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    Deep Learning Toward Autonomous Ship Navigation and Possible COLREGs Failures 

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2020:;volume( 142 ):;issue: 003:;page 031102-1
    Author(s): Perera, Lokukaluge P.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A structured technology framework to address navigation considerations, including collision avoidance, of autonomous ships is the focus of this study. That consists of adequate maritime technologies to achieve the required ...
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    Marine Engine-Centered Data Analytics for Ship Performance Monitoring 

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2017:;volume( 139 ):;issue: 002:;page 21301
    Author(s): Perera, Lokukaluge P.; Mo, Brage
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP). The SEEMP enforces various emission control measures to improve ship energy efficiency by considering ...
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    System Identification of Nonlinear Vessel Steering 

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2015:;volume( 137 ):;issue: 003:;page 31302
    Author(s): Perera, Lokukaluge P.; Oliveira, P.; Guedes Soares, C.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In this paper, the stochastic parameters describing a nonlinear ocean vessel steering model are identified, resorting to an extended Kalman filter (EKF). The proposed method is applied to a secondorder modified Nomoto model ...
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    DSpace software copyright © 2002-2015  DuraSpace
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