2022.03.25
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Abstract

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (133Ba, 22Na, 137Cs, and 60Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an insitu monitoring system.

Keywords

Acknowledgement

This research was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (grant No. 2019-0-00831) and the Korea Atomic Energy Research Institute funded by the Ministry of Science and ICT (2020M2C9A106861712).

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