A finite element model of the deformation zone during cold drawing on a movable mandrel has been developed and justified. This makes it possible to determine the state of the metal, calculate its damage and the shape of the die channel, while the configuration of the first transition is taken as the initial one to obtain the second transition.

Keywords: Simulation of the process of long-mandrel drawing of profile pipes

The article solves the problem of automated generation of user roles using machine learning methods. To solve the problem, cluster data analysis methods implemented in Python in the Google Colab development environment are used. Based on the results obtained, a method for generating user roles was developed and tested, which allows reducing the time for generating a role-based access control model.

Keywords: machine learning, role-based access control model, clustering, k-means method, hierarchical clustering, DBSCAN method

This study analyzes classical machine learning methods applied to the prediction of corporate bond yield spreads. Both linear methods, such as Principal Component Analysis and Partial Least Squares, and nonlinear methods, such as copula regression and adaptive regression splines, are examined. The study also explores the potential application of Random Forest models and classical neural networks. It includes a description of the data used for forecasting and presents some results of the empirical analysis. The findings have the potential to significantly impact practitioners and the scientific community striving to improve forecasting accuracy and optimize investment strategies.

Keywords: Machine Learning, Financial Engineering, Stock Market Modeling, Bond Market

In order to optimize the operation of dust-settling chambers of steelmaking furnace emission purification systems and increase the overall efficiency of the cleaning system, the movement of gas-air flows and dust particles of different diameters inside dust-collecting chambers was studied using the SolidWorks software product with the FlowSimulation application, which allowed us to investigate the influence of a number of factors, for example, fractional composition, the condition of the working surfaces of chambers, on the movement of gas-air the flow.

Keywords: steelmaking furnace, gas-air flow, dust-settling chamber, cleaning efficiency, dust, dispersed composition, modeling

The work includes an analysis of the mathematical apparatus determining the influence of parasitic parameters of the capacitor, the topology of the printed circuit board on the effective range of the decoupling capacitor. A mathematical apparatus is presented that determines the shift in the resonant frequency of the connected decoupling capacitor, taking into account the parasitic parameters of the topology.

Keywords: power distribution system, decoupling capacitor, self-resonance frequency, anti-resonance frequency, effective range, parasitic parameters, topology

This article is devoted to the development of a collision detection technique using a polygonal mesh and neural networks. Collisions are an important aspect of realistically simulating physical interactions. Traditional collision detection methods have certain limitations related to computational accuracy and computational complexity. A new approach based on the use of neural networks for collision detection with polygonal meshes is proposed. Neural networks have shown excellent results in various computer vision and image processing tasks, and in this context they can be effectively applied to polygon pattern analysis and collision detection. The main idea of ​​the technique is to train a neural network on a large data set containing information about the geometry of objects and their movement for automatic collision detection. To train the network, it is necessary to create a special module responsible for storing and preparing the dataset. This module will provide collection, structuring and storage of data about polygonal models, their movements and collisions. The work includes the development and testing of a neural network training algorithm on the created dataset, as well as assessing the quality of network predictions in a controlled environment with various collision conditions.

Keywords: modeling, collision detection techniques using polygonal meshes and neural networks, dataset, assessing the quality of network predictions

The step-by-step construction of a computer model of the process of long-angle drawing of profile pipes is considered. The minimum dimensions of the blanks have been determined, the use of which ensures the necessary dimensions of the finished product. The scheme of applying a deforming force with size adjustment in the current state during step-by-step deformation is taken into account. Geometric and finite element models have been obtained that make it possible to find all the parameters of the deformation site during the drawing process.

Keywords: dimensions of the workpiece, profile pipe, boundary conditions, load application, physical model, finite element grid

The paper considers the experience of using neural networks in construction. The widespread coverage of AI success in various areas of construction has led to an increase in business and public interest in the successful implementation of AI in various construction areas. Examples of the use of neural networks in the construction expertise of the working documentation "Metal structures" and "Metal detailed structures" are given. The process of solving the assigned tasks by an expert builder in comparison with the answers received by the neural network is described. A comparative analysis of the quality of the results obtained by the expert builder and artificial intelligence is given. As part of this study, the main algorithms for training neural networks that are applicable to solving the problem were analyzed. Particular attention is paid to algorithms capable of efficiently handling parameter variations and new configurations not represented in the training dataset. The use of these algorithms will provide increased accuracy when scaling the solution. A neural network forecast for this area of construction expertise is given.

