The article is devoted to development of the concept of the integrated intelligent control system for complex manufacturing process. The base of the system is recurrent decomposition method, which allows reconfiguring systems logical structure in case of the given optimum criteria. A recurrent decomposition model of the general problem of manufacturing process automation and the algorithm of multi-level control for automated facilities with scalable tree structures are suggested. In this paper, the design of processes, as well as the problem of automating the lifecycle of the product are considered within the concept of Industry 4.0. The methods of final product quality control based on the control and testing processes optimization are considered as well as methods of structural and parametric optimization of technological processes. Some application examples of the developed mathematical models for technological systems are given.
Abstract. The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P=0,964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (PCFAR<0.01). The reliability was confirmed by checking the initial data using principal component analysis.
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