For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

Maritime traffic prediction is a crucial task for increasing the efficiency of port operations and safety, especially in congested regions. A huge amount of automatic identification system (AIS) data is constantly transmitting from vessels to receivers that contain information about vessels' movements and characteristics. These historical AIS data can be utilized in movement analyses of vessels. This paper proposes a novel point-based model for location and traffic prediction using vessels' trajectories adapted from AIS measures. The location prediction procedure is setup based on similarity analysis of historical AIS data. The model is applied to a real dataset of hundreds of vessels' trajectories in the Strait of Georgia, USA. The correlation results of 0.9976, 0.9887, and 0.9794 for the next 10, 20, and 30 minutes, respectively, imply sufficient correspondence between predicted and actual coordinates. The traffic prediction procedure considers the probability of the appearance of new vessels inside an area of interest (AoI) at different time intervals. The Sorenson similarity index (SSI) is used to measure the accuracy of the traffic prediction model. The SSIs for time intervals of 10, 20, and 30 minutes are 70%, 66%, and 59%, respectively, which show the robustness of the model to predict hot spots inside the AoI.

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Synthesis of spiro 1,3-oxazines via three-component reaction of conjugated imines, dialkyl acetylenedicarboxylates and N,N'-disubstituted parabanic acids

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Controlling nonlinear systems have always been a challenging problem. Complexity is mostly the result of nonlinear systems behavior dependence on initial conditions and input. Linearization techniques are such kinds of nonlinear systems analysis tools, which can give suitable results in neighborhood of equilibrium points. In addition, phase portraits are very efficient for visualizing the behavior of system in equilibrium points’ neighborhood. In this paper, designing a controller for a low order dynamic system with multiple equilibrium points in presence of a sinusoidal disturbance with unknown amplitude and unknown bounded frequency is investigated. The proposed controller is based on a combination of the backstepping method, Lyapunov based techniques and neuro-fuzzy strategies. At first, the mathematical dynamic model is presented and all equilibrium points are found. Lyapunov-based techniques and backstepping method are combined in order to control the system under influence of sinusoidal disturbance with unknown amplitude but fixed frequency. Finally, a neuro-fuzzy controller is designed and added to main controller to tolerate the frequency variation of the disturbance. Simulation results are presented to demonstrate the performance of the controller in omission of a sinusoidal disturbance with random variable frequency and amplitude.

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