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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Journal ArticleDOI
Fault classification in power systems using EMD and SVM
N. Ramesh Babu,B. Jagan Mohan +1 more
TL;DR: Results demonstrate that the combination of EMD and SVM can be an efficient classifier with acceptable levels of accuracy.
Journal ArticleDOI
Global streamflows - Part 1: Characteristics of annual streamflows
TL;DR: In this article, a set of 1221 global rivers are compared with the rest of the world and the results show that there are large differences in hydrologic characteristics between Australia and southern Africa.
Journal ArticleDOI
Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform
TL;DR: In this paper, a combination of intrinsic characteristic-scale decomposition (ICD) and TQWT is proposed to diagnose the early fault of rolling bearings, which has significant advantages on computation efficiency and alleviation of mode mixing.
Journal ArticleDOI
A Methodology for Validating Artifact Removal Techniques for Physiological Signals
TL;DR: A more empirical approach to the modeling of the desired signal is described that is demonstrated for functional brain monitoring tasks which allows for the procurement of a “ground truth” signal which is highly correlated to a true desired signal that has been contaminated with artifacts.
Journal ArticleDOI
Nonlinear mode decomposition: a noise-robust, adaptive decomposition method.
TL;DR: Nonlinear mode decomposition (NMD) as discussed by the authors decomposes a given signal into a set of physically meaningful oscillations for any wave form, simultaneously removing the noise, based on the powerful combination of time-frequency analysis techniques, together with the adaptive choice of their parameters.
References
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Journal ArticleDOI
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TL;DR: In this paper, a general overview of the nonlinear theory of water wave dynamics is presented, including the Wave Equation, the Wave Hierarchies, and the Variational Method of Wave Dispersion.
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TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.
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TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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