Automatic location of relevant time slices and patterns in both signals and video-movies: real-time and off-line visualization

Next generation nuclear fusion devices, (for instance ITER), will generate Pbytes of data. To gain knowledge about the nature of thermonuclear plasmas, an in depth analysis of such massive databases is required. Typically, to get statistical relevance in the study of the plasma properties, particular databases around specific plasma events are created. This means their location not only in discharges but also in the corresponding times. In this respect, visualization tools are essential. Of course, manual location of any relevant phenomenology by means of visual analysis is no longer valid. Instead, automatic software based methods are necessary. These methods have to be applied in both real-time and off-line not only for visualization purposes but also for data access. Candidates for their implementation are machine learning techniques.

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Publication Details

Journal
Frontiers in Physics
Published
2025-04-24
DOI
https://doi.org/10.3389/fphy.2025.1538107
Primary Topic
Data Stream Mining Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Automatic location of relevant time slices and patterns in both signals and video-movies: real-time and off-line visualization

R. Castro, J. Vega
Frontiers in Physics
Data Stream Mining Techniques
article

Automatic location of relevant time slices and patterns in both signals and video-movies: real-time and off-line visualization

R. Castro, J. Vega
article en

Abstract

Next generation nuclear fusion devices, (for instance ITER), will generate Pbytes of data. To gain knowledge about the nature of thermonuclear plasmas, an in depth analysis of such massive databases is required. Typically, to get statistical relevance in the study of the plasma properties, particular databases around specific plasma events are created. This means their location not only in discharges but also in the corresponding times. In this respect, visualization tools are essential. Of course, manual location of any relevant phenomenology by means of visual analysis is no longer valid. Instead, automatic software based methods are necessary. These methods have to be applied in both real-time and off-line not only for visualization purposes but also for data access. Candidates for their implementation are machine learning techniques.

Frontiers in PhysicsVol. 13
Ministerio de Ciencia e Innovación
Openalex Percentile: Top 96%
Data Stream Mining Techniques
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