Results 51 to 60 of about 5,348,114 (153)
A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction
IEEE Access, 2020 Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon ...
Hai Tao +7 more
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients.
Yi Tao +9 more
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International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.Nowadays, carbon trading policy is regarded as an effective method to control carbon emissions. Accurate prediction of carbon trading price can benefit both governments and enterprises. To enhance the accuracy of prediction, this paper proposes a novel three‐stage carbon trading price prediction model combining time–frequency decomposition and hybrid ...
Haochu Cui, Yan Sun, Peiyue Cheng
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Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.Forecasting relative humidity remains challenging due to its nonlinear, nonstationary characteristics and long‐memory dependence. This study proposes a hybrid decomposition‐ensemble model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Sample Entropy–Gated Recurrent Unit–Ridge Regression (CEEMDAN‐SE‐GRU‐Ridge), for one‐step‐ahead ...
John Kamwele Mutinda +4 more
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IEEE Access, 2019 olling bearings are crucial components in mechanical, civil and aerospace engineering. The practical working conditions of rolling bearings are complex and tough, hence fault diagnosis of rolling bearings under varying operating conditions is very ...
Rui Yuan +3 more
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Ibrain, Volume 11, Issue 3, Page 347-363, Fall 2025.Overview of the EEG‐based emotion classification framework. The process starts with EEG signal recording while subjects watch five emotional videos. The raw EEG data undergoes preprocessing, including noise and artifact removal, followed by segmentation, feature extraction, and selection.
Afshin S. Asl, Sahar Karimpour
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Multivariate Empirical Mode Decomposition analysis of Swarm data [PDF]
, 2018 The magnetosphere and the ionosphere of the Earth are populated by different current systems with well-structured spatial patterns. In this paper, by using low-resolution (1 Hz) Swarm A data, we present an application of the Multivariate Empirical Mode ...
Alberti, T.
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Weighted Window Sliding Multivariate Empirical Mode Decomposition for Online Multichannel Filtering
IEEE Access, 2018 Affected by nonlinear and non-stationary problems, classical linear analysis approaches may fail in analyzing real-world signals, such as the biomedical data.
Songbing Tao +3 more
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Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition
Frontiers in Neuroscience, 2020 Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels.
Andres Soler +5 more
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WIREs Data Mining and Knowledge Discovery, Volume 15, Issue 2, June 2025.ABSTRACT The integration of wavelet transformation and artificial intelligence techniques has demonstrated significant potential in healthcare applications. Wavelet analysis enables multi‐scale signal decomposition and feature extraction that, when combined with machine and deep learning approaches, enhance the accuracy and efficiency of medical data ...
Samiul Based Shuvo +8 more
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