CNN-Based Fault Detection in Nuclear Power Reactors Using Real-Time Sensor Data
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3980-6_55
Reference12 articles.
1. Ezzat, D., Hassanien, A.E., Darwish, A., Yahia, M., Ahmed, A., Abdelghafar, S.: Multi-objective hybrid artificial intelligence approach for fault diagnosis of aerospace systems. IEEE Access 9, 41717–41730 (2021). https://doi.org/10.1109/ACCESS.2021.3064976
2. Ni, P., Li, G., Hung, P.C.K., Chang, V.: StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence. Appl. Soft Comput. 113 (2021). https://doi.org/10.1016/j.asoc.2021.107975
3. Mohammed, K.K., Hassanien, A.E., Afify, H.M.: Classification of ear imagery database using Bayesian optimization based on CNN-LSTM architecture. J. Digit. Imag. 35, 947–961 (2022). https://doi.org/10.1007/s10278-022-00617-8
4. Choi, J., Lee, S.J.: RNN-based integrated system for real-time sensor fault detection and fault-informed accident diagnosis in nuclear power plant accidents. Nucl. Eng. Technol. 55(3), 814–826 (2023). https://doi.org/10.1016/j.net.2022.10.035
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