Results 81 to 90 of about 8,576 (212)
Advanced Intelligent Systems, Volume 8, Issue 5, May 2026.This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
Ensemble empirical mode decomposition for high frequency ECG noise reduction [PDF]
, 2012 [[abstract]]An electrocardiogram (ECG) is measured from the body surface and is often corrupted by various noises, such as high-frequency muscle contraction.
張剛鳴;Chang, Kang-Ming
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Engineering Reports, Volume 8, Issue 5, May 2026.This graphical abstract compares circuit‐based, signal‐processing‐based, and intelligent‐learning‐based approaches for secondary arc‐fault detection, highlighting their key principles and practical advantages. ABSTRACT Reliable detection of free‐air electric arc faults in high‐voltage overhead transmission lines, together with accurate discrimination ...
Mahyar Abasi +2 more
wiley +1 more source
Accounting for nonstationarity in the condition monitoring of wind turbine gearboxes [PDF]
, 2013 Increasing growth of wind turbine systems suggests a more systematic research around their design, operation and maintenance is needed. These systems operate under challenging enviromental conditions and failure of some of their parts, for the time being,
Antoniadou, Ifigeneia
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Empirical Mode Decomposition Method Based on Wavelet with Translation Invariance
EURASIP Journal on Advances in Signal Processing, 2008 For the mode mixing problem caused by intermittency signal in empirical mode decomposition (EMD), a novel filtering method is proposed in this paper. In this new method, the original data is pretreated by using wavelet denoising method to avoid the mode ...
Chen Ming, Lin Yan, Qin Pinle
doaj +1 more source
Objective Empirical Mode Decomposition metric [PDF]
, 2015 Empirical Mode Decomposition (EMD) is a data driven technique for extraction of oscillatory components from data. Although it has been introduced over 15 years ago, its mathematical foundations are still missing which also implies lack of objective ...
Laszuk, Dawid +7 more
core +2 more sources
IEEE AccessThis paper presents a hybrid forecasting model for a wind power named Secondary Hybrid Decomposition (SHD)-Long Short-Term Memory (LSTM) with Crisscross Optimization (CSO).The model integrates three key techniques to improve prediction accuracy. Firstly,
Yoseph Mekonnen Abebe +4 more
doaj +1 more source
Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition [PDF]
, 2012 [[abstract]]A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces.
張剛鳴;Chang, Kang-Ming
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Empirical mode decomposition of wind speed signals [PDF]
, 2023 Empirical Mode Decomposition (EMD) is a powerful signal processing technique with diverse applications, particularly in the analysis of non-stationary data.
Pinto Molina, Ines
core +2 more sources
Analysis the Spike Wave of Double Channel MGG Signal based on Empirical Mode Decomposition [PDF]
, 2014 The empirical mode decomposition (EMD) has been introduced quite recently to adaptively decompose non-stationary and/or nonlinear time series. Spike wave of the MGG signal is very important and standard diagnose method for medical and clinical research ...
Miao L(缪磊), Xu BL(徐保磊)
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