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

송지헌; 정세민; 김영철; Hahn Sang-Hee; 장주혁; 이정표

doi:10.1016/j.net.2022.08.026

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자료유형: 학술저널

저자정보: 송지헌 (한양대학교) 정세민 (한국과학기술원) 김영철 (한국과학기술원) Hahn Sang-Hee (Korea Institute of Fusion Energy) 장주혁 (한국핵융합에너지연구원) 이정표 (한양대학교)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제55권 제1호

발행연도: 2023.1

수록면: 100 - 108 (9page)

DOI: 10.1016/j.net.2022.08.026

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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

#Fusion system #Line Radiation Diagnostics #Machine learning #Convolutional neural network #data analysis #Peak detection #Tokamak

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