Results 51 to 60 of about 573 (180)

Multichannel Signal Denoising Using Multivariate Variational Mode Decomposition With Subspace Projection

IEEE Access, 2020
This paper describes a novel multichannel signal denoising approach based on multivariate variational mode decomposition (MVMD). MVMD is the extended version of the variational mode decomposition (VMD) algorithm for multichannel data sets.
Peipei Cao, Huali Wang, Kaijie Zhou
doaj   +1 more source

Bypassing the axial anomalies

, 2005
Many meson processes are related to the U_A(1) axial anomaly, present in the Feynman graphs where fermion loops connect axial vertices with vector vertices.
DALIBOR KEKEZ, DUBRAVKO KLABUČAR, Georgi H., M. D. SCADRON   +3 more
core   +1 more source

The Multivariate Mixture Dynamics Model: Shifted dynamics and correlation skew

, 2018
The Multi Variate Mixture Dynamics model is a tractable, dynamical, arbitrage-free multivariate model characterized by transparency on the dependence structure, since closed form formulae for terminal correlations, average correlations and copula ...
Brigo, Damiano, Pisani, Camilla, Rapisarda, Francesco   +2 more
core   +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

Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer

Energies
Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy management.
Haoda Ye, Qiuyu Zhu, Xuefan Zhang
doaj   +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

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

Complexity, Volume 2019, Issue 1, 2019., 2019
In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy.
Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du, Danilo Comminiello   +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

Circumventing the axial anomalies and the strong CP problem [PDF]

, 2005
Many meson processes are related to the U_A(1) axial anomaly, present in the Feynman graphs where fermion loops connect axial vertices with vector vertices.
Kekez, D., Klabucar, D., Scadron, M. D.
core   +3 more sources