English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Poster

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 Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons109653

König,  R.       
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons209371

Krause,  M.
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons206336

Puig Sitjes,  A.       
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Szűcs, 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. Poster presented at 32nd Symposium on Fusion Technology (SOFT 2022), Dubrovnik, Virtual.


Cite as: https://hdl.handle.net/21.11116/0000-000B-2FB7-3
Abstract
There is no abstract available