The article considers the parameter identification issues of linear non-stationary dynamic systems adaptive models using the example of a linearized adjustable model of a DC motor with independent excitation. A new method for estimating the parameters of adjustable models from a small number of observations is developed based on projection identification and the apparatus of linear algebra and analytical geometry. To evaluate the developed identification method, a comparison of the transient processes of the adaptive model of a DC motor with independent excitation with the obtained parameter estimates with reference characteristics was carried out. The efficiency of the proposed identification method in problems of DC electric drive control is shown.

Keywords: DC motor, projection identification, dynamic system parameter estimation, adaptive model of non-stationary dynamic system

Analysis of a digital data transmission system through a noisy communication channel based on the Huffman compression method and encoding using cyclic Bose-Chowdhury-Hockingham codes This article examines the effectiveness of a digital data transmission system through a noisy communication channel using the Huffman compression method and cyclic BCH encoding (Bose-Chowdhury-Hockingham). Huffman compression reduces data redundancy, which increases the effective transmission rate, while BCH codes detect and correct errors caused by channel noise. The analysis likely includes evaluating parameters such as compression ratio, data transmission rate, error probability after decoding, and computational complexity of the algorithms. The results demonstrate the effectiveness of this combination of techniques in improving data transmission reliability in noisy environments.

Keywords: " digital transmission system, cyclic coding, compression ratio, decoding, encoding"

This article examines the growing threat of web scraping (parsing) as a form of automated cyberattack, particularly aimed. Although scraping publicly available data is often legal, its misuse can lead to serious consequences, including server overload, data breaches and intellectual property infringement. Recent court cases against OpenAI and ChatGPT highlight the legal uncertainty associated with unauthorized data collection. The study presents a dual approach to combat malicious scraping. Traffic Classification Model - a machine learning based solution using Random Forest algorithms results in performance that achieves 89% accuracy in distinguishing between legitimate and malicious bot traffic, enabling early detection of scraping attempts. Data Deception Technique - the countermeasure dynamically modifies HTML content to convey false information to scrapers while maintaining the original look of the page. This technique prevents data collection without affecting the user experience. Performance results include real-time traffic monitoring, dynamic page obfuscation, and automatic response systems. The proposed system demonstrates effectiveness in mitigating the risks associated with scraping and emphasizes the need for adaptive cybersecurity measures in evolving digital technologies.

Keywords: parsing, automated attacks, data protection, bot detection, traffic classification, machine learning, attack analysis, data spoofing, web security

The paper considers the effect of particle size on the dynamics of suspended sediments in a riverbed. The EcoGIS-Simulation computing complex is used to simulate the joint dynamics of surface waters and sediments in the Volga River model below the Volga hydroelectric dam. The most important factor in the variability of the riverbed is the spring releases of water from the Volgograd reservoir, when water consumption increases fivefold. Some integral and local characteristics of the riverbed are calculated depending on the particle size coefficient.

Keywords: suspended sediment, soil particle size, sediment dynamics, diffusion, bottom sediments, channel morphology, relief, particle gravitational settling velocity, EcoGIS-Simulation software and hardware complex, Wexler formula, water flow

The article examines the influence of the data processing direction on the results of the discrete cosine transform (DCT). Based on the theory of groups, the symmetries of the basic functions of the DCT are considered, and the changes that occur when the direction of signal processing is changed are analyzed. It is shown that the antisymmetric components of the basis change sign in the reverse order of counts, while the symmetric ones remain unchanged. Modified expressions for block PREP are proposed, taking into account the change in the processing direction. The invariance of the frequency composition of the transform to the data processing direction has been experimentally confirmed. The results demonstrate the possibility of applying the proposed approach to the analysis of arbitrary signals, including image processing and data compression.

