과제정보
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No. 2018M2B2B1065651).
참고문헌
- "Operational Performance Information System for Nuclear Power Plant", Nuclear Accident and Failure Status, 2020 last modified Oct 26, http://opis.kins.re.kr/opis?act=KROBA3400R. (Accessed 13 January 2021). accessed.
- 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. https://doi.org/10.1016/j.net.2020.02.002
- J. Yang, J. Kim, Accident diagnosis algorithm with untrained accident identification during power-increasing operation, Reliab. Eng. Syst. Saf. 202 (2020) 107032. https://doi.org/10.1016/j.ress.2020.107032
- 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. https://doi.org/10.1016/j.anucene.2020.108077
- 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. https://doi.org/10.1016/j.net.2018.03.007
- A. Ayodeji, Y.-k. Liu, Support vector ensemble for incipient fault diagnosis in nuclear plant components, Nucl. Eng. Technol. 50 (8) (2018) 1306-1313. https://doi.org/10.1016/j.net.2018.07.013
- 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. https://doi.org/10.1016/j.pnucene.2017.12.013
- 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. https://doi.org/10.1080/00223131.2017.1394228
- 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. https://doi.org/10.1016/j.net.2017.11.014
- 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. https://doi.org/10.1016/j.promfg.2015.07.604
- M.A. Kramer, Nonlinear principle component analysis using autoassociative neural networks, AIChE J. 37 (2) (1991) 233-243. https://doi.org/10.1002/aic.690370209
- 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.
- R. Chalapathy, A.K. Menon, S. Chawla, Anomaly Detection Using One-Class Neural Networks, 2018 arXiv preprint arXiv: 1802.06360.
- S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8) (1997) 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- 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.
- J.H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Stat. 29 (5) (2001) 1189-1232. https://doi.org/10.1214/aos/1013203451
- J.H. Friedman, Stochastic gradient boosting, Comput. Stat. Data Anal. 38 (4) (2002) 367-378. https://doi.org/10.1016/S0167-9473(01)00065-2
- Ke Guolin, et al., LightGBM: a highly efficient gradient boosting decision tree, in: Proc. Advances in neural information processing systems, 2017, pp. 3146-3154.
- A.B. Arrieta, et al., Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion 58 (2020) 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
- J.S. Bridle, Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, Neurocomputing (1990) 227-236.
- D. Gunning, Explainable Artificial Intelligence (XAI), Tech. Rep., Defense Advanced Research Projects Agency (DARPA), 2017.
- S.M. Lundberg, G.G. Erion, Su-In Lee, Consistent Individualized Feature Attribution for Tree Ensembles, arXiv preprint arXiv:1802.03888, 2018.
- S.M. Lundberg, et al., From local explanation to global understanding with explainable AI for trees, Nat Mach Intell 2 (1) (2020) 2522-5839.
- L.S. Shapley, A.E. Roth, The Shapley Value: Essays in Honor of Lloyd S. Shapley, Cambridge University Press, 1988.
- Hayes-Roth, Frederick, Rule-based systems, Commun. ACM 28 (9) (1985) 921-932. https://doi.org/10.1145/4284.4286
- J.-c. Park, Equipment and Performance Upgrade of Compact Nuclear Simulator, KAERI/RR-1967/1999, KAERI:Daejeon, Korea, 1999.
- 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.
- C. Tang, N. Luktarhan, Y. Zhao, An efficient intrusion detection method based on LightGBM and autoencoder, Symmetry 12 (9) (2020) 1458. https://doi.org/10.3390/sym12091458
- 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.
- 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. https://doi.org/10.3348/kjr.2004.5.1.11
- J.N. Mandrekar, Receiver operating characteristic curve in diagnostic test assessment, J. Thorac. Oncol. 5 (9) (2010) 1315-1316. https://doi.org/10.1097/jto.0b013e3181ec173d