Results 71 to 80 of about 44,291 (189)

Reduced Order Optimal Control of the Convective FitzHugh-Nagumo Equation

, 2019
In this paper, we compare three model order reduction methods: the proper orthogonal decomposition (POD), discrete empirical interpolation method (DEIM) and dynamic mode decomposition (DMD) for the optimal control of the convective FitzHugh-Nagumo (FHN ...
Karasözen, Bülent, Küçükseyhan, Tuğba, Uzunca, Murat   +2 more
core   +1 more source

A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer

Scientific Reports
Fault diagnosis for gearbox by robust variational mode decomposition (RVMD) and twin extreme learning machine (TELM) with composite chaotic grey wolf optimizer (CCGWO) is proposed in this study. Robust variational mode decomposition is an advanced signal
Xuebin Huang, Anfeng Xu, Hongbing Liu, Bingcheng Ye   +3 more
doaj   +1 more source

Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method

IEEE Access
To enhance the precision of rolling bearing fault detection and lessen the likelihood of safety mishaps, this paper proposes a fault detection method grounded in improved variational mode decomposition. This technique initially employs the Northern Eagle
Xiaoli Huang, Haifeng Xu, Junying Cui
doaj   +1 more source

3D Common-Refinement Method for Non-Matching Meshes in Partitioned Variational Fluid-Structure Analysis

, 2017
We present a three-dimensional (3D) common-refinement method for non-matching meshes between discrete non-overlapping subdomains of incompressible fluid and nonlinear hyperelastic structure.
Jaiman, Rajeev Kumar, Joshi, Vaibhav, Law, Yun Zhi, Li, Yulong   +3 more
core   +1 more source

EEG Signal Classification using Variational Mode Decomposition

, 2020
Epilepsy affects about 1% of the population every year, and is characterized by abnormal and sudden hyper-synchronous excitation of the neurons in the brain. The electroencephalogram(EEG) is the most widely used method to record brain signals and diagnose epilepsy and seizure cases. In this paper we use the method of Variational Mode Decomposition (VMD)
Ullal, Akshith, Pachori, Ram Bilas
openaire   +3 more sources

The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies, 2019
The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode ...
Yuxing Li, Xiao Chen, Jing Yu, Xiaohui Yang, Huijun Yang   +4 more
doaj   +1 more source

Resonance-based sparse adaptive variational mode decomposition and its application to the feature extraction of planetary gearboxes.

PLoS ONE, 2020
Due to the assumption that the VMD technique is essentially a set of adaptive Wiener filter banks and its performance depends to a large extent on the preset parameter K (the number of decomposition).
Jing Zhu, Aidong Deng, Jing Li, Minqiang Deng, Wenqing Sun, Qiang Cheng, Yang Liu   +6 more
doaj   +1 more source

Consistent Dynamic Mode Decomposition

, 2019
We propose a new method for computing Dynamic Mode Decomposition (DMD) evolution matrices, which we use to analyze dynamical systems. Unlike the majority of existing methods, our approach is based on a variational formulation consisting of data alignment
Azencot, Omri, Bertozzi, Andrea, Yin, Wotao   +2 more
core  

A Variational Principle for the Axisymmetric Stability of Rotating Relativistic Stars

, 2016
It is well known that all rotating perfect fluid stars in general relativity are unstable to certain non-axisymmetric perturbations via the Chandrasekhar-Friedman-Schutz (CFS) instability.
Prabhu, Kartik, Schiffrin, Joshua S., Wald, Robert M.   +2 more
core   +1 more source

A decomposition clustering ensemble learning approach for forecasting foreign exchange rates

Journal of Management Science and Engineering, 2019
A decomposition clustering ensemble (DCE) learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition (VMD), the self-organizing map (SOM) network, and the kernel extreme learning machine (KELM).
Yunjie Wei, Shaolong Sun, Jian Ma, Shouyang Wang, Kin Keung Lai   +4 more
doaj   +1 more source