Results 161 to 170 of about 49,831 (187)

Analysis of ElectroGlottoGraph signal using Ensemble Empirical Mode Decomposition

2014 Annual IEEE India Conference (INDICON), 2014
The analysis of various components of the Electroglottograph (EGG) signal, obtained after Ensemble Empirical Mode Decomposition (EEMD) is the primary objective of this paper. The ability of EEMD to detect intermittent high frequency data embedded in the data of lower frequency is exploited to segregate the Epoch locations and the Periodic nature of EGG
Rajib Sharma, null Ramesh K., S. R. M. Prasanna   +2 more
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Mining anomalous electricity consumption using Ensemble Empirical Mode Decomposition

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
Sensor deployments in large buildings allow the administrators to supervise the building infrastructure and identify abnormalities. Nevertheless, the numerous data streams reported by the increasing number of sensors overwhelm the building administrators. We propose a methodology that assists them to identify abnormal devices usages.
Romain Fontugne, Nicolas Tremblay, Pierre Borgnat, Patrick Flandrin, Hiroshi Esaki   +4 more
openaire   +1 more source

Hyperspectral Image Classification Based on Ensemble Empirical Mode Decomposition

2012
Ensemble empirical mode decomposition (EEMD) has been shown in the literature to be a more suitable for nonlinear and non-stationary signals than empirical mode decomposition (EMD). This paper proposes support vector machine classification based on EEMD for hyperspectral images (2D-EEMD-SVM).
Yi Shen, Min Zhang
openaire   +1 more source

Reflection wave analysis based on ensemble empirical mode decomposition

2013 E-Health and Bioengineering Conference (EHB), 2013
In recent studies, the reflection waveform analysis (RWA) in arterial blood pressure (ABP) is the important method for cardiovascular system assessment. But conventional RWA contains some limitations during several clinical experiments, such as the unrecognizable reflection waveform morphology during Valsalva maneuver (VM).
null Sheng-Chi Kao, Chia-Chi Chang, Tzu-Chien Hsiao, Hung-Yi Hsu   +3 more
openaire   +1 more source

Hyperspectral data discrimination based on Ensemble Empirical Mode Decomposition

2011 International Conference on Remote Sensing, Environment and Transportation Engineering, 2011
The classification of hyperspectral data is an important issue. This investigation adopts a novel hyperspectral data classification approach using Ensemble Empirical Mode Decomposition (EEMD). First, the EEMD is applied to decompose the spectra into several components.
Ming-Shu Wang, Tee-Ann Teo
openaire   +1 more source

ENERGY PRODUCTION TREND EXTRACTION USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

International Journal of Energy and Statistics, 2013
The purpose of this study is to illustrate, by example, a method called Ensemble Empirical Mode Decomposition (EEMD). Specifically, the method was used to extract the long-term trend from a time series of energy production data from the United Kingdom for the period from January 1978 through July 2011.
openaire   +1 more source

Ensemble empirical mode decomposition based ECG noise filtering method

2010 International Conference on Machine Learning and Cybernetics, 2010
Electrocardiogram is often corrupted by various noises, such as high-frequency muscle contraction. In this study, ensemble empirical mode decomposition (EEMD) was used for ECG noise reduction. Gaussian noise was applied and the average (ensemble) intrinsic mode function (IMF) was used for ECG reconstruction.
openaire   +1 more source

Robust Pitch Estimation using Ensemble Empirical Mode Decomposition

Speech Prosody 2014, 2014
Sujan Kumar Roy, Md. Khademul Islam Molla, Keikichi Hirose   +2 more
openaire   +1 more source

Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants

Applied Energy, 2021
Piero Baraldi, Enrico Zio
exaly  

Improved Ensemble Empirical Mode Decomposition Method and Its Simulation

2012
Ensemble empirical mode decomposition (EEMD) is a powerful tool for processing signals with intermittency. However, a problem existing in the EEMD method is the absent guide to how much amplitude of the added white noise should be appropriate for the researched signal.
openaire   +1 more source