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Towards intelligent air quality forecasting using integrated machine learning framework with variational mode decomposition and catboost feature selection. [PDF]

Sci Rep
Ahmadianfar I, Yaseen ZM, Marhoon HA, Halder B, Tan ML, Kilinc HC, Abba SI, Heddam S, Goliatt L, Demir V, Al-Areeq AM.   +10 more
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A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model. [PDF]

Sci Rep
Xu Y, Ji X, Zhu Z.
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Filter Bank Property of Multivariate Empirical Mode Decomposition

IEEE Transactions on Signal Processing, 2011
The multivariate empirical mode decomposition (MEMD) algorithm has been recently proposed in order to make empirical mode decomposition (EMD) suitable for processing of multichannel signals. To shed further light on its performance, we analyze the behavior of MEMD in the presence of white Gaussian noise.
Rehman, Naveed ur, Mandic, Danilo P.
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A joint framework for multivariate signal denoising using multivariate empirical mode decomposition

Signal Processing, 2017
In this paper, a novel multivariate denoising scheme using multivariate empirical mode decomposition (MEMD) is proposed. Unlike previous EMD-based denoising methods, the proposed scheme can align common frequency modes across multiple channels of a multivariate data, thus, facilitating direct multichannel data denoising. The key idea in this work is to
Hao, Huan, Wang, H.L., Rehman, N.U.
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Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals

IEEE Signal Processing Letters, 2018
Over the last decade, empirical mode decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering
Mruthun R. Thirumalaisamy, Phillip J. Ansell   +1 more
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Multivariate empirical mode decomposition

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2009
Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis.
Rehman, N., Mandic, D. P.
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Ring-down oscillation mode identification using multivariate Empirical Mode Decomposition

2016 IEEE Power and Energy Society General Meeting (PESGM), 2016
Inter-area oscillation in a large power systems draws much attention because it might severely influence system security and reduce transmission capability. The recent large-scale deployment of phasor measurement units (PMUs) enables online measurement-based monitoring and analysis on inter-area oscillatory modes.
Shutang You, Jiahui Guo, null Wenxuan Yao, null Siqi Wang, Yong Liu, Yilu Liu   +5 more
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Speech Enhancement: A Multivariate Empirical Mode Decomposition Approach

2013
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Solé-Casals, Jordi, Gallego Jutglà, Esteve, Martí i Puig, Pere, Travieso, Carlos M., Alonso, Jesús B.   +4 more
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Characterisation of Physiological Tremor using Multivariate Empirical Mode Decomposition and Hilbert Transform

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
Fatigue-induced physiological tremor (FIPT) is undesirable when performing micromanipulation tasks that require high precision. It is important to characterise this form of tremor to aid in identifying and suppressing it from the intended micromanipulation task. Researchers have used surface electromyography (sEMG) and mechanomyography (MMG) separately
Poongavanam, Palani, Vignesh, Sompur, Asokan, Thondiyath   +2 more
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Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain–Computer Interfaces

IEEE Journal of Biomedical and Health Informatics, 2018
A brain-computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that ...
Sheng Ge, Hai-Xian Wang, Wen-Ming Zheng, Yan-Hua Shi, Rui-Min Wang, Pan Lin, Jun-Feng Gao, Gao-Peng Sun, Keiji Iramina, Yuan-Kui Yang, Yue Leng   +10 more
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