Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods [PDF]
, 2020 Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life. This chapter designs M5 tree-based machine learning model integrated with advanced multivariate empirical mode decomposition (i.e., MEMD-M5 Tree) for daily
Deo, Ravinesh C. +11 more
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Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
IEEE Access, 2021 Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis.
Haiyang Pan +3 more
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Multivariate Swarm Decomposition [PDF]
, 2023 Adaptive signal decomposition methods are widespread in the field of nonstationary signal analysis. One such method is the Swarm Decomposition (SwD), which relies on the collective dynamics of a virtual swarm-prey model, in order to analyze a given ...
Georgios Apostolidis (16622763) +2 more
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Applied Sciences, 2022 Although widely used in various fields due to its powerful capability of signal processing, empirical mode decomposition has to decompose signals separately, which limits its application for multivariate data such as the structural monitoring data ...
Mingfeng Huang +3 more
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Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications. [PDF]
Philos Trans A Math Phys Eng Sci, 2016 An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown
Hemakom A +3 more
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Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific [PDF]
Weather and Climate Dynamics, 2023 A variety of statistical tools have been used in climate science to gain a better understanding of the climate system's variability on various temporal and spatial scales. However, these tools are mostly linear, stationary, or both. In this study, we use
L. Boljka +5 more
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Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network in bivariate and multiscale time series. [PDF]
, 2022 Objective.Noise-assisted multivariate empirical mode decomposition (NA-MEMD) based causal decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series.
Ran, Y +8 more
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Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach [PDF]
, 2019 Soil moisture forecasts are vital for environmental monitoring, the health of ecological systems, hydrology, agriculture and understanding the soil characteristics. In this study, we design a new multivariate sequential predictive model that utilizes the
Deo, Ravinesh C. +3 more
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Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach [PDF]
, 2022 Accurate water level forecasting is important to understand and provide an early warning of food risk and discharge. It is also crucial for many plants and animal species that needs specific ranges of water level. This research focused on long term multi-
Mehdi Jamei +11 more
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A Method for Blind Source Separation of Multichannel Electromagnetic Radiation in the Field
IEEE Access, 2020 Considering the multichannel instability, spectral overlap and strong interference of electromagnetic radiation signals in the integrated electric propulsion systems of ships, a new method is proposed which combines multivariate empirical mode ...
Sheng Liu, Bangmin Wang, Lanyong Zhang
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