Simulator of the JET real-time disruption predictor


Autoria(s): López Navarro, Juan Manuel; Ruiz González, Mariano; Arcas Castro, Guillermo de; Dormido-Canto, Sebastián; Vega Sánchez, Jesús; Murari, Andrea; Ramirez Pérez, Jesús Manuel
Data(s)

01/03/2012

Resumo

A disruption predictor based on support vector machines (SVM) has been developed to be used in JET. The training process uses thousands of discharges and, therefore, high performance computing has been necessary to obtain the models. To this respect, several models have been generated with data from different JET campaigns. In addition, various kernels (mainly linear and RBF) and parameters have been tested. The main objective of this work has been the implementation of the predictor model under real-time constraints. A “C-code” software application has been developed to simulate the real-time behavior of the predictor. The application reads the signals from the JET database and simulates the real-time data processing, in particular, the specific data hold method to be developed when reading data from the JET ATM real time network. The simulator is fully configurable by means of text files to select models, signal thresholds, sampling rates, etc. Results with data between campaigns C23and C28 will be shown.

Formato

application/pdf

Identificador

http://oa.upm.es/19818/

Idioma(s)

eng

Publicador

E.U.I.T. Telecomunicación (UPM)

Relação

http://oa.upm.es/19818/1/INVE_MEM_2012_132864.pdf

http://www.fusione.enea.it/EVENTS/validation2012-frascati/

info:eu-repo/semantics/altIdentifier/doi/null

Direitos

http://creativecommons.org/licenses/by-nc-nd/3.0/es/

info:eu-repo/semantics/openAccess

Fonte

Publicado en página web del Congreso 7 th Workshop on Fusion Data Processing Validation and Analysis, March 27, 2012 | 7 th Workshop on Fusion Data Processing Validation and Analysis, March 27, 2012 | 26/03/2012 - 28/03/2012 | Frascati, Roma, Italia

Palavras-Chave #Electrónica
Tipo

info:eu-repo/semantics/conferenceObject

Ponencia en Congreso o Jornada

PeerReviewed