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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 …
[HTML][HTML] A novel model for the study of future maritime climate using Artificial Neural Networks and Monte Carlo simulations under the context of climate change.
NP Juan, VN Valdecantos - Ocean Modelling, 2024 - Elsevier
This paper proposes a new model to study future coastal maritime climate under climate
change context. This new model combines statistical analysis, Monte Carlo simulations and …
change context. This new model combines statistical analysis, Monte Carlo simulations and …
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 …
Development of pyramid neural networks for prediction of significant wave height for renewable energy farms
A Mahdavi-Meymand, W Sulisz - Applied Energy, 2024 - Elsevier
Significant wave height (H s) is a critical parameter in the design, operation, and
maintenance of nearshore and offshore wind and wave farms. In this study, the original …
maintenance of nearshore and offshore wind and wave farms. In this study, the original …
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 …
A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city
Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with
bearings playing a pivotal role as key mechanical transmission components. Therefore, the …
bearings playing a pivotal role as key mechanical transmission components. Therefore, the …
[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 …
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 …