Data analysis techniques: Difference between revisions
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=== Non-linear analysis === | === Non-linear analysis === | ||
* [[ | * [[Bicoherence]], bispectrum | ||
* Chaos analysis ([[:Wikipedia:Attractor|Strange attractor]], [[:Wikipedia:Fractal dimension|Fractal dimension]], [[:Wikipedia:Mutual information|Mutual information]], [[:Wikipedia:Lyapunov exponent|Lyapunov exponent]]) | * Chaos analysis ([[:Wikipedia:Attractor|Strange attractor]], [[:Wikipedia:Fractal dimension|Fractal dimension]], [[:Wikipedia:Mutual information|Mutual information]], [[:Wikipedia:Lyapunov exponent|Lyapunov exponent]]) | ||
* [[:Wikipedia:Hilbert-Huang transform|Hilbert-Huang transform]] <ref>N. Huang and S. Shen, ''Hilbert-Huang transform and its applications'' (World Scientific, London, 2005) ISBN 978-9812563767</ref> | * [[:Wikipedia:Hilbert-Huang transform|Hilbert-Huang transform]] <ref>N. Huang and S. Shen, ''Hilbert-Huang transform and its applications'' (World Scientific, London, 2005) ISBN 978-9812563767</ref> | ||
Revision as of 21:59, 24 September 2010
This page collects information on data analysis techniques used in fusion research.
Temporal analysis
Linear analysis
- Correlation
- 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]
Self-similarity
- Rescaled range or Hurst analysis; Structure functions
- Waiting times, quiet times [2]
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
Image analysis
- Twodimensional Fourier analysis
- Twodimensional wavelet analysis
- Event detection using thresholding
- Optical flow (for movies)
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
- ↑ 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)