The results of fire dynamics simulation based on the FDS software kernel are a large amount of data describing the dynamics of various parameters in the space of the studied object. Solving various research problems based on them may require quite complex processing, which goes beyond the functionality of existing software solutions. The article is devoted to the method of efficiency increasing for numerical fire dynamics simulation results processing by automating the implementation of relevant operations. The article describes the functional model of the developed technology and its main stages. Approbation of the proposed method was carried out using the example of solving the problem of forming initial data arrays in high spatial and time resolution for the subsequent study of enclosing tunnel structures heating in case of fire. Graphs of the gas medium temperature at various points under the roof of the tunnel structure from the coordinate are presented, as well as temperature fields in the vertical section of the investigated structure in the plane passing through the fire focus at different times. Based on the comparative analysis, it was shown that the speed of calculation results automated processing is several orders of magnitude higher compared to methods that use the functionality of existing software solutions designed to view the output of the fire dynamics simulation.

Keywords: fire dynamics simulation, automation, data processing, tunnel structures, mathematical model, FDS

The article discusses current issues related to the design of a smart home wireless local area network based on splitter-repeater modules. Special attention in the study is paid to the modules of wired and wireless hubs and switches. The results of the comparative characteristics of PLC and FBT splitter-repeaters are also presented. Particular emphasis is placed on the network topology and its main components.

Keywords: wireless network, topology, data, transmission, power, traffic, packet, failures, adapter, cable, connection

The article explores the actor model as implemented in the Elixir programming language, which builds upon the principles of the Erlang language. The actor model is an approach to parallel programming where independent entities, called actors, communicate with each other through asynchronous messages. The article details the main concepts of Elixir, such as comparison with a sample, data immutability, types and collections, and mechanisms for working with the actors. Special attention is paid to the practical aspects of creating and managing actors, their interaction and maintenance. This article will be valuable for researchers and developers interested in parallel programming and functional programming languages.

Keywords: actor model, elixir, parallel programming, pattern matching, data immutability, processes, messages, mailbox, state, recursion, asynchrony, distributed systems, functional programming, fault tolerance, scalability

The transition from scheduled maintenance and repair of equipment to maintenance based on its actual technical state requires the use of new methods of data analysis based on machine learning. Modern data collection systems such as robotic unmanned complexes allow generating large volumes of graphic data in various spectra. The increase in data volume leads to the task of automating their processing and analysis to identify defects in high-voltage equipment. This article analyzes the features of using computer vision algorithms for images of high-voltage equipment of power plants and substations in the infrared spectrum and presents a method for their analysis, which can be used to create intelligent decision support systems in the field of technical diagnostics of equipment. The proposed method uses both deterministic algorithms and machine learning. Classical computer vision algorithms are applied for preliminary data processing in order to highlight significant features, and models based on unsupervised machine learning are applied to recognize graphic images of equipment in a feature space optimized for information space. Image segmentation using a spatial clustering algorithm based on the density distribution of values ​​taking into account outliers allows detecting and grouping image fragments with statistically close distributions of line orientations. Such fragments characterize certain structural elements of the equipment. The article describes an algorithm that implements the proposed method using the example of solving the problem of detecting defects in current transformers, and presents a visualization of its intermediate steps.

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

The annual growth of the load on data centers increases many times over, which is due to the growing growth of users of the information and telecommunications network Internet. Users access various resources and sources, using search engines and services for this. Installing equipment that processes telecommunications traffic faster requires significant financial costs, and can also significantly increase the downtime of the data center due to possible problems during routine maintenance. It is more expedient to focus resources on improving the software, rather than the hardware of the equipment. The article provides an algorithm that can reduce the load on telecommunications equipment by searching for information within a specific subject area, as well as by using the features of natural language and the process of forming words, sentences and texts in it. It is proposed to analyze the request based on the formation of a prefix tree and clustering, as well as by calculating the probability of the occurrence of the desired word based on the three sigma rule and Zipf's Law.

Keywords: Three Sigma Rule, Zipf's Law, Clusters, Language Analysis, Morphemes, Prefix Tree, Probability Distribution

The article presents the existing methods of reducing the dimensionality of data for teaching machine models of natural language. The concepts of text vectorization and word-form embedding are introduced. The task of text classification is being formed. The stages of classifier training are being formed. A classifying neural network is being designed. A series of experiments is being conducted to determine the effect of reducing the dimension of word-form embeddings on the quality of text classification. The results of evaluating the work of trained classifiers are compared.

