Optimization of automated management systems for facility protection complexes remains relevant today. This research paper provides an overview of the tools for implementing separate monitoring processes: device polling, processing of the received data, and transferring data to the graphic user interface. Based on the analysis of the reviewed information, a basis of solutions for developing management system of the technical means complex is planned to be formed. During the research, it was found that the combination of multi-threading architecture and adaptive polling algorithm allows to implement a large-scale polling; the clustering algorithm and special settings of frameworks for processing large-scale datasets can enhance job performance; WebSocket protocol has proved its efficiency for transferring the real-time data. The result of the evaluation of solutions was a set of tools for implementation of a hardware-software complex.

Keywords: sensor, management system, monitoring, SNMP manager, clustering, Hadoop, MapReduce, Spark, Apache Kafka, WebSocket

The paper examines the current state of the industrial Internet of Things market in Russia and around the world, the main areas of its application, as well as the prospects and challenges that businesses and industrial enterprises will face in implementing this technology. Special attention is paid to the advantages of implementing IIoT, such as increased productivity, reduced costs, improved security and transparency of processes. The barriers specific to the Russian market are discussed, including cybersecurity, hardware compatibility, and significant initial costs. Examples of successful implementations of IIoT technologies in various industries such as the oil and gas industry, logistics and chemical production are given. The emphasis is placed on the need for government support and adaptation of the regulatory framework to accelerate implementation. The article highlights the importance of an integrated approach to IIoT implementation, including using international experience and consolidating efforts to develop the digital economy in the face of global and local challenges.

Keywords: industrial Internet of Things, IIoT, industry 4.0, 5G, production automation, digital transformation

The article examines the use of unmanned complexes for finding and identifying defects in the construction of buildings and structures. The use of unmanned complexes integrated into practice for quality control of construction works of concrete and steel surfaces, as well as for regular inspections of buildings, insulation or ventilation systems is given. The prospects of using unmanned complexes for repair work, which contributes to improving the performance of the construction organization, were confirmed.

Keywords: machine vision, software, unmanned complex, survey, defect

In this article, we examined the permeability of concrete and the effect of corrosion processes on the durability and reliability of reinforced concrete structures. Attention is paid not only to the causes and mechanisms of corrosion, but also modern methods and strategies for protecting concrete and reinforced concrete structures from it are provided, aimed at extending their service life and ensuring operational safety. This knowledge will allow engineers and builders to plan and implement projects more efficiently, reducing the risks and economic losses associated with corrosion processes.

Keywords: corrosion of concrete, corrosion of steel reinforcement, permeability, reinforced concrete, durability, strength, reliability

This article reveals the features of the operation of information and measuring systems during gas transportation. The issues reflected in the article are particularly relevant in the context of the need to achieve the efficiency of information and measuring systems in the oil and gas industry. The purpose of the scientific research is to develop an approach to information and measuring systems in oil and gas organizations based on the digital twin model. To achieve this goal, the article analyzes practical cases of information and measuring systems in oil and gas organizations, reflects the features of certification of information and measuring systems used in oil and gas organizations, and presents the results of developing an approach to information and measuring systems in oil and gas organizations based on the digital twin model.

Keywords: information and measuring systems; oil and gas industry; certification; digital twin model; gas transportation; approach; work efficiency

The article provides a comprehensive analysis of defects in monolithic reinforced concrete structures, commonly encountered during the construction of multi-storey buildings. The main types of defects and damage are discussed, such as cracks, concrete spalling, destruction of the protective layer, exposure and corrosion of reinforcement bars, formation of cavities, concrete overflow, gravelly texture, geometric deviations, and cold joints in concreting. Their general description, causes of occurrence, classifications, parameters, and consequences for the operational characteristics of the structures are presented. Special attention is given to modern diagnostic methods and repair technologies, which significantly extend the service life of buildings and enhance their safety throughout their lifecycle. The results presented can be used by engineers, builders, and repair specialists to optimize construction processes, control the quality of work, and ensure the timely elimination of identified defects.

Keywords: Inspection of structural elements, reinforced concrete, defects of monolithic structures, cracks, reinforcement corrosion, repair, concrete quality, cold joint, monolithic construction, concrete surface quality

A project is a single system, an interconnected network of elements: participants, resources, processes, and goals. This relationship is critically important for successful implementation by the technical customer service. Processes that are clearly defined and managed ensure consistency and efficiency. Therefore, project design and management requires a systematic approach that takes into account all relationships and strives to optimize each element to achieve a common goal. A system approach is a method that considers a problem or task in terms of its interrelationships and interactions of all elements of the system. In the context of a technical customer service, this approach can be particularly useful for project management, process optimization, and work efficiency improvement.

