On 8/7/2024, Dong Feiyan, Demachi lab, Department of Nuclear Engineering and Management, (D2）, received “Best Paper Award” at 31st International Conference on Nuclear Engineering31st International Conference on Nuclear Engineering.

〈Name of award and short explanation about the award〉

The “Best Paper Award” was presented to researchers for their outstanding and high-quality papers submitted to the student paper competition track.

〈About awarded research〉

Time Series Analysis with Combined Learning Approach for Anomaly Detection in Nuclear Power Plants

Abstract: Timely and accurate anomaly detection is essential to ensure nuclear safety, maintain normal operation of nuclear facilities, and prevent serious accidents. Due to the complexity of condition-based monitoring datasets, advanced data-driven deep learning algorithms with state-of-the-art performance are adopted for automatic anomaly detection in nuclear power plants (NPPs). In this study, we developed a deep learning model using a composite learning approach of unsupervised learning (UL) and an attention mechanism to achieve high-performance anomaly detection.

〈Your impression & future plan〉

Receiving the Best Paper Award at ICONE31 is a tremendous honor and a significant milestone in my research journey following the Best Poster award I received at ICONE29 two years ago. This progression highlights my growth and development in the field.

I would like to express my heartfelt gratitude to my advisor Prof. Demachi and Prof. Kasahara, and Dr. Chen, for their invaluable guidance and support throughout this research journey. My sincere thanks also go to Dr.Yoshikawa, Dr.Seki and Dr. Takaya their crucial contributions to this study. I am also grateful to Kasahara-Demachi Lab. for their insightful feedback, as well as to the conference organizers for their excellent arrangements, which have made this event both impressive and memorable.

This year’s conference is especially significant as it marks my first in-person participation in the student sessions at international conference after COVID-19. I have greatly enjoyed the opportunity to engage with many researchers and discuss my work face-to-face.

Looking ahead, this award motivates me to continue advancing research in anomaly detection and safety monitoring in nuclear power plants. My future work will focus on improving the accuracy and robustness of anomaly detection systems and developing innovative solutions to address emerging challenges in the field.