Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6743601 | Fusion Engineering and Design | 2018 | 4 Pages |
Abstract
Method for automatic detection of L-H transition using Support Vector Machine (SVM), a popular tool of supervised machine learning tools, has been evaluated in order to improve plasma density control in KSTAR. Through the SVM, a nonlinear classifier is trained to distinguish L-mode and H-mode using two kinds of diagnostic data measured in KSTAR. The trained classifier has been analyzed for possible usage on the real-time detection through the truncation of the training samples. Study on the optimization of the training samples, and corresponding accuracy change is made for evaluating feasibility for real-time implementations.
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Authors
Gi Wook Shin, J.-W. Juhn, G.I. Kwon, S.H. Son, S.H. Hahn,