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

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

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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Bibliographic Details
Main Authors: Jiheon Song, Semin Joung, Young-Chul Ghim, Sang-hee Hahn, Juhyeok Jang, Jungpyo Lee
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:Nuclear Engineering and Technology
Subjects:
Fusion system
Line Radiation Diagnostics
Machine learning
Convolutional neural network
data analysis
Peak detection
Online Access:http://www.sciencedirect.com/science/article/pii/S1738573322004077

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