Results 71 to 80 of about 8,576 (212)

GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP

Materials Genome Engineering Advances, Volume 4, Issue 2, June 2026.
GPUMDkit is a comprehensive and user‐friendly toolkit for GPUMD and NEP programs, integrating format conversion, structure sampling, property calculation, and visualization into a unified interface, substantially lowering the barrier to entry for machine‐learning molecular dynamics simulations with GPUMD and NEP.
Zihan Yan, Denan Li, Xin Wu, Zhoulin Liu, Chen Hua, Boyi Situ, Hao Yang, Shengjie Tang, Benrui Tang, Ziyang Wang, Shangzhao Yi, Huan Wang, Dian Huang, Ke Li, Qilin Guo, Zherui Chen, Ke Xu, Yanzhou Wang, Ziliang Wang, Gang Tang, Shi Liu, Zheyong Fan, Yizhou Zhu   +22 more
wiley   +1 more source

Hybrid Approach of an Empirical Mode Decomposition and Wavelet Support Vector Machine for Forecasting Singapore Tourist Arrivals to Malaysia [PDF]

, 2018
Time series modelling and forecasting has fundamental importance to various practical domains. Thus, a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the
A. Shabri, A. Rafidah, (UniKL MITEC)
core  

A Novel Empirical Mode Decomposition Denoising Scheme [PDF]

, 2010
This paper presents a novel and fast scheme for signal denoising by using Empirical mode decomposition (EMD). The EMD involves the adaptive decomposition of signal into a series of oscillating components, Intrinsic mode functions(IMFs), by means of a ...
Wei Du, Quan Liu
core   +1 more source

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Energies, 2017
Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the ...
Yaping Huang, Haibin Di, Reza Malekian, Xuemei Qi, Zhixiong Li   +4 more
doaj   +1 more source

Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning

NMR in Biomedicine, Volume 39, Issue 6, June 2026.
We propose a novel deep learning algorithm for predicting the myelin water fraction from multiple gradient‐echo or spin‐echo pulse sequences arising in magnetic resonance relaxometry (MRR) measurements of the human brain. Our method incorporates both regularized nonlinear least squares and pure deep learning through a concatenation paradigm known as ...
Mirage Modi, Shashank Sule, Jonathan Palumbo, Michael Rozowski, Griffin S. Hampton, Mustapha Bouhrara, Wojciech Czaja, Richard G. Spencer   +7 more
wiley   +1 more source

Gravity Wave Activity in the Stratosphere and Mesosphere During Hurricane Sam

Journal of Geophysical Research: Space Physics, Volume 131, Issue 6, June 2026.
Abstract Multi‐instrument observations of gravity wave (GW) activity during Hurricane Sam (2021) were made using AIRS (Atmospheric Infrared Sounder) satellite data, ERA5 reanalysis, and TIMED/SABER temperature profiles. Two GW extraction methods, vertical high‐pass filtering and empirical mode decomposition, were applied to quantify wave‐induced ...
Ayden L. S. Gann, Erdal Yiğit
wiley   +1 more source

A Novel Source Number Enumeration Approach Based on Ensemble Learning and SEEMD

IEEE Access
Empirical Mode Decomposition (EMD) is a widely adopted adaptive signal analysis method, particularly effective for source number estimation in non-stationary environments. It decomposes complex signals into a series of Intrinsic Mode Functions (IMFs) and
Zhu Cairong, Ge Shengguo, Xu Yihan
doaj   +1 more source

An HHT–ANN Framework for Short‐Term Kp Forecasting

Space Weather, Volume 24, Issue 6, June 2026.
Abstract The geomagnetic activity index Kp is an important indicator of solar wind–magnetosphere coupling, and accurate 3‐hr‐ahead forecasting is important for space‐weather monitoring and warning. Because upstream solar wind and interplanetary magnetic field (IMF) signals are strongly nonlinear and nonstationary, methods based only on conventional ...
P. Yang, H. S. Fu, H. Lan, C. X. Du, W. D. Fu, J. B. Cao   +5 more
wiley   +1 more source

Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition

Shock and Vibration, 2016
This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are
Yifan Li, Jianxin Liu, Yan Wang
doaj   +1 more source

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Motor imagery refers to the brain’s response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals.
Hyunsoo Yu, Suwhan Baek, Jiwoon Lee, Illsoo Sohn, Bosun Hwang, Cheolsoo Park   +5 more
doaj   +1 more source