Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life
prediction by combining the empirical model decomposition algorithm and long short-term …
prediction by combining the empirical model decomposition algorithm and long short-term …
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
Background There is a broad need in neuroscience to understand and visualize large-scale
recordings of neural activity, big data acquired by tens or hundreds of electrodes recording …
recordings of neural activity, big data acquired by tens or hundreds of electrodes recording …
Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks
Y Wei, MC Chen - Transportation Research Part C: Emerging …, 2012 - Elsevier
Short-term passenger flow forecasting is a vital component of transportation systems. The
forecasting results can be applied to support transportation system management such as …
forecasting results can be applied to support transportation system management such as …
Cycle-by-cycle analysis of neural oscillations
Neural oscillations are widely studied using methods based on the Fourier transform, which
models data as sums of sinusoids. This has successfully uncovered numerous links …
models data as sums of sinusoids. This has successfully uncovered numerous links …
Forecasting wind speed using empirical mode decomposition and Elman neural network
J Wang, W Zhang, Y Li, J Wang, Z Dang - Applied soft computing, 2014 - Elsevier
Because of the chaotic nature and intrinsic complexity of wind speed, it is difficult to describe
the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD …
the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD …
Sleep spindles as an electrographic element: description and automatic detection methods
D Coppieters't Wallant, P Maquet, C Phillips - Neural Plasticity, 2016 - Wiley Online Library
Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a
number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual …
number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual …
A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks
Wind speed forecasting is one of the most important technologies to guarantee the wind
energy integrated into the whole power system smoothly. In this paper a hybrid model …
energy integrated into the whole power system smoothly. In this paper a hybrid model …
On intrinsic mode function
Empirical Mode Decomposition (EMD) has been widely used to analyze non-stationary and
nonlinear signal by decomposing data into a series of intrinsic mode functions (IMFs) and a …
nonlinear signal by decomposing data into a series of intrinsic mode functions (IMFs) and a …
EMD interval thresholding denoising based on similarity measure to select relevant modes
G Yang, Y Liu, Y Wang, Z Zhu - Signal Processing, 2015 - Elsevier
This paper introduces a novel EMD interval thresholding (EMD-IT) denoising, where
relevant modes are selected using al 2-norm measure between the probability density …
relevant modes are selected using al 2-norm measure between the probability density …
Robust image watermarking based on multiband wavelets and empirical mode decomposition
N Bi, Q Sun, D Huang, Z Yang… - IEEE Transactions on …, 2007 - ieeexplore.ieee.org
In this paper, we propose a blind image watermarking algorithm based on the multiband
wavelet transformation and the empirical mode decomposition. Unlike the watermark …
wavelet transformation and the empirical mode decomposition. Unlike the watermark …