Approach to diagnosing multiple abnormal events with single-event training data
Tài liệu tham khảo
Lee, 2007, Development of an integrated decision support system to aid cognitive activities of operators, Nuclear Engineering and Technology, 39, 703, 10.5516/NET.2007.39.6.703
Mathew, 2020, Deep learning techniques: an overview, advanced machine learning technologies and applications, Proceedings of AMLTA, 2021, 599
Ma, 2011, Applications of fault detection and diagnosis methods in nuclear power plants: a review, Progress in Nuclear Energy, 53, 255, 10.1016/j.pnucene.2010.12.001
Embrechts, 2004, Hybrid identification of nuclear power plant transients with artificial neural networks, IEEE Transactions on Industrial Electronics, 51, 686, 10.1109/TIE.2004.824874
Mo, 2007, A dynamic neural network aggregation model for transient diagnosis in nuclear power plants, Progress in Nuclear Energy, 49, 262, 10.1016/j.pnucene.2007.01.002
V Santosh, 2009, Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks, Reliab Eng Syst Saf, 94, 759, 10.1016/j.ress.2008.08.005
Bae, 2021, Real-time prediction of nuclear power plant parameter trends following operator actions, Expert Syst Appl, 186, 10.1016/j.eswa.2021.115848
Kim, 2020, Abnormality diagnosis model for nuclear power plants using two-stage gated recurrent units, Nuclear Engineering and Technology, 52, 2009, 10.1016/j.net.2020.02.002
Kim, 2022, Consistency check algorithm for validation and re-diagnosis to improve the accuracy of abnormality diagnosis in nuclear power plants, Nuclear Engineering and Technology, 54, 3620, 10.1016/j.net.2022.05.031
Yu, 2021, A continuous learning monitoring strategy for multi-condition of nuclear power plant, Ann Nucl Energy, 164, 10.1016/j.anucene.2021.108544
Pereira, 2018, Categorizing feature selection methods for multi-label classification, Artif Intell Rev, 49, 57, 10.1007/s10462-016-9516-4
Spolaôr, 2013, A comparison of multi-label feature selection methods using the problem transformation approach, Electron Notes Theor Comput Sci, 292, 135, 10.1016/j.entcs.2013.02.010
Kim, 2010, Development of extended speech act coding scheme to observe communication characteristics of human operators of nuclear power plants under abnormal conditions, J Loss Prev Process Ind, 23, 539, 10.1016/j.jlp.2010.04.005
Geurts, 2006, Extremely randomized trees, Mach Learn, 63, 3, 10.1007/s10994-006-6226-1
Ho, 1995, 278
Breiman, 2001, Mach Learn, 45, 5, 10.1023/A:1010933404324
Lee, 2021, A convolutional neural network model for abnormality diagnosis in a nuclear power plant, Appl Soft Comput, 99, 10.1016/j.asoc.2020.106874
Shwartz-Ziv, 2022, Tabular data: deep learning is not all you need, Information Fusion, 81, 84, 10.1016/j.inffus.2021.11.011
Azizjon, 2020, 1D CNN based network intrusion detection with normalization on imbalanced data, 218
2013
Durand, 2019, Learning a deep convnet for multi-label classification with partial labels, 647
Nair, 2010, Rectified linear units improve restricted Boltzmann machines, 807
Kingma, 2014
Cortes, 1995, Support-vector networks, Mach Learn, 20, 273, 10.1007/BF00994018
Ke, 2017, Lightgbm: A highly efficient gradient boosting decision tree, Adv Neural Inf Process Syst, 30
Ryua, 2020, Development to Diagnose Model of Abnormal Status in Nuclear Power Plant Operation using Machine Learning Algorithms
Shin, 2021, Abnormal state diagnosis model tolerant to noise in plant data, Nuclear Engineering and Technology, 53, 1181, 10.1016/j.net.2020.09.025
Lee, 2022, Concept of robust AI with meta-learning for accident diagnosis
