Article Sidebar

PDF
Published: 2024-02-20
Abstract Views: 71
Views PDF: 53
DOI: https://doi.org/10.53747/nst.v12i4.347
Issue
Vol. 12 No. 4 (2022)
Section
Articles
How to Cite
Cao, H., Dinh, T. H., Dinh, K. C., Nguyen, T. T., Pham, D. K., & Nguyen, X. H. (2024). Nuclide identification algorithm for Polyvinyl Toluene scintillation detector based on Deep Neural Network. Nuclear Science and Technology, 12(4), 46-55. https://doi.org/10.53747/nst.v12i4.347

Nuclide identification algorithm for Polyvinyl Toluene scintillation detector based on Deep Neural Network

Hiep Cao1, Tien Hung Dinh2, Kim Chien Dinh3, Thi Thoa Nguyen3, Dinh Khang Pham4, Xuan Hai Nguyen5
1 s:68:"Vietnam Military Institute of Chemical and Environmental Engineering";
2 1Military Institute of Chemical and Environmental Engineering
3 Military Institute of Chemical and Environmental Engineering
4 Hanoi University of Science and Technology
5 Dalat Nuclear Rerearch Institute

Main Article Content

Abstract

Radiation portal monitors (RPMs) are now stationed at strategic areas (airports, ports, etc.) to identify the illegal transportation of radioactive sources and nuclear items. RPMs are typically fitted with a PVT detector with a high recording efficiency. Radioisotope identification from the gamma spectrum acquired on this detector is normally not regarded due to the low resolution. This research describes an artificial neural network-based isotope identification algorithm that was applied to the gamma spectrum collected from the RPM's PVT detector. With excellent precision, this approach can detect one or a mixture of isotopes on the spectrum. The model still recognizes the training isotopes with >89 percent accuracy for spectra with the gain displacement in the range of 20 percent.

Article Details

Keywords

Artificial Neural Network, PVT scintillation detector, Nuclide identification algorithm

References

[1]. P. Olmos et al., “A New Approach to Automatic Radiation Spectrum Analysis,” IEEE Trans. Nucl. Sci., vol. 38, no. 4, pp. 971–975, 1991, doi: 10.1109/23.83860.
[2]. C. Bobin, O. Bichler, V. Lourenço, C. Thiam, and M. Thévenin, “Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network,” Appl. Radiat. Isot., vol. 109, pp. 405–409, 2016, doi: 10.1016/j.apradiso.2015.12.029.
[3]. L. F. Blázquez, F. Aller, S. Vrublevskaya, J. Fombellida, and E. Valtuille, “Classification of radionuclides on polyvinyl toluene radiation portal monitors by a neural network based system,” IFAC-PapersOnLine, vol. 28, no. 21, pp. 852–857, 2015, doi: 10.1016/j.ifacol.2015.09.633.
[4]. L. Chen and Y. X. Wei, “Nuclide identification algorithm based on K-L transform and neural networks,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 598, no. 2, pp. 450–453, 2009, doi: 10.1016/j.nima.2008.09.035.
[5]. J. Fombellida, L. F. Blazquez, F. Aller, S. Vrublevskaya, and E. Valtuille, “Neural network based radioisotope discrimination on polyvinyl toluene radiation portal monitors,” 2014 22nd Mediterr. Conf. Control Autom. MED 2014, pp. 1099–1104, 2014, doi: 10.1109/MED.2014.6961521.
[6]. M. Kamuda and C. J. Sullivan, “An automated isotope identification and quantification algorithm for isotope mixtures in low-resolution gamma-ray spectra,” Radiat. Phys. Chem., vol. 155, no. August 2017, pp. 281–286, 2019, doi: 10.1016/j.radphyschem.2018.06.017.
[7]. L. J. Kangas, P. E. Keller, E. R. Siciliano, R. T. Kouzes, and J. H. Ely, “The use of artificial neural networks in PVT-based radiation portal monitors,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 587, no. 2–3, pp. 398–412, 2008, doi: 10.1016/j.nima.2008.01.065.
[8]. J. Kim, K. Park, and G. Cho, “Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma
spectra,” Appl. Radiat. Isot., vol. 147, no. November 2018, pp. 83–90, 2019, doi: 10.1016/j.apradiso.2019.01.005.
[9]. D. Liang et al., “Rapid nuclide identification algorithm based on convolutional neural network,” Ann. Nucl. Energy, vol. 133, pp. 483–490, 2019, doi: 10.1016/j.anucene.2019.05.051.
[10]. V. Pilato, F. Tola, J. M. Martinez, and M. Huver, “Application of neural networks to quantitative spectrometry analysis,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 422, no. 1–3, pp. 423–427, 1999, doi: 10.1016/S0168-9002(98)01110-3.
[11]. E. Yoshida, K. Shizuma, S. Endo, and T. Oka, “Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 484, no. 1–3, pp. 557–563, 2002, doi: 10.1016/S0168-9002(01)01962-3.
[12]. D. T. Hung et al., “Gamma spetrum stabilization for environmental radiation monitoring stations using NaI(Tl) detector,” Radiat. Prot. Dosimetry, vol. 189, no. 1, pp. 48–55, Mar. 2020, doi: 10.1093/rpd/ncaa011.