Results 1 to 10 of about 10,183 (167)

A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition [PDF]

Sensors, 2021
Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics.
Kun Yang, Manjin Xu, Xiaotong Yang, Runhuai Yang, Yueming Chen   +4 more
doaj   +5 more sources

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

Scientific Reports
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
Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza   +3 more
doaj   +4 more sources

Deep Learning Method Based on Multivariate Variational Mode Decomposition for Classification of Epileptic Signals [PDF]

Brain Sciences
Background/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types.
Shang Zhang, Guangda Liu, Shiqing Sun, Jing Cai   +3 more
doaj   +4 more sources

Seismic attenuation estimation using multivariate variational mode decomposition

Frontiers in Earth Science, 2022
A seismic attenuation estimation approach is proposed based on multivariate variational mode decomposition (MVMD). MVMD, as a multivariable or multichannel signal processing tool, can extract several predefined multivariable modulation oscillations from ...
Jun-Zhou Liu, Jun-Zhou Liu, Jun-Zhou Liu, Ya-juan Xue, Lei Shi, Lei Shi, Lei Shi, Lei Han, Lei Han, Lei Han   +9 more
doaj   +2 more sources

Multichannel Signal Denoising Using Multivariate Variational Mode Decomposition With Subspace Projection [PDF]

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   +2 more sources

Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition. [PDF]

Comput Intell Neurosci, 2022
Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode ...
Wu G, Liu G, Wang J, Fan P.
europepmc   +4 more sources

Successive multivariate variational mode decomposition based on instantaneous linear mixing model

Signal Processing, 2022
Abstract In this paper, a novel Successive Multivariate Variational Mode Decomposition (SMVMD) is presented. Different from most existing multichannel signal decomposition approaches, the proposed SMVMD does not need to predefine the mode number and is able to extract the joint or common modes successively.
Shuaishuai Liu, Kaiping Yu
exaly   +2 more sources

GPR Energy Attribute Slices Based on Multivariate Variational Mode Decomposition and Teager–Kaiser Energy Operator

Remote Sensing, 2022
The GPR signals appear nonlinear and nonstationary during propagation. To evaluate the nonstationarity, the empirical mode decomposition (EMD) and its modifications have been proposed to localize the variations of energy and frequency components over ...
Xuebing Zhang, Yuxiang Qin, Zhengkun Hu, Xin Hu, Xuan Feng, Yuan Chai   +5 more
doaj   +2 more sources

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Remote Sensing
Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band.
Kang Xing, Shiyan Li, Zhijie Qu, Xiaojuan Zhang   +3 more
doaj   +2 more sources

Fault Diagnosis of Wind Turbine Gearbox Based on Improved Multivariate Variational Mode Decomposition and Ensemble Refined Composite Multivariate Multiscale Dispersion Entropy [PDF]

Entropy
Wind turbine planetary gearboxes have complex structures and operating environments, which makes it difficult to extract fault features effectively. In addition, it is difficult to achieve efficient fault diagnosis.
Xin Xia, Xiaolu Wang, Weilin Chen
doaj   +2 more sources