Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F20%3A00538109" target="_blank" >RIV/61389021:_____/20:00538109 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/20:00538109 RIV/68407700:21230/20:00344433 RIV/68407700:21340/20:00344433
Result on the web
<a href="https://www.tandfonline.com/doi/pdf/10.1080/15361055.2020.1820805?needAccess=true&" target="_blank" >https://www.tandfonline.com/doi/pdf/10.1080/15361055.2020.1820805?needAccess=true&</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/15361055.2020.1820805" target="_blank" >10.1080/15361055.2020.1820805</a>
Alternative languages
Result language
angličtina
Original language name
Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks
Original language description
Chirping Alfvén eigenmodes were observed at the COMPASS tokamak. They are believed to be driven by runaway electrons (REs), and as such, they provide a unique opportunity to study the physics of nonlinear interaction between REs and electromagnetic instabilities, including important topics of RE mitigation and losses. On COMPASS, they can be detected from spectrograms of certain magnetic probes. So far, their detection has required much manual effort since they occur rarely. We strive to automate this process using machine learning techniques based on generative neural networks. We present two different models that are trained using a smaller, manually labeled database and a larger unlabeled database from COMPASS experiments. In a number of experiments, we demonstrate that our approach is a viable option for automated detection of rare instabilities in tokamak plasma.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Fusion Science and Technology
ISSN
1536-1055
e-ISSN
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Volume of the periodical
76
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
962-971
UT code for WoS article
000586815000001
EID of the result in the Scopus database
2-s2.0-85095722107