Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation
Significant wave height is an average of the largest ocean waves, which are important for
renewable and sustainable energy resource generation. A large significant wave height can …
renewable and sustainable energy resource generation. A large significant wave height can …
[HTML][HTML] A deep learning method for the prediction of ship fuel consumption in real operational conditions
In recent years, the European Commission and the International Maritime Organization
(IMO) implemented various operational measures and policies to reduce ship fuel …
(IMO) implemented various operational measures and policies to reduce ship fuel …
Semantic attention and relative scene depth-guided network for underwater image enhancement
In this paper, to solve unique underwater degradation challenges covering low contrast,
color deviation and blurring, etc., a novel semantic attention and relative scene depth …
color deviation and blurring, etc., a novel semantic attention and relative scene depth …
[HTML][HTML] A spectral-ensemble deep random vector functional link network for passive brain–computer interface
Randomized neural networks (RNNs) have shown outstanding performance in many
different fields. The superiority of having fewer training parameters and closed-form …
different fields. The superiority of having fewer training parameters and closed-form …
Human-cognition-inspired deep model with its application to ocean wave height forecasting
Ocean wave height (OWH) forecasting is indispensable but challenging task since that the
series evolution involves mixed effects of numerous factors. However, most deep models …
series evolution involves mixed effects of numerous factors. However, most deep models …
Benchmarking feed-forward randomized neural networks for vessel trajectory prediction
The burgeoning scale and speed of maritime vessels present escalating challenges to
navigational safety. Perceiving the motions of vessels, identifying anomalies, and risk …
navigational safety. Perceiving the motions of vessels, identifying anomalies, and risk …
Ship order book forecasting by an ensemble deep parsimonious random vector functional link network
Efficient forecasting of ship order books holds immense significance in the maritime industry,
enabling companies to optimize their operations, allocate resources effectively, and make …
enabling companies to optimize their operations, allocate resources effectively, and make …
[HTML][HTML] A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region
Storm surge and waves are responsible for a substantial portion of tropical and extratropical
cyclones-related damages. While high-fidelity numerical models have significantly …
cyclones-related damages. While high-fidelity numerical models have significantly …
Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions
Time series analysis has been widely employed in various domains, including finance,
healthcare, meteorology, and economics. This approach is crucial in extracting patterns …
healthcare, meteorology, and economics. This approach is crucial in extracting patterns …
Revolutionizing low-carbon marine transportation: Prediction of wave energy via adaptive neuro-fuzzy inference framework in East China Sea
M Abbas, D Zhang - Arabian Journal for Science and Engineering, 2023 - Springer
Carbon neutrality hinges on effectively harnessing renewable energy sources, a critical
factor as countries worldwide enact low-carbon legislation to mitigate global warming. In this …
factor as countries worldwide enact low-carbon legislation to mitigate global warming. In this …