A Deep Learning-Based Method to Detect Hot-Spots in the Visible Video 
Diagnostics of Wendelstein 7-X

Szücs, M.; Szepesi, T.; Biedermann, C.; Cseh, G.; Jakubowski, M.; Kocsis, G.; König, R.; Krause, M.; Puig Sitjes, A.; W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society

doi:10.3390/jne3040033

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A Deep Learning-Based Method to Detect Hot-Spots in the Visible Video Diagnostics of Wendelstein 7-X

MPS-Authors

/persons/resource/persons108701

Biedermann, C.
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons109498

Jakubowski, M.
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons109653

König, R.
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons209371

Krause, M.
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons206336

Puig Sitjes, A.
Stellarator Dynamics and Transport (E5), Max Planck Institute for Plasma Physics, Max Planck Society;

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https://doi.org/10.3390/jne3040033
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Citation

Szücs, M., Szepesi, T., Biedermann, C., Cseh, G., Jakubowski, M., Kocsis, G., et al. (2022). A Deep Learning-Based Method to Detect Hot-Spots in the Visible Video Diagnostics of Wendelstein 7-X. Journal of Nuclear Engineering, 3(4), 473-479. doi:10.3390/jne3040033.

Cite as: https://hdl.handle.net/21.11116/0000-000C-BC21-B

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