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Deep neural networks for plasma tomography with applications to JET and COMPASS
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SYSNO ASEP 0522747 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Deep neural networks for plasma tomography with applications to JET and COMPASS Author(s) Carvalho, D. D. (PT)
Ferreira, D. R. (PT)
Carvalho, P. J. (PT)
Imríšek, Martin (UFP-V) RID
Mlynář, Jan (UFP-V) RID
Fernandes, H. (PT)Number of authors 6 Article number C09011 Source Title Journal of Instrumentation. - : Institute of Physics Publishing - ISSN 1748-0221
Roč. 14, č. 9 (2019)Number of pages 8 s. Language eng - English Country GB - United Kingdom Keywords Computerized Tomography (CT) and Computed Radiography (CR) ; Plasma diagnostics-interferometry, spectroscopy and imaging OECD category Fluids and plasma physics (including surface physics) R&D Projects LM2015045 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support UFP-V - RVO:61389021 UT WOS 000486989800011 EID SCOPUS 85074284403 DOI 10.1088/1748-0221/14/09/C09011 Annotation Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. Workplace Institute of Plasma Physics Contact Vladimíra Kebza, kebza@ipp.cas.cz, Tel.: 266 052 975 Year of Publishing 2020 Electronic address https://iopscience.iop.org/article/10.1088/1748-0221/14/09/C09011/pdf
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