Results 31 to 40 of about 4,055 (251)

A novel method to identify the flow pattern of oil–water two-phase flow

Journal of Petroleum Exploration and Production Technology, 2020
This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of
Zhong-Cheng Li, Chun-Ling Fan
doaj   +1 more source

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

Frontiers in Physics, 2023
Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference noises produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure.
Zhichao Yu, Zhichao Yu, Yingkun Huang, Zisen Fang, Yuyang Tan, Chuan He   +5 more
doaj   +1 more source

Hyperparameter optimization of support vector machine using adaptive differential evolution for electricity load forecasting

Energy Reports, 2022
Peak load forecasting plays an integral part in the planning and operating of energy plants for the utility companies and policymakers to devise reliable and stable power infrastructure.
M. Zulfiqar, M. Kamran, M.B. Rasheed, T. Alquthami, A.H. Milyani   +4 more
doaj   +1 more source

Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition [PDF]

Computers in Biology and Medicine, 2017
We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T-F) analysis of non-stationary ...
Asmat Zahra, Nadia Kanwal, Naveed ur Rehman, Shoaib Ehsan, Klaus D. McDonald-Maier   +4 more
openaire   +3 more sources

Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition

智能科学与技术学报, 2020
The brain-computer interface is an emerging technology,which can analyze the collected motor imagery signals to control the external auxiliary equipment.A new method based on the noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD ...
Qian ZHENG, Dan QIAO, Xun LANG, Lei XIE, Dongliu Li, Qibing Wang, Hongye SU   +6 more
doaj  

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
doaj   +1 more source

THEORY OF DIGITAL FILTER BANKS REALIZED VIA MULTIVARIATE EMPIRICAL MODE DECOMPOSITION [PDF]

Advances in Adaptive Data Analysis, 2014
Undecimated and decimated multivariate empirical mode decomposition filter banks (MEMDFBs) are introduced in order to incorporate MEMD equipped with downsampling into any arbitrary tree structure and provide flexibility in the choice of frequency bands.
Min-Sung Koh, Danilo P. Mandic, Anthony G. Constantinides   +2 more
openaire   +1 more source

A Novel Algorithm for the Decomposition of Non-Stationary Multidimensional and Multivariate Signals

Computation
The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more.
Roberto Cavassi, Antonio Cicone, Enza Pellegrino, Haomin Zhou   +3 more
doaj   +1 more source

Developing stage–discharge relationships using multivariate empirical mode decomposition-based hybrid modeling

Applied Water Science, 2018
This paper proposes an alternative method for modeling stage–discharge relationships by accounting significant information from different process scales employing the multivariate empirical mode decomposition (MEMD).
S. Adarsh, Ajin P. John, R. N. Anagha, Abi Abraham, M. P. Afiya, K. K. Arathi, Aaliya Azeem   +6 more
doaj   +1 more source

Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals

IEEE Transactions on Signal Processing, 2011
A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic ...
Fleureau, Julien, Nunes, Jean-Claude, Kachenoura, Amar, Albera, Laurent, Senhadji, Lotfi   +4 more
openaire   +4 more sources