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Semi-supervised deep networks for plasma state identification
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SYSNO ASEP 0564063 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Semi-supervised deep networks for plasma state identification Author(s) Zorek, M. (CZ)
Škvára, V. (CZ)
Smidl, L. (CZ)
Pevný, T. (CZ)
Seidl, Jakub (UFP-V) RID
Grover, Ondřej (UFP-V) ORCIDNumber of authors 6 Article number 125004 Source Title Plasma Physics and Controlled Fusion. - : Institute of Physics Publishing - ISSN 0741-3335
Roč. 64, č. 12 (2022)Number of pages 16 s. Language eng - English Country GB - United Kingdom Keywords plasma ; neural networks ; semi-supervised learning ; classification Subject RIV BL - Plasma and Gas Discharge Physics OECD category Fluids and plasma physics (including surface physics) R&D Projects LM2018117 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA19-15229S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UFP-V - RVO:61389021 UT WOS 000876069600001 EID SCOPUS 85141293718 DOI 10.1088/1361-6587/ac9926 Annotation Correct and timely detection of plasma confinement regimes and edge localized modes (ELMs) is important for improving the operation of tokamaks. Existing machine learning approaches detect these regimes as a form of post-processing of experimental data. Moreover, they are typically trained on a large dataset of tens of labeled discharges, which may be costly to build. We investigate the ability of current machine learning approaches to detect the confinement regime and ELMs with the smallest possible delay after the latest measurement. We also demonstrate that including unlabeled data into the training process can improve the results in a situation where only a limited set of reliable labels is available. All training and validation is performed on data from the COMPASS tokamak. The InceptionTime architecture trained using a semi-supervised approach was found to be the most accurate method based on the set of tested variants. It is able to achieve good overall accuracy of the regime classification at the time instant of 100 mu s delayed behind the latest data record. We also evaluate the capability of the model to correctly predict class transitions. While ELM occurrence can be detected with a tolerance smaller than 50 mu s, detection of the confinement regime transition is more demanding and it was successful with 2 ms tolerance. Sensitivity studies to different values of model parameters are provided. We believe that the achieved accuracy is acceptable in practice and the method could be used in real-time operation. Workplace Institute of Plasma Physics Contact Vladimíra Kebza, kebza@ipp.cas.cz, Tel.: 266 052 975 Year of Publishing 2023 Electronic address https://iopscience.iop.org/article/10.1088/1361-6587/ac9926
Number of the records: 1