“This PDF file contains the front matter associated with SPIE Proceedings Volume 13217, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.”

The paper considers the process of developing and manufacturing prototypes of antenna devices based on printing technology. During the development of the model and its optimization, CST Studio Suite software was used, followed by processing of the antenna model in Autodesk Inventor and Altium Designer. Three methods were used for manufacturing, which were compared in terms of the speed of manufacturing antenna prototypes. Particular emphasis in the production process was placed on the introduction of additive technologies in the antenna manufacturing process.

In this work, the microhardness of the surfacing-steel structure after vacuum annealing was studied. Structural changes resulting from temperature exposure were studied using metallographic analysis methods. The paper discusses possible reasons leading to changes in the microhardness of modified L-53 steel.

The paper presents the results of mathematical modeling of the physical, technical and geometric parameters of the micro-optical system of a fiber-optic refractometric measuring transducer, which is a transparent tube of small diameter comparable to the diameter of the optical fiber used in the sensor design. It is shown that in order to ensure the maximum signal difference in the measuring transducer corresponding to situations when a calibration substance is in the tube, and then the substance under study, it is necessary to increase the sensitivity of the conversion of fiber-optic measuring transducers of liquid media quality sensors used in environmental systems. The calculated parameters, which ensure an increase in the sensitivity of the conversion, will be used in the design of fiber-optic refractometric sensors for rapid analysis of the quality of liquid substances in environmental protection systems.

This work aims to study plastic deformation inhomogeneity of laser-cladded flat samples under uniaxial tensile. High-nitrogen Cr-Mn-Ni austenitic stainless steel was chosen as the substrate material. Laser cladding was implemented by applying Ni-Cr-Fe + WC powder at varying laser power (1-2 kW) and scanning speed (0.008-0.035 m/s). The penetration depth of a single roller decreased with the increase in scanning speed. The microhardness exhibited wide ranging with the cladding thickness (from 700 to 1350 HV) and decreased with increasing scanning rate. The mechanical test data showed that the surface hardening of austenitic steel led to an increase in the tensile strength and a decrease in ductility. The effect of laser cladding modes on the degree of plastic flow inhomogeneity within the micro-volumes of the cladded layer and the surface was established using digital speckle photography. The local strain variation in the sample increased with an increase in the specific energy of laser cladding. The structural heterogeneity near the interface between the cladded layer and the substrate exerts an impact on the development regularity of localized plastic flow. Unfavorable choice of laser parameters leads to formation of micro-cracks and porosities in the microstructure.

The paper studies the patterns of localization of plastic deformation of a corrosion-resistant bimetal under uniaxial tension via speckle photography method. It was found that at different stages of work hardening, the distributions of local elongations have a spatio-temporal order in the form of macroscopic deformation zones. The parameters of localized plasticity in the layers of the base (ASTM A414 grade A) and cladding (AISI 304) metal are determined. The process of deformation accumulation in the fracture site of the composite is analyzed.

The article is devoted to the study of corrosion-mechanical strength in TiNi alloy in various solutions by means of preliminary soaking for 30 days followed by mechanical tensile tests. The studies carried out showed a decrease in mechanical strength in corrosive environments. The greatest reduction of 32% is observed in Hanks' solution, while the smallest change in ultimate strength is observed in solutions of 0.9% NaCl, 1M H₂SO₄. The fracture character does not change during preliminary exposure in corrosive environment. On the surface of specimens there are observed corrosive damages in the form of pittings, the size of pittings varies depending on the environment.

The article describes the development of a software system for calculating the main modes of the technological process to produce antifriction coatings based on fluoroplastic powders with fillers. The research presents A calculation algorithm and formulae for calculating the strength of the resulting connection and the wear rate of the coating based on fluoroplastic powder with fillers. The developed software product allows based on the calculation of technological process parameters, from the basic values of the coating parameters σп and Ih. According to the developed algorithm, the current values of the main parameters of the technological process are calculated. The information database is implemented in a software shell that serves 3 purposes: entering and searching for information in the database by fields; linking information fields on topics, technologies, materials for an expert technologist’s working; working in information mode and in the mode of issuing an information array to control the process. The created models and databases made it possible to create an algorithm for automating the diffusion coating process with the determination of the main modes.

