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Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning
(
Elsevier Ltd
, 2023 , Article)
The reliable control of wave energy devices highly relies on the forecasts of wave heights. However, the dynamic characteristics and significant fluctuation of waves’ historical data pose challenges to precise predictions. ...
Dynamic ensemble deep echo state network for significant wave height forecasting
(
Elsevier Ltd
, 2023 , Article)
Forecasts of the wave heights can assist in the data-driven control of wave energy systems. However, the dynamic properties and extreme fluctuations of the historical observations pose challenges to the construction of ...
A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition
(
Elsevier Inc.
, 2023 , Article)
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary ...
Situation Awareness Recognition Using EEG and Eye-Tracking data: a pilot study
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Conference Paper)
Since situation awareness (SA) plays an important role in many fields, the measure of SA is one of the most concerning problems. Using physiological signals to evaluate SA is becoming a popular research topic because of ...
Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Article)
Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve ...