Here is a task of classifying aeroballistic vehicle path in atmospheric phase. It has been suggested the task to be solved by means of pattern recognition technique involving tutor-assisted training with a variety of classification samples, i.e., flying vehicle paths in atmospheric phase targeted at different ground objects. The pattern recognition technique has been developed, which involves minimum distance to a class standard and artificial neural network such as multilayer perceptron for the task solution. The novelty of the developed technique includes generation of gliding aircraft path through simulation of spline approximation of sectional polyline defined with a set of fixed points which include origin and end of the path as well as disturbing points. The path recognition system quality is to be assessed through probability measures. The path recognition quality assessment during study of various recognition techniques involved calculations of probability measures for different values of class numbers, path types (with changing number and steepness of maneuvers), and time interval assigned for decision-making. There is a set of programs involving mathematical modeling of aircraft paths in atmospheric phase, recognition procedures based on minimum distance to class standard method and artificial neural network such as multilayer perceptron as well as recognition quality check program through statistical assessment of correct recognition probabilities. The developed set of programs is a basis for study of various aircraft path recognition techniques with different initial data. There are computer modeling results and calculations of aircraft path classification error probabilities using the mentioned techniques in case of various path class number and limitations of decision-making time.

The paper contains the detailed description of major requirements which should be taken into account in the design of the library of metaheuristic algorithms of discrete optimization. The proposed requirements provide an opportunity to create a large number of different population-based algorithms by the designed library, including classical and modified variations of genetic algorithms, ant colony algorithms, bees algorithms as well as many other optimization algorithms applicable for solving the problems of discrete optimization.

The problem of emergency combinations of events during the inspection of industrial facilities by unmanned aerial vehicles is investigated. Emergency combinations are made up of relatively harmless individually events. A formal and meaningful formulation of this problem is proposed. The first stage of solving the problem is considered: representation of the processes of development of accidents from a combination of individually non-hazardous events. For such a representation, fault trees and their minimum sections are used. At the same time, failures are understood not only as manifestations of hardware and software malfunctions, but also operator errors, as well as environmental influences. Each minimum section of such a fault tree underlies one or more scenarios for the development of accidents. An example of a fault tree with a set of minimum sections and examples of scenarios for the development of an accident during the inspection of industrial facilities by unmanned aerial vehicles is given.

5G technology is designed to resolve issues such as the growth of mobile traffic, the increase in the number of devices connected to the network, reducing delays for the implementation of new services and the lack of a frequency spectrum. The main services in future 5G networks are enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC) and Ultra Reliable Low Latency Communications (URLLC). In this paper, we consider two cases of joint eMBB and URLLC transmissions. In the first case, when all resources are occupied, the blocking of new requests happens, and in the second case, it is possible to reduce the transmission speed of the eMBB application and accept the new URLLC request for service.

Evolution of random undirected graphs by the clustering attachment (CA) without node and edge deletion and with uniform node deletion is investigated. The CA causes clusters of consecutive exceedances of the modularity over a sufficiently high threshold. The modularity is a measure that allows us to divide graphs into communities. It shows the connectivity of nodes in the community. An extremal index (a local dependence measure) approximates the mean cluster size of exceedances over a sufficiently high threshold. Considering the change of the modularity at each evolution step, the extremal index of the latter random sequence indicates the consecutive large connectivity of nodes and thus, it reflects the community appearance during the network evolution. This allows to consider the community structure of the network from perspectives of the extreme value analysis. By simulation study we show that estimates of the extremal index of the modularity and the tail index of node degrees depend on the CA parameters. The latter estimates are compared both for evolution without node and edge deletion and with uniform node deletion.