Keywords: neural network, construction, construction expertise, expert builder, comparative analysis, training sample, neural network forecast

At the moment, quantum key distribution (QKD) technology guarantees the highest level of data exchange security, which makes QKD networks one of the most promising areas in the field of computer security. Unfortunately, the problem of topology optimization when planning and extending QKD networks has not attracted enough attention. This paper reviews approaches that use analytical models in the topology optimization problem of quantum key distribution networks. Different methods that solve problems of network capacity and security maximization and cost minimization are reviewed, the utilized algorithms are described, and conclusions about possible further research in this area are drawn.

Keywords: quantum key distribution, mathematical modeling, network topology, analytical modeling, topology optimization

The simulation model of Apache HTTP Server as a mass service system is considered, the parameters of the corresponding system and Apache HTTP Server are compared using GPSS World environment. The comparison of the simulation model with a real web server is based on the construction of a test server. using Apache JMeter application, which can be used to simulate high load on the server. Query generation and statistics collection was done by Apache JMeter application. A comparison of both reports was given, differences in characteristics were pointed out, and assumptions about the reasons for the differences were outlined. The model can be applied to establish requirements for Apache HTTP Server in order to optimise its performance.

Keywords: simulation modelling, mass service system, efficiency characteristics, test server, flow of requests, service channels, queue

The article analyzes the impact of transformation types on the learning quality of neural network classification models, and also suggests a new approach to expanding image sets using reinforcement learning.

Keywords: neural network model, training dataset, data set expansion, image transformation, recognition accuracy, reinforcement learning, image vector

This paper investigates the effectiveness of the distance fields method for building 3D graphics in comparison with the traditional polygonal approach. The main attention is paid to the use of analytical representation of models, which allows to determine the shortest distance to the objects of the scene and provides high speed even on weak hardware. Comparative analysis is made on the possibility of wide model detailing, applicability of different lighting sources, reflection mapping and model transformation. Conclusions are drawn about the promising potential of the distance field method for 3D graphics, especially in real-time rendering systems. It is also emphasized that further research and development in this area is relevant. Within the framework of this work, a universal software implementation of the distance fields method was realized.

Keywords: computer graphics, rendering, 3D graphics, ray marching, polygonal graphics, 3D graphics development, modeling, 3D models

The article describes the methodology for developing a client-server application intended for constructing a virtual museum. The creation of the server part of the application with the functions of processing and executing requests from the client part, as well as the creation of a database and interaction with it, is discussed in detail. The client part is developed using the Angular framework and the TypeScript language; the three-dimensional implementation is based on the three.js library, which is an add-on to WebGL technology. The server part is developed on the ASP.NET Core platform in C#. The database schema is based on a Code-First approach using Entity Framework Core. Microsoft SQL Server is used as the database management system.

Keywords: client-server application, virtual tour designer, virtual museum, three.js library, framework, Angular, ASP.NET Core, Entity Framework Core, Code-First, WebGL

The work is devoted to the analysis of machine learning methods for solving problems of automatic document processing. The study considers such methods as classification, information extraction, pattern recognition and natural language processing and their application in the analysis of text data. An analysis of existing algorithms and models, including linear models, decision trees, support vector methods, and a comparison of their effectiveness depending on various conditions and parameters is carried out. Particular attention is paid to the problems that specialists face when using machine learning methods in working with documents, such as data quality, the need for pre-processing and tuning of model parameters. Prospects for further research in this area and examples of possible integration of modern machine learning methods to improve the efficiency and accuracy of automatic document processing in various industries are given.

