참고문헌 (31)

1. "Operational Performance Information System for Nuclear Power Plant", Nuclear Accident and Failure Status, 2020 last modified Oct 26, http://opis.kins.re.kr/opis?actKROBA3400R. (Accessed 13 January 2021). accessed. 

2. J.M. Kim, G. Lee, C. Lee, S.J. Lee, Abnormality diagnosis model for nuclear power plants using two-stage gated recurrent units, Nucl. Eng. Technol. 52 (9) (2020) 2009-2016. 

   원문보기 상세보기

3. J. Yang, J. Kim, Accident diagnosis algorithm with untrained accident identification during power-increasing operation, Reliab. Eng. Syst. Saf. 202 (2020) 107032. 

   상세보기

4. H. Kim, A.M. Arigi, J. Kim, Development of a diagnostic algorithm for abnormal situations using long short-term memory and variational autoencoder, Ann. Nucl. Energy 153 (2021) 108077. 

   상세보기

5. K.H. Yoo, J.H. Back, M.G. Na, S. Hur, H. Kim, Smart support system for diagnosing severe accidents in nuclear power plants, Nucl. Eng. Technol. 50 (4) (2018) 562-569. 

   원문보기 상세보기

6. A. Ayodeji, Y.-k. Liu, Support vector ensemble for incipient fault diagnosis in nuclear plant components, Nucl. Eng. Technol. 50 (8) (2018) 1306-1313. 

   원문보기 상세보기

7. A. Ayodeji, Y.-k. Liu, H. Xia, Knowledge base operator support system for nuclear power plant fault diagnosis, Prog. Nucl. Energy 105 (2018) 42-50. 

   상세보기

8. Y.-k. Liu, A. Ayodeji, Z.-b. Wen, M.-p. Wu, M.-j. Peng, W.-f. Yu, A cascade intelligent fault diagnostic technique for nuclear power plants, J. Nucl. Sci. Technol. 55 (3) (2018) 254-266. 

   상세보기

9. M.-j. Peng, H. Wang, S.-s. Chen, G.-l. Xia, Y.-k. Liu, X. Yang, A. Ayodeji, An intelligent hybrid methodology of on-line system-level fault diagnosis for nuclear power plant, Nucl. Eng. Technol. 50 (3) (2018) 396-410. 

   원문보기 상세보기

10. R.L. Boring, K.D. Thomas, T.A. Ulrich, R.T. Lew, Computerized operator support systems to aid decision making in nuclear power plants, Procedia Manuf. 3 (2015) 5261-5268. 

    상세보기

11. M.A. Kramer, Nonlinear principle component analysis using autoassociative neural networks, AIChE J. 37 (2) (1991) 233-243. 

    상세보기

12. M. Sakurada, T. Yairi, Anomaly detection using autoencoders with nonlinear dimensionality reduction, in: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, 2014, pp. 4-11. 

13. R. Chalapathy, A.K. Menon, S. Chawla, Anomaly Detection Using One-Class Neural Networks, 2018 arXiv preprint arXiv: 1802.06360. 

14. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8) (1997) 1735-1780. 

    상세보기

15. K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation, arXiv preprint arXiv: 1406.1078, 2014. 

16. J.H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Stat. 29 (5) (2001) 1189-1232. 

    상세보기

17. J.H. Friedman, Stochastic gradient boosting, Comput. Stat. Data Anal. 38 (4) (2002) 367-378. 

    상세보기

18. Ke Guolin, et al., LightGBM: a highly efficient gradient boosting decision tree, in: Proc. Advances in neural information processing systems, 2017, pp. 3146-3154. 

19. A.B. Arrieta, et al., Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion 58 (2020) 82-115. 

    상세보기

20. J.S. Bridle, Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, Neurocomputing (1990) 227-236. 

21. D. Gunning, Explainable Artificial Intelligence (XAI), Tech. Rep., Defense Advanced Research Projects Agency (DARPA), 2017. 

22. S.M. Lundberg, G.G. Erion, Su-In Lee, Consistent Individualized Feature Attribution for Tree Ensembles, arXiv preprint arXiv:1802.03888, 2018. 

23. S.M. Lundberg, et al., From local explanation to global understanding with explainable AI for trees, Nat Mach Intell 2 (1) (2020) 2522-5839. 

24. L.S. Shapley, A.E. Roth, The Shapley Value: Essays in Honor of Lloyd S. Shapley, Cambridge University Press, 1988. 

25. Hayes-Roth, Frederick, Rule-based systems, Commun. ACM 28 (9) (1985) 921-932. 

    상세보기

26. J.-c. Park, Equipment and Performance Upgrade of Compact Nuclear Simulator, KAERI/RR-1967/1999, KAERI:Daejeon, Korea, 1999. 

27. J. Miettinen, Development and assessment of the SBLOCA code SMABRE, in: Proceedings of the CSNI Specialists' Meeting on Small Break LOCA Analyses in LWRs vols. 23-27, June 1985, pp. 481-495. Pisa, Italy. 

28. C. Tang, N. Luktarhan, Y. Zhao, An efficient intrusion detection method based on LightGBM and autoencoder, Symmetry 12 (9) (2020) 1458. 

    상세보기

29. H.S. Hota, R. Handa, A.K. Shrivas, Time series data predicting using sliding window based RBF neural network, Int. J. Comput. Intell. Res. 13 (5) (2017) 1145-1156. 

30. S.H. Park, J.M. Goo, C.H. Jo, Receiver operating characteristics (ROC) curve: practical review for radiologists, Korean J. Radiol. 5 (2004) 11-18. 

    상세보기

31. J.N. Mandrekar, Receiver operating characteristic curve in diagnostic test assessment, J. Thorac. Oncol. 5 (9) (2010) 1315-1316. 

    상세보기