Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TLDR
In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.Abstract:
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...read more
Citations
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Strong Turbulence in the Wave Crest Region
TL;DR: In this paper, the authors estimate the dissipation rates of turbulence kinetic energy based on centered second-order structure functions at 4-Hz sampling and show a clear threshold behavior in accordance with the onset of wave breaking.
Journal ArticleDOI
Laboratory measurements of limiting freak waves on currents
Chin H. Wu,Aifeng Yao +1 more
TL;DR: In this article, both dispersive spatial-temporal focusing and wave-current interaction are used to generate freak wave formation in a partial random wave field in the presence of currents.
Journal ArticleDOI
Instantaneous frequency estimation based on synchrosqueezing wavelet transform
Qingtang Jiang,Bruce W. Suter +1 more
TL;DR: The numerical experiments show that the instantaneous frequency-embedded synchrosqueezing wavelet transform (IFE-SST) outperforms the CWT-based SST in IF estimation and separation of multicomponent signals.
Journal ArticleDOI
Modal identification of Shanghai World Financial Center both from free and ambient vibration response
TL;DR: In this paper, three output-only modal identification techniques are applied to the ambient and forced vibration measurements of Shanghai World Financial Center (SWFC) to identify the dynamic properties of the building.
Journal ArticleDOI
Persistent homology for time series and spatial data clustering
TL;DR: The main contribution of the approach is enabling the clustering of time series that have similar recurrent behavior characterized by their attractors in phase space and spatial data that havesimilar scale-invariant spatial distributions, as traditional clustering techniques ignore that information as they rely on point-to-point dissimilarity measures such as Euclidean distance or elastic measures.
References
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