Applications of discriminant analysis on shear turbulence data in wavenumber domain
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Authors
Yongfang Wang
- School of Informatics, Linyi University, Linyi, Shandong 276005, P. R. China.
Xin Luan
- College of Information Science & Engineering, Ocean University of China, Qingdao, Shandong 266100, P. R. China.
Tongxing Li
- School of Informatics, Linyi University, Linyi, Shandong 276005, P. R. China.
Abstract
This paper proposed a discriminant analysis method to realize the auto matching of shear spectra and
improve the precision of the shear turbulence data. The discriminant analysis method includes two parts,
firstly, in order to eliminate noise data, cross validation method is used to data preprocessing, and secondly,
maximum likelihood method is used to get discriminant function to realize the auto matching of the spectra.
South China Sea experiment is used to verify the validity of the method.
Share and Cite
ISRP Style
Yongfang Wang, Xin Luan, Tongxing Li, Applications of discriminant analysis on shear turbulence data in wavenumber domain, Journal of Nonlinear Sciences and Applications, 8 (2015), no. 1, 40--45
AMA Style
Wang Yongfang, Luan Xin, Li Tongxing, Applications of discriminant analysis on shear turbulence data in wavenumber domain. J. Nonlinear Sci. Appl. (2015); 8(1):40--45
Chicago/Turabian Style
Wang, Yongfang, Luan, Xin, Li, Tongxing. "Applications of discriminant analysis on shear turbulence data in wavenumber domain." Journal of Nonlinear Sciences and Applications, 8, no. 1 (2015): 40--45
Keywords
- Discriminant analysis
- cross validation
- data preprocessing
- maximum likelihood.
MSC
References
-
[1]
George E. P. Box, George C. Tiao, Bayesian Inference in Statistical Analysis, John Wiley & Sons, Inc., (1992)
-
[2]
H. Dong, S. E. Dosso, Bayesian inversion of interface-wave dispersion for seabed shear-wave speed profiles, IEEE Journal of Oceanic Engineering, 36 (2011), 1-11.
-
[3]
F. Ince , Maximum likelihood classification, optimal or problematic? A comparison with the nearest neighbour classification, International Journal of Remote Sensing, 12 (1987), 1829-1838.
-
[4]
A. N. Kolmogorov, Dissipation of energy in the locally isotropic turbulence, Proceedings of the USSR Academy of Sciences(Russian), 32 (1941), 16-18.
-
[5]
J. Liang, S. Yang, A. Winstanley, Invariant optimal feature selection: A distance discriminant and feature ranking based solution, Pattern Recognition, 41 (2008), 1429-1439.
-
[6]
G. J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, John Wiley & Sons, Inc., (1992)
-
[7]
S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.-R. Müller, Fisher discriminant analysis with kernels, Neural networks for signal processing IX, Processing of the 1999 IEEE Signal Processing Society Workshop, (1999), 41-48.
-
[8]
P. W. Nasmyth, Ocean turbulence, Ph. D. Thesis, University of British Columbia, Vancouver (1970)
-
[9]
N. S. Oakey, Determination of the rate of dissipation of turbulent energy from simultaneous temperature and velocity shear microstructure measurements, Journal of Physical Oceanography, 12 (1982), 256-271.
-
[10]
J. Piera, Signal processing of microstructure profiles: integrating turbulent spatial scales in aquatic ecological modeling, University of Girona, (2001)
-
[11]
J. Piera, E. Roget, J. Catalan, Turbulent patch identification in microstructure profiles: A method based on wavelet denoising and Thorpe displacement analysis, Journal of Atmospheric and Oceanic Technology, 19 (2002), 1390-1402.
-
[12]
J. Shao, Linear model selection by cross-validation, J. Amer. Statist. Assoc., 88 (1993), 486-494.
-
[13]
G. I. Taylor, The spectrum of turbulence, Proceedings of the Royal Society of London. Series A-Mathematical and Physical Sciences, 164 (1938), 476-490.
-
[14]
A. A. Townsend, The Structure of Turbulent Shear Flow, Cambridge University Press, Cambridge (1980)
-
[15]
X.-H. Xie, X.-D. Shang, G.-Y. Shang, L. Sun, Variations of diurnal and inertial spectral peaks near the bi-diurnal critical latitude, Geophysical Research Letters, 36 (2009), L02606.