Results 111 to 120 of about 10,183 (167)

An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection. [PDF]

Brain Inform
Islam M, Lee T.
europepmc   +1 more source

Unveiling individual and collective temporal patterns in the tanker shipping network. [PDF]

Nat Commun
Teo K, Arnold N, Hone A, Coulon M, Ireland M, Santillana M, Kiss IZ.   +6 more
europepmc   +1 more source

A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer. [PDF]

Sensors (Basel)
Zheng Q, Li C, Yang B, Yan Z, Qin Z.
europepmc   +1 more source

Quantum-enhanced dual-layer graph attention network for time-series forecasting. [PDF]

Sci Rep
Tang Y, Cai Z, Zhang Y, Gao Z, Yu J.
europepmc   +1 more source

Classical simulations of noisy variational quantum circuits. [PDF]

npj Quantum Inf
Fontana E, Rudolph MS, Duncan R, Rungger I, Cîrstoiu C.   +4 more
europepmc   +1 more source

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Successive multivariate variational mode decomposition

Multidimensional Systems and Signal Processing, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuaishuai Liu, Kaiping Yu
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Oscillation Mode Assessment in Power System Using Multivariate Variational Mode Decomposition

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 2021
The identification of electromechanical modes in the power system has been researched extensively in the last decade. Nonlinear nonstationary techniques like variational mode decomposition (VMD) are much effective in determining mode parameters. However, these methods experiment with single-channel data and while computing for other channels needs ...
S. Rahul, Sunitha Rajan, V. M. Akhil
openaire   +1 more source

Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition

IEEE Transactions on Emerging Topics in Computational Intelligence, 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 in frequency scale across all channels.
Muhammad Tariq Sadiq, Xiaojun Yu, Zhaohui Yuan, Muhammad Zulkifal Aziz, Naveed ur Rehman, Weiping Ding 0001, Gaoxi Xiao   +6 more
openaire   +3 more sources