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
Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
TL;DR: The results show that the proposed method can effectively get the signal feature to diagnose the occurrence of early fault of rotating machinery.
Journal ArticleDOI
A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans
David Blondeau-Patissier,James F.R. Gower,Arnold G. Dekker,Arnold G. Dekker,Stuart R. Phinn,Vittorio E. Brando,Vittorio E. Brando +6 more
TL;DR: In this paper, a comprehensive review of multispectral ocean color sensor data is presented to understand the spatio-temporal patterns of phytoplankton blooms and their triggering factors.
Journal ArticleDOI
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
TL;DR: The DMD approach is validated on sub-dural electrode array recordings from human subjects performing a known motor task, and the resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings.
Journal ArticleDOI
Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs
TL;DR: The proposed method for fault diagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs) show that the multiple ANFIS combination can reliably recognise different fault categories and severities.
Journal ArticleDOI
Filter Bank Property of Multivariate Empirical Mode Decomposition
TL;DR: It is found that, similarly to EMD, MEMD also essentially acts as a dyadic filter bank on each channel of the multivariate input signal, but better aligns the corresponding intrinsic mode functions from different channels across the same frequency range which is crucial for real world applications.
References
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TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
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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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