Results 51 to 60 of about 3,491,274 (129)

Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection. [PDF]

Sci Rep
Accurate forecasting of shipboard electricity demand is essential for optimizing Energy Management Systems (EMSs), which are crucial for efficient and profitable operation of shipboard power grids. To address this challenge, this paper introduces a novel
Fazzini P, La Tona G, Diez M, Di Piazza MC.   +3 more
europepmc   +2 more sources

Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition

, 2022
In this article, a novel computer-aided diagnosis framework is proposed for the classification of motor imagery (MI) electroencephalogram (EEG) signals. First, a multivariate variational mode decomposition (MVMD) method was employed to obtain joint modes
Yu, Xiaojun, Yuan, Zhaohui, Rehman, Naveed ur, Ding, Weiping, Xiao, Gaoxi, Sadiq, Muhammad Tariq, Aziz, Muhammad Zulkifal, Sadiq, Muhammad Tariq; id_orcid   +7 more
core   +1 more source

Fault feature extraction method for rolling bearing based on MVMD and complex Fourier transform [PDF]

, 2022
The vibration signals caused by rolling bearing defects in different directions may be different, and the fault diagnosis based on single channel vibration signals may be made incorrectly, and the observation results may be understood wrong. To avoid it,
Huang, Chuanjin, Song, Haijun
core   +1 more source

Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology

, 2021
Forecasting of solar radiation (Radn) can provide an insight vision for the amount of green and friendly energy sources. Owing to the non-linearity and non-stationarity challenges caused by meteorological variables in forecasting Radn, a variational mode
Yaseen, Zaher Mundher, Zong, Tianrui, Prasad, Ramendra, Ali, Mumtaz, Xiang, Yong, Khan, Mohsin, Farooque, Aitazaz Ahsan   +6 more
core   +1 more source

Improving Relative Humidity Forecasting Accuracy Using a Hybrid CEEMDAN‐Based Deep Learning and Machine Learning Approach: A Comparative Analysis

Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.
Forecasting relative humidity remains challenging due to its nonlinear, nonstationary characteristics and long‐memory dependence. This study proposes a hybrid decomposition‐ensemble model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Sample Entropy–Gated Recurrent Unit–Ridge Regression (CEEMDAN‐SE‐GRU‐Ridge), for one‐step‐ahead ...
John Kamwele Mutinda, Tecla Mutave Kyalo, Jackson Ndoto Munyao, Amos Kipkorir Langat, Man Leung Wong   +4 more
wiley   +1 more source

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

Machines, 2022
Deep learning based on vibration signal image representation has proven to be effective for the intelligent fault diagnosis of bearings. However, previous studies have focused primarily on dealing with single-channel vibration signal processing, which ...
Bin Pang, Jiaxun Liang, Han Liu, Jiahao Dong, Zhenli Xu, Xin Zhao   +5 more
doaj   +1 more source

GSVMD: A High‐Performance Method for Denoising Surface‐Electromyography Signals With Generalized Successive Variational Mode Decomposition

IET Signal Processing, Volume 2025, Issue 1, 2025.
Surface electromyography (sEMG) has been used for decades to diagnose movement and neuromuscular disorders; however, sEMG signals are noisy and interfered with, and the nonstationary, nonlinear nature of sEMG signals complicates their use for diagnostic purposes.
Seyyed Ali Zendehbad, Abdollah PourMottaghi, Hamid Reza Kobravi, Elias Mazrooei Rad, Athena Sharifi-Razavi, Zahra Sedaghat, Hadi Dehbovid, Richard Dansereau   +7 more
wiley   +1 more source

SALF: A Self‐Adaptive Learning Framework for Short‐Term Load Forecasting in Smart Grid

International Journal of Energy Research, Volume 2025, Issue 1, 2025.
The energy sector’s rapid expansion necessitates accurate, dependable, and computationally efficient short‐term load forecasting (STLF) models to assure real‐time balance between energy supply and demand. However, the stochastic nature of the energy usage and its reliance on changing weather conditions make accurate forecasting difficult.
Muhammad Sajid Iqbal, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Sharanabasava Ganachari   +4 more
wiley   +1 more source

Multiscale New Energy Price Forecasting Integrating Feature Selection and Parallel Parameter Optimization of IMVMD

International Journal of Energy Research, Volume 2025, Issue 1, 2025.
The article proposes a feature selection framework that integrates principal component analysis (PCA) and random forest (RF) to identify the key factors influencing fluctuations in China’s new energy prices. Based on this, a parallel optimization comparison mechanism is constructed by integrating the enhanced whale optimization algorithm (EWOA ...
JingYe Lyu, Chong Li, Huaiyu Wang
wiley   +1 more source

Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks

Wave power is an emerging renewable energy technology that has not reached its full potential. For wave power plants, a reliable forecast system is crucial to managing intermittency. We propose a novel robust short-term wave power (Pw) forecasting method,
Deo, Ravinesh C., Farooque, Aitazaz A., Prasad, Ramendra, Malik, Anurag, Abdulla, Shahab, Ali, Mumtaz, Labban, Abdulhaleem H., Jamei, Mehdi, Xiang, Yong   +8 more
core   +2 more sources