Data analysis techniques: Difference between revisions
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* [[Biorthogonal decomposition]] | * [[Biorthogonal decomposition]] | ||
I really wish there were more atrilces like this on the web. | |||
== Integrated data analysis == | == Integrated data analysis == | ||
Revision as of 02:54, 16 July 2011
This page collects information on data analysis techniques used in fusion research.
Temporal analysis
Linear analysis
- Correlation analysis
- Fourier analysis
- Wavelet analysis
- Conditional analysis
- Probability distribution, Cumulative distribution function (rank)
Non-linear analysis
- Bicoherence, bispectrum
- Chaos analysis (Strange attractor, Fractal dimension, Mutual information, Lyapunov exponent)
- Hilbert-Huang transform [1][2]
Self-similarity
- Rescaled range or Hurst analysis; Structure functions
- Waiting times, quiet times [3]
Spatial analysis
Most of the techniques listed under 'temporal analysis' can of course be applied to spatial data.
- Tomography (cf. TJ-II:Tomography)
Spatio-temporal analysis
I really wish there were more atrilces like this on the web.
Integrated data analysis
The goal of integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement.
See also
References
- ↑ N. Huang and S. Shen, Hilbert-Huang transform and its applications (World Scientific, London, 2005) ISBN 978-9812563767
- ↑ B.A. Carreras et al., Reconstruction of intermittent waveforms associated with the zonal flow at the transition leading to the edge shear flow layer, Nucl. Fusion 51 (2011) 053022
- ↑ R. Sánchez et al., Quiet-time statistics: A tool to probe the dynamics of self-organized-criticality systems from within the strong overlapping regime, Phys. Rev. E 66, 036124 (2002)