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Development of machine learning model for automatic ELM-burst detection without hyperparameter adjustment in KSTAR tokamak 원문보기

Nuclear engineering and technology : an international journal of the Korean Nuclear Society, v.55 no.1, 2023년, pp.100 - 108  

Jiheon Song (Hanyang University, Department of Nuclear Engineering) ,  Semin Joung (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ,  Young-Chul Ghim (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ,  Sang-hee Hahn (Korea Institute of Fusion Energy) ,  Juhyeok Jang (Korea Institute of Fusion Energy) ,  Jungpyo Lee (Hanyang University, Department of Nuclear Engineering)

Abstract AI-Helper 아이콘AI-Helper

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recog...

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