YIN Jiahui, a first-year doctoral student in the Network Design Program of the Graduate School of Advanced Mathematical Sciences (Fukuyama Laboratory), received the Outstanding Paper Presentation Award at the Annual Conference of the Institute of Electrical Engineers of Japan (IEEJ), held on March 12, 2026, at the Tohoku Gakuin University Itsutsubashi Campus.
The Outstanding Paper Presentation Award is presented at the conference to recognize excellent presentations by young researchers under the age of 35, in recognition of outstanding papers deemed appropriate for early-career engineers.
Title: Explanation of Gas Turbine Generator Anomaly Detection using Contextual Outlier Interpretation with Fast Gaussian Support Vector Machine
Authors: Jiahui Yin (Meiji University), Yoshikazu Fukuyama (Meiji University), Kenya Murakami (Fuji Electric Co., Ltd.), Satoshi Suzuki (Fuji Electric Co., Ltd.), Tatsuya Iizaka (Fuji Electric Co., Ltd.)
Keywords: contextual outlier interpretation, explainable artificial intelligence, anomaly detection, contextual outlier interpretation, non-linear support vector machines, parallel computinggas turbine generator
Abstract: This paper proposes explanation of gas turbine generator anomaly detection using Contextual Outlier Interpretation (COIN) with fast Support Vector Machine (SVM) with a gaussian kernel. For anomaly detection with AI models, it is essential to provide users with convincing explanations of the model’s decision. Therefore, it is necessary to apply explainable artificial intelligence for anomaly detection AI models. The effectiveness of the proposed method is confirmed through a comparison with conventional methods using actual gas turbine operation data.
≪
Japanese version≫