In this work, the thermodynamic parameters and phase diagrams of some high-entropy ceramics systems (carbide and boride) have been calculated. The analysis allowed us to determine the optimal system: HfZrTiTaNb for synthesizing high-entropy carbide and boride. It was found that this composition has the smallest difference of interatomic distances 4.24 %, low value of enthalpy of mixing 2.64 kJ/mol and high thermodynamic parameter, which takes into account the average melting point of elements, enthalpy and entropy of mixing 12.78.

In this paper, on the basis of literature analysis, calculation of thermodynamic parameters and construction of phase diagrams, the following systems were selected: Fe_xCo₆Al₃Ni₂Si (x = 5; 6 and 8) and Fe₆Co₆Al₄Ni₃MnSi_x (x = 0; 0.513 and 1.053) with different iron and silicon contents. It is revealed that the compositions possess thermodynamic parameters: for the Fe_xCo₆Al₃Ni₂Si system, the interatomic state difference is 4.86-5.34 %, the mixing enthalpy values are -(16.39-17.89) kJ/mol, the thermodynamic parameter including the average melting point of the elements, entropy and mixing enthalpy are 1.07-1.22, for the system Fe6Co6Al4Ni3MnSix the difference of interatomic states 4.99-5.64 %, the values of enthalpy of mixing -(18.28-21.40) kJ/mol, thermodynamic parameter including average melting point of elements, entropy and enthalpy of mixing 1.00-1.07, respectively.

In this work the influence of ion-plasma treatment and high-power ion beams on the change of coherent scattering areas, dislocation density, and tribotechnical characteristics of tool hard alloys has been investigated. Processing modes of high-power ion beams, which provide the maximum degree of hardening of tungsten carbide-based hard alloys, have been established. It is shown that complex modification by ion-plasma surface treatment in combination with powerful pulse beams contributes to the increase of wear resistance of hard alloys under cutting conditions of structural steels.

The paper presents some experimental studies of the formation of methane gas hydrate in a cell containing methane and a solution of sodium chloride in the initial state. On the obtained time dependences of temperature and pressure in the cell, areas corresponding to the process of gas hydrate formation are highlighted. The dependence of the time of onset of hydrate formation on salt concentration was experimentally studied. It has been shown that with increasing salt concentration in the solution, the time for the onset of hydrate formation increases according to a linear law. The dependence of the equilibrium temperature on the pressure in the cell was constructed. It has been shown that as the concentration of the salt solution increases, the equilibrium temperature of hydrate formation decreases.

The paper presents some studies of the formation features of gas hydrates in solutions of table salt and calcium chloride. From the pressure drop curve, the moments at which gas hydrate begins to form in the cell, as well as the equilibrium parameters of its formation, are determined. Using these parameters, the dependences of equilibrium temperature on pressure were constructed for solutions of table salt and calcium chloride of various concentrations. It has been established that with increasing salt concentration in the solution, the temperature of gas hydrate formation decreases according to a linear law. Moreover, for a sodium chloride solution this change is less pronounced than for a calcium chloride solution. It has been shown that a solution of calcium chloride does not allow the formation of gas hydrate down to a temperature of -10 0C. It has been established that an increase in gas temperature in the cell leads to an increase in equilibrium pressure. Moreover, in the case of a certain pressure value, the equilibrium temperature of hydrate formation for calcium chloride solutions is significantly lower than for a sodium chloride solution.

This paper describes a codebook generation algorithm for the sparse code multiple access (SCMA). In this paper, we propose an algorithm to construct MC based on amplitude and phase-shift keying (APSK) modulation constellations using optimization characteristics. Each dimension of multidimensional MC is formed by some APSK constellations with different amplitudes and initial phases. The proposed method makes it possible to construct MC with zero peak-toaverage power ratio and best characteristics. For the proposed algorithm, a simulation model of the communication system was developed and simulation was performed. The results show that the proposed method achieves better bit error rate performance when transmitting signals in the uplink Rayleigh channel. The resulting codebooks can be used in the following generations of communication systems.

