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... the limited applications in maritime research and lack of detailed analyses from trajectory to trajectory points, which are necessary to identify and delineate the unique behaviour of particular ship, authors feel the need to develop a semantic model with multiple levels of ontology networks. As shown in Figure 1, this study developed semantic model of ship behaviour and related recognition methods, which allows the easy annotation, expression, and acquisition of information about trajectories from complex situation and big data. And go a step further, the semantic model can express ship behaviours in natural language or queried by ontology query language, which is easily to understand by port authorities, coast guard, pilots, tug operators, crewmembers, etc. ...
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This chapter explores the data management challenges inherent in a digital twin framework and elucidates the pivotal role of knowledge graphs in meeting these demands. The author introduces a functional metamodel designed to semantically convert scenarios related to the green transition into a standardized format. This metamodel emerges as a vital instrument for both business and technical stakeholders, streamlining the intricate interplay among operational requisites, environmental factors, optimization strategies, and decarbonization technologies. It adeptly encapsulates the ship's environmental and economic performance indicators. In synergy with the metamodel, a knowledge graph (KG) encodes industry-specific parameters, laying a robust groundwork that depicts the vessel's functionalities, variables, and operational processes, and serves as a repository for model metadata. This facilitates the automation of shortest path computations, effectively bridging the divide between overarching platform operations and the complex nuances of maritime assets and activities. The utility of the metamodel and knowledge graph in translating real-world issues into a digital twin-compatible format is exemplified through a case study focusing on the application of variable frequency drives for enhancing the efficiency of the high-temperature cooling system. The chapter concludes by summarizing the data management hurdles encountered in maritime digital twins and offering a perspective on future developments.
The essence of the path planning problems is multi-modality constraint. However, most of the current literature has not mentioned this issue. This paper introduces the research progress of path planning based on the multi-modality constraint. The path planning of multi-modality constraint research can be classified into three stages in terms of its basic ingredients (such as shape, kinematics and dynamics et al.): Route Planning, Trajectory Planning and Motion Planning. It then reviews the research methods and classical algorithms, especially those applied to the Unmanned Surface Vehicle (USV) in every stage. Finally, the paper points out some existing problems in every stage and suggestions for future research.
Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.
