Software and hardware module to support decision-making of qualitative abnormal changes presence in sample data based on information metrics

Y.E. Shishkin1, A.V. Skatkov2

 1Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28

2Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33


DOI: 10.33075/2220-5861-2021-2-142-151

UDC 681.3


   The key task of society development is to ensure rational use of natural resources and related continuous monitoring of natural and technical systems state. Regarding the growing problems of  ensuring operational control of critical infrastructure facilities, tasks of epidemiological and environmental protection, solving the issues of developing new information technologies that meet modern requirements for scientific and practical activities and implementing their software and hardware modules for supporting decision-making on the presence of qualitative anomalous changes in monitoring data aimed at ensuring information and metrological reliability of control systems, becomes critical for the life support of the population.

   An information technology and a software and hardware module for supporting decision-making on the presence of qualitative abnormal changes in sample data, which are predictors of significant changes in the internal state of monitored objects, natural-technical systems or control devices, are proposed. A method for choosing parametric criteria for the difference in monitoring data using numerical measures of Shannon information entropy and Kullback-Leibler divergence is presented. The use of the developed and demonstrated in practice methodology makes it possible to achieve an increase in the accuracy, convergence and reproducibility of measurements through the use of numerical statistical modeling to obtain a numerical estimate of confident recognition boundaries of a qualitative anomalous change in the shape and shift of the sample distribution of monitoring data, including small samples.

Keywords: statistical clustering, data mining, machine learning, metrological reliability, anomaly detection, numerical modeling.

To quote: Shishkin, Y.E., and A.V. Skatkov. “Software and Hardware Module to Support Decision-Making of Qualitative Abnormal Changes Presence in Sample Data Based on Information Metrics.” Monitoring Systems of Environment no. 2 (June 24, 2021): 142–151. doi:10.33075/2220-5861-2021-2-142-151.

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