A method for developing mathematical models of nonlinear non-stationary random processes in the form of stochastic differential equations is considered. Methods for synthesizing such equations for scalar and vector non-Gaussian random processes are presented. Examples of modeling for various probability density distributions are provided. These mathematical models find application in the study of various complex control and information transmission systems affected by random disturbances.

The study is devoted to the problem of verifying the entry of correct data into a distributed registry system for accounting and monitoring of forest resources based on blockchain technology. The features and significance of preprocessing data entered into the system for the presence of anomalies are considered. A method for identifying anomalies in data entered into such a system is proposed using the example of processing data on wood reserves in the territory. The technique is based on mixing statistical approaches and machine learning models. The technique was tested on data from automated thematic interpretation of images. Methods for scaling the methodology to identify data anomalies throughout the entire cycle of the forestry industry are described.

The presented article describes the application of the finite difference method to the problem of synthesis of control systems. In this research work the weight (or system) function is considered. The paper describes the method of a finite difference algorithm for a linear system function. The error of the results obtained using the finite difference method are estimated with respect to the most common numerical methods such as the Runge-Kutta method and the Euler method. This research paper presents the results of simulation modeling. The publication compares the model outputs obtained using various numerical methods. The comparison demonstrates that the finite difference method is as good as other numerical methods such as the Runge-Kutta method and the Euler method. The article presents the advantages of using the finite difference method.

This paper presents models for improving accuracy and efficiency in recognizing a person's emotional state and age based on facial expressions using the combination of Local Binary Patterns and Squeeze-and-Excitation Block methods. The proposed models were trained on the AffectNet dataset for emotion recognition and the Adience dataset for age recognition with four classes [angry, happy, neutral, sad] and [6-20, 25-30, 42-48, 60-98]. The accuracy and speed of these models, achieved after training, were compared with the results described in scientific articles published in the Scopus database. According to the obtained experimental data, the first and second developed models achieved validation accuracy of 81.5% and 72.3%, respectively.

To solve the problems of increasing the environmental safety of the motor transport complex, this article proposes to use a decision support system, the intellectual center of which is a simulation model (virtual twin) of an emergency-hazardous section of the road network. An analysis of the traffic situation in an emergency-prone area of a large industrial center with a population of more than 500 thousand residents was carried out. To improve environmental safety parameters, it is proposed to use the microscopic simulation method. A 3D model of this section was built using GIS technologies based on a discrete event approach using a traffic library. The developed model was validated and virtual experiments were carried out. High model validity indicators indicate the correctness of the results of virtual experiments. Measures for the reconstruction of this site have been proposed, throughput and environmental safety indicators have been calculated, and the optimal option has been selected.

This paper suggests using existing neural networks and photogrammetry methods to build a point cloud model of a room and highlight floor surfaces. The work uses neural networks for segmentation: Boundary-guided Context Aggregation Network, Deep Convolutional Neural Networks with a fully connected Conditional Random Field, Spatial information Guided Convolutional Network and Swin Transformer with Shifted Windows. Also work uses DPT-L for depth prediction. The proposed ensemble demonstrated the viability of the concept. Three shortcomings were also identified: high peak load on hardware, sensitivity to the color palette of the input image, and the need for fine-tuning. When solving or reducing the impact of identified problems, the ensemble can be used for remotely controlled land-based unmanned vehicle.

A scheme and a basic set of software experiments for time series forecasting using an integrated autoregressive-moving average model (Box-Jenkins model) are presented. The model is based on the assumption that there is some relationship between neighboring values of a time series. In particular, the hypothesis is accepted that the time series contains three components: autoregressive, integrated and moving average. The application of the ARIMA model for forecasting time series using the statistical modelling language R - from the stage of data loading and preprocessing to the prediction of future values - is presented.

