Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vessel sailing pattern within harbour waters. The Naïve Bayes classifier of vessel behaviour is built to classify and detect anomalous vessel behaviour. The proposed method has been tested and validated on the vessel trajectories from AIS data within the waters of Xiamen Bay and Chengsanjiao, China. The results indicate that the proposed method is effective and helpful, thus enhancing maritime situational awareness in coastal waters.

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This paper presents a fuzzy set-based approach to the evaluation of information technology (IT) projects. We assume a multi-criteria decision-making framework, where sets of general and domain-specific criteria are used to judge the relative performance of alternative technologies. The methodology was originally developed for DIAS.net, an EU project aiming at the development of the Information Society in insular and isolated regions of Europe. In this paper, we present many aspects of our evaluation framework, including the synthesis of evaluation teams, the assessment of the importance of criteria, the evaluation of the performance of the alternatives and the final ranking and selection of projects. The methodology presented has the innovative feature of embodying techniques of fuzzy sets theory into the classical multi-criteria decision analysis. This combination enables us to handle efficiently the subjectiveness that often characterizes expert judgements on a decision problem. Fuzzy linguistic terms, such as "poor," "fair," "very important," etc. are proposed for assessing the relative merit of alternatives and criteria. The paper concludes by exploring the potentiality of the above methodology in providing a flexible and robust IT evaluation framework.

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