Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system
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-consis...
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| Published in | Nuclear engineering and technology Vol. 54; no. 3; pp. 1037 - 1048 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2022
Elsevier 한국원자력학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1738-5733 2234-358X |
| DOI | 10.1016/j.net.2021.09.032 |
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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 in-situ monitoring system. |
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| AbstractList | 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 in-situ monitoring system. A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detectorapplied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionarylearning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVDwas 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 aslearning samples. Experimental measurement was conducted to obtain practical spectra. After determiningthe hyper parameters, two dictionaries tailored to the learning samples were tested by varyingwith the source position and the measurement time. They achieved average accuracies of 97.6% and98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measuredover 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, theproposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plasticscintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing tothe outstanding properties of sparse representation, the proposed approach can easily be built into an insitumonitoring system KCI Citation Count: 0 |
| Author | Kim, Junhyeok Hwang, Jisung Kim, Jinhwan Lee, Daehee Kim, Giyoon Kim, Wonku Cho, Gyuseong |
| Author_xml | – sequence: 1 givenname: Junhyeok surname: Kim fullname: Kim, Junhyeok organization: Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea – sequence: 2 givenname: Daehee surname: Lee fullname: Lee, Daehee organization: Fuze Laboratory, Agency for Defense Development, Yuseong-gu, P.O. Box 35-5, Daejeon, 305-600, Republic of Korea – sequence: 3 givenname: Jinhwan surname: Kim fullname: Kim, Jinhwan organization: Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea – sequence: 4 givenname: Giyoon surname: Kim fullname: Kim, Giyoon organization: Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea – sequence: 5 givenname: Jisung surname: Hwang fullname: Hwang, Jisung organization: Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea – sequence: 6 givenname: Wonku surname: Kim fullname: Kim, Wonku organization: Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea – sequence: 7 givenname: Gyuseong surname: Cho fullname: Cho, Gyuseong email: gscho@kaist.ac.kr organization: Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea |
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| Cites_doi | 10.1007/BF02678430 10.1016/j.anucene.2019.05.051 10.1109/49.932703 10.1016/j.apradiso.2019.01.005 10.1109/TSP.2006.881199 10.1016/j.radphyschem.2018.07.022 10.3390/s21041051 10.1016/j.apradiso.2015.10.019 10.1016/j.radmeas.2012.06.001 10.1109/PROC.1985.13340 10.1016/j.net.2019.01.017 10.1016/j.nima.2015.06.007 10.3390/a2010339 10.1109/TPAMI.2013.88 10.1109/TPAMI.2016.2545661 10.1016/j.radmeas.2013.01.049 |
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| Keywords | Radioisotope identification Sparse representation Discriminative dictionary Dictionary learning |
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| SubjectTerms | Dictionary learning Discriminative dictionary Radioisotope identification Sparse representation 원자력공학 |
| Title | Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system |
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