Automatic disruption classification based on manifold learning for real-time applications on JET (Articolo in rivista)

Type
Label
  • Automatic disruption classification based on manifold learning for real-time applications on JET (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1088/0029-5515/53/9/093023 (literal)
Alternative label
  • B. Cannas; A. Fanni; A. Murari; A. Pau; G. Sias; JET EFDA Contributorsa (2013)
    Automatic disruption classification based on manifold learning for real-time applications on JET
    in Nuclear fusion; IOP PUBLISHING LTD, BRISTOL BS1 6BE (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • B. Cannas; A. Fanni; A. Murari; A. Pau; G. Sias; JET EFDA Contributorsa (literal)
Pagina inizio
  • 093023 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Article Number: 093023. La rivista è pubblicata anche online con ISSN 1741-4326. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://iopscience.iop.org/0029-5515/53/9/093023/pdf/0029-5515_53_9_093023.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 53 (literal)
Rivista
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  • 11 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 9 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1,2,4,5 : Electrical and Electronic Engineering Department, University of Cagliari, Italy / 3 : Consorzio RFX-Associazione EURATOM ENEA per la Fusione, I-35127 Padova, Italy / 6 : JET-EFDA Culham Science Centre, Abingdon, OX14 3DB, UK. (literal)
Titolo
  • Automatic disruption classification based on manifold learning for real-time applications on JET (literal)
Abstract
  • Disruptions remain the biggest threat to the safe operation of tokamaks. To efficiently mitigate the negative effects, it is now considered important not only to predict their occurrence but also to be able to determine, with high probability, the type of disruption about to occur. This paper reports the results obtained using the nonlinear generative topographic map manifold learning technique for the automatic classification of disruption types. It has been tested using an extensive database of JET discharges selected from JET campaigns from C15 (year 2005) up to C27 (year 2009). The success rate of the classification is extremely high, sometimes reaching 100%, and therefore the prospects for the deployment of this tool in real time are very promising. (literal)
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