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Träfflista för sökning "WFRF:(Murari Andrea) ;pers:(Eriksson Jacob Dr 1985);pers:(Bykov Igor)"

Search: WFRF:(Murari Andrea) > Eriksson Jacob Dr 1985 > Bykov Igor

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1.
  • Murari, Andrea, et al. (author)
  • Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds
  • 2017
  • In: Entropy. - : MDPI. - 1099-4300. ; 19:10
  • Journal article (peer-reviewed)abstract
    • 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.
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2.
  • Murari, Andrea, et al. (author)
  • On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals
  • 2018
  • In: Entropy. - : MDPI. - 1099-4300. ; 20:9
  • Journal article (peer-reviewed)abstract
    • Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.
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