Nuclear Engineering and Technology

  • 제54권3호
  • /
  • Pages.1037-1048
  • /
  • 2022
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2021.08.13
  • 심사 : 2021.09.26
  • 발행 : 2022.03.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

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).

참고문헌

  1. J. Kim, K. Park, J. Hwang, H. Kim, J. Kim, H. Kim, S.-H. Jung, Y. Kim, G. Cho, Efficient design of a Ø2×2 inch NaI(Tl)scintillation detector coupled with a SiPM in an aquatic environment, Nucl. Eng. Technol. 51 (2019), https://doi.org/10.1016/j.net.2019.01.017.
  2. L. Marques, A. Vale, P. Vaz, State-of-the-art mobile radiation detection systems for different scenarios, Sensors 21 (2021) 1-67, https://doi.org/10.3390/s21041051.
  3. F.G. Knoll, Radiation Detection and Measurement, third ed. -, Glenn F.pdf, 2000, p. 802.
  4. T. Burr, M. Hamada, Radio-Isotope identification algorithms for NaI γ spectra, Algorithms 2 (2009) 339-360, https://doi.org/10.3390/a2010339.
  5. Y. Kim, M. Kim, K.T. Lim, J. Kim, G. Cho, Inverse calibration matrix algorithm for radiation detection portal monitors, Radiat. Phys. Chem. 155 (2019) 127-132, https://doi.org/10.1016/j.radphyschem.2018.07.022.
  6. H.C. Lee, W.G. Shin, H.J. Park, D.H. Yoo, C. Il Choi, C.S. Park, H.S. Kim, C.H. Min, Validation of energy-weighted algorithm for radiation portal monitor using plastic scintillator, Appl. Radiat. Isot. 107 (2016) 160-164, https://doi.org/10.1016/j.apradiso.2015.10.019.
  7. J. Kim, K. Park, G. Cho, Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma spectra, Appl. Radiat. Isot. 147 (2019) 83-90, https://doi.org/10.1016/j.apradiso.2019.01.005.
  8. D. Liang, P. Gong, X. Tang, P. Wang, L. Gao, Z. Wang, R. Zhang, Rapid nuclide identification algorithm based on convolutional neural network, Ann. Nucl. Energy 133 (2019) 483-490, https://doi.org/10.1016/j.anucene.2019.05.051.
  9. C.-C. Hung, E. Song, Y. Lan, Image Texture Analysis, Springer International Publishing, 2019, https://doi.org/10.1007/978-3-030-13773-1.
  10. M. Aharon, M. Elad, A. Bruckstein, K-SVD, An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Trans. Signal Process. 54 (2006) 4311-4322, https://doi.org/10.1109/TSP.2006.881199.
  11. Z. Jiang, Z. Lin, L.S. Davis, Label consistent K-SVD: learning a discriminative dictionary for recognition, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013) 2651-2664, https://doi.org/10.1109/TPAMI.2013.88.
  12. D.B. Pelowitz, J.T. Goorley, M.R. James, T.E. Booth, F.B. Brown, J.S. Bull, L.J. Cox, J.W. Durkee, J.S. Elson, M.L. Fensin, R.A. Forster, J.S. Hendricks, H.G. Hughes, R.C. Johns, B.C. Kiedrowski, S.G. Mashnik, MCNP6 User's Manual, 2013.
  13. G. Davis, S. Mallat, M. Avellaneda, Adaptive greedy approximations, Constr, Approx 13 (1997) 57-98, https://doi.org/10.1007/BF02678430.
  14. T.T. Cai, L. Wang, Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise 57 (2011) 4680-4688. https://doi.org/10.1109/TIT.2011.2146090
  15. I. Kviatkovsky, M. Gabel, E. Rivlin, I. Shimshoni, On the equivalence of the LC-KSVD and the D-KSVD algorithms, IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017) 411-416, https://doi.org/10.1109/TPAMI.2016.2545661.
  16. Z. Jiang, Z. Lin, L.S. Davis, Learning A Discriminative Dictionary for Sparse Coding via Label Consistent K-SVD, n.D.
  17. C. Kim, Y. Kim, M. Moon, G. Cho, Iterative Monte Carlo simulation with the Compton kinematics-based GEB in a plastic scintillation detector, Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip. 795 (2015) 298-304, https://doi.org/10.1016/j.nima.2015.06.007.
  18. R. Casanovas, J.J. Morant, M. Salvado, Temperature peak-shift correction methods for NaI(Tl) and LaBr 3(Ce) gamma-ray spectrum stabilisation, Radiat. Meas. 47 (2012) 588-595, https://doi.org/10.1016/j.radmeas.2012.06.001.
  19. S. Buranurak, C.E. Andersen, A.R. Beierholm, L.R. Lindvold, Temperature variations as a source of uncertainty in medical fiber-coupled organic plastic scintillator dosimetry, Radiat. Meas. 56 (2013) 307-311, https://doi.org/10.1016/j.radmeas.2013.01.049.
  20. J. Kim, K.T. Lim, J. Kim, C. jong Kim, B. Jeon, K. Park, G. Kim, H. Kim, G. Cho, Quantitative analysis of NaI(Tl) gamma-ray spectrometry using an artificial neural network, Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip. 944 (2019), https://doi.org/10.1016/j.nima.2019.162549.
  21. G.C.J. Kim, K.T. Lim, K. Ko, E. Ko, Radioisotope Identification and Nonintrusive Depth Estimation of Localized Low-Level Radioactive Contaminants Using Bayesian Inference, Sensors (Switzerland), 2020, p. 20.
  22. M.A. El Khaddar, H. Harroud, M. Boulmalf, M. Elkoutbi, A. Habbani, Emerging wireless technologies in e-health: trends, challenges, and framework design issues, in: Int. Conf. Multimed. Comput. Syst. ICMCS 2012, 2012, pp. 440-445, https://doi.org/10.1109/ICMCS.2012.6320276.
  23. J. Makhoul, S. Roucos, H. Gish, Vector quantization in speech coding, Proc. IEEE 73 (1985) 1551-1588, https://doi.org/10.1109/PROC.1985.13340.
  24. W.W. Chang, H. Tan, D.Y. Wang, Robust vector quantization for wireless channels, IEEE J. Sel. Areas Commun. 19 (2001) 1365-1373, https://doi.org/10.1109/49.932703.