Application of Artificial Intelligence in Maritime Transportation
Maritime transportation assumes a large number of cargo-delivering tasks in world trade. It is noted that maritime traffic safety and efficiency may be affected by varied factors such as weather, ship crew proficiency, etc. The topic Reprint focuses on the use of artificial intelligence techniques t...
Wedi'i Gadw mewn:
| Fformat: | Online |
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| Iaith: | Saesneg |
| Cyhoeddwyd: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Pynciau: | |
| Mynediad Ar-lein: | ONIX_20240514_9783725806553_404 |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| _version_ | 1869527658441736192 |
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| collection | Directory of Open Access Books |
| description | Maritime transportation assumes a large number of cargo-delivering tasks in world trade. It is noted that maritime traffic safety and efficiency may be affected by varied factors such as weather, ship crew proficiency, etc. The topic Reprint focuses on the use of artificial intelligence techniques to enhance maritime transportation efficiency. More specifically, the Reprint unveils cutting-edge machine learning-supported studies, including autonomous guide vehicle path optimization, ship arrival and departure time estimation from insufficient/biased maritime data, anomaly ship kinematic data cleansing, ship collision avoidance, etc. |
| format | Online |
| id | doab-20.500.12854ir-137808 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1378082024-05-14T14:32:47Z Application of Artificial Intelligence in Maritime Transportation Chen, Xinqiang Ma, Dongfang Liu, Ryan Wen U-shaped automated container terminal double cantilever rail crane refined collaborative scheduling equipment ratio water transportation target detection unmanned ship deep learning attention mechanism rim-driven thruster (RDT) electric power propulsion ship system permanent magnet synchronous motor (PMSM) position-sensorless control sliding mode observer (SMO) phase-locked loop (PLL) ship movement characteristics polar waters correlation analysis polar navigation maritime search and rescue path planning unmanned air vehicle multi-objective optimization non-dominated sorting genetic algorithm-II multi-task optimization crankshaft angle of marine main engines angular displacement sensor magnetic focusing induced voltage analysis linearity error optimization eddy current loss water surface segmentation edge detection shoreline detection local path planning dynamic window method USV accident analysis offshore wind farm STAMP CAST complex network ship speed extraction image dehaze ship detection ship tracking spatiotemporal graph neural network traffic flow prediction ship big data AIS port traffic prediction autonomous berthing CMA-ES LQR berthing strategy ship global path planning A-star algorithm navigational safety path optimization automatic identification system spoofing ship missing points jumping points trajectory segmentation isolation forest ship trajectory prediction AIS data neural network encoder–decoder model multiple feature fusion convolutional neural network low-visibility image enhancement maritime surveillance visual perception cooperative USV-UAV system YOLOX PIDNet monocular camera vision adverse weather image restoration improved YOLOv5 intelligent maritime transportation spatial-temporal density maritime transportation network analysis Delaunay triangulation online traffic monitoring ETL pipeline vessel trajectory prediction dead reckoning GIWW travel time ETA prediction XGBoost n/a Maritime transportation assumes a large number of cargo-delivering tasks in world trade. It is noted that maritime traffic safety and efficiency may be affected by varied factors such as weather, ship crew proficiency, etc. The topic Reprint focuses on the use of artificial intelligence techniques to enhance maritime transportation efficiency. More specifically, the Reprint unveils cutting-edge machine learning-supported studies, including autonomous guide vehicle path optimization, ship arrival and departure time estimation from insufficient/biased maritime data, anomaly ship kinematic data cleansing, ship collision avoidance, etc. 2024-05-14T14:32:32Z 2024-05-14T14:32:32Z 2024 book ONIX_20240514_9783725806553_404 9783725806553 9783725806560 https://directory.doabooks.org/handle/20.500.12854/137808 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9043 https://mdpi.com/books/pdfview/book/9043 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0656-0 10.3390/books978-3-7258-0656-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725806553 9783725806560 434 open access |
| spellingShingle | U-shaped automated container terminal double cantilever rail crane refined collaborative scheduling equipment ratio water transportation target detection unmanned ship deep learning attention mechanism rim-driven thruster (RDT) electric power propulsion ship system permanent magnet synchronous motor (PMSM) position-sensorless control sliding mode observer (SMO) phase-locked loop (PLL) ship movement characteristics polar waters correlation analysis polar navigation maritime search and rescue path planning unmanned air vehicle multi-objective optimization non-dominated sorting genetic algorithm-II multi-task optimization crankshaft angle of marine main engines angular displacement sensor magnetic focusing induced voltage analysis linearity error optimization eddy current loss water surface segmentation edge detection shoreline detection