A.V. Skatkov, A.A. Bryukhovetsky, D.V. Moiseev
Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33
Currently, it is of particular importance to obtain complete and reliable information concerning physical processes occurring in sea and ocean waters, and in most cases it is impossible to limit yourself to remote observations only, and you have to place measuring equipment directly in the sea. Such equipment operates in the automatic, maintenance-free mode of low-power autonomous power sources. These autonomous information and measurement systems consume resources during their operation, the main ones being the communication channel, processor, memory, and battery.
In this paper, we consider an algorithmic approach based on the methods of adaptive intelligent technology for monitoring the state of objects in computer systems. The approach is focused on detecting changes in the state of controlled resources of autonomous information and measurement systems. An adaptive model is presented using a Bayesian classifier for estimating changes in resource states of autonomous information and measurement systems. The model is based on a probabilistic automaton with adaptive self-tuning. On the basis of the proposed model, the problem of assessing the state of resources is solved in order to increase the reliability of the results of classification of information situations. The paper describes an approach that allows increasing the duration of continuous operation of the environmental monitoring system. This approach is based on adaptive correction of primary meter readings in the event of a decrease in their accuracy due to degradation failures. The structure and equations of such a system are proposed, and the task of developing a simulation model of the system is set.
Keywords: probabilistic automaton, Bayesian classifier, dynamic resource estimation, adaptive model, self-tuning.
To quote: Skatkov, A.V., A.A. Bryukhovetsky, and D.V. Moiseev. “Investigation of a Model of Probabilistic Automata for Detecting Changes in the State of Resources of Autonomous Measurement Systems.” Monitoring Systems of Environment no. 4 (December 24, 2020): 58–64. doi:10.33075/2220-5861-2020-4-58-64.
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