Mode decomposition-based time-varying phase synchronization for fMRI
NeuroImage, 2022 Recently, there has been significant interest in measuring time-varying functional connectivity (TVC) between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data.
Hamed Honari, Martin A. Lindquist
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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
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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
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Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach [PDF]
, 2022 Accurate forecasting of the wave energy is crucial and has significant potential because every wave meter possesses an energy amount ranging from 30 to 40 kW along the shore.
Yaseen, Zaher Mundher +5 more
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An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting [PDF]
, 2022 Accurate prediction of PM2.5 concentration for half a day can provide valuable guidance for urban air pollution prevention and daily travel planning. In this paper, combining adaptive variational mode decomposition (AVMD) and multivariate temporal graph ...
Yamin Shen +3 more
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Multivariate enhanced adaptive empirical Fourier decomposition and its application in rolling bearing fault diagnosis [PDF]
, 2023 Enhanced adaptive empirical Fourier decomposition EAEFD is a recently developed single channel signal separation algorithm which has attracted increasing attention for diagnosing localized rolling bearing failures Even though the EAEFD approach can ...
Cao, S +11 more
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Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks [PDF]
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. +8 more
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Gaussian mixture model decomposition of multivariate signals [PDF]
, 2022 We propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model.
Yarman, Can Evren,, Zickert, Gustav,
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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
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Frontiers in Neuroscience, 2022 As one of the most common neurological disorders, epilepsy causes great physical and psychological damage to the patients. The long-term recurrent and unprovoked seizures make the prediction necessary.
Xiao Wu +3 more
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