Results 61 to 70 of about 5,348,114 (153)

Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification

Sensors, 2018
As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data.
Yong Lv, Houzhuang Zhang, Cancan Yi
doaj   +1 more source

Cause‐and‐Effect Relationships Between Sea Surface Temperature Changes in Different Regions During the Past 4.5 Million Years

Paleoceanography and Paleoclimatology, Volume 40, Issue 6, June 2025.
Abstract Understanding long‐term cause‐and‐effect relationships between changes at different locations and temporal scales is crucial for unraveling the key mechanisms that drive climate dynamics and their impacts on ecosystems and society. Here, we focus on deconvolving different modes of Sea Surface Temperature (SST) variability and the coupling ...
T. Alberti, M. Stumpo, F. Florindo, E. J. Rohling   +3 more
wiley   +1 more source

Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Measurement + Control
Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be ...
Hui Ali, Yu Jie, Lu Weiqiang
doaj   +1 more source

FREQ‐NESS Reveals the Dynamic Reconfiguration of Frequency‐Resolved Brain Networks During Auditory Stimulation

Advanced Science, Volume 12, Issue 20, May 29, 2025.
A new analytical pipeline, FREQuency‐resolved Network Estimation via Source Separation (FREQ‐NESS), reveals how the brain at rest is organized in frequency‐specific networks and dynamically rearranges during auditory stimulation. By analyzing source‐reconstructed magnetoencephalography (MEG) data, FREQ‐NESS uncovers the networks' prominence, spatial ...
Mattia Rosso, Gemma Fernández‐Rubio, Peter Erik Keller, Elvira Brattico, Peter Vuust, Morten L Kringelbach, Leonardo Bonetti   +6 more
wiley   +1 more source

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface [PDF]

, 2017
Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain–computer interface (BCI) applications.
Yi-Feng Chen, Kiran Atal, Xie, S-Q, Quan Liu, Atal, K, Sheng-Quan Xie, Liu, Q, Chen, Y-F   +7 more
core   +1 more source

Forecasting Airtel Stock Prices Through Decomposition and Integration: A Novel VMD‐GARCH‐LSTM Framework

International Journal of Mathematics and Mathematical Sciences, Volume 2025, Issue 1, 2025.
Stock price forecasting is complex due to the nonlinear and nonstationary nature of financial time series. This study proposes a hybrid variational mode decomposition (VMD)–generalized autoregressive conditional heteroskedasticity (GARCH)–long short‐term memory (LSTM) model to predict Airtel’s stock prices, integrating VMD, GARCH, and LSTM networks ...
John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili, Andrzej Skowron   +3 more
wiley   +1 more source

Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes [PDF]

, 2022
In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification. MEMD is a data-adaptive analysis approach which is suitable particularly for multi-
Islam, Monira, Lee, Tan
core  

A dynamic graph deep learning model with multivariate empirical mode decomposition for network‐wide metro passenger flow prediction

Computer-Aided Civil and Infrastructure Engineering, Volume 39, Issue 17, Page 2596-2618, 1 September 2024.
Abstract Network‐wide short‐term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non‐stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow.
Hao Huang, Jiannan Mao, Leilei Kang, Weike Lu, Sijia Zhang, Lan Liu   +5 more
wiley   +1 more source

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

IET Renewable Power Generation, Volume 18, Issue 3, Page 331-347, 24 February 2024.
This study proposes a new wind power forecasting model based on a deep learning model, the long‐short term memory neural network, enhanced by multiple signal decomposition techniques. While the empirical mode decomposition initially generates more stable, stationary, and regular patterns of the original signal, the variational mode decomposition ...
Mehmet Balci, Emrah Dokur, Ugur Yuzgec, Nuh Erdogan   +3 more
wiley   +1 more source

Multi Sensor Image Fusion using Empirical Mode Decomposition [PDF]

, 2013
Image fusion is a process of combining relevant information from two or more images from different sensors based on certain algorithm. Many algorithms have been proposed for pixel level image fusion.
Naidu, VPS, Shakthipriya, VS
core