Keywords: discrete transforms, basic functions, invariance, symmetry, processing direction, matrix representation, correlation

The article analyzes various approaches to the generation and detection of audio deepfakes. Particular attention is paid to the preprocessing of acoustic signals, extraction of voice signal parameters, and data classification. The study examines three groups of classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and neural networks. For each group, effective methods were identified, and the most successful approaches were determined based on a comprehensive analysis. The study revealed two approaches demonstrating high accuracy and reliability: a detector based on temporal convolutional networks analyzing MFCC-cepstrogram achieved an EER metric of 0.07%, while the Support Vector Machine with a radial basis function kernel reached an EER of 0.5%. Additionally, the latter method demonstrated the following metrics on the ASVspoof 2021 dataset: Accuracy = 99.6%, F1-score = 0.997, Precision = 0.998, and Recall = 0.994.

Keywords: audio deepfakes, preprocessing of acoustic signals, support vector machine, k-nearest neighbors, neural networks, temporal convolutional networks, deepfake detection

Modern engineering equipment operation necessitates solving optimal control problems based on measurement data from numerous physical and technological process parameters. The analysis of multidimensional data arrays for their approximation with analytical dependencies represents both current and practically significant challenges. Existing software solutions demonstrate limitations when working with multidimensional data or provide only fixed sets of basis functions. Objectives. The aim of this study is to develop software for multidimensional regression based on the least squares method and a library of constructible basis functions, enabling users to create and utilize diverse basis functions for approximating multidimensional data. Methods. The development employs a generalized least squares method model with loss function minimization in the form of a multidimensional elliptical paraboloid. LASSO (L1), ridge regression (L2), and Elastic Net regularization mechanisms enhance model generalization and numerical stability. A precomputation strategy reduces asymptotic complexity from O(b²·N·f·log₂(p)) to O(b·N·(b+f·log₂(p))). The software architecture includes recursive algorithms for basis function generation, WebAssembly for computationally intensive operations, and modern web technologies including Vue3, TypeScript, and visualization libraries. Results. The developed web application provides efficient approximation of multidimensional data with 2D and 3D visualization capabilities. Quality assessment employs MSE, R², and AIC metrics. The software supports XLSX data loading and intuitive basis function construction through a user-friendly interface. Conclusion. The practical value lies in creating a publicly accessible tool at https://datapprox.com for analyzing and modeling complex multidimensional dependencies without requiring additional software installation.

Keywords: approximation, least squares method, basic functions, multidimensional regression, L1/L2 regularization, web-based

A two-level hierarchically organized model of managing the interaction of the director with the personnel in a company related to the development of projects in the construction sector is presented. The director acts as the leader, and the company's employees act as followers. Both management entities strive to maximize their target functions, reflecting their income and expenses. The study of the model was conducted taking into account its hierarchical structure. An algorithm for constructing a Stackelberg solution under inducement has been developed. A numerical study of the model has been conducted using the scenario method by partially enumerating the areas of admissible controls of subjects with a certain step. When conducting simulation experiments, all input parameters of the model varied in a fairly wide range. The results of the simulation experiments have been analyzed, and some patterns of system development have been identified.

Keywords: hierarchy, incentive, control system, solution algorithm, Stackelberg equilibrium, leader, follower, imitation, experiment, investment project

In this paper, methods for estimating one's own position from a video image are considered. A robust two-stage algorithm for reconstructing the scene structure from its observed video images is proposed. In the proposed algorithm, at the feature extraction and matching stage, a random sample based on the neighborhood graph cuts is used to select the most probable matching feature pairs. At the nonlinear optimization stage, an improved optimization algorithm with an adaptive attenuation coefficient and dynamic adjustment of the trust region is used. Compared with the classical Levenberg-Marquard (LM) algorithm, global and local convergence can be better balanced. To simplify the system's decisions, the Schur complement method is used at the group tuning stage, which allows for a significant reduction in the amount of computation. The experiments confirmed the operability and effectiveness of the proposed algorithm.

Keywords: 3D reconstruction，graph-cut, Structure-from-Motion (SfM)，RANSAC，Bundle Adjustment optimization，Levenberg-Marquardt algorithm，Robust feature matching

The paper considers a lightweight modified version of the YOLO-v5 neural network, which is used to recognize road scene objects in the task of controlling an unmanned vehicle. In the proposed model, the pooling layer is replaced by the ADown module in order to reduce the complexity of the model. The C2f module is added as a feature extraction module to improve accuracy by combining features. Experiments using snowy road scenes are presented and the effectiveness of the proposed model for object recognition is demonstrated.

