With the development of artificial intelligence, intelligent and unmanned driving has received extensive attention. Compared with the rapid technological advance of unmanned vehicles, the research on unmanned ship technology is relatively rare. The autonomous navigation of cargo ships needs to meet their huge inertia and obey existing complex rules. Therefore, the requirements for smart ships are much higher than those for unmanned vehicles. A smart ship has to realise autonomous driving instead of manual operation, which consists of path planning and controlling.Toward to this goal, this research proposes a path planning and manipulating approach based on Qlearning, which can drive a cargo ship by itself without requiring any input from human experiences or guidance rules. At the very beginning, a ship is modelled in a simulation waterway. Then, a number of simple rules of navigation are introduced and regularized as rewards or punishments, which are used to judge the performance, or manipulation decisions of the ship. Subsequently, Qlearning is introduced to learn the action-reward model and the learning outcome is used to manipulate the ship's movement. By chasing higher reward values, the ship can find an appropriate path or navigation strategies by itself. After a sufficient number of rounds of training, a convincing path and manipulating strategies will likely be produced. By comparing the proposed approach with the existing Rapid-exploring Random Tree (RRT) and the Artificial Potential Field A* methods, it is shown that this approach is more effective in self-learning and continuous optimisation, and therefore closer to human manoeuvring.

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Marine intelligent anti-collision regulations have been a means of dealing with a particularly dangerous problem for many years. As the foundation for making anti-collision decisions, the International Regulations for Preventing Collisions at Sea formulated by the International Maritime Organization should always be considered. Based on the International Regulations for Preventing Collisions at Sea, the minimum distance required for anti-collision by only the give-way ship (under normal situations) and by both the give-way ship and the stand-on ship steering simultaneously (under critical situations) under all possible encounter situations are studied respectively. Rather than regarding ships as a point, a restricted area where no evasive action of other ships is allowed is introduced. Furthermore, the ship's manoeuvrability is taken into account. The proposed model is also assessed by the traditional parameters used in anti-collision such as the closest point of approach, the distance to the closest point of approach and the time to the closest point of approach. The study shows that the results obtained in this paper are important and complement the above-mentioned regulations so that navigators can make wise decisions.

SUMMARYA new formulation of the Navier-Stokes equations is introduced to solve incompressible ow problems. When ÿnite element methods are used under this formulation there is no need to worry whether Babuska-Brezzi condition is satisÿed or not. Both velocity and pressure can be obtained separately and the pressure can be simply obtained by a substitution. Moreover, fully explicit time integration can be applied for easy implementation. Implementation issues are discussed and a couple of ow examples are simulated. Parallel implementation based on domain decomposition is incorporated as well.

This study proposes a new, simple but effective technique to detect and restore colour cast images, named modified grey world method. This method detects colour cast images of outdoor surveillance videos by computing the values in the YUV colour space, which makes it much easier than classic methods. Specific colour cast can be found out by calculating the hue values. Additionally, this method can detect not only simple colour cast images but also multiple colour cast images simultaneously. To detect and restore a colour cast image, the authors first remove all grey pixels and separate it into multiple parts with a maze-solving algorithm. Then, they compute the YUV colour values of each part. If the values are too high or too low, this part of the input image is designated as a colour cast. Finally, they carry out a restoration procedure, in which they calculate weights by matching average colour value with a grey reference value in YUV colour space. This method has been tested in the Safety City surveillance system in Wuhan city, China. The results show that the proposed method leads to better results in detecting and restoring colour cast imaging than classic methods in outdoor surveillance videos.