김종현 교수

The power start-up operation of a nuclear power plant (NPP) increases the reactor power to the full-power condition for electricity generation. Compared to full-power operation, the power-increase operation requires significantly more decision-making and therefore increases the potential for human errors. While previous studies have investigated the use of artificial intelligence (AI) techniques for NPP control, none of them have addressed making the relatively complicated power-increase operati

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. Howe

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give i

This study introduces the application of explainable artificial intelligence (XAI) techniques to enhance nuclear power plant diagnostics through effective feature selection. We compared various XAI methods, including gradient-based techniques, layer-wise relevance propagation, DeepSHAP, integrated gradients, local interpretable model-agnostic explanation(LIME), and saliency maps, with traditional approaches such as principal component analysis (PCA). By applying these methods to data from an IAE

Interest in small modular reactors (SMRs) has been growing for their enhanced safety design and operational flexibility. For their adoption, one related challenge to be resolved is the increase in the task load of operators, as SMRs are designed for multi-module operation. This challenge is further heightened during the power-increase operation, which requires continuous monitoring and manual adjustments for an extended period. To address this, this study proposes an autonomous algorithm for the

This study introduces an enhanced active learning framework utilizing Bayesian neural networks for nuclear power plant condition diagnoses. A novel multi-component information need metric combining uncertainty, density, entropy, and diversity with adaptive weighting is proposed to efficiently identify informative training samples. Validation using the International Atomic Energy Agency's integral pressurized water reactor simulator with 26 abnormal conditions across 10 independent runs demonstra

Communication errors contribute to safety in nuclear power plants (NPPs), yet they are not explicitly analyzed in conventional human reliability analysis. This limitation makes it challenging to estimate communication error probabilities (CEPs), which play a significant role in understanding human failure events. Current approaches rely on qualitative assessments or lack statistical foundations, impeding the quantification of CEPs in both intra-organizational and inter-organizational communicati