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Neural network for predicting ship magnetic signatures at arbitrary depths and courses: a comparison with the multi-dipole model. [PDF]
Sci RepZielonacki K, Tarnawski J, Woloszyn M.
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Hull form optimization with a new three-dimensional deformation strategy. [PDF]
Sci RepWei D +5 more
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Aerosol-cloud-climate cooling overestimated by ship-track data
Science, 2021Making tracks The magnitude of the effect of anthropogenic aerosols on the formation of clouds is an important unknown about how humans are affecting climate.
Franziska Glassmeier +2 more
exaly +2 more sources
Research progress on ship power systems integrated with new energy sources: A review
Renewable and Sustainable Energy Reviews, 2021The global shipping industry faces huge pressure to reduce its greenhouse (GHG) emissions due to the International Maritime Organization (IMO) has introduced strict regulations to decrease GHG emissions from ships.
Yuwei Sun, Chengqing Yuan, Xinping Yan
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A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2023Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique ...
Yongsheng Zhou +4 more
semanticscholar +1 more source
HOG-ShipCLSNet: A Novel Deep Learning Network With HOG Feature Fusion for SAR Ship Classification
IEEE Transactions on Geoscience and Remote Sensing, 2022Ship classification in synthetic aperture radar (SAR) images is a fundamental and significant step in ocean surveillance. Recently, with the rise of deep learning (DL), modern abstract features from convolutional neural networks (CNNs) have hugely ...
Tianwen Zhang +13 more
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IEEE Transactions on Geoscience and Remote Sensing, 2021
Recently, deep-learning methods have been successfully applied to the ship detection in the synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR ships due to the broad diversity of the scales and the strong ...
Jiamei Fu, Xian Sun, Zhirui Wang, Kun Fu
semanticscholar +1 more source
Recently, deep-learning methods have been successfully applied to the ship detection in the synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR ships due to the broad diversity of the scales and the strong ...
Jiamei Fu, Xian Sun, Zhirui Wang, Kun Fu
semanticscholar +1 more source
Efficient COLREG-Compliant Collision Avoidance in Multi-Ship Encounter Situations
IEEE transactions on intelligent transportation systems (Print), 2022Ship collisions are major types of maritime accidents which may involve the loss of life and significant damage to property and the environment. Although many automatic ship collision avoidance algorithms have been suggested, most of them are only ...
Yonghoon Cho, Jungwook Han, Jinwhan Kim
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