Nuclear Engineering and Technology

  • 제55권1호
  • /
  • Pages.100-108
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  • 2023
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Development of machine learning model for automatic ELM-burst detection without hyperparameter adjustment in KSTAR tokamak

  • 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)
  • 투고 : 2022.05.26
  • 심사 : 2022.08.24
  • 발행 : 2023.01.25
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초록

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 recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

키워드

과제정보

J.H.S. and J.P.L. were supported by the National R&D Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF-2021M1A7A4091137, NRF-2021M3F7A1084421). Y.C.G was supported by NRF Grant No. RS2022-00155917. This work was also supported by Ministry of Science and ICT under KFE R&D Program of "KSTAR Experimental Collaboration and Fusion Plasma Research (KFE-EN2201-13)".

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