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
A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks
TL;DR: The results show that the proposed EMD–ANN hybrid method is robust in dealing with jumping samplings in non-stationary wind series and the performance of the proposed model is highly satisfactory.
Journal ArticleDOI
Feature extraction and recognition of ictal EEG using EMD and SVM
TL;DR: A novel method for feature extraction and pattern recognition of ictal EEG, based upon empirical mode decomposition (EMD) and support vector machine (SVM), where the EEG signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and then the coefficient of variation and fluctuation index of IMFs are extracted as features.
Journal ArticleDOI
Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines
Xiaoyuan Zhang,Jianzhong Zhou +1 more
TL;DR: The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures.
Journal ArticleDOI
Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN
TL;DR: The proposed fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool can increase reliability for the faults diagnosis of ball bearing.
Journal ArticleDOI
Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating
TL;DR: A single-channel EEG based method for sleep staging using recently introduced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bootstrap Aggregating (Bagging) is proposed and gives high detection accuracy for sleep stages S1 and REM.
References
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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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