Keywords: construction project, technical customer, planning, technologies, risks, system approach, project management, documentation of processes, construction site, system

Zero-day attacks are one of the most dangerous threats to the security of modern systems, applications and infrastructure because they are unpredictable. Due to the unknown signatures of zero-day attacks, traditional signature-based defences are unable to detect them. Countering such attacks in IoT networks requires both in-depth research and the implementation of practical measures. The present review of state-of-the-art zero-day attack detection research has shown that deep learning approaches are best at detecting zero-day attacks and botnets in IoT networks. These approaches can analyse anomalies in network traffic and identify new threats and zero-day attacks while minimising the number of false positives.

Keywords: Zero-Day Attack, vulnerability, Internet of Things, machine learning, anomaly, signature-based defence method, autoencoder, network traffic

In this article, an analysis of the main methods for solving the linear cutting problem (LCP) with the criterion of minimizing the number of knife rearrangements is presented. The linear cutting problem in its general form represents an optimization problem that involves placing given types of material (rolls) in such a way as to minimize waste and/or maximize the use of raw materials, taking into account constraints on the number of knives, the width of the master roll, and the required orders. This article discusses a specific case of the problem with an additional condition for minimizing knives' changes and the following approaches for its solution: the exhaustive search method, which ensures finding a global optimal solution but can be extremely inefficient for problems with a large number of orders, as well as random search based on genetic and evolutionary algorithms that model natural selection processes to find good solutions. Pseudocode is provided for various methods of solving the LCP. A comparison is made in terms of algorithmic complexity, controllability of execution time, and accuracy. The random search based on genetic and evolutionary algorithms proved to be more suited for solving the LCP with the minimization of waste and knife rearrangements.

Keywords: paper production planning, linear cutting, exhaustive search, genetic algorithm, waste minimization, knife permutation minimization

Steel hoisting ropes play an important role in metallurgical equipment, ensuring reliability and efficiency of lifting operations. One of the key features of their operation is the high level of contamination typical of metallurgical operations. Metallurgical processes are often accompanied by dust, metal chips and other abrasive particles that can significantly degrade ropes, causing wear and corrosion. To maintain the efficient operation of equipment it is necessary to monitor the condition of hoisting ropes in real time, which makes the task of improving automatic systems for monitoring the condition of ropes urgent. The paper reviews the methods of optical control of defects in hoisting steel ropes, the advantages and limitations of different approaches are considered. The aim of the work is to justify the effectiveness of the authors' developed method of analyzing rope defect images using neural networks in relation to the method based on the discrete Fourier transform. It is revealed that one of the most promising in terms of technical and economic efficiency of inspection methods is the application of vision system with image processing based on convolutional neural network technology, which allows to effectively detect defects in complex and changing operating conditions, such as metallurgical and mining production, where the background of the image may be non-uniform, and the distance between the camera and the rope varies.

Keywords: lifting ropes, vision systems, optical control methods, fast Fourier transform, hidden Markov models, convolutional neural networks

The article is devoted to the creation of a highly specialized automated information system for automated processing of orders for the production of abrasive tools. The development of such software products will improve production efficiency through the transition from order-based production to batch production.

Keywords: automated information system, production order processing system, Rammler-Breich diagram, role-based data access system

The article is the result of an analytical study on the topic of risk management in the creation and modernization of business processes. The article proposes risk management methods using the organization's human resources and methods for training personnel taking into account trends in the labor market. The effect of implementing risk management measures and the method for assessing the effectiveness of the implemented training are separately noted.

Keywords: risk management, human resources, employee training, experts, SWOT analysis

Deviation of forestry equipment from the designated route leads to environmental, legal, and economic issues, such as soil damage, tree destruction, and fines. Autonomous route correction systems are essential to address these problems. The aim of this study is to develop a system for deviation detection and trajectory calculation to return to the designated route. The system determines the current position of the equipment using global positioning sensors and an inertial measurement unit. The Kalman filter ensures positioning accuracy, while the A* algorithm and trajectory smoothing methods are used to compute efficient routes considering obstacles and turning radii. The proposed solution effectively detects deviations and calculates a trajectory for returning to the route.

Keywords: deviation detection, route correction, mobile application, Kalman filter, logging operations

In this work, an approach is considered, which makes it possible to obtain scientific-based management decisions on the development of organizational systems. The purpose of the work is to show in a particular example the development of a fuzzy cognitive model development process, to analyze the model for sustainability, to determine the activation vertex complexes. The toolkit for describing and analyzing a fuzzy cognitive model is based on the basic concepts of fuzzy graph theory. Simulation results are given, directed to the development of possible scenarios for the development of situations of a socio-economic system of the Rostov region. The results are clearly illustrated by examples of fourteen scenarios, the matching of which to the main factors made it possible to determine a plurality of pessimistic and optimistic and to give recommendations to the face receiving the decision.

