Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships
Maritime traffic data (e.g., radar data, AIS data, and CCTV data) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, representing a treasure trove for behavior analysis. Additionally, navigation rules and regula...
সংরক্ষণ করুন:
| বিন্যাস: | Online |
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| ভাষা: | ইংরেজি |
| প্রকাশিত: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20230623_9783036574431_21 |
| ট্যাগগুলো: |
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| _version_ | 1869523786224631808 |
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| collection | Directory of Open Access Books |
| description | Maritime traffic data (e.g., radar data, AIS data, and CCTV data) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, representing a treasure trove for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multisource heterogeneous big data and artificial intelligence techniques inspires innovative and important means for the development of MASS. This reprint collects twelve contributions published in “Data-/Knowledge-Driven Behavior Analysis of Maritime Autonomous Surface Ships” Special Issue during 2021–2022, aiming to provide new views on data-/knowledge-driven analytical tools for maritime autonomous surface ships, including data-driven behavior modeling, knowledge-driven behavior modeling, multisource heterogeneous traffic data fusion, risk analysis and management of MASS, etc. |
| format | Online |
| id | doab-20.500.12854ir-100789 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1007892024-04-11T15:11:08Z Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships Wen, Yuanqiao Hahn, Axel Valdez Banda, Osiris Huang, Yamin unmanned surface vehicle velocity obstacle collision avoidance obstacles classification fuzzy rules mixed waterborne traffic ship behavior ship autonomy information perception intelligent decision-making execution COLREGs ship object formal expression complex waters ship traffic flow spatiotemporal dependence gate recurrent unit motion planning unmanned surface vehicle (USV) effects of wind and current regularization-trajectory cell inland waterway transportation AIS data trajectory classification clustering deep convolutional neural network ship intention identification AIS RANSAC Bayesian framework YOLO intersection maritime autonomous surface ships hybrid causal logic preliminary hazard analysis risk assessment hazard identification autonomous ship ship manoeuvrability deduction of the manoeuvring process ship exhaust behavior detection and tracking multi-sensor deep learning morphological operation collision alert system (CAS) available maneuvering margins (AMM) ship domain ship stability maritime safety semantic modeling cognitive space multi-scale analysis ontology n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades Maritime traffic data (e.g., radar data, AIS data, and CCTV data) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, representing a treasure trove for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multisource heterogeneous big data and artificial intelligence techniques inspires innovative and important means for the development of MASS. This reprint collects twelve contributions published in “Data-/Knowledge-Driven Behavior Analysis of Maritime Autonomous Surface Ships” Special Issue during 2021–2022, aiming to provide new views on data-/knowledge-driven analytical tools for maritime autonomous surface ships, including data-driven behavior modeling, knowledge-driven behavior modeling, multisource heterogeneous traffic data fusion, risk analysis and management of MASS, etc. 2023-06-23T09:41:50Z 2023-06-23T09:41:50Z 2023 book ONIX_20230623_9783036574431_21 9783036574431 9783036574424 https://directory.doabooks.org/handle/20.500.12854/100789 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7251 https://mdpi.com/books/pdfview/book/7251 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7442-4 10.3390/books978-3-0365-7442-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036574431 9783036574424 262 Basel open access |
| spellingShingle | unmanned surface vehicle velocity obstacle collision avoidance obstacles classification fuzzy rules mixed waterborne traffic ship behavior ship autonomy information perception intelligent decision-making execution COLREGs ship object formal expression complex waters ship traffic flow spatiotemporal dependence gate recurrent unit motion planning unmanned surface vehicle (USV) effects of wind and current regularization-trajectory cell inland waterway transportation AIS data trajectory classification clustering deep convolutional neural network ship intention identification AIS RANSAC Bayesian framework YOLO intersection maritime autonomous surface ships hybrid causal logic preliminary hazard analysis risk assessment hazard identification autonomous ship ship manoeuvrability deduction of the manoeuvring process ship exhaust behavior detection and tracking multi-sensor deep learning morphological operation collision alert system (CAS) available maneuvering margins (AMM) ship domain ship stability maritime safety semantic modeling cognitive space multi-scale analysis ontology n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships |
| title | Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships |
| title_full | Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships |
| title_fullStr | Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships |
| title_full_unstemmed | Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships |
| title_short | Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships |
| title_sort | data knowledge driven behaviour analysis for maritime autonomous surface ships |
| topic | unmanned surface vehicle velocity obstacle collision avoidance obstacles classification fuzzy rules mixed waterborne traffic ship behavior ship autonomy information perception intelligent decision-making execution COLREGs ship object formal expression complex waters ship traffic flow spatiotemporal dependence gate recurrent unit motion planning unmanned surface vehicle (USV) effects of wind and current regularization-trajectory cell inland waterway transportation AIS data trajectory classification clustering deep convolutional neural network ship intention identification AIS RANSAC Bayesian framework YOLO intersection maritime autonomous surface ships hybrid causal logic preliminary hazard analysis risk assessment hazard identification autonomous ship ship manoeuvrability deduction of the manoeuvring process ship exhaust behavior detection and tracking multi-sensor deep learning morphological operation collision alert system (CAS) available maneuvering margins (AMM) ship domain ship stability maritime safety semantic modeling cognitive space multi-scale analysis ontology n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades |
| topic_facet | unmanned surface vehicle velocity obstacle collision avoidance obstacles classification fuzzy rules mixed waterborne traffic ship behavior ship autonomy information perception intelligent decision-making execution COLREGs ship object formal expression complex waters ship traffic flow spatiotemporal dependence gate recurrent unit motion planning unmanned surface vehicle (USV) effects of wind and current regularization-trajectory cell inland waterway transportation AIS data trajectory classification clustering deep convolutional neural network ship intention identification AIS RANSAC Bayesian framework YOLO intersection maritime autonomous surface ships hybrid causal logic preliminary hazard analysis risk assessment hazard identification autonomous ship ship manoeuvrability deduction of the manoeuvring process ship exhaust behavior detection and tracking multi-sensor deep learning morphological operation collision alert system (CAS) available maneuvering margins (AMM) ship domain ship stability maritime safety semantic modeling cognitive space multi-scale analysis ontology n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades |
| url | ONIX_20230623_9783036574431_21 |