In the presented article, problems associated with the modeling features in automation and control of processes occurring in a primary oil refining technological installation are studied and investigated. In the article, when solving the modeling problem, special attention is paid to the problem of increasing the completeness of the received information used for processing, and in this connection, based on an active experiment, the “noise” characteristics inherent to the main indicators of the technological process under study are determined. It also addresses issues related to the correlation regression analysis features and the basic principles of designing an optimal filter that ensures the necessary cleaning of the received information from harmful interference and noise inherent in information communication lines. The article proposes an adaptive filter with an adaptation algorithm, which, analyzing the current state of the process under study and taking into account the disturbing influences constantly acting on the technological process, promptly corrects the optimal tuning parameters of the filter, minimizing distortion of the useful signal for the automatic control system.

The work solves the problem of developing a system for making diagnostic decisions regarding the condition of electrical equipment. A system of hierarchical mixed production rules using fuzzy logic tools is proposed. This system allows you to evaluate the state of electrical equipment taking into account the combination of clear and fuzzy parameter values in rules using fuzzy logic tools. In addition, this system also allows you to make scientifically based decisions about the technical condition of electrical equipment in conditions of uncertain and incomplete information and increase the compactness of the knowledge base presentation.

The results of the leaves and needles samples reflection spectra measurements on a laboratory installation in the range of 0.4-2.4 μm during the summer-autumn period are presented. It has been shown that there may be cases when the summer (for green leaves) and autumn (for yellow-green leaves) leaves or needles samples reflection spectra differ slightly in the visible range (0.4-0.75 μm), and in the near-infrared range (1.4-2.4 μm) stronger. It has also been noted that the information index R value (which is equal to the ratio of the vegetation reflectivity in the narrow spectral bands near wavelengths of 1.65 and 2.03 μm) is significantly higher for green vegetation samples (in summer period) compared to yellow or began to turn yellow samples (in autumn period).

Laboratory experimental studies have been carried out on a method for detecting oil pollution on the earth's surface in the near-infrared range. In the spectral range of 900-2500 nm, reflection spectra of samples were obtained after spills on soil and sand of various brands of gasoline, commercial oil and motor oil, as well as kerosene, diesel fuel, gas condensate, engine coolant and vegetable oil. It has been shown that in the spectral range of about 1730 nm, dips appear in the reflection spectra caused by the absorption of hydrocarbons on a surface contaminated with oil products, which are especially pronounced in the case of an oil spill on sand. In the spectral range around 2300 nm, dips in the reflectance spectra are more noticeable in the case of oil spills on soil. Therefore, to increase the efficiency of the method for detecting oil pollution on the earth's surface in the near-infrared range, it is necessary to use measurement data both in the spectral region of about 1730 nm and in the spectral region of about 2300 nm.

This article explores and develops a method for the functioning of quantum algorithms and models of quantum computing devices to transform a classical image into a quantum state, highlight boundaries and convert a halftone image into a binary one. The presented quantum algorithm demonstrates the prospects for applying quantum information theory to solve classical problems. The main emphasis of the work is on computer simulation of a quantum algorithm and the study of its effectiveness when using quantum computing tools and methods. The relevance of this research stems from the constant updating and expansion of the field of quantum research, as well as from the lack of computer simulations of quantum physical phenomena. The scientific novelty of the work lies in the development of an effective pattern recognition model using the properties and methods of quantum computing, which contributes to the development of this area of scientific research.

Quantum methods for image analysis and processing in the frequency domain represent an innovative approach to image processing using quantum computing. One of the key tools in this field is the quantum Fourier transform (QFT), which can be applied to quantum images to analyze and process data in the frequency domain. The process involves several steps, including quantum image preparation, QFT application, frequency domain data processing, and inverse Fourier transform to reconstruct the spatial domain image. Quantum image processing techniques offer new opportunities for solving complex problems in the field of image processing, especially in the context of the use of quantum computing. However, this area is still under active research and requires further research and technology development for practical application.

