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  • The influence of annealing technology of piezoceramics of Bi0.67Ba0.33Fe0.67Ti0.33O3 composition on its dielectric properties

    The article considers one of the problems hindering the use of ceramic piezoelectric materials based on the BiFeO3 - BaTiO3 phases, which is caused by high values ​​of the dielectric loss tangent of these materials. It is assumed that this is due to different electrical conductivity of individual grains of this type of piezoelectric ceramics (Maxwell-Wagner effects), caused by the presence of iron ions in their composition, which have different oxidation states. In order to equalize the oxidation states of iron ions found in the grains of the considered ceramics, we used annealing of its samples in an atmosphere formed as a result of thermal decomposition of ammonium carbonate. It was found that this technique allows: a) to reduce the values ​​of tgδ of the CPM by 6 - 7 times; b) increase the polarizing field strength by at least 40%, and the value of the longitudinal piezoelectric modulus to 140 pC/N.

    Keywords: piezoelectric material, electrical conductivity, dielectric loss, defect, iron ions, Maxwell-Wagner effect, reducing atmosphere, ammonium carbonate

  • The k-e Realizable turbulence model in calculations of aerodynamic characteristics of rectangular cylinders with different aspect ratios

    The article examines the two-dimensional flow around rectangular cylinders with an aspect ratio from 0.1 to 2.0 using the k-Realizable turbulence model with a Reynolds number of 2×E5. Numerical calculations in the ANSYS Fluent program have obtained changes in the coefficients of drag, transverse force and the Struhal number depending on the size of the prism section. The calculations were carried out at the intensity of turbulence of the incoming flow 2% and 4%. According to the results of calculations, it was found that with turbulence 4%, there is a good coincidence of the total aerodynamic characteristics with the available experimental data.

    Keywords: flow around a rectangular cylinder, k-e Realizable turbulence model, aerodynamic characteristics of a rectangular cylinder

  • Simulation of the design activity diversification of innovative enterprise

    A two-dimensional coefficient inverse problem of thermal conductivity for a finite functionally graded cylinder is investigated. The thermal conductivity coefficient is considered to be variable along the radial and axial coordinates. The direct problem of finding the temperature distribution at different moments of time with known boundary conditions and the thermal conductivity coefficient is formulated in a weak statement and solved in the FreeFem++ finite element package. The influence of various two-dimensional power laws of the thermal conductivity coefficient on additional information (the temperature of the outer surface of the cylinder) is investigated. A projection-iteration scheme is constructed to solve the inverse problem. The thermal conductivity coefficient is presented as the sum of the initial approximation and the correction function specified as an expansion in a system of polynomials. At each stage of the iteration process, the expansion coefficients are calculated from the solution of the system of algebraic equations obtained by discretizing the operator equation of the first kind. The results of computational experiments on restoring various two-dimensional laws of change in the thermal conductivity coefficient are presented.

    Keywords: functionally graded cylinder, finite element package FreeFem++, identification, thermal conductivity coefficient, inverse problem, iterative-projection approach, operator equation

  • Reports on current research

  • Detection of illegitimate computing processes in the automated control system using machine learning

    Protecting the endpoints of an information system from cyber attacks determines the search and development of methods for detecting such attacks using artificial intelligence. The dynamics of the increase in the number of information threats of various types leads to the need to use machine learning methods to classify the functioning of automated control systems, including computing processes in automated control systems. The purpose of the study is to classify the computational processes of the created database for detecting illegitimate processes, taking into account minimizing the number of process parameters to achieve acceptable detection quality. Methods: as a mathematical tool, it is proposed to use a model trained on the created dataset and a correlation matrix based on Pearson coefficients to determine a group of parameters of computational processes. Results: an analysis of the data set based on Pearson correlation coefficients was carried out, which allows minimizing the number of parameters of the input data of the model. It is proposed to use the random forest method for the functioning of the model in solving the binary classification problem of detecting illegitimate computing processes in the automated control system. The effectiveness of the proposed model is evaluated by classification metrics: Precision, Recall, The developed model was tested at fixed volumes, training and testing samples. The work of the model was evaluated using the ROC curve and the PR curve.

