Graph neural network based multiple accident diagnosis in nuclear power plants: Data optimization to represent the system configuration-Reference-Cited by-同舟云学术

Graph neural network based multiple accident diagnosis in nuclear power plants: Data optimization to represent the system configuration

Published:2022-08 Issue:8 Volume:54 Page:2859-2870
ISSN:1738-5733
Container-title:Nuclear Engineering and Technology
language:en
Short-container-title:Nuclear Engineering and Technology

Author:

Chae Young Ho,Lee Chanyoung^ORCID,Han Sang Min^ORCID,Seong Poong Hyun

Funder

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference26 articles.

1. An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference;Kim;Reliab. Eng. Syst. Saf.,2006

2. Human reliability assessment in context;Hollnagel;Nucl. Eng. Technol.,2005

3. A Study on the Systematic Framework to Develop an Effective Diagnosis Procedure of the Nuclear Power Plants;Park,2003

4. A Study on the Operator's Errors of Commission (EOC) in Accident Scenarios of Nuclear Power Plants: Methodology Development and Application;Kim,2003

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of an open-set recognition method for detecting untrained accident scenarios in a nuclear power plant accident diagnosis model;Nuclear Engineering and Design;2024-10

2. Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning;Applied Energy;2024-09

3. Enhancing accident diagnosis in nuclear power plants through knowledge Distillation: Bridging the gap between simulation and Real-World scenarios;Nuclear Engineering and Design;2024-09

4. Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation;Korean Journal of Chemical Engineering;2024-08-05

5. Establishing operator trust in machine learning for enhanced reliability and safety in nuclear Power Plants;Progress in Nuclear Energy;2024-08