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Semi-supervised deep networks for plasma state identification

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    0564063 - ÚFP 2023 RIV GB eng J - Journal Article
    Zorek, M. - Škvára, V. - Smidl, L. - Pevný, T. - Seidl, Jakub - Grover, Ondřej
    Semi-supervised deep networks for plasma state identification.
    Plasma Physics and Controlled Fusion. Roč. 64, č. 12 (2022), č. článku 125004. ISSN 0741-3335. E-ISSN 1361-6587
    R&D Projects: GA MŠk(CZ) LM2018117; GA ČR(CZ) GA19-15229S
    Institutional support: RVO:61389021
    Keywords : plasma * neural networks * semi-supervised learning * classification
    OECD category: Fluids and plasma physics (including surface physics)
    Impact factor: 2.200, year: 2022
    Method of publishing: Limited access

    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.
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    Scientific data in ASEP :
    Plasma state identification in the COMPASS tokamak
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