    Keywords: machine learning, binary classification, computational processes, database, data processing, model testing

  • Human Handwritten Signature Recognition Using Neural Networks

    In this paper, we present the implementation of a neural network approach to solving the problem of handwritten signature recognition. We analyzed the main approaches to handwritten signature recognition. We identified the features of using a handwritten signature as an identification method, including the variability of a handwritten signature and the possibility of forgery. We identified the relevance of using neural networks to solve the signature recognition problem. We developed a neural network model for recognizing handwritten signatures, presented its architecture containing convolutional and fully connected layers, and trained the neural network model based on handwritten signatures "Handwritten Signatures" containing 2263 signature samples. The accuracy of the developed model was 92% on the test sample. We developed a web application "Recognition of a static handwritten signature" based on the developed neural network model on the Amvera cloud hosting. The web application allows identifying users based on a handwritten signature sample.

    Keywords: handwritten signature, neural networks, signature recognition, image processing, machine learning, web application, cloud hosting, identification, verification, artificial intelligence

  • The device for determining the edge angle of wetting liquids on flat surfaces

    A device has been developed that allows measuring the edge angle of wetting of flat surfaces with liquid. The device is characterized by simplicity of implementation, low cost, allows you to simplify the measurement process and eliminate errors associated with the individual characteristics of the observer. As an example, by determining the wetting edge angle, the hydrophilicity (hydrophobicity) of surfaces – fluoroplast, steel and steel with a separation coating based on the composition of Penta-100 when they are wetted with water. The dependence of the wetting edge angle of the initial composition of Penta-100 in the liquid state on the viability of the solution has been studied. The correlation of the marginal wetting angle of the Penta-100 solution with the porosity of the formed coating with the surface is shown. The proposed device can be used in educational and industrial laboratories for rapid assessment of the condition of various surfaces.

    Keywords: surface, wetting edge angle, water, coating

  • Simulation of the design activity diversification of innovative enterprise

    This paper discusses statistical methods, as well as machine learning methods for choosing the optimal way to establish authorship for a passage of a work. The authors create a dataset from the passages of the corresponding authors, create a set of numerical features corresponding to each passage and apply various approaches to analyze authorship, such as correlation, similarity, t-test. An attempt is made to find the optimal method for the output layer of a graph convolutional neural network used for data preprocessing. The GCN neural network is being trained.

    Keywords: t-test, cosine similarity, correlation, graph convolutional neural networks, natural language analysis

  • Evaluation of wear resistance by the coefficient of friction taking into account the dependence of viscosity on pressure and temperature of a modified radial bearing design

    The wear resistance of friction units with a polymer coating and a special groove in their supporting structure is increased by minimizing the heating of the contact zone of the rubbing surfaces through effective heat removal due to the presence of a transverse recess (groove). In addition, this design helps to minimize the dry friction process, since it directly affects the distribution of the lubricating fluid. Increased loads of friction units entail characteristic changes in the properties of lubricants. In our opinion, taking into account the viscosity indicators, depending on temperature and pressure, will allow us to more accurately characterize the operation of the structure in various friction modes. The effect of a modified friction unit design (with a polymer coating and a groove) on improving performance in general is described. In addition, comparing the standard and modified designs, it can be noted that the load capacity has significantly increased and the friction coefficient has decreased. At the same time, the service life and the overhaul period of the friction unit have increased, which is a significant effect for the mechanical engineering industry.

    Keywords: friction unit, index, friction coefficient, polymer coating, load, tribocoupling

  • Design and Integration of a Neural Network Model for Face Recognition

    In this paper, we present a study dedicated to implementing a neural network approach to face recognition. We conducted a comprehensive review of existing face recognition methods. We developed a neural network model, trained on the DigiFace-1M dataset. This paper details the architecture of our developed neural network model and the step-by-step training process. The model achieved an accuracy of 78% on the validation set and 92% on the training set. We also addressed the integration of our model into the Russian Amvera Cloud service. As a result, we created a web application that allows users to identify themselves using uploaded images of their faces. This research demonstrates the potential of neural networks for face recognition tasks and offers a practical solution for implementing such systems in various fields.

    Keywords: face recognition, deep learning, neural networks, user identification, model architecture, model training, model integration, cloud services, security, biometric technologies

  • The theory of calculation of the main parameters of the cable system of a transport and cargo balloon

    This article presents the results of theoretical research in the field of methods for determining the forces arising in the traction return ropes of the balloon system used in transport, cargo and construction works. The emphasis is placed on the accuracy of the swivel positioning when exposed to a wind flow on the balloon. Theoretical calculations of the main parameters are given, such as: high-speed head, resultant lifting force, rope span, rope force, rope deflection, swivel movement and others. A technique is proposed that allows for a relatively simple and rapid calculation of rope forces in the process of performing cargo operations by an aerostat crane on a construction and installation site.

    Keywords: balloon system, balloon crane, traction and return ropes, rope forces, drag, aerial construction and installation work, swivel