Korea Advanced Institute of Science and Technology · 意思決定科学
Professor Poong Hyun Seong's research lab specializes in intelligent safety systems for nuclear power plants, focusing on leveraging artificial intelligence to enhance accident diagnosis and decision support. The lab develops advanced machine learning techniques—particularly graph neural networks—to enable high-accuracy diagnosis with limited sensor data, improving operator response during abnormal or severe accident scenarios. Research also emphasizes real-time prediction of critical event timing to support timely severe accident management. The lab's work bridges nuclear engineering and AI, aiming to strengthen the safety and reliability of nuclear energy systems.
Figures are computed from collected data and may differ slightly.
Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) ba
If a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident managemen
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