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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EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition
TL;DR: It was found that PLVs calculated with the NA-MEMD algorithm could be used as a potential biological marker for seizure prediction, and the gamma frequency band was useful for discriminating between interictal and preictal stages.
Journal ArticleDOI
Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm
Liye Xiao,Feng Qian,Wei Shao +2 more
TL;DR: A new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting, and the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-stepWind speed forecasting.
Journal ArticleDOI
Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions
TL;DR: The developed diagnostic system for detecting the onset of degradation, isolating the degrading bearing, classifying the type of defect is based on an hierarchical structure of K-Nearest Neighbours classifiers.
Journal ArticleDOI
Identification of Natural Frequencies and Dampings of In Situ Tall Buildings Using Ambient Wind Vibration Data
TL;DR: In this paper, the Hilbert transform is applied to each free vibration modal response to identify natural frequencies and damping ratios of in situ tall buildings using ambient wind vibration data, which is based on the empirical mode decomposition (EMD) method, the random decrement technique (RDT), and the Hilbert-Huang transform.
Journal ArticleDOI
Deep-Learning-Based Fault Classification Using Hilbert–Huang Transform and Convolutional Neural Network in Power Distribution Systems
TL;DR: A deep-learning-based fault classification method in small current grounding power distribution systems is presented and has the characteristics of high accuracy and adaptability in fault classification of power Distribution systems.
References
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