Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception.
Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. However, when analyzing individual countries, we find that some exhibit a high prevalence of unstable triads or a low prevalence of stabilizing triads, suggesting that vulnerabilities in global energy trade are more pronounced in some countries than others.ĭue to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. We further find that the global energy trade network is quite robust against disruptions, maintaining its topological characteristics even after random removal of 80% of the network's nodes. We then analyze whether various triads are under-or over-represented in our networks and find that triads associated with stability appear more frequently than expected, whereas triads associated with conflict appear less frequently than expected. Using the network science technique of triad analysis, which examines the prevalence of 3-node subnetworks in a target network, we calculate triad significance profiles for each network. We analyze the topology of the global energy trade network in aggregate, for various energy commodities, and for individual countries. As trade patterns begin to reconfigure, it is important that policy makers understand how vulnerabilities of the existing network may present obstacles to a smooth energy transition. The data analysed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.Īn international push to decarbonize economies has initiated a major transition in the global energy system and has begun to disrupt the intricate network of energy trade. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. In particular, most ships can be classified into three categories: bulk dry carriers, container ships and oil tankers. We show that the network has several features that set it apart from other transportation networks. Here, we use information about the itineraries of 16 363 cargo ships during the year 2007 to construct a network of links between ports. With 90 per cent of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Transportation networks play a crucial role in human mobility, the exchange of goods and the spread of invasive species.
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