Keywords: natural language processing, vectorization, word-form embedding, text classification, data dimensionality reduction, classifier

Two capacitive methods of measuring the linear density of one-dimensional fibrous products are considered. The sensitivity of the measurement results to variations in the geometric and physical parameters of the measuring device for the differential and resonance measurement methods is estimated. A weak, almost linear dependence of the measurement error on parameter variations in a wide variation range is established. The good suitability of both methods for measuring the linear density of one-dimensional products by the capacitive method and the high correlation between the measured value and the measurement results are substantiated.

Keywords: fibrous materials, one-dimensional products, linear density, capacitive measurement method, capacitive method, differential circuit, resonant measurement circuit, parameter variations

The paper discusses the use of the M/M/n mass service model to analyze the performance of cloud storage systems. Simulations are performed to identify the impact of system parameters on average latency, blocking probability, and throughput. The results demonstrate how optimizing the number of servers and service intensity can improve system performance and minimize latency. The relevance of the study is due to the need to improve the performance of cloud solutions in the context of growing data volumes and increasing load on storage systems.

Keywords: cloud storage, mass service theory, M/M/n model, Python, modeling, performance analysis

Increasing the accuracy of steady-state calculation is possible by taking into account the thermal processes occurring in electrical energy conductors. The wind flow velocity, in turn, is of significant importance in determining the conductor temperature. In this paper, the values of wind speed for an 11-year period are considered. The time series is analyzed and the prediction models of the target variable are tested and the prediction results are compared.

Keywords: power grid mode calculation, thermal processes, wind flow velocity, prediction models, feed forward neural network, ensemble methods

This article explores visual data processing methods for underwater navigation and environmental reconstruction, based on modern approaches in computer vision and robotics. A system implemented in the ROS (Robot Operating System) environment is proposed, enabling simultaneous localization and mapping (SLAM) in underwater conditions. The system evaluation methodology includes virtual experiments using the Gazebo simulator, which replicate realistic underwater operational scenarios. The study demonstrates the feasibility of integrating stereo cameras, confirms the effectiveness of image processing methods in underwater environments, and presents quantitative metrics for navigation accuracy and object reconstruction. The results validate the proposed solutions as promising for real-world applications in autonomous underwater exploration.

Keywords: underwater navigation, environmental reconstruction, computer vision, visual odometry, SLAM, ROS, Gazebo, simulation, visual data, robotics

For the development of automated systems for designing ultraviolet irradiators intended to compensate for the deficiency of natural ultraviolet, it is critically important to know the spatial distribution of the erythemal radiation power. However, there are no suitable sensors for direct measurement of this value on the Russian market. In this regard, an alternative method for determining the erythemal radiation power is considered, which does not require the use of specialized erythemal-sensitive receivers. The method is based on obtaining the spatial distribution of the erythemal radiation power by taking into account the curve of the relative spectral erythemal efficiency of radiation and preliminary measurements on a gonioradiometric setup of the distribution of energy illuminance in the UVA (320 - 400 nm), UVB (280 - 320 nm) regions and the relative spectral distribution of the irradiator or radiation source for one arbitrarily selected direction in the wavelength range of 280 - 400 nm.

Keywords: ultraviolet radiation; erythemal radiation; irradiation units; measurement method, radiation strength; spatial distribution of erythemal radiation strength, method

During the research, a new prestressing system was developed for carbon fiber reinforced polymer plates to reinforce damaged steel beams. A parametric analysis was performed using finite element modeling. The results showed that satisfactory amplification efficiency can be achieved using the new pre-voltage system. The prestressed carbon fiber significantly increased the performance when bending beams at the elastic and elastic-plastic stages due to the use of high-strength carbon fiber plates. In addition, as the pre-voltage level increased, the amplification efficiency increased. A simple increase in the area or modulus of elasticity of the carbon fiber plate slightly improved the hardening efficiency, while the simultaneous application of prestressing clearly increases the hardening efficiency.

Keywords: reinforcement, steel beam, prestressing, new system, carbon fiber plate

This paper considered the problem of detection and classification of surface objects in low visibility conditions such as rain and fog. The focus is on the application of state-of-the-art deep learning algorithms, in particular the YOLO architecture , to improve detection accuracy and speed. The introduction to the problem includes a discussion of the limitations of visibility degradation, the change in shape and size of objects depending on the viewing angle, and the lack of training data. The paper also presents the use of discrete wavelet transform to improve image quality and increase the robustness of the systems to adverse conditions. Experimental results show that the proposed algorithm achieves high accuracy and speed, which makes it suitable for application in drone video monitoring systems.

