A study of the characteristics of white noise using the empirical mode decomposition method

By Zhaohua Wu, Norden E. Huang. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 2004

1,735 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/eed59d698af4a7a4cda37c94db27cc07975b1e03

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Citations in the corpus

Intrinsic mode functions and a strategy for forecasting Indian monsoon rainfall (2005, background): “Wu and Huang (2003) have recently proposed an indirect way of testing the importance of the IMF’s by comparing them with the IMF’s of a known white noise data series.”
A Refined Hilbert–Huang Transform With Applications to Interarea Oscillation Monitoring (2009, background): “The cornerstone to the whole HHT procedure is the empirical mode decomposition (EMD) that separates a signal into a series of amplitude—as well as frequency—modulated signal components [20], [26].”
Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method (2009, methodology): “The relatively large deviation for the fundamental signal from the theoretical line fitting is understandable: the spread of error for low-frequency signals is large, as pointed by Wu and Huang.(6)”
Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model (2018, methodology): “In order to promote the development of multiple time-scale analysis approaches, Huang proposed a novel signal analysis method in 1998 called empirical mode decomposition (EMD) [26].”
Analysis of daily river flow fluctuations using Empirical Mode Decomposition and arbitrary order Hilbert spectral analysis (2009, methodology): “…elsewhere (Huang et al., 2009) that the334 influence of periodic components is stronger on structure function than on335 HSA exponents, which can be linked to the fact that EMD acts a filter336 bank (Flandrin and Gonçalvès, 2004; Flandrin et al., 2004; Huang et al.,337 2008; Wu and Huang, 2004).”
Efficacy of Hilbert and Wavelet Transforms for Time-Frequency Analysis (2006, methodology): “This was demonstrated by Wu and Huang 2004 , where the authors effectively establish EMD as a dyadic filter, and IMFs are shown to concentrate their Fourier spectrum at distinct frequencies but with considerable overlap in bandwidth.”
On empirical mode decomposition and its algorithms (2003, result): “This supports an interpretation of EMD in terms of a constant-Q filter bank, in close agreement with the findings reported in [1, 4, 7] in stochastic situations involving broadband noise.”
An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality (2007, methodology): “Further experiments established the ability of the EMD method to decompose white noise into IMFs whose frequency spectrum comprises an octave [26].”
Empirical mode decomposition as a filter bank (2004, background): “It is worth to point out that similar results have been obtained independently by Wu and Huang [5] in the case of white noise (corresponding here toH = 1/2).”
The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling (2020, background): “It is interesting to observe that EEMD acts as a self-adaptive filter [55,56].”