Keywords: machine learning, automatic document processing, computational experiment, artificial intelligence, classification models, software package

Flight safety is one of the most important priorities of civil aviation. In recent years, there has been a downward trend in the number of aviation accidents, which is associated with the introduction of new technologies and training methods. One of these methods is computer simulator training (CTP).KTP is a training method in which LE specialists practice skills and procedures in a virtual environment that simulates real flight conditions. KTP allows you to increase the effectiveness of training, reduce the risk of errors and ensure that training meets modern safety requirements aviation simulators, development of simulator systems, simulators of aviation instrumentation.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production

Models and a software package have been developed that allow for the analysis of counterparties for the probability of fulfilling government contracts. A comparative analysis of machine learning models has been conducted: logistic regression, decision forest, clustering, and neural network. A software package has been developed that allows for contract forecasting. A computational experiment has been conducted to analyze counterparties taking into account contracts that have been fulfilled or not completed by them. The best model has been established, demonstrating a forecast accuracy of 97.89% by the accuracy metric.

Keywords: mathematical modeling, cybersecurity, intelligent models, financial sector, government contracts, information infrastructure

Multilayer edge commutation in 3D integration technologies can simplify the design of microassemblies and reduce the length of edge electrical connections. However, this commutation is vulnerable to thermomechanical stresses and requires preliminary analysis of the product design. This paper shows the results of modeling various variants of multilayer edge commutation for 3D microassemblies, differing both in the dielectric material used at the edge redistribution layer and in the material for sealing the microassembly volume. It has been established that the lowest values ​​of thermomechanical stresses in commutation are characteristic of materials whose temperature coefficient of linear expansion is as close as possible to this parameter of conductors. At the same time, the use of composite dielectrics in redistribution layers leads to a more significant decrease in stresses than the use of more thermally stable unfilled polymers.

Keywords: 3D integration, packaging, thermomechanical stresses, polyimide, redistribution layer

The article is devoted to the development of an algorithm for three-dimensional terrain reconstruction based on single satellite images. The algorithm is based on the algorithmic formation of three-dimensional models based on the output data of two deep learning models to solve the problems of elevation restoration and instance segmentation, respectively. The paper also presents methods for processing large satellite images with deep learning models. The algorithm proposed in the framework of the work makes it possible to significantly reduce the requirements for input data in the problem of three-dimensional reconstruction.

Keywords: three-dimensional reconstruction, deep learning, computer vision, elevation restoration, segmentation, depth determination, contour approximation

The influence of the diffraction divergence of an acoustic beam on the characteristics of a single-channel acousto-optic correlator (AOC) is considered. A mathematical model is being developed to optimize the divergence of the acoustic beam of a single-channel acousto-optic correlator. It is shown that it boils down to amplitude and phase modulation of the pulse response of the device, and the nature of the parasitic modulation turns out to be invariant to the type of the correlator reference signal. As a result of numerical calculations, dependences were obtained that allow us to quantify the effect of diffraction divergence on the operation of the ACS. Methods of compensation for parasitic modulation are proposed, which makes it possible to improve the functionality of acousto-optic correlators.

Keywords: diffraction divergence of an acoustic beam, mathematical model of an acousto-optic correlator, ultrasonic light modulator, electroacoustic transducer, reference transparency

The paper proposes a hybrid multi-agent solution search algorithm containing procedures that simulate the behavior of a bee colony, a swarm of agents and co-evolution methods, with a reconfigurable architecture. The developed hybrid algorithm is based on a hierarchical multi-population approach, which allows, using the diversity of a set of solutions, to expand the areas of search for solutions. Formulations of metaheuristics for a bee colony and a swarm of agents of a canonical species are presented. As a measure of the similarity of two solutions, affinity is used - a measure of equivalence, relatedness (similarity, closeness) of two solutions. The principle of operation and application of the directed mutation operator is revealed. A description of the modified chromosome swarm paradigm is given, which provides the ability to search for solutions with integer parameter values, in contrast to canonical methods. The time complexity of the algorithm is O(n2)-O(n3).

Keywords: swarm of agents, bee colony, co-evolution, search space, hybridization, reconfigurable architecture

The article offers a variant of the development of lighting design projects for outdoor architectural lighting. Based on the modeling of light distribution in the DIALux 4.13 program, brushes have been created using specific lighting devices that simulate lighting effects from real lighting devices. A variant of the sketch of outdoor architectural lighting using Adobe Photoshop has been created with the implementation of local lighting techniques using the example of a drama theater building. Using a three-dimensional model of the object, a light design project was created in the DIALux EVO program. The proposed method of creating sketches is useful in professional activities related to the development of sketches of lighting design projects based on their high-quality photographs without the need to develop three-dimensional models, for conceptual proposals of fragments of the urban light environment and landscape territories. Having developed a base of brushes (based on real light distributions of lighting devices), it is possible to create sketches of architectural lighting of buildings that implement various lighting techniques.

