Results 141 to 150 of about 50,188 (187)

Median ensemble empirical mode decomposition

Signal Processing, 2020
Abstract Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm.
Xun Lang, Naveed Ur Rehman, Yufeng Zhang   +2 more
exaly   +2 more sources

THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD

Advances in Adaptive Data Analysis, 2009
A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved.
Zhaohua Wu, Nordén E Huang, Xianyao Chen   +2 more
exaly   +2 more sources

Performance enhancement of ensemble empirical mode decomposition

Mechanical Systems and Signal Processing, 2010
Ensemble empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the amplitude of added white noise and the number of ensemble trials.
Ruqiang Yan, Robert X Gao, Zhihua Feng
exaly   +2 more sources

ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

Advances in Adaptive Data Analysis, 2009
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the ...
Zhaohua Wu, Nordén E Huang
exaly   +2 more sources

ENSEMBLE EMPIRICAL MODE DECOMPOSITION WITH SUPERVISED CLUSTER ANALYSIS

Advances in Adaptive Data Analysis, 2013
Ensemble empirical mode decomposition (EEMD) is a noise-assisted data analysis method which decomposes a signal into a collection of intrinsic mode functions (IMFs). There nevertheless appears a multi-mode problem where signals with a similar timescale are decomposed into different IMF components.
Chih-Yu Kuo, Shao-Kuan Wei, Pi-Wen Tsai
openaire   +1 more source

MODEL VALIDATION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis, 2010
We proposed a new model validation method through ensemble empirical mode decomposition (EEMD) and scale separate correlation. EEMD is used to analyze the nonlinear and nonstationary ozone concentration data and the data simulated from the Taiwan Air Quality Model (TAQM).
Yu-Mei Chang, Zhaohua Wu, Julius Chang, Norden E. Huang   +3 more
openaire   +1 more source

ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Advances in Adaptive Data Analysis, 2012
Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary ...
Min Zhang, Yi Shen
openaire   +1 more source

A complete ensemble empirical mode decomposition with adaptive noise

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In
María Eugenia Torres, Marcelo Alejandro Colominas, Gastón Schlotthauer, Patrick Flandrin   +3 more
openaire   +1 more source

Recognizing thoracic breathing by ensemble empirical mode decomposition

2013 9th International Conference on Information, Communications & Signal Processing, 2013
Recognizing breathing pattern is important in many fields of medicine. Ensemble empirical mode decomposition (an adaptive algorithm) was used to investigate breathing pattern, including thoracic breathing (TB) and abdominal breathing (AB). This study recognizes TB and AB by correlation coefficient and power proportion.
Jin-Long Chen, Ya-Chen Chen, Tzu-Chien Hsiao   +2 more
openaire   +1 more source

IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING

Advances in Adaptive Data Analysis, 2013
The empirical mode decomposition (EMD) is a useful method for the analysis of nonlinear and nonstationary signals and found immediate applications in diverse areas of signal processing. However, the major inconvenience of EMD is the mode mixing. The ensemble EMD (EEMD) was proposed to solve the problem of mode-mixing with the assistance of added noises
Raïs El'hadi Bekka, Yaakoub Berrouche
openaire   +1 more source