Recovery of Electron Density Signals Beyond the Operating Range of the Measuring Instrument
( Pp. 152-159)
More about authors
Leshov Nikolai V.
postgraduate student, Department of Mathematical Cybernetics and Information Technologies
Moscow Technical University of Communications and Informatics (MTUCI)
Moscow, Russian Federation Shcherbak Anastasia N. leading engineer, Laboratory of Tokamak Plasma Diagnostics and Plasma Physics, Department of Tokamak and Current-Carrying Plasma Physics
State Research Centre of the Russian Federation Troitsk Institute for Innovation and Fusion Research
Troitsk, Moscow, Russian Federation Gorodnichev Mikhail G. Cand. Sci. (Eng.), Associate Professor; Dean, Faculty of Information Technology
Moscow Technical University of Communications and Informatics (MTUCI)
Moscow, Russian Federation
Moscow Technical University of Communications and Informatics (MTUCI)
Moscow, Russian Federation Shcherbak Anastasia N. leading engineer, Laboratory of Tokamak Plasma Diagnostics and Plasma Physics, Department of Tokamak and Current-Carrying Plasma Physics
State Research Centre of the Russian Federation Troitsk Institute for Innovation and Fusion Research
Troitsk, Moscow, Russian Federation Gorodnichev Mikhail G. Cand. Sci. (Eng.), Associate Professor; Dean, Faculty of Information Technology
Moscow Technical University of Communications and Informatics (MTUCI)
Moscow, Russian Federation
Abstract:
Machine learning models have been widely incorparated into control systems aimed at improving the operational efficiency of tokamaks. The training machine learning models requires substantial datasets. However, data collection is limited because experimental campaigns on tokamaks are prolonged in time. Furthermore, the amount of suitable training data may decrease due to the present of faulty diagnostic signals. Additionally, the frequency of faulty signal occurrences increases while initial operation of a new tokamak or specialized equipment. This work examines the possibility of recovering faulty signals using machine learning techniques. Particularly, we focus on recovering signals obtained beyond the operating range of measuring instruments. Thus, recovering such kind of signals should increase the volume of available training data, consequently enhancing the efficacy of machine learning-based model training.
How to Cite:
Leshov N.V., Shcherbak A.N., and Gorodnichev M.G. Recovery of electron density signals beyond the operating range of the measuring instrument. Computational Nanotechnology. 12, 3 (2025), 152–159. DOI: 10.33693/2313-223X-2025-12-3-152-159. EDN: BRJDPD
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Matos F. et al. Classification of tokamak plasma confinement states with convolutional recurrent neural networks. Nuclear Fusion. 2020. Vol. 60. No. 3. 036022.
Akiba T. et al. Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery data mining. 2019. Pp. 2623 2631.
Kingma D. P. et al. Adam: A method for stochastic optimization. Published as a conference paper at the 3rd International Conference for Learning Representations. San Diego, 2015.
Paszke A. et al. Pytorch: An imperative style, high-performance deep learning library. In: Advances in neural information processing systems. 2019. Vol. 32.
Keywords:
tokamak, plasma density, interferometry, artificial neural network, signal recovery.