The paper discusses the application of large language models for the development of decision support systems, particularly in the agro-industry. The thesis that decision support is necessary in the sphere of agriculture is confirmed. In order to optimize the activity of smart greenhouses it is reasonable to implement information systems and technologies using large language models. The architecture of decision support system with Retrieval Augmented Generation technology for controlling disease incidence of plants cultivated is proposed. Experiments with the implemented decision support system have been carried out. It is concluded that the application of large language models in the operation of smart greenhouses is promising for use in agriculture in general.

In this work, the degree of automation of mass transfer devices for processing by-products of ethyl alcohol production is analyzed; in this regard, a number of shortcomings are identified, one of which is the optimal automated control of the process of rectification of by-products for the extraction of high-molecular-weight products. alcohols, in our case from non-standard fusel oils. In accordance with the topic, an automated advanced control device for a distillation unit for processing by-products of ethanol production has been developed. An optimal automated control device was developed and a comparative analysis was carried out in the Simulink environment of PID and MPC controllers, as a result of which a more advanced device for automated control of mass transfer processes was proposed.

The paper solves the problem of reducing the dimension of input features when constructing neural models for the SuperMAG electrojet index forecast. In order to decrease the input data dimension, estimates of correlation indexes values and computations of neural network errors are used. Feature sets providing the least error for the SuperMAG electrojet index modeling are obtained. Modelling of the geomagnetic data was performed based on the obtained feature sets. Recurrent neural networks were used for modelling. The dependence of the modelling quality on the input data is shown.

The paper presents a new approach to quality management in mechanical engineering production using machine vision. The proposed development is based on the implementation of a "Quality Guarantor" model. In particular, an algorithm is proposed for identifying external threads using 3D point cloud scanning data. Based on the results of a comparative analysis of different methods of approximation analysis, a combined smoothing algorithm was proposed that allows identifying key points of the model. The proposed approach allows us to expand the capabilities of computer vision for analyzing the quality of mechanical engineering products.

In this study, the problem of forecasting and analyzing the urbanization process using machine learning methods was solved using the example of Samarkand region. The official data of the State Statistics Committee of the Republic of Uzbekistan was used, and 25 important features affecting the level of urbanization were identified. Subsequently, various machine learning models were built, and their effectiveness was compared, with the artificial neural network model showing the highest result. With the help of this model, the levels of urbanization in Samarkand region for 2023-2025 were predicted. The obtained results are of practical importance for managing and regulating urbanization processes.

The paper discusses main paths to improve the effectiveness of railway intellectual marshalling yard monitoring and control systems and its land devices. It shows which environment conditions and other factors affect the accuracy of land device measurements particularly park track Occupancy Monitor Block which measures the length of unoccupied track portion. The paper shows that the measurements could be corrected by means of classification algorithms. It is suggested to use hierarchic decision trees as a classification rule. It discusses a method to improve classical decision trees building algorithms based on bootstrap method ideas. The suggested method improves accuracy of built decision trees.

A novel method for detecting synchronization of pulse oscillator signals via entropy measures is introduced. Entropy is calculated for the time series derived from the ratios of the periods of the analyzed signals at intervals of the multiplication signal. We explore a model dynamic system consisting of two resistively coupled pulse oscillators. By adjusting the resistance, it is possible to induce various synchronization states in the output signals. This method has been employed to analyze the maps of oscillator synchronization states using four different entropy measures: FuzzyEn, SampEn, SVDEn and NNetEn. A detailed comparative analysis of these measures was conducted. The developed approach showcases its potential for implementation in mobile digital platforms for analyzing and diagnosing synchronization failures across diverse systems.

The mathematical model is proposed for evaluating the noise immunity characteristics of a coherent modem reception in a message transmission system that functions under the influence of unintended interference sources. The developed of a mathematical model takes into account the demodulator synthesis algorithm, decoding methods, effective M-PSK modulations and Reed-Solomon codes in the modem. Integrated expressions are obtained that evaluate the noise immunity characteristics of a modem receive, taking into account the energy performance of the receiver.

