과제정보
This work was supported by the KETEP funded by the Korea government Ministry of Trade, Industry and Energy (20206510100040 and 2021040101002D).
참고문헌
- I.A.E. Agency, Safety Margins of Operating Reactors-Analysis of Uncertainties and Implications for Decision Making, International Atomic Energy Agency, 2003.
- A. International Atomic Energy, Deterministic Safety Analysis for Nuclear Power Plants : Specific Safety Guide, IAEA, Vienna, 2019.
- KHNP, Korea Non-LOCA Analysis Package, KHNP, TR-KHNP-0009, 2007 (in Korean).
- M.G. Genton, Classes of kernels for machine learning: a statistics perspective, J. Mach. Learn. Res. 2 (2) (2002) 299-312.
- H. Mannila, Data mining: machine learning, statistics, and databases, in: Eighth International Conference on Scientific and Statistical Database Systems, Proceedings, 1996, pp. 2-9.
- V. Gudivada, et al., Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations, Int. J. Adv. Software 10 (1) (2017) 1-20.
- X. Shi, et al., An accident prediction approach based on XGBoost, in: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, 2017.
- Y. Wang, et al., A hybrid ensemble method for pulsar candidate classification, Astrophys. Space Sci. 364 (8) (2019).
- T. Chen, C. Guestrin, XGboost: a scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
- S.S. Dhaliwal, et al., Effective intrusion detection system using XGBoost, Information 9 (7) (2018) 149.
- H.T. Bang, et al., Application of machine learning methods to predict a thermal conductivity model for compacted bentonite, Ann. Nucl. Energy 142 (2020), 107395.
- J. Cai, et al., An assembly-level neutronic calculation method based on LightGBM algorithm, Ann. Nucl. Energy 150 (2021), 107871.
- C. Xu, et al., A study of predicting irradiation-induced transition temperature shift for RPV steels with XGBoost modeling, Nucl. Eng. Technol. 53 (8) (2021) 2610-2615. https://doi.org/10.1016/j.net.2021.02.015
- B.D. Chung, et al., MARS CODE MANAUAL VOLUME IV-Developmental Assessment Report, Korea Atomic Energy Research Institute, 2010.
- NUREG-0800, Standard review plan for the review of safety analysis reports for nuclear power plants, Rev. (2007).
- (Chapter 15), June, APR1400 Standard Safety Analysis Report, 2002.
- D.H. Shin, et al., Application of a Combined Safety Approach for the Evaluation of Safety Margin during a Loss of Condenser Vacuum Event, Nuclear Engineering and Technology, 2021.
- R. Cuocolo, et al., Current applications of big data and machine learning in cardiology, J. Geriatr. Cardiol.: JGC 16 (8) (2019) 601.
- X. Ying, An overview of overfitting and its solutions, in: Journal of Physics: Conference Series, IOP Publishing, 2019.
- A.N. Richter, T.M. Khoshgoftaar, Learning curve estimation with large imbalanced datasets, in: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2019.
- C. D, J. R, Statistics without Maths for Psychology, Prentice Hall, 2011, p. 175, 5th edition.
- A. Gomez-Rios, ' et al., A study on the noise label influence in boosting algorithms: AdaBoos, GBM and XGBoost, in: International Conference on Hybrid Artificial Intelligence Systems, Springer, 2017.
- S. Sun, et al., A survey of optimization methods from a machine learning perspective, IEEE Trans. Cybern. 50 (8) (2019) 3668-3681. https://doi.org/10.1109/TCYB.2019.2950779
- S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing, Science 220 (4598) (1983) 671-680. https://doi.org/10.1126/science.220.4598.671
- dmlc XGBoost stable", https://xgboost.readthedocs.io/en/stable/parameter.html, accessed 28 June 2022.
- R. Poli, et al., Particle swarm optimization, Swarm Intell. 1 (1) (2007) 33-57. https://doi.org/10.1007/s11721-007-0002-0
- M. Paulsen, et al., RETRAN-3D-A Program for Transient Thermal-Hydraulic Analysis of Complex Fluid Flow Systems, 1996. NP-7450 1(4).
- Combustion Engineering Inc, CESEC Digital Simulation of a Combustion Engineering Nuclear Steam Supply System, 1981. LD-82-001.
- C. Liao, H. Iyer, A tolerance interval for the normal distribution with several variance components, Stat. Sin. (2004) 217-229.
- H. Arsham, M. Lovric, Bartlett's test, Int. encycl. Stat. Sci. 1 (2011) 87-88. https://doi.org/10.1007/978-3-642-04898-2_132
- M.G. Vangel, One-sided nonparametric tolerance limits, Commun. Stat. Simulat. Comput. 23 (4) (1994) 1137-1154. https://doi.org/10.1080/03610919408813222
- D.S. Young, Tolerance: an R package for estimating tolerance intervals, J. Stat. Software 36 (2010) 1-39. https://doi.org/10.18637/jss.v036.i05