Confident and accurate license plate recognition is challenging due to the large number of noise factors. Such factors as: dirt on the plate, background of the plate, light, etc. To solve the problem under such conditions, a three-stage algorithm based on several convolutional neural networks is proposed. At the first stage, the plate is detected using a fast convolutional neural network SSD, in the second stage, the location of each character is quickly searched using the ResNet convolutional neural network, and in the third stage, each character is recognized using several ResNet convolutional neural networks. The experimental results on the sets show that the proposed approach surpasses the existing methods in terms of performance and recognition speed, while maintaining the same accuracy.

This paper presents the results of primary data processing of the experiment carried out at the Institute of Control Sciences to study the aging of the U5 KV400 electric motor, developed by the T-motor company for use in copters. As the engine deteriorates, an increase in the amplitude of the vibration and sound signals is observed and the frequencies at which this occurs are determined. In particular, it is expected that an increase in the amplitude of vibrations and sound at the magnetostriction frequency can serve as an informative sign of the increased engine wear.

To make a management decision in the UAV group, a multi-valued associative memory is used. Such a memory is a new concept, generalizing the similar in fuzzy neural networks. In a multi-valued neural network, inputs are elements or subsets of the lattice of linguistic variables. The lattice is assumed to be distributive and defines the implication used to determine the output of a neural network.

In this paper, we develop an agent-based model to explore how agents’ activity patterns affect echo chamber formation when their mutual interactions are controlled using a personalization system algorithm that decides what information users will be exposed to. In our model, agents can undertake two types of actions: publish a post and like a post. Our experiments revealed that the key parameter that guides agents’ opinion dynamics is the probability of publishing a post: agents who often publish posts tend to enter echo chambers. In contrast, the roles of network topology and liking behavior are far less influential.

An effective (performance- and accuracy-wise) computational method for pattern recognition in a continuous video stream using deep neural networks for access control systems is proposed. The class of recognition problems solved by the method using a sequence of video stream frames is identified: the vehicle itself and the characters on its license plate (LP), faces of people, and abnormal situations. In contrast to the known solutions, a classification with a subsequent reinforcement based on multiple frames of a video stream and with an algorithm for the automatic annotation of images is used. Neural network architectures with independent recurrent layers for classifying video fragments adapted for the problems, a dual network for face recognition, and a deep neural network for vehicle character recognition are proposed. New databases for neural network training are created. A schematic diagram of an intelligent access control system for ensuring the security of an enterprise, a distinctive feature of which is the use of a multirotor unmanned aerial vehicle with a computing unit, is proposed. Field experiments are carried out, and the accuracy and performance of the computational method in solving each problem are assessed. Software modules in the Python language for solving tasks of the intelligent access control system are developed.