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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Journal ArticleDOI
EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum
Donghoh Kim,Hee-Seok Oh +1 more
TL;DR: An R package called EMD is introduced that performs oneand twodimensional EMD and HS and the Hilbert spectral analysis of intrinsic mode functions provides frequency information evolving with time and quantifies the amount of variation due to oscillation at different time scales and time locations.
Journal ArticleDOI
PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors
Suling Zhu,Xiuyuan Lian,Lin Wei,Jinxing Che,Xiping Shen,Ling Yang,Xuanlin Qiu,Xiaoning Liu,Wenlong Gao,Xiaowei Ren,Juansheng Li +10 more
TL;DR: The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models and can be used to develop air quality forecasting and warnings.
Proceedings ArticleDOI
ECG denoising based on the empirical mode decomposition.
TL;DR: A new ECG denoising method based on the recently developed Empirical Mode Decomposition (EMD) is proposed, able to remove high frequency noise with minimum signal distortion.
Journal ArticleDOI
An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting.
TL;DR: The E-LSTM model inspired by ensemble learning, which constructs multiple LSTMs in different modes, obtained better forecasting performance than that using the single LSTM and feed forward neural network in terms of mean absolute percentage error.
Journal ArticleDOI
Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals
TL;DR: The proposed method has provided better TF representation as compared to existing EWT method and Hilbert–Huang transform (HHT) method, especially when analyzed signal possesses closed frequency components and of short time duration.
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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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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