Keywords: YOLO, wavelet transform, overwater objects, drones, low visibility condition, Fourier transforms, Haar

In the work describes the extreme filtering method and the author's approaches that allow adapting it to work in real time: frame-by-frame processing and the method with signal loading. Further, solutions are presented that can be used to implement the above on real devices. The first solution is to use the Multiprocessing library for the Python language. The second approach involves creating a client-server application and sending asynchronous POST requests to implement the frame-by-frame signal processing method. The third method is also associated with the development of a client-server application, but with the WebSocket protocol, not HTTP, as in the previous approach. Then, the results are presented, and conclusions are made about the suitability of the author's approaches and solutions for working on real devices. It is noted that the solution based on the use of the WebSocket protocol is of particular interest. This solution is suitable for both the frame-by-frame signal processing method and the method with value loading. It is also noted that all approaches proposed by the author are workable, which is confirmed by the time values ​​and the coincidence of the graphs.

Keywords: extreme filtering, frame-by-frame signal processing method, method with value loading, Multiprocessing, HTTP, WebSocket, REST, JSON, Python, microcontrollers, single-board computers

Diagnosing the technical condition of complex objects and planning repair-restoration work is a pressing challenge in today’s economy. This paper presents an approach to solving this problem using an integral indicator developed by the authors: the Modified Technical Condition Index. A conceptual model for a continuous (cyclical) monitoring system of this integral indicator’s levels is proposed. To assess an object’s technical condition, a linguistic scale is introduced, which generates recommendations for repair-restoration actions. Additionally, a resource-constrained planning system for repair-restoration work is developed. The novelty of this work lies in the proposed methodology for creating an evaluative linguistic scale based on the integral indicator. This scale enables qualitative assessment of an object’s condition and provides actionable recommendations for repair-restoration efforts. The paper also addresses resource-constrained planning of repair-restoration work using the Modified Technical Condition Index for complex technical objects.

Keywords: express diagnostics, planning repair-restoration work, integral technical condition indicator, conceptual model of continuous (cyclical) monitoring, technical condition level assessment, technical condition quality evaluation, linguistic scale

The article proposes an approach to automate the detection of polishing defects in blades using luminescent testing (LUM). Instead of manual visual inspection, a system was developed that utilizes a deep learning neural network for defect segmentation on images and a robotic setup for precise positioning of the camera and the blank. This ensures the repeatability of the inspection. The relevance is driven by the industry's need for high-precision and reliable real-time quality control methods. The mathematical model of the process, software architecture, hardware components, and the data collection process for neural network training are described. The results of applying the system for defect detection are presented. The development optimizes polishing processes.

Keywords: industrial blade polishing, intelligent video analytics, robotic optical scheme, mathematical model of technological process, Lum control

The main maintenance of a diversification of production as activity of subjects of managing is considered. being shown in purchase of the operating enterprises, the organizations of the new enterprises, redistribution of investments in interests of the organization and development of new production on available floor spaces. The most important organizational economic targets of a diversification of management are presented by innovative activity of the industrial enterprise.

Keywords: software systems, visualization, data, graphic systems, parts, models, diagrams, drawings

The paper considers two machine learning methods for predicting the growth of watercut on an oil field with the calculation of basic convergence metrics.

Keywords: watercut, statistics, machine learning, time series, oil production

The article is devoted to the developed code designer for the Scilab environment, which is intended to automate the process of creating software modules. The program allows you to generate code for Scilab through an intuitive interface, providing users with tools for working with variables, loops, graphs, system analysis and user-defined functions. The constructor allows you to write programs for Scilab without knowledge of a programming language.

Keywords: Scilab, code designer, programming automation, code generation, visual programming

Linear feedback shift registers (LFSR) and the pseudo-random sequences of maximum length (m-sequences) generated by them have become widely used in solving problems of mathematical modeling, cryptography, radar and communications. The wide distribution is due to their special properties, such as correlation. An interesting, but rarely discussed in the scientific literature of recent years, property of these sequences is the possibility of forming quasi-orthogonal matrices on their basis.In this paper, was conducted a study of methods for generating quasi-orthogonal matrices based on pseudo-random sequences of maximum length (m-sequences). An analysis of the existing method based on the cyclic shift of the m-sequence and the addition of a border to the resulting cyclic matrix is carried out. Proposed an alternative method based on the relationship between pseudo-random sequences of maximum length and quasi-orthogonal Mersenne and Hadamard matrices, which allows generating cyclic quasi-orthogonal matrices of symmetric structure without a border. A comparative analysis of the correlation properties of the matrices obtained by both methods and the original m-sequences is performed. It is shown that the proposed method inherits the correlation properties of m-sequences, provides more efficient storage, and is potentially better suited for privacy problems.

