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
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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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Towards a proper estimation of phase synchronization from time series.
TL;DR: This work illustrates how the complex representation of a continuous phase variable may fail if the signal possesses a multi-component or a broadband spectra and points out some criteria that the pair [A(t), phi(t)] must observe to unambiguously define the instantaneous amplitude and phase of the observed signal.
Journal ArticleDOI
Mirror extending and circular spline function for empirical mode decomposition method
Zhao Jinping,Huang Da-ji +1 more
TL;DR: In this paper, the Mirror Extending (ME) approach is proposed for solving the end extending issue in the empirical mode decomposition (EMD) method, which eliminates the possible problems in reliability and uniqueness in the original EMD method.
Journal ArticleDOI
Frequency content and characteristics of ventricular conduction.
TL;DR: The novel method of the conjoint analysis of the ECG signal in six dimensions is discussed, in the domain of three space dimensions, in time domain, and in frequency domain, to characterize electrophysiological substrate.
Journal ArticleDOI
Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China
TL;DR: Wang et al. as discussed by the authors investigated the predictability of monthly streamflow using support vector machine model coupled with discrete wavelet transform (DWT) and empirical mode decomposition (EMD), and the influence of the noise component of the decomposed time series on the forecast accuracy.
Journal ArticleDOI
Analysis of Nonstationary Power-Quality Waveforms Using Iterative Hilbert Huang Transform and SAX Algorithm
TL;DR: In this article, a new method using the Symbolic Aggregate ApproXimation (SAX) method is proposed to overcome the problem in identifying instants of sudden changes in the waveform, which can be used to both identify and later isolate unique signatures within a provider's distribution system.
References
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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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Mathematical analysis of random noise
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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