Enhancing accident diagnosis in nuclear power plants through knowledge Distillation: Bridging the gap between simulation and Real-World scenarios

Author:

Chae Young Ho,Geun Kim Seung,Ryong Koo Seo

Funder

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Korea Ministry of Science and ICT

Publisher

Elsevier BV

Reference28 articles.

1. Limit surface/states searching algorithm with a deep neural network and Monte Carlo dropout for nuclear power plant safety assessment;Bae;Appl. Soft Comput.,2022

2. Graph neural network based multiple accident diagnosis in nuclear power plants: Data optimization to represent the system configuration;Chae;Nucl. Eng. Technol.,2022

3. Chin, T., Zhang, C. and Marculescu, D., 2020. Improving the adversarial robustness of transfer learning via noisy feature distillation.arXiv preprint arXiv:2002.02998.

4. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks;Croce,2020

5. A survey of uncertainty in deep neural networks;Gawlikowski;Artif Intell Rev,2023

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