Vol 23, No 1 (2021)

The article discusses issues of automation of diagnostics and control of PCM parameters based on FPGAs and microcontrollers.

This article draws attention to the difficulties in developing an integrated approach, especially the evaluation and evaluation of the main helicopter technology. Fiber optic technology is a technology that allows you to create a unified system. This allows the measurement of various physical parameters such as vibration, distortion, temperature, volume and other parameters, given the magnitude of optical fiber science. RMB can be integrated into production, which is an important factor in the amount of RMB related to the design and manufacture of helicopters. The test results, the test bench, and the rotor of the helicopter and light using optical fiber transmission in the Prague fiber optic filter emphasize the importance of developing technology to track the helicopter’s use of optical fiber. Other embodiments of the use of optical transmission systems that use fiber-optic technology, such as cable warning of explosives and helicopter damage as well as system weight and transitions. The conduction system monitors air technology.

The article analyzes the most successful results of the use of metal-polymer composite materials (MPCM), reflects the advantages at the current stage of development. The analysis of the use of composite materials in the design of helicopter gliders is carried out. The use of microtubules in the manufacture of double curvature panels using MPCM is considered. Improvement of technologies in the production of structures made of MPCM. The search for optimal parameters of the technological process in the production of panels of double curvature of components based on SIAL used in the aircraft design is carried out. The optimal technological process in terms of time resources, material costs, and design weight at different levels of serial production were considered as optimization criteria in this study. Numerical modeling and optimization of workflow parameters were performed in the NASTRAN CAE system (NX). Each of the combinations of optimized process variables was determined based on numerical modeling. Analysis of the optimization results showed that the growth of parameters of the working process leads to an expansion of the area of locally optimal parameters. With optimal parameters of the working process, it is assumed that the cost of work will be reduced.

This article test chip reactivity of carbon fiber (CFRP) samples were analyzed in a standard format. First, the nature of ultrasound uncertainty is determined, and this information is used to establish the value of the control that characterized the production of crystals up to 30 layers, with different procedures. Different input and set some Teflon to simulate the presence of desalination. Article explains that the choice for special disability (flexible, medium frequency and configuration templates), collapse can be carried out, the face of the new field in the form the respiratory tract.

The article presents a universal simulation model of aggregate and Assembly production processes based on IDEF0 methods and numerical modeling methods. The problem statement is formed as follows: it is necessary to analyze the existing simulation tools, structure the Assembly production processes into separate classes and subclasses, describe the processes using the IDEF0 method, create a simulation model of the aggregate-Assembly production process with a link between economic and labor costs. The total labor intensity of Assembly processes and economic costs were considered as optimization criteria in this study. Numerical modeling and optimization of workflow parameters were performed in the AnyLogic simulation environment. The obtained simulation models can be used in the future in the management system of aviation enterprises, as well as in other production areas where the product is a complex system.

This article focuses on automation of personnel quality control into the production process organization for the cost reduction, the elimination of discrepancies, meeting social distancing requirements, human capital growth.

Prevention of emergencies in aviation technology is largely ensured by diagnostics of the functioning of its units. Often the performance criterion is the level of vibration that influences the decision to adjust the load or shut down the unit. The article discusses mathematical models when applying neural network methods for vibration diagnostics. When using cross-validation, the initial data set with vibration data is divided into several blocks, which are grouped into three sets: training, validation, and test. To assess the effectiveness of diagnostics, three different quality criteria were used: mean error in the test set, AUC, and F-measure. For a given set of initial data, the best fitted configuration turned out to be a neural network of three layers with 18 neurons in each layer, implemented in the MATLAB package. It uses a Bayesian regularization algorithm as a learning function. The percentage of the average error in recognizing the state of the considered aggregate using the neural network turned out to be 4.85, the AUC value was 0.885, and the F-measure was 0.827. Compared to a network built in automatic mode using the Statistics and Machine Learning Toolbox and Neural Network Toolbox machine learning libraries, the F-measure of the fitted network configuration is 6.7% higher.

The work is devoted to the study of the effectiveness of the application of models of Gaussian mixtures for the recognition of abnormal deviations in the speaker’s speech. The practical application of the developed algorithms for revealing the emotional state of the crew member by the phrase uttered by such crew member is proposed. The spectral characteristics of the speech signal are used as the main criterion for distinguishing using the Gaussian mixture model. In connection with a rather small sampling step in frequency and, accordingly, with the presence of 255 frequency components in the signal spectrum, it is proposed to compress the spectrum to 10 components. This approach made it possible to reduce the number of key parameters in the Gaussian model to 10, which, in turn, made it possible to simplify the analysis process when constructing multivariate distributions. To assess the quality of the proposed algorithm, test phrases were recorded. At the same time, various psychological states of the speaker were imitated. We used both simple unregulated speech structures and messages regulated in accordance with the Federal Aviation Rules when conducting radio exchange in civil aviation on the territory of the Russian Federation. Taking into account the limitations on the prior knowledge of the model and clustering by spectral characteristics, all recordings of the model were made by one speaker. Three classes of the speaker’s emotional state were considered. At the output, the recognition system put such marks as a calm state, a tired state, a stressful state. Various states were artificially simulated during data preparation. On a test sample of 48 messages, a Gaussian model of 3 components and 10 parameters without preliminary training immediately allowed to achieve a result of about 65%, while the probability of recognizing the correct class with 3 equal classes a priori is 33%. As further research, it is proposed to apply preliminary training using neural networks or correlation algorithms. This approach will allow further clustering at a deeper level, when, for example, the gender of the speaker is determined, a typical message of the radio exchange is determined, and then the emotional state of the speaker is revealed.

To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% -9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.

This article discusses the main prerequisites for the creation of a transport and logistics center on the territory of the Ulyanovsk region.

The article considers the state and dynamics and prospects of the development of water transport in the Ulyanovsk region. The identification of the main problems provides an opportunity to accelerate further targeted investment.