Automatic detection of L-H transition in KSTAR by support vector machine

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...

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Bibliographic Details
Published in:Fusion engineering and design 2018-04, Vol.129, p.341-344
Main Authors: Shin, Gi Wook, Juhn, J.-W., Kwon, G.I., Son, S.H., Hahn, S.H.
Format: Article
Language:English
Subjects:
Algebra
Artificial intelligence
Classifiers
Diagnostic systems
Feasibility
Feasibility studies
KSTAR
L-H transition
Machine learning
Optimization
Optimization algorithms
Plasma control
Plasma density
Real time
Support vector machine
Support vector machines
Training
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Summary: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.
ISSN:0920-3796
1873-7196
DOI:10.1016/j.fusengdes.2017.12.011