Keywords: road scene object recognition, YOLOv5, Adown, C2f, deep learning, pooling layer, neural network, lightweight network, dataset

The characteristics of a submersible induction motor are described with sufficient reliability for practice by the theory of multi-motor electric drive. In this case, the classical circuit of a submersible induction motor is a coupled system of several equivalent-T circuits. In turn, this significantly increases its computational complexity and reduces the speed of ACS. It is proposed to construct a mathematical model of the submersible electric motor in the form of polynomials with significantly higher speed using the methods of experiment planning. In the area of applicability, the differences in the estimation of energy performance do not exceed 1.1%, between the proposed models and classical equivalent-T circuits.

Keywords: automated control system, mathematical model, polynomial, mean absolute percentage error, computational complexity, design of experiment, scatter diagram, modal interval, submersible electrical motor, rotor package

The article presents a mathematical model that formalizes the process of managing the scientific activities of an organization. The model based on the theory of queuing. The principle of death - reproduction used in the construction. For a special case, a graph of states and a system of Kolmogorov differential equations are given. The intensity of the input and output streams are time-dependent non-stationary streams. The model allows us to consider various structures and schemes of interaction between scientific departments and various sce-narios for setting scientific tasks and the intensity of their solution by employees of the organization. A software package for decision-making has developed for the model for optimal management of the scientific activities of the department. The article presents one of the results of an experimental and model study of the influence of the motivational component and the level of competence of employees. Graphs of the system states given for the resulting solution. The research can used for comprehensive evaluation of results, planning, resource allocation and management of scientific activities.

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

The article presents the results of a study on the effectiveness of the hashing algorithms Argon2, Scrypt, and Bcrypt in the context of developing web applications with user registration and authentication features. The main focus of this research is on analyzing the algorithms' resilience to brute-force attacks, hardware attacks (GPU/ASIC), as well as evaluating their computational performance. The results of the experiments demonstrate the advantages of Scrypt in terms of balancing execution time and security. Recommendations for selecting algorithms based on security and performance requirements are also provided.

Keywords: hashing algorithm, user registration interface, user authentication interface, privacy protection

The article discusses the structure and principle of operation of an improved centrifugal unit for mixing bulk materials. A special feature of which is the ability to control mixing modes. Due to its design, the selection of a rational position of the bump makes it possible to provide such conditions for the impact interaction of particle flows, in which a high-quality homogeneous mixture of components is formed, the particles of which have different sizes, shapes and other parameters. To characterize the resulting mixture, the coefficient of heterogeneity was used, the conclusion of which is based on a probabilistic approach. A computational scheme of the rarefied flow formation process is given. An expression is derived for calculating the coefficient of heterogeneity when mixing bulk media, the particles of which have different sizes, shapes and other parameters. The research conducted in the article allows not only to predict the quality of the resulting mixture, but also to identify the factors that have the greatest impact on achieving the required uniformity.

Keywords: aggregate, bulk media, mixing, coefficient of heterogeneity, concentration, design scheme, particle size

The problem of substantiating the grid step of an electronic map used to establish the fact that a vehicle, whose coordinates are read from an on-board GPS sensor, enters the control area is considered. The proposed decision support system determines the maximum allowable step for marking a geographical area for subsequent information processing, in which the amount of resulting data is close to the minimum, taking into account the preservation of processing time and acceptable the level of error of the data for analytics. To solve the problem, it is proposed to use the regression analysis apparatus to determine the dependence of the expected verdict error on the grid step of the electronic map. The analysis of residual variances was used to prove the applicability of the proposed device. The Laplace formula was used to estimate the confidence interval of erroneous verdicts.

Keywords: grid step, confidence probability, percentage of erroneous verdicts, regression analysis, coefficient of determination