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230811 ||| eng |
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|a 9783036574424
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|a books978-3-0365-7442-4
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|a 9783036574431
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| 100 |
1 |
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|a Wen, Yuanqiao
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| 245 |
0 |
0 |
|a Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (262 p.)
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| 653 |
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|a Transport technology and trades / bicssc
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| 653 |
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|a complex waters
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| 653 |
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|a velocity obstacle
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| 653 |
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|a intelligent decision-making
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| 653 |
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|a AIS
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| 653 |
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|a AIS data
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| 653 |
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|a ship behavior
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| 653 |
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|a regularization-trajectory cell
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| 653 |
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|a n/a
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| 653 |
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|a COLREGs
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| 653 |
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|a mixed waterborne traffic
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| 653 |
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|a deep learning
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| 653 |
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|a hazard identification
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| 653 |
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|a ship object
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| 653 |
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|a effects of wind and current
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| 653 |
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|a maritime safety
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a inland waterway transportation
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| 653 |
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|a ship domain
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| 653 |
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|a cognitive space
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| 653 |
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|a ship traffic flow
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| 653 |
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|a collision alert system (CAS)
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| 653 |
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|a unmanned surface vehicle
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| 653 |
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|a unmanned surface vehicle (USV)
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| 653 |
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|a available maneuvering margins (AMM)
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a YOLO
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| 653 |
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|a autonomous ship
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| 653 |
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|a information perception
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| 653 |
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|a motion planning
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| 653 |
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|a intersection
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| 653 |
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|a risk assessment
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| 653 |
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|a multi-sensor
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| 653 |
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|a obstacles classification
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| 653 |
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|a collision avoidance
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| 653 |
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|a ontology
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| 653 |
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|a clustering
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| 653 |
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|a deep convolutional neural network
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| 653 |
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|a ship autonomy
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| 653 |
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|a spatiotemporal dependence
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| 653 |
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|a morphological operation
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| 653 |
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|a ship manoeuvrability
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| 653 |
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|a execution
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| 653 |
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|a multi-scale analysis
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| 653 |
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|a semantic modeling
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| 653 |
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|a ship exhaust behavior
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| 653 |
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|a formal expression
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| 653 |
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|a gate recurrent unit
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| 653 |
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|a trajectory classification
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| 653 |
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|a RANSAC
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| 653 |
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|a preliminary hazard analysis
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| 653 |
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|a maritime autonomous surface ships
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| 653 |
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|a fuzzy rules
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| 653 |
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|a Bayesian framework
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| 653 |
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|a detection and tracking
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| 653 |
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|a ship stability
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| 653 |
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|a hybrid causal logic
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| 653 |
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|a ship intention identification
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| 653 |
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|a deduction of the manoeuvring process
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| 700 |
1 |
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|a Hahn, Axel
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| 700 |
1 |
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|a Valdez Banda, Osiris
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| 700 |
1 |
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|a Huang, Yamin
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-7442-4
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/100789
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/7251
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 900
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| 082 |
0 |
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|a 000
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| 082 |
0 |
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|a 380
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|a 600
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|a 620
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|a 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.
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