Results 11 to 20 of about 338,685 (349)

EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python

Journal of Open Source Software, 2021
The Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum
A. Quinn, V. Lopes-dos-Santos, D. Dupret, A. Nobre, M. Woolrich   +4 more
semanticscholar   +1 more source

Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction

IEEE Access, 2021
Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of ...
Haichao Huang, Jingya Chen, Xinting Huo, Yufei Qiao, Lei Ma   +4 more
doaj   +1 more source

DC Arc-Fault Detection Based on Empirical Mode Decomposition of Arc Signatures and Support Vector Machine

IEEE Sensors Journal, 2021
Protection devices are extensively utilized in direct current (DC) systems to ensure their normal operation and safety. However, series arc faults that establish current paths in the air between conductors introduce arc impedance to the system ...
Wenchao Miao, Qi Xu, K. Lam, P. Pong, Senior Member, H. Poor, Life Fellow   +6 more
semanticscholar   +1 more source

Ground Roll Attenuation of Multicomponent Seismic Data with the Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) Method

Applied Sciences, 2022
Multicomponent seismic exploration provides more wavefield information for imaging complex subsurface structures and predicting reservoirs. Ground roll is strongly coherent noise in land multicomponent seismic data and exhibits similar features, which ...
Liying Xiao, Zhifu Zhang, Jianjun Gao
doaj   +1 more source

Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Italian National Conference on Sensors, 2022
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low
Lei Hu, Ligui Wang, Yanlu Chen, N. Hu, Yu Jiang   +4 more
semanticscholar   +1 more source

Bivariate Empirical Mode Decomposition [PDF]

, 2007
10 pages, 3 figures. Submitted to Signal Processing Letters, IEEE. Matlab/C codes and additional material are downloadable from http://perso.ens-lyon.fr/patrick.flandrinThe Empirical Mode Decomposition (EMD) has been introduced quite recently to ...
Flandrin, Patrick, Gonçalves, Paulo, Lilly, Jonathan M., Rilling, Gabriel   +3 more
core   +4 more sources

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the ‘empirical mode decomposition’ method with which any complicated data set can be decomposed into a finite and often small number of ...
Norden E. Huang, Zheng Shen, S. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi-Chao Tung, Henry H. Liu   +8 more
semanticscholar   +1 more source

Enhancing Performance of Single-Channel SSVEP-Based Visual Acuity Assessment via Mode Decomposition

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
This study aimed to improve the performance of single-channel steady-state visual evoked potential (SSVEP)-based visual acuity assessment by mode decomposition methods.
Xiaowei Zheng, Xun Zhang, Guanghua Xu, Rui Zhang   +3 more
doaj   +1 more source

An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition

Energies, 2019
Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect.
Zhongzhe Chen, Baqiao Liu, Xiaogang Yan, Hongquan Yang   +3 more
doaj   +1 more source

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

Journal of Marine Science and Engineering, 2021
Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations.
Shuyi Zhou, Brandon J. Bethel, Wenjin Sun, Yang Zhao, Wenhong Xie, C. Dong   +5 more
semanticscholar   +1 more source