A method of risk analysis in information systems is being developed. The ways of ensuring the efficiency of control systems in the conditions of information confrontation with the use of the game theory apparatus are investigated. The desire to ensure high efficiency of modern management information systems, minimize financial costs, provide energy and information protection of the management system, highlights the creation of a system of analysis and risk management in information systems. It is assumed that the control system can implement the following behavioral strategies in a conflict situation: the control system does not change the algorithm, but changes the class of algorithms used to achieve the maximum value of the average quality by choosing the probability Pij for a given set of countermeasures, the control system changes the algorithm operation, the class of operating algorithms used to maximize the average quality of fixed countermeasures, the control system changes the operating algorithm and the class of operating algorithms used depending on the countermeasure strategy in order to achieve maximum quality. Using the apparatus of game theory, an analysis was performed and a method for estimating the average value of the quality of the communication system with different strategies of the conflicting parties was developed. The technique of estimation of average value of an indicator of quality of functioning of a control system is developed and expressions for an estimation of average value of an indicator at various strategies of behavior are received. It is shown that the solution to the problem of improving the quality of the control system is possible through the use of a mixed strategy of system behavior and the choice of structure and parameters of the control system that increase the partial quality of its operation.
This paper addresses the task of identifying the parameters of a linear object in the presence of non-Gaussian interference. The identification algorithm is a gradient procedure for minimizing the combined functional. The combined functional, in turn, consists of the fourth-degree functional and a modular functional, whose weights are set using a mixing parameter. Such a combination of functionals makes it possible to obtain estimates that demonstrate robust properties. We have determined the conditions for the convergence of the applied procedure in the mean and root-mean-square measurements in the presence of non-Gaussian interference. In addition, expressions have been obtained to determine the optimal values of the algorithm's parameters, which ensure its maximum convergence rate. Based on the estimates obtained, the asymptomatic and non-asymptotic values of errors in estimating the parameters and identification errors. Because the resulting expressions contain a series of unknown parameters (the values of signal and interference variances), their practical application requires that the estimates of these parameters should be used. We have investigated the issue of stability of the steady identification process and determined the conditions for this stability. It has been shown that determining these conditions necessitates solving the third-degree equations, whose coefficients depend on the specificity of the problem to be solved. The resulting ratios are rather cumbersome but their simplification allows for a qualitative analysis of stability issues. It should be noted that all the estimates reported in this work depend on the choice of a mixing parameter, the task of determining which remains to be explored. The estimates obtained in this paper allow the researcher to pre-evaluate the capabilities of the identification algorithm and the effectiveness of its use in solving practical problems
A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network. To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method. The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal. When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU. The results obtained confirm the effectiveness of the proposed method in solving practical problems.
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