Identification of the accidenttype at critical infrastructure facilities using hidden Markov models.

Keywords: critical infrastructure objects, emergency state, anomalous patterns, probability density function, hidden Markov model, hidden semi-Markov model, mixed Gauss model


Modern methods of recognizing anomalous patterns in samples of code sequences for identifying the type of accident at critical infrastructure facilities using hidden Markov models are considered. A complex technique based on the analysis of the probability density function of anomalous patterns, which is based on a hidden Markov and semi-Markov model, is presented. It is indicated that this approach makes it possible to determine the function of the time dependence of the data series; accordingly, the classification is carried out on the basis of sets of anomalous data that do not correspond to the classes of the training sample. On the basis of a mathematical model, it is shown that the presented methodology makes it possible to optimize the efficiency of recognizing anomalous patterns when identifying an emergency state at an infrastructure facility.


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How to Cite
Labzhynskyi , V. (2021). Identification of the accidenttype at critical infrastructure facilities using hidden Markov models. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (44), 25-29.