Automatic recognition of plasma relevant events: implications for ITER

Vega Sánchez, Jesús and Castro, Rodrigo and Dormido-Canto, Sebastian and Ratta, Giuseppe and Ruiz Gonzalez, Mariano (2020). Automatic recognition of plasma relevant events: implications for ITER. In: "The 12th IAEA Technical Meeting on Control, Data Acquisition and Remote Participation for Fusion Research (CODAC 2019)", 13/05/2019 - 17/05/2019, Daejeon, Korea. pp. 1-20. https://doi.org/10.1016/j.fusengdes.2020.111638.

Description

Title: Automatic recognition of plasma relevant events: implications for ITER
Author/s:
  • Vega Sánchez, Jesús
  • Castro, Rodrigo
  • Dormido-Canto, Sebastian
  • Ratta, Giuseppe
  • Ruiz Gonzalez, Mariano
Item Type: Presentation at Congress or Conference (Article)
Event Title: The 12th IAEA Technical Meeting on Control, Data Acquisition and Remote Participation for Fusion Research (CODAC 2019)
Event Dates: 13/05/2019 - 17/05/2019
Event Location: Daejeon, Korea
Title of Book: Proceedings of the 12th IAEA Technical Meeting on Control, Data Acquisition, and Remote Participation for Fusion Research
Título de Revista/Publicación: Fusion Engineering and Design
Date: May 2020
ISSN: 0920 - 3796
Subjects:
Freetext Keywords: Automatic recognition of events; Massive databases; ITER; Nuclear fusion; Big data
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Ingeniería Telemática y Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This work makes a proposal about the use of big data techniques for the automatic recognition and classification of plasma relevant events in huge databases of nuclear fusion devices. A relevant event can be any kind of anomaly (or perturbation) in the plasma evolution. This is revealed in the temporal evolution signals as (typically) abrupt variations (for instance in amplitude, noise, or sudden presence/suppression of patterns with periodical structure). A general algorithm based on five steps is presented here for the automatic location and unsupervised classification of plasma events: dataset selection, location of anomalies in individual signals, definition of multi-signal patterns, unsupervised clustering of multi-signal patterns and creation of supervised classifiers. It is important to note that the algorithm implementation is for off-line analysis but supervised classifiers could be implemented under real-time conditions.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainENE2015-64914-C3-1-RUnspecifiedUniversidad Politécnica de MadridTomas de decisión en tiempo real para la selección de métodos de elusión y mitigación de disrupciones en tokamaks
Government of SpainENE2015-64914-C3-2-RUnspecifiedUniversidad Nacional de Educación a DistanciaTomas de decisión en tiempo real para la selección de métodos de elusión y mitigación de disrupciones en Tomkamaks
Government of SpainENE2015-64914-C3-3-RUnspecifiedCentro de Investigación energética medioambiental y tecnológica (CIEMAT)Tomas de decisión en tiempo real para la selección de métodos de elusión y mitigación de disrupciones en Tomkamaks

More information

Item ID: 65010
DC Identifier: https://oa.upm.es/65010/
OAI Identifier: oai:oa.upm.es:65010
DOI: 10.1016/j.fusengdes.2020.111638
Official URL: https://conferences.iaea.org/event/180/
Deposited by: Memoria Investigacion
Deposited on: 02 Mar 2021 12:21
Last Modified: 03 Mar 2021 10:51
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