Acknowledgement
본 연구는 한국원자력연구원의 도전창의 집단기술연구사업 지원을 받았으며(NRF-2021M2D2A20184452161082139290201), 한국수력원자력(주)의 지원(No. 2020-Tech-14), 한국에너지기술평가원의 에너지기술개발사업지원(No. 20203210100390)으로 수행되었습니다.
References
- Kim CL. 2016. Nuclear Decommissioning Waste Management Trends. Konetic Report 2016-138
- Kim TW, Choi KS, Kang KD and Ha JH. 2008. Method for Determining Transportation Grade for HIC Containing Spent Resin Using Radioactivity Analysis. J. Nucl. Fuel Cycle Waste Technol. 6(1): 11-15.
- Nuclear Safety and Security Commission. 2019. Investigation Results on the Errors in Nuclide Analysis of Radioactive Waste at the Korea Atomic Energy Research Institute, 103rd Nuclear Safety and Security Commission, Report: 1.
- Lee KM, Hong DS, Kim TG, Bae SM, Shon JS and Hong GP. 2004. Radioactive Wastes Assay Technique & Equipment. KAERI/TR-2879/2004.
- Li F, Wang J, Ge L, Hu F, Cheng F and Sun K. 2019. Research on gamma spectrum semi-quantitative analysis based on convolutional neural network. J. Phys.: Conf. Ser. 1423:012005.
- Kamuda M, Zhao J and Huff K. 2020. A comparison of machine learning methods for automated gamma-ray spectroscopy. Nucl. Instrum. Methods Phys. Res. A. 954:161385. https://doi.org/10.1016/j.nima.2018.10.063
- Shumaker E, Stinnett J and Hero AO. 2020. Twin Artificial Neural Networks for Gamma Ray Spectra Comparison. 2020 ETI Annual Workshop.
- Gomez-Fernandez M, Wong WK, Tokuhiro A, Welter K, Alhawsawi AM, Yang H and Higley K. 2021. Isotope identification using deep learning: An explanation. Nucl Instrum Methods Phys. Res. A. 988:164925. https://doi.org/10.1016/j.nima.2020.164925
- Chalapathy R and Chawla S. 2019. Deep Learning for Anomaly Detection: A Survey. arXiv:1901.03407v2.
- Pang G, Shen C, Cao L and Hengel AV. 2020. Deep Learning for Anomaly Detection: A Review. arXiv:2007.02500v3.
- Lee GJ, Paek MH, Park JG, Han BS, Cheong JH, Lee HC, Yang JY, Hong HK and Park JB. 1995. A Study on the Establishment of the Regulatory Guide to the Characteristics and Classi fication Cri teria of Low and Intermediate Level Radioactive Waste. KINS/HR-111.
- Han SJ, Lee HY, Kim BG and An EM. 2021. Analysis and Consideration of the Establishment of a Multiplexed Channel for Domestic RI Waste Nuclide Analysis. J. Radiol. Sci. Technol. 44(4): 351-358. https://doi.org/10.17946/JRST.2021.44.4.351
- Best WT and Miller AD. 1987. Updated Scailing Factors in Low-Level Radwaste. EPRI NP-5077, Project 1557-6, Final Report.
- Tyagi AK and Chahal P. 2019. Artificial Intelligence and Machine Learning Algorithms. pp. 188-219. In: Challenges and Applications for Implementing Machine Learning in Computer Vision(Kashyap R and Kumar AV), IGI Global, Hershey. PA.
- Wang J, Xu M, Wang H and Zhang J. 2006. Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding. ICSP2006 Proceeding.
- Teknomo K. 2006. K-means clustering tutorial. Available online at http://people.revoledu.com/kardi/tutorial/kMean/