Artículo
Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds
Autor/es | Murari, A,
Craciunescu, T. Peluso, E. Gelfusa, M. Jet Contributors García Muñoz, Manuel |
Departamento | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Fecha de publicación | 2017-10 |
Fecha de depósito | 2020-07-31 |
Publicado en |
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Resumen | Modern experiments in Magnetic Confinement Nuclear Fusion can produce Gigabytes of
data, mainly in form of time series. The acquired signals, composing massive databases, are typically
affected by significant levels of ... Modern experiments in Magnetic Confinement Nuclear Fusion can produce Gigabytes of data, mainly in form of time series. The acquired signals, composing massive databases, are typically affected by significant levels of noise. The interpretation of the time series can therefore become quite involved, particularly when tenuous causal relations have to be investigated. In the last years, synchronization experiments, to control potentially dangerous instabilities, have become a subject of intensive research. Their interpretation requires quite delicate causality analysis. In this paper, the approach of Information Geometry is applied to the problem of assessing the effectiveness of synchronization experiments on JET (Joint European Torus). In particular, the use of the Geodesic Distance on Gaussian Manifolds is shown to improve the results of advanced techniques such as Recurrent Plots and Complex Networks, when the noise level is not negligible. In cases affected by particularly high levels of noise, compromising the traditional treatments, the use of the Geodesic Distance on Gaussian Manifolds allows deriving quite encouraging results. In addition to consolidating conclusions previously quite uncertain, it has been demonstrated that the proposed approach permit to successfully analyze signals of discharges which were otherwise unusable, therefore salvaging the interpretation of those experiments. |
Identificador del proyecto | 633053 |
Cita | Murari, A., Craciunescu, T., Peluso, E., Gelfusa, M., Jet Contributors, y García Muñoz, M. (2017). Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds. Entropy, 19 (10), 1-12. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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entropy-19-00569-v2.pdf | 2.192Mb | [PDF] | Ver/ | |