Tool condition monitoring for high-performance machining systems—A review
In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems
have gained considerable interest in high-value manufacturing industries to cope with the …
have gained considerable interest in high-value manufacturing industries to cope with the …
Empirical mode decomposition and its variants: A review with applications in structural health monitoring
Structural health monitoring (SHM) is one of the most emerging approaches for early
damage detection, which leads to improved safety and efficient maintenance of large-scale …
damage detection, which leads to improved safety and efficient maintenance of large-scale …
Multivariate variational mode decomposition
N ur Rehman, H Aftab - IEEE Transactions on signal …, 2019 - ieeexplore.ieee.org
We present a generic extension of variational mode decomposition (VMD) algorithm to
multivariate or multichannel data. The proposed method utilizes a model for multivariate …
multivariate or multichannel data. The proposed method utilizes a model for multivariate …
A short-term wind power prediction model based on CEEMD and WOA-KELM
Y Ding, Z Chen, H Zhang, X Wang, Y Guo - Renewable Energy, 2022 - Elsevier
Effective short-term wind power prediction is crucial to the optimal dispatching, system
stability, and operation cost control of a power system. In order to deal with the intermittent …
stability, and operation cost control of a power system. In order to deal with the intermittent …
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution …
Developing an accurate and robust prediction of long-term average global solar irradiation
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition
Accurate and reliable multi-step-ahead forecasting of stock price indexes over long-term
future trends is challenging for capital investors and decision-makers. This study developed …
future trends is challenging for capital investors and decision-makers. This study developed …
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 …
Smart multichannel mode extraction for enhanced bearing fault diagnosis
In bearing fault diagnosis, multichannel data can contain more abundant and complete fault
information to alleviate the influence of accidental factors in a single channel. To fully …
information to alleviate the influence of accidental factors in a single channel. To fully …
Human emotion recognition based on time–frequency analysis of multivariate EEG signal
V Padhmashree, A Bhattacharyya - Knowledge-Based Systems, 2022 - Elsevier
Understanding the expression of human emotional states plays a prominent role in
interactive multimodal interfaces, affective computing, and the healthcare sector. Emotion …
interactive multimodal interfaces, affective computing, and the healthcare sector. Emotion …
A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
A brain-computer interface (BCI) facilitates a medium to translate the human motion
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …