Classification of nuclear activity types for neighboring countries of South Korea using machine learning techniques with xenon isotopic activity ratios
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The discrimination of the source for xenon gases’ release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases’ release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate th
Abstract
The discrimination of the source for xenon gases’ release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases’ release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate th
참고문헌 (17)
- P. Gaebler, "A multi-technology analysis of the 2017 North Korean nuclear test", Solid Earth, 10, pp.59~78 (2019)
- "CTBTO"[URL]
- M. B. Kalinowski, "Discrimination of nuclear explosions against civilian sources based on atmospheric xenon isotopic activity ratios", Pure Appl. Geophys, 167, pp.517~539 (2010)
- S. Shai, "Understanding Machine Learning : from Theory to Algorithms" (2014), Cambridge University Press
- SCALE CODE SYSTEM, "B. T. Rearden", Oak Ridge National Laboratory, ~, pp.ORNL (2018), TM-2005[DOI]
- J. Leppanen, "The serpent Monte Carlo code:status, development and application in 2013", Ann. Nucl. Energy, 82, pp.142~150 (2015)
- J. Brownlee, "Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in python" (2020), Machine learning mastery
- F. Pedregosa, "Scikit-learn : machine learning in Python", J. Mach. Learn. Res, 12, pp.2825~2830 (2011)
- G. H. Park, "Analysis and DB Construction of Radioactive Xenon Isotope Characteristics for Various Types of Nuclear Threat in Neighboring Countries", pp.2021
- B. Ade, "ORIGEN Reactor Libraries", Oak Ridge National Laboratory, ~ (2016)
- G. H. Park, "An estimation of weapon-grade plutonium production from 5MWe Yongbyun reactor through MCNP6 core depletion analysis", Prog. Nucl. Energy, 130, pp.103533~ (2020)
- D. R. Cox, "The regression analysis of binary sequences", J. R. Stat. Soc. Series B Stat. Methodol, 20, pp.215~232 (1958)
- C. J. Burges, "A tutorial on support vector machines for pattern recognition", Data Min. Knowl. Discov, 2, pp.121~167 (1998)
- C. M. Bishop, "Pattern Recognition and Machine Learning" (2006), Springer
- A. Patle, "SVM kernel functions for classification", International Conference on Advances in Technology and Engineering, ICATE) (2013)
- K. Chomboon, "An empirical study of distance metrics for K-nearest neighbor algorithm", Proceedings of the 3rd International Conference on Industrial Application Engineering (2015)
- Y. Bengio, "No unbiased estimator of the variance of K-fold crossvalidation", J. Mach. Learn. Res, 5, pp.1089~1105 (2004)