Improved complete ensemble EMD: A suitable tool for biomedical signal processing
The empirical mode decomposition (EMD) decomposes non-stationary signals that may
stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted …
stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted …
Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental …
Accurate prediction of water quality parameters plays a crucial and decisive role in
environmental monitoring, ecological systems sustainability, human health, aquaculture and …
environmental monitoring, ecological systems sustainability, human health, aquaculture and …
Self-attention deep image prior network for unsupervised 3-D seismic data enhancement
We develop a deep learning framework based on deep image prior (DIP) and attention
networks for 3-D seismic data enhancement. First, the 3-D noisy data are divided into …
networks for 3-D seismic data enhancement. First, the 3-D noisy data are divided into …
Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)
Reliable prediction of reservoir displacement is essential for practical applications. Machine
learning offers an attractive and accessible set of tools for the displacement prediction of …
learning offers an attractive and accessible set of tools for the displacement prediction of …
An ensemble hybrid forecasting model for annual runoff based on sample entropy, secondary decomposition, and long short-term memory neural network
Accurate and consistent annual runoff prediction in a region is a hot topic in management,
optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD …
optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD …
A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition …
Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to
public health continues to face significant challenges considering the chaotic, non-linear …
public health continues to face significant challenges considering the chaotic, non-linear …
A blended approach incorporating TVFEMD, PSR, NNCT-based multi-model fusion and hierarchy-based merged optimization algorithm for multi-step wind speed …
D Xiong, W Fu, K Wang, P Fang, T Chen… - Energy Conversion and …, 2021 - Elsevier
Precise wind speed prediction plays an essential role in wind farm planning. To enhance the
wind speed forecasting accuracy, a blended approach incorporating time varying filtering …
wind speed forecasting accuracy, a blended approach incorporating time varying filtering …
Introducing libeemd: A program package for performing the ensemble empirical mode decomposition
The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN)
are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical …
are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical …
Unsupervised deep learning for single-channel earthquake data denoising and its applications in event detection and fully automatic location
We propose to use unsupervised deep learning (DL) and attention networks to mute the
unwanted components of the single-channel earthquake data. The proposed algorithm is an …
unwanted components of the single-channel earthquake data. The proposed algorithm is an …
[HTML][HTML] Universities power energy management: A novel hybrid model based on iCEEMDAN and Bayesian optimized LSTM
Rapid growth and development around the world will lead to a gradual increase in electricity
consumption. At present, colleges and universities have become the primary unit of daily …
consumption. At present, colleges and universities have become the primary unit of daily …