local path planning dynamic window method USV accident analysis offshore wind farm STAMP CAST complex network ship speed extraction image dehaze ship detection ship tracking spatiotemporal graph neural network traffic flow prediction ship big data AIS port traffic prediction autonomous berthing CMA-ES LQR berthing strategy ship global path planning A-star algorithm navigational safety path optimization automatic identification system spoofing ship missing points jumping points trajectory segmentation isolation forest ship trajectory prediction AIS data neural network encoder–decoder model multiple feature fusion convolutional neural network low-visibility image enhancement maritime surveillance visual perception cooperative USV-UAV system YOLOX PIDNet monocular camera vision adverse weather image restoration improved YOLOv5 intelligent maritime transportation spatial-temporal density maritime transportation network analysis Delaunay triangulation online traffic monitoring ETL pipeline vessel trajectory prediction dead reckoning GIWW travel time ETA prediction XGBoost n/a Application of Artificial Intelligence in Maritime Transportation |
| title | Application of Artificial Intelligence in Maritime Transportation |
| title_full | Application of Artificial Intelligence in Maritime Transportation |
| title_fullStr | Application of Artificial Intelligence in Maritime Transportation |
| title_full_unstemmed | Application of Artificial Intelligence in Maritime Transportation |
| title_short | Application of Artificial Intelligence in Maritime Transportation |
| title_sort | application of artificial intelligence in maritime transportation |
| topic | U-shaped automated container terminal double cantilever rail crane refined collaborative scheduling equipment ratio water transportation target detection unmanned ship deep learning attention mechanism rim-driven thruster (RDT) electric power propulsion ship system permanent magnet synchronous motor (PMSM) position-sensorless control sliding mode observer (SMO) phase-locked loop (PLL) ship movement characteristics polar waters correlation analysis polar navigation maritime search and rescue path planning unmanned air vehicle multi-objective optimization non-dominated sorting genetic algorithm-II multi-task optimization crankshaft angle of marine main engines angular displacement sensor magnetic focusing induced voltage analysis linearity error optimization eddy current loss water surface segmentation edge detection shoreline detection local path planning dynamic window method USV accident analysis offshore wind farm STAMP CAST complex network ship speed extraction image dehaze ship detection ship tracking spatiotemporal graph neural network traffic flow prediction ship big data AIS port traffic prediction autonomous berthing CMA-ES LQR berthing strategy ship global path planning A-star algorithm navigational safety path optimization automatic identification system spoofing ship missing points jumping points trajectory segmentation isolation forest ship trajectory prediction AIS data neural network encoder–decoder model multiple feature fusion convolutional neural network low-visibility image enhancement maritime surveillance visual perception cooperative USV-UAV system YOLOX PIDNet monocular camera vision adverse weather image restoration improved YOLOv5 intelligent maritime transportation spatial-temporal density maritime transportation network analysis Delaunay triangulation online traffic monitoring ETL pipeline vessel trajectory prediction dead reckoning GIWW travel time ETA prediction XGBoost n/a |
| topic_facet | U-shaped automated container terminal double cantilever rail crane refined collaborative scheduling equipment ratio water transportation target detection unmanned ship deep learning attention mechanism rim-driven thruster (RDT) electric power propulsion ship system permanent magnet synchronous motor (PMSM) position-sensorless control sliding mode observer (SMO) phase-locked loop (PLL) ship movement characteristics polar waters correlation analysis polar navigation maritime search and rescue path planning unmanned air vehicle multi-objective optimization non-dominated sorting genetic algorithm-II multi-task optimization crankshaft angle of marine main engines angular displacement sensor magnetic focusing induced voltage analysis linearity error optimization eddy current loss water surface segmentation edge detection shoreline detection local path planning dynamic window method USV accident analysis offshore wind farm STAMP CAST complex network ship speed extraction image dehaze ship detection ship tracking spatiotemporal graph neural network traffic flow prediction ship big data AIS port traffic prediction autonomous berthing CMA-ES LQR berthing strategy ship global path planning A-star algorithm navigational safety path optimization automatic identification system spoofing ship missing points jumping points trajectory segmentation isolation forest ship trajectory prediction AIS data neural network encoder–decoder model multiple feature fusion convolutional neural network low-visibility image enhancement maritime surveillance visual perception cooperative USV-UAV system YOLOX PIDNet monocular camera vision adverse weather image restoration improved YOLOv5 intelligent maritime transportation spatial-temporal density maritime transportation network analysis Delaunay triangulation online traffic monitoring ETL pipeline vessel trajectory prediction dead reckoning GIWW travel time ETA prediction XGBoost n/a |
| url | ONIX_20240514_9783725806553_404 |