Semi-supervised deep networks for plasma state identification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F22%3A00564063" target="_blank" >RIV/61389021:_____/22:00564063 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/22:00362328 RIV/68407700:21340/22:00362328

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1361-6587/ac9926" target="_blank" >https://iopscience.iop.org/article/10.1088/1361-6587/ac9926</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1361-6587/ac9926" target="_blank" >10.1088/1361-6587/ac9926</a>

Result language

angličtina

Original language name

Semi-supervised deep networks for plasma state identification

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10305 - Fluids and plasma physics (including surface physics)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Plasma Physics and Controlled Fusion

ISSN

0741-3335

e-ISSN

1361-6587

Volume of the periodical

64

Issue of the periodical within the volume

12

Country of publishing house

GB - UNITED KINGDOM

Number of pages

16

Pages from-to

125004

UT code for WoS article

000876069600001

EID of the result in the Scopus database

2-s2.0-85141293718