Abstract. In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

This paper describes the creation of a system that acquires building energy sub-metering data. The system is designed in a way that allows it to use different sensors for different energy resources sub-metering. A wireless communication standard that allows to achieve the best combination of communication range, low power sub-metering and to give the system more flexibility is chosen. This paper shows how electrical energy sub-metering data is extracted by the sensor node from the current wire without any physical invasion and wirelessly passed to the concentrator. Non-standard error processing solutions and their application to the prototype, and issue-specific solutions related to energy transmission and processing using standard wired and wireless transmission protocols are analyzed. Ill. 4, bibl. 8 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.366

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