Support vector machine-based feature extractor for L/H transitions in JET (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Support vector machine-based feature extractor for L/H transitions in JET (Articolo in rivista) (literal)

Anno

• 2010-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1063/1.3502327 (literal)

Alternative label

• Gonzalez S.; Vega J.; Murari A.; Pereira A.; Ramirez J.M.; Dormido-Canto S.; JET-EFDA Contributors (2010)
  Support vector machine-based feature extractor for L/H transitions in JET
  in Review of scientific instruments; American Institute of Physics, Melville [NY] (Stati Uniti d'America)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Gonzalez S.; Vega J.; Murari A.; Pereira A.; Ramirez J.M.; Dormido-Canto S.; JET-EFDA Contributors (literal)

Pagina inizio

• 10E123 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• Article Number: 10E123. La rivista è pubblicata anche online con ISSN 1089-7623. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://rsi.aip.org/resource/1/rsinak/v81/i10/p10E123_s1 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 81 (literal)

Rivista

• Review of scientific instruments (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• Issue 10, Article Number 10E123 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 3 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 10 (literal)

Note

• Scopu (literal)
• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• 1,2,4: Asociación EURATOM/CIEMAT para Fusión, Madrid 28040, Spain / - 3: Consorzio RFX, Associazione EURATOM ENEA per la Fusione, Padova 4-35127, Italy / - 5,6: Departamento de Informática y Automática, UNED, Madrid 28040, Spain / - 7: JET-EFDA, Culham Science Center, Abingdon OX14 3DB, United Kingdom. (literal)

Titolo

• Support vector machine-based feature extractor for L/H transitions in JET (literal)

Abstract

• Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained. (literal)

Editore

• American Institute of Physics (Editore)

Prodotto di

• Esperimento RFX ed attività collegate (ET.P05.003.001) (Modulo)
• Institute of ionized gas (IGI) (Istituto)

Autore CNR

• ANDREA MURARI (Unità di personale interno)

Incoming links:

Prodotto

• Institute of ionized gas (IGI) (Istituto)
• Esperimento RFX ed attività collegate (ET.P05.003.001) (Modulo)

Autore CNR di

• ANDREA MURARI (Unità di personale interno)

Editore di

• American Institute of Physics (Editore)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Review of scientific instruments (Rivista)