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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Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary
TL;DR: The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional SVD method.
Journal ArticleDOI
Structural system identification based on variational mode decomposition
TL;DR: A new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD), which decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies.
Nonlinear indicial response of complex nonstationary oscillations as pulmonary hypertension responding to step hypoxia (degree of nonlinearityyFourier spectrumyHilbert spectrumynonlinear relationships)
TL;DR: Theoretical extension of the experimental nonlinear indicial functions to arbitrary history of hypoxia is proposed and application of the results to tissue remodeling and tissue engineering of blood vessels is discussed.
Journal ArticleDOI
Forecasting stochastic neural network based on financial empirical mode decomposition
TL;DR: A new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN) and the empirical results show that the proposed model indeed displays a good performance in forecasting stock market fluctuations.
Journal ArticleDOI
Integration of EEMD and ICA for wind turbine gearbox diagnosis
TL;DR: In this article, an effective fault component separation method that integrates ensemble empirical mode decomposition (EEMD; an adaptive signal decomposition method in time-frequency domain) with independent component analysis (ICA; a blind source separation technique) is presented.
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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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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