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024 7 $a10.3390/books978-3-0365-7442-4
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100   $a20230729d2023      uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aData/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships /$fedited by Yuanqiao Wen, Axel Hahn, Osiris Valdez Banda
210  1$aBasel :$cMDPI - Multidisciplinary Digital Publishing Institute,$d2023.
210  4$dİ2023
215   $a1 online resource (262 pages)
311   $a3-0365-7442-5 
320   $aIncludes bibliographical references and index.
330   $aMaritime 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.
606   $aDialectical behavior therapy
615  0$aDialectical behavior therapy.
676   $a616.89142
702   $aValdez Banda$b Osiris
702   $aHahn$b Axel
702   $aWen$b Yuanqiao
801  0$bNjHacI
801  1$bNjHacl
906   $aBOOK
912   $a9910729781103321
996   $aData$9892730
997   $aUNINA