Artificial Neural Network-Based Tomography Reconstruction of Plasma Radiation Distribution at GOLEM Tokamak
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F24%3A00376800" target="_blank" >RIV/68407700:21340/24:00376800 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s10894-024-00458-z" target="_blank" >https://doi.org/10.1007/s10894-024-00458-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10894-024-00458-z" target="_blank" >10.1007/s10894-024-00458-z</a>
Alternative languages
Result language
angličtina
Original language name
Artificial Neural Network-Based Tomography Reconstruction of Plasma Radiation Distribution at GOLEM Tokamak
Original language description
The paper presents an artificial neural network-based model for tomography reconstruction of visible plasma radiation distribution at the GOLEM tokamak. The model was trained using a dataset from emissivity phantoms and associated synthetic measurements from a poloidal cross-section of the GOLEM tokamak. The model validation was performed on the prediction of various unseen phantom samples with shapes similar to those in the training dataset. The backfit of line-integrated measurements indicates the considerable potential of the proposed model for reconstructing the position, size, shape and intensity of the radiation function of one cross section. Additionally, the neural network-based model offers a significantly shorter prediction time compared to traditional tomography methods, providing a substantial advantage.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10305 - Fluids and plasma physics (including surface physics)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Fusion Energy
ISSN
0164-0313
e-ISSN
1572-9591
Volume of the periodical
43
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
6
Pages from-to
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UT code for WoS article
001304506000001
EID of the result in the Scopus database
2-s2.0-85203049921