The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.
Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light. However, if there are many planes in the work scene, it is difficult to identify objects and process the resulting huge amount of data. In view of this problem and targeted at object calibration, this paper puts forward a depth data calibration method based on Gauss mixture model. The method converts the depth data to point cloud, filters the noise and collects samples, which effectively reduces the computational load in the following steps. Besides, the authors cluster the point cloud vector with the Gaussian mixture model, and obtain the target and background planes by using the random sampling consensus algorithm to fit the planes. The combination of target Region Of Intelligent agent (ROI) and point cloud significantly reduces the computational load and improves the computing speed. The effect and accuracy of the algorithm is verified by the test of the actual object.
. Distrib. , 2012, vol. 6, no. 9, pp. 874–883.
 S. Kim and T. J. Overbye, “Mixed Power Flow Analysis using AC and DC Models”, IET Gener. Transm. Distrib. , 2012, vol. 6, no. 10, pp. 1053–1059.
 Y. Mishra, Z. Y. Dong, J. Ma and D. J. Hill, “Induction Motor Load Impact on Power System Eigenvalue Sensitivity Analysis”, IET Gener. Transm. Distrib. , 2009, vol. 3, no. 7, pp. 690–700.
 R. Agrawal and D. Thukaram, “Support VectorClustering-based Direct Coherency Identification of Generators a Multi-machine Power System”, IET Gener. Transm. Distrib
Łukasz Bartczuk, Andrzej Przybył and Krzysztof Cpałka
concepts and open issues, Information Sciences 251 : 22–46.
Malchiodi, D. and Pedrycz, W. (2013). Learning membership functions for fuzzy sets through modified support vectorclustering, in F. Masulli et al. (Eds.), Fuzzy Logic and Applications , Springer, Cham, pp. 52–59.
Medasani, S., Kim, J. and Krishnapuram, R. (1998). An overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning 19 (3): 391–417.
Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits