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    A Hybrid Approach Integrating Physics-Based Models and Expert-Augmented Neural Networks for Ship Fuel Consumption Prediction 

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2024:;volume( 147 ):;issue: 003:;page 31408-1
    Author(s): Liang, Qin; Han, Peihua; Vanem, Erik; Erik Knutsen, Knut; Zhang, Houxiang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The International Maritime Organization’s recent approval of the 2023 strategy on reduction of greenhouse gas emissions amplifies the pressure on stakeholders to achieve net-zero emissions in shipping by 2050. Considering ...
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    A Deep Generative Model for Multi-Ship Trajectory Forecasting With Interaction Modeling 

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2024:;volume( 147 ):;issue: 003:;page 31402-1
    Author(s): Zhu, Mingda; Han, Peihua; Tian, Weiwei; Skulstad, Robert; Zhang, Houxiang; Li, Guoyuan
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Multi-agent modeling is a challenging issue in intelligent systems, which is further compounded by heavy and complex traffic in maritime contexts. Trajectory forecasting can enhance operation safety. Nonetheless, effectively ...
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    DSpace software copyright © 2002-2015  DuraSpace
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