The article considers the issue of developing an object-oriented database model of the electronic library of the works of medieval scientists of Uzbekistan. Based on the formulated requirements for the software platform and its information content, the main objects, their attributes and permissible connections between objects were determined. The ER model and the corresponding logical and physical database models were developed taking into account common approaches to the formalization of bibliographic information and the general trend in the evolutionary development of the architecture and functionality of electronic libraries. The developed object-oriented model and database structure made it possible to optimize the composition and structure of the source information for the tasks of the electronic library system. Subsequent analysis of the developed model from the point of view of how successfully it allows solving the tasks of readers showed that the designed database, with minimal redundancy, has good data consistency and operability, has the properties of flexibility and extensibility to adapt to more complex tasks that may arise with further development of the system.

The development of software for computer modeling of the transport and diffusion of harmful substances in the atmospheric air is a valuable contribution to environmental monitoring and forecasting. The developed software tool provides monitoring and forecasting of the processes of transport and diffusion of these substances emitted by industrial enterprises. This feature can provide valuable information about the nature of dispersion and the potential environmental impact of pollutants. By simulating the velocities and trajectories of particles carrying harmful substances, the software can help identify areas with high levels of pollution, evaluate the effectiveness of mitigation measures and support decision-making processes related to the control of industrial emissions.

The paper considers the ADMM (Alternating Direction Method of Multipliers) optimizer, which is a powerful tool for solving complex optimization problems, especially in the context of mixed-binary constrained optimization (MBCO). This study presents a new ADMM optimizer that demonstrates efficiency in solving optimization problems in the field of energy. The developed ADMM optimizer represents an innovative approach to solving optimization problems in the field of energy using quantum computing. It allows you to effectively solve MBCO tasks, taking into account various constraints and requirements related to energy production and distribution. The results of the study demonstrate the excellent performance and accuracy of the ADMM optimizer in comparison with other optimization methods. The developed ADMM optimizer represents a significant contribution to the field of optimization in the energy industry and has the potential for wide application in solving various tasks related to energy production and management.

The article presents results of route detection and roadway conditions in front of autonomous vehicle using machine vision system combining image with light markers creating grid of laser lines. Tracking the roadway parameters is implemented by registering the light markers distortion. Also, an analysis of illuminance effect to green and red laser lines shape detection quality in concrete surface with machine vision is given. The experimental results of the illumination effect on the quality of laser line recognition by machine vision inside industrial premises for a concrete surface are shown. To achieve high-quality recognition, the main variable parameters are recursively changed: image filtering, binary threshold, morphology functions.

The paper deals with the development of an adaptive digital system of modal control of technological processes. The analysis of a typical adaptive digital control system with an adjustable model is carried out, in which optimization of digital regulator settings is performed at each quantization tact according to the adaptable model of the object, which leads to a significant increase in the computational load on the system. Determination of the optimal tuning parameters of the digital regulator is carried out by the criterion of ensuring the necessary margin of stability of the system. With this purpose mathematical and software research of stability margin at drift of system parameters of variability of external conditions is developed. The connection between the stability margin and dynamic characteristic of the closed-loop control system is established. Synthesized adaptive digital system of modal control providing sufficient stability margin, given stability margin, differs from the typical adaptive system by the possibility of inclusion of the analysis block, designed to assess the stability margin, as well as to determine the moments of optimization of the tuning parameters of the digital regulator. The stability margin of the control system is set according to the requirements for specific technological processes. Conducted studies with different parameters of models of the control object showed the effectiveness of the proposed adaptive digital control system of a dynamic object relative to the known.

The paper solves the problem of SuperMAG electrojet index real-time forecast based on the combination of neural networks and wavelet processing. The scheme of training data generation and architecture of neural networks are illustrated. The quality of modeling of neural networks, trained for different periods, is assessed. Timely forecast of the geomagnetic SuperMAG electrojet index makes it possible to obtain information on expected disturbances in the geomagnetic field. The results of neural network model performance during geomagnetic disturbances are presented. The neural networks show the dependence of forecast quality on the interplanetary magnetic field data.