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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Damage detection in structures under traveling loads by Hilbert–Huang transform
N. Roveri,Antonio Carcaterra +1 more
TL;DR: In this article, a novel HHT-based method for damage detection of bridge structures under a traveling load is proposed, which uses a single point measurement and is able to identify the presence and the location of the damage along the beam.
Journal ArticleDOI
Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression
TL;DR: A new procedure is described, which exploits the Empirical Mode Decomposition method to disaggregate a time series into two sets of components, respectively describing the trend and the local oscillations of the energy consumption values.
Journal ArticleDOI
Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.
TL;DR: EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.
Journal ArticleDOI
Adaptive variational mode decomposition method for signal processing based on mode characteristic
TL;DR: The proposed Adaptive Variational Mode Decomposition (AVMD) method has strong adaptability and is robust to noise and can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.
Journal ArticleDOI
Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings
TL;DR: In this article, a sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in the EWT for fault diagnosis of rolling element bearings, which can detect single and multiple railway axle bearing defects.
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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