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[null,"1.","Record Nr.",null,"UNINA9910729781103321"]
[null,null,"Titolo",null,"Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships / / edited by Yuanqiao Wen, Axel Hahn, Osiris Valdez Banda"]
[null,null,"Pubbl/distr/stampa",null,"Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"Descrizione fisica",null,"1 online resource (262 pages)"]
[null,null,"Disciplina",null,"616.89142"]
[null,null,"Soggetti",null,"Dialectical behavior therapy"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,null,"Sommario/riassunto","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."]