Keywords: orthogonal matrices, quasi-orthogonal matrices, Hadamard matrices, m-sequences

The article considers the options for visual programming of information support means for software and information complexes for UAV operators training. The main criterion indicators for systematically organizing the set of components for reusing program code are identified. An example of an unmanned payload carrier in various representative forms of visualization is given. A comparison of the labor intensity of developing the specified software and information implementations for the same unmanned robotics object with their normative labor intensity is shown. The variants of content filling during the development of the same material part of the considered device for various aspects of training specialists in the management and operation of UAV are considered. The principle of systematization of components by means of ordering the complexity of presentation and softwarе implementation is shown.

Keywords: risk forecasting, information support, training of unmanned aircraft systems operators, labor intensity assessment

The paper presents a method for quantitative assessment of zigzag trajectories of vehicles, which allows to identify potentially dangerous behavior of drivers. The algorithm analyzes changes in direction between trajectory segments and includes data preprocessing steps: merging of closely spaced points and trajectory simplification using a modified Ramer-Douglas-Pecker algorithm. Experiments on a balanced data set (20 trajectories) confirmed the effectiveness of the method: accuracy - 0.8, completeness - 1.0, F1-measure - 0.833. The developed approach can be applied in traffic monitoring, accident prevention and hazardous driving detection systems. Further research is aimed at improving the accuracy and adapting the method to real-world conditions.

Keywords: trajectory, trajectory analysis, zigzag, trajectory simplification, Ramer-Douglas-Pecker algorithm, yolo, object detection

The article explores the implementation of digital and mathematical technologies in decision support systems (DSS) aimed at enhancing the efficiency of livestock enterprises. In the context of digital transformation and increasing uncertainty in agriculture, the authors emphasize the importance of intelligent DSS capable of processing large datasets and supporting rapid, evidence-based decision-making. The purpose of the study is to identify effective technological and methodological approaches for optimizing livestock management, particularly in the area of animal feeding. Methods include the use of mathematical models, predictive algorithms, automated control systems, and big data analytics. The proposed DSS architecture enables real-time monitoring, adaptive ration formulation, and integration of physiological, environmental, and economic data. The paper provides practical examples of successful DSS applications, such as automated milking systems and health monitoring technologies, and analyzes their impact on productivity and cost reduction. A set of methodological recommendations is formulated to enhance management efficiency, including modular system design, staff training, and integration of IoT and AI technologies. The article concludes that intelligent DSS not only reduce feeding costs but also improve animal health, optimize resource use, and support sustainable agricultural practices. The results are of practical significance for researchers, developers, and farm managers aiming to implement data-driven solutions in livestock production.

Keywords: decision support system, animal husbandry, feeding optimization, mathematical models, big data, artificial intelligence, automation, predictive algorithms, Internet of things, digital agriculture

The article discusses a mathematical model of the production application distribution process on an aggregator platform. A formal approach based on a finite state machine and Petri nets is proposed to describe the states of orders, their transitions and constraints. A comprehensive model has been developed that takes into account the criteria for selecting a contractor, selecting equipment, and analyzing the production process, taking into account time, cost, and resource availability. The presented approaches ensure accuracy and efficiency in order management on digital production platforms.

Keywords: mathematical modeling, finite state machine, Petri net, digital production, service aggregator, choice of contractor

A class of mathematical methods for code channel division has been developed based on the use of pairs of orthogonal encoding and decoding matrices, the components of which are polynomials and integers. The principles of constructing schemes for implementing code channel combining on the transmitting side and arithmetic code channel division on the receiving side of the communication system and examples of such schemes are presented. The proposed approach will significantly simplify the design of encoding and decoding devices used in space and satellite communication systems.

Keywords: telecommunications systems, telecommunications devices, multiplexing, code division of channels, matrix analysis, encoding matrices, synthesis method, orthogonal matrices, integers