Keywords: adobe photoshop, dialux 4.13, dialux evo, sketch, brush, building facade, outdoor architectural lighting, lighting effect, lighting technique, architectural lighting concept

To check the efficiency and correctness of the implementation of primary and secondary signal processing algorithms in onboard radar systems for Arctic purposes in the functional tasks of detecting weather conditions that are potentially hazardous to flight, it is advisable to use numerical modeling of radar signal simulators. This is due to the fact that during preliminary tests under adverse weather conditions there is a potential danger of losing control over the flight of the radar carrier, especially in the case of developing unmanned aircraft platforms. In addition, there are very rare weather phenomena, such as wind shear, the detection of which during tests is an unlikely event. All this leads to the fact that the development and debugging of onboard radars for low-altitude carriers that solve the problem of meteorological navigation during flight, it is advisable to carry out the method of semi-naturalistic modeling, using databases for the formation of reflected signals that contain a set of initial parameters that allow imitation either in real time or according to a pre-planned flight scenario and a prepared special set of signal signature records. This article proposes an algorithm for working with a database and subsequent numerical modeling, which allows estimating the necessary spectral components of signal signatures for a pulse-Doppler radar that estimates the radial component of wind speed in each resolution element, which is used for further calculation of the F-factor of wind shear hazard.

Keywords: airborne radar, database, simulation, numerical modeling, meteorological navigation, Arctic, wind shear

In this paper, heat transfer in a staggered tube bundle under steady and pulsating flow conditions is analyzed using numerical simulation. The numerical study was conducted for tube bundles with 5, 10, and 15 longitudinal rows. The Reynolds number Re and the Prandtl number Pr were 3400 and 3 respectively. Flow pulsations were characterized by both symmetrical and asymmetrical reciprocating flow. The effect of pulsations was estimated using the product of the relative dimensionless pulsation amplitude and the Strouhal number A/DSh, which corresponded to values of 0.1, 0.25, and 0.4. The numerical study was conducted using Ansys Fluent. The flow hydrodynamics in the tube bundle was described using the Reynolds-averaged unsteady Navier-Stokes equations. Based on the results of numerical simulation, it was found that the effect of pulsations on heat transfer in the tube bundle varies depending on the number of longitudinal rows. It is shown that an increase in the number of rows leads to a decrease in the Nusselt number ratio in a pulsating flow compared to a steady flow. It is established that the thermal-hydraulic efficiency increases with an increase in the number of rows. It is shown that asymmetric pulsations are more effective than symmetric ones for intensifying heat transfer when taking into account energy costs

Keywords: heat transfer intensification, staggered tube bundle, heat transfer, numerical simulation, flow pulsations

The article is devoted to the consideration of multi-criteria Pareto optimization methods based on genetic algorithms. The NSGA-III and AGE-MOEA-II methods are considered, and their comparative analysis is given. The results obtained are important both for theoretical research in the field of genetic algorithms and for practical application in engineering and other fields where multicriteria optimization plays a key role.

Keywords: multicriteria optimization problem, Pareto front, genetic algorithm, NSGA-III, AGE-MOEA-II

The purpose of this work is to study the applicability of the U-Net architecture for automatically determining the contours of natural skins using the TensorFlow and Keras libraries in Python. A software application has been developed based on methods including OpenCV libraries, as well as a model for implementing a deep convolutional neural network. The dataset for training and testing the network was created using augmentation. Training was carried out using the stochastic gradient descent method after splitting the data sample into training and test images. In the future, the results obtained will be used to create an automated system that will make it possible to determine the contours of the skin and its defects, which in turn will open up the possibility of calculating the useful area of ​​the skin and creating an automated layout of patterns taking into account the identified defects.

Keywords: computer vision, edge detection, natural skin, machine learning, convolutional neural networks, U-Net architecture, deep learning