Radionuclide identification based on energy-weighted algorithm and machine learning applied to a multi-array plastic scintillator
4
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm2. The energy spectra of 137Cs, 60Co, 226Ra, and 4K (KCl) were measured at speeds of 10–30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions
Abstract
Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm2. The energy spectra of 137Cs, 60Co, 226Ra, and 4K (KCl) were measured at speeds of 10–30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions
참고문헌 (23)
- P. Dorenbos, "Non-proportionality in the scintillation response and the energy resolution obtainable with scintillation crystals", IEEE Trans. Nucl. Sci, 42(6), pp.2190~2202 (1995)
- B. L. Rupert, "Bismuth-loaded plastic scintillators for gamma-ray spectroscopy", Europhys. Lett, 97(2), pp.22002~ (2012)
- Progress Report on the Advanced Large-Area Plastic Scintillators (ALPS) Project, "D.V. Jordan", Pacific Northwest National Laboratory (PNNL), ~, pp.PNNL-14283 (2003)
- H. C. Lee, "Dynamic radionuclide identification using energy weighted algorithm with commercialized radiation portal monitor based on plastic scintillators", Radiat. Phys. Chem, 170, pp.108645~ (2020)
- H. C. Lee, "Validation of energy-weighted algorithm for radiation portal monitor using plastic scintillator", Appl. Radiat. Isot, 107, pp.160~164 (2016)
- W. G. Shin, "A Monte Carlo study of an energy-weighted algorithm for radionuclide analysis with a plastic scintillation detector", Appl. Radiat. Isot, 101, pp.53~59 (2015)
- H. C. Lee, "Radioisotope identification using an energy-weighted algorithm with a proof-of-principle radiation portal monitor based on plastic scintillators", Appl. Radiat. Isot, 156, pp.109010~ (2020)
- American National Standard for Performance Criteria for Spectroscopy-Based Portal Monitors Used in Homeland Security, "A.N.S. Institute", American National Standards Institute, 45~ (2015)
- B. T. Koo, "Feasibility study for radionuclide identification using multi-array plastic scintillator and energy weighted algorithm of radiation portal monitors: a Monte Carlo study", J. Instrum, 14(12), pp.P12015~ (2019)
- D. Oneata, "Spatio-temporal object detection proposals", European Conference on Computer Vision (2014)
- A. Krizhevsky, "Imagenet classification with deep convolutional neural networks", Commun. ACM, 60(6), pp.84~90 (2017)
- J. Long, "Fully convolutional networks for semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
- J. R. Koza, "Automated design of both the topology and sizing of analog electrical circuits using genetic programming", Artificial Intelligence in Design’96 (1996)
- A. Ratner, "Snorkel metal: weak supervision for multi-task learning", Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning (2018)
- T. Hastie, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" (2009), Springer
- Y. LeCun, "Gradient-based learning applied to document recognition", Proc. IEEE, 86(11), pp.2278~2324 (1998)
- M. Abadi, "Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems", arXiv preprint arXiv:1603.04467, ~ (2016)
- D. Mishkin, "Systematic evaluation of convolution neural network advances on the imagenet", Comput. Vis. Image Underst, 161, pp.11~19 (2017)
- R. H. R. Hahnloser, "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit", Nature, 405(6789), pp.947~951 (2000)
- H. Ide, "Improvement of learning for CNN with ReLU activation by sparse regularization", 2017 International Joint Conference on Neural Networks (IJCNN) (2017)
- T. Fawcett, "An introduction to ROC analysis", Pattern recognit. Lett, 27(8), pp.861~874 (2006)
- D.M.W. Powers, "Evaluation: from Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation", arXiv preprint arXiv:2010.16061, ~ (2020)
- C. J. Van Rijsbergen, "The Geometry of Information Retrieval" (2004), Cambridge University Press