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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Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition
Varun Bajaj,Ram Bilas Pachori +1 more
TL;DR: The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010.
Journal ArticleDOI
Natural demodulation of two-dimensional fringe patterns. I. General background of the spiral phase quadrature transform.
TL;DR: A novel two-dimensional transform is developed in terms of two multiplicative operators: a spiral phase spectral (Fourier) operator and an orientational phase spatial operator that results in a meaningfulTwo-dimensional quadrature (or Hilbert) transform.
Journal ArticleDOI
Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition
TL;DR: Wang et al. as mentioned in this paper proposed an ensemble empirical mode decomposition (EEMD)-ARIMA model for forecasting annual runoff time series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir in China.
Journal ArticleDOI
An improved Hilbert Huang transform and its application in vibration signal analysis
TL;DR: In this article, new techniques have been applied to improve the result of the Hilbert-Huang Transform (HHT) and the improved HHT is a precise method for nonlinear and non-stationary signal analysis.
Journal ArticleDOI
Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation
TL;DR: A hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-L STM) for short- term load forecasting is presented.
References
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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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