Results 21 to 30 of about 10,183 (167)

Airborne Radio-Echo Sounding Data Denoising Using Particle Swarm Optimization and Multivariate Variational Mode Decomposition

Remote Sensing, 2023
Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational mode
Yuhan Chen, Sixin Liu, Kun Luo, Lijuan Wang, Xueyuan Tang   +4 more
doaj   +1 more source

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

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

IEEE Access, 2022
In process control system, nonlinearity-induced unit-wide oscillations are a common fault, which degrades the control performance and threaten the stability.
Zhuliang Lin, Min Sun, Xialai Wu
doaj   +1 more source

A literature survey of low-rank tensor approximation techniques [PDF]

, 2013
During the last years, low-rank tensor approximation has been established as a new tool in scientific computing to address large-scale linear and multilinear algebra problems, which would be intractable by classical techniques.
Grasedyck, Lars, Kressner, Daniel, Tobler, Christine   +2 more
core   +1 more source

Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology

Energy Reports, 2021
Forecasting of solar radiation (Radn) can provide an insight vision for the amount of green and friendly energy sources. Owing to the non-linearity and non-stationarity challenges caused by meteorological variables in forecasting Radn, a variational mode
Mumtaz Ali, Ramendra Prasad, Yong Xiang, Mohsin Khan, Aitazaz Ahsan Farooque, Tianrui Zong, Zaher Mundher Yaseen   +6 more
doaj   +1 more source

A reduced-order strategy for 4D-Var data assimilation [PDF]

, 2005
This paper presents a reduced-order approach for four-dimensional variational data assimilation, based on a prior EO F analysis of a model trajectory. This method implies two main advantages: a natural model-based definition of a mul tivariate background
Bennett, Blayo, Blum, Brasseur, Buehner, C. Robert, Cane, Courtier, D'Andréa, De Witte, Devenon, E. Blayo, F.-X. Le Dimet, Faugeras, Gilbert, Greiner, Greiner, Hoang, Hoteit, Ide, J. Blum, J. Verron, Lawson, Legras, Lellouche, Leredde, Lermusiaux, Lions, Luong, Madec, Moore, Mu, Pham, Rabier, Ricci, S. Durbiano, Schröter, Spitz, Toth, Veersé, Vialard, Vialard, Weaver, Weaver, Wenzel   +44 more
core   +7 more sources

A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network

Sensors, 2022
Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the ...
Xu Yang, Rui Yuan, Yong Lv, Li Li, Hao Song   +4 more
doaj   +1 more source

Customized design of hearing aids using statistical shape learning [PDF]

, 2008
3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy.
K. Zhou, N. Paragios, R. Paulsen, R.R. Paulsen, S. Darkner, T. Cootes, W.H. Press   +6 more
core   +2 more sources

Variational Data Assimilation via Sparse Regularization [PDF]

, 2013
This paper studies the role of sparse regularization in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest ...
Ebtehaj, A. M., Foufoula-Georgiou, E., Lerman, G., Zupanski, M.   +3 more
core   +2 more sources

Modeling heterogeneity in random graphs through latent space models: a selective review [PDF]

, 2014
We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and their extensions that have undergone major developments in the last five ...
Matias, Catherine, Robin, Stéphane
core   +5 more sources