Noise-Assisted Multivariate Variational Mode Decomposition [PDF]
, 2021 The variational mode decomposition (VMD) is a widely applied optimization-based method, which analyzes non-stationary signals concurrently. Correspondingly, its recently proposed multivariate extension, i.e., MVMD, has shown great potentials in analyzing
Hadjileontiadis, Leontios +5 more
core +1 more source
IEEE Access, 2022 In process control system, nonlinearity-induced unit-wide oscillations are a common fault, which degrades the control performance and threaten the stability.
Zhuliang Lin, Min Sun, Xialai Wu
doaj +1 more source
Gearbox fault diagnosis method based on MVMD-MOMEDA [PDF]
Journal of Hebei University of Science and Technology, 2023 Aiming at the problem that the early weak fault diagnosis of gearbox vibration signal is difficult due to the influence of complex transmission path and strong background noise, a gearbox fault diagnosis method based on multivariate variational mode ...
Suxiao CUI +4 more
doaj +1 more source
Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation [PDF]
, 2023 Significant wave height is an average of the largest ocean waves, which are important for renewable and sustainable energy resource generation. A large significant wave height can cause beach erosion, and marine navigation problems in a storm.
Mehdi Jamei +13 more
core +1 more source
Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition [PDF]
, 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 +7 more
core +1 more source
A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization
Journal of Advanced Transportation, Volume 2023, Issue 1, 2023., 2023 The link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved ...
Chunguang He +4 more
wiley +1 more source
Design data decomposition-based reference evapotranspiration forecasting model: A soft feature filter based deep learning driven approach [PDF]
, 2023 Reference evapotranspiration can cause huge discrepancies in soil moisture and runoff which is responsible for uncertainties in drought warning systems.
Mehdi Jamei +13 more
core +3 more sources
RF‐Gait: Gait‐Based Person Identification with COTS RFID
Wireless Communications and Mobile Computing, Volume 2022, Issue 1, 2022., 2022 Recently, person identification has been a prerequisite in many applications of the Internet of Things. As a new biometric identification technology, gait recognition has a wide application prospect with the advantages of long‐distance recognition and difficulty to forge.
Shang Jiang +7 more
wiley +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
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis [PDF]
, 2023 We propose a novel multivariate signal denoising method that performs long-range correlation analysis of multiple modes in input data by considering inherent inter-channel dependencies of the data.
Mukhtar, Sidra +2 more
core +2 more sources