Keywords: control, organizational system, cognitive model, simulation modeling, fuzzy graph, sustainability

The article considers the issues of imitation modeling of fibrous material mixing processes using Markov processes. The correct combination and redistribution of components in a two-component mixture significantly affects their physical properties, and the developed model makes it possible to optimize this process. The authors propose an algorithm for modeling transitions between mixture states based on Markov processes.

Keywords: modeling, imitation, mixture, mixing, fibrous materials

The number of information security incidents and the amount of damage caused by them are increasing every year. The relevance of information security incident response remains high. The primary response action is an information security incident localization. The purpose of this study is to reduce the time taken to localize detected information security incidents by information security incident response teams. The purpose is achieved by issuing and using a mandate for information security incident localization by orchestration tools and/or artificial intelligence systems in an automated mode. Analysis and synthesis of available publicly materials were applied as research methods. The results of the analysis and evaluation of different criteria applicability for granting a mandate to localize an information security incident in an incident response are presented in the article. A mandate is granted to orchestration tools and/or artificial intelligence systems to perform localization of an information security incident in an automatic mode, i.e., without the involvement of information security incident response team forces. In evaluation the applicability of various criteria for granting a mandate, unlike the known ones, the level of difficulty in determining values for the criteria in question by information security incident response team forces alone was assessed. Criteria and their values are defined, which, unlike the known ones, highlight the area for information security incidents localization in automatic mode.

Keywords: response team, response area, response, automatic localization, orchestration, artificial intelligence

The use of recurrent neural networks to predict the water level in the Amur River are consider. The advantages of using such networks in comparison with traditional machine learning methods are described. Various architectures of recurrent networks are compared, and hyperparameters of the model are optimized. The developed model based on long-term short-term memory (LSTM) has demonstrated high prediction accuracy, surpassing traditional methods. The results obtained can be used to improve the effectiveness of monitoring water resources and flood prevention.

Keywords: time series analysis, Amur, water level, forecasting, neural networks, recurrent network

The relevance of the research is determined by the need to solve complex optimization problems under conditions of high dimensionality, noisy data, and dynamically changing environments. Classical methods, such as genetic algorithms, often encounter the problem of premature convergence and fail to effectively adapt to changes in the problem. Therefore, this article focuses on identifying opportunities to enhance the flexibility and efficiency of evolutionary algorithms through integration with artificial neural networks, which allow for dynamically adjusting search parameters during the evolutionary process. The leading approach to addressing this problem is the development of a hybrid system that combines genetic algorithms with neural networks. This approach enables the neural network to adaptively regulate mutation and crossover probabilities based on the analysis of the current state of the population, preventing premature convergence and accelerating the search for the global extremum. The article presents methods for dynamic adjustment of evolutionary parameters using a neural network approach, reveals the principles of the hybrid system's operation, and provides results from testing on the Rastrigin function. The materials of the article hold practical value for further research in the field of optimization, particularly in solving problems with many local minima, where traditional methods may be ineffective. The application of the proposed hybrid model opens new perspectives for developing adaptive algorithms that can be used in various fields of science and engineering, where high accuracy and robustness to environmental changes are required.

Keywords: genetic algorithm, artificial neural network, dynamic tuning, hybrid method, global optimization, adaptive algorithm

The article explores the use of computer vision technologies to automate the process of observing animals in open spaces, with the aim of counting and identifying species. It discusses advanced methods of animal detection and recognition through the use of highly accurate neural networks. A significant challenge addressed in the study is the issue of duplicate animal counts in image data. To overcome this, two approaches are proposed: the analysis of video data sequences and the individual recognition of animals. The advantages and limitations of each method are analyzed in detail, alongside the potential benefits of combining both techniques to enhance the system's accuracy. The study also describes the process of training a neural network using a specialized dataset. Particular attention is given to the steps involved in data preparation, augmentation, and the application of neural networks like YOLO for efficient detection and classification. Testing results highlight the system's success in detecting animals, even under challenging conditions. Moreover, the article emphasizes the practical applications and potential of these technologies in monitoring animal populations and improving livestock management. It is noted that these advancements could contribute significantly to the development of similar systems in agriculture. The integration of such technologies is presented as a promising solution for tracking animal movement, assessing their health, and minimizing livestock losses across vast grazing areas.

Keywords: algorithm, computer vision, monitoring, pasture-based, livestock farming

This article will present the mlreflect package, written in Python, which is an optimized data pipeline for automated analysis of reflectometry data using machine learning. This package combines several methods of training and data processing. The predictions made by the neural network are accurate and reliable enough to serve as good starting parameters for subsequent data fitting using the least-mean-squares (LSC) method. For a large dataset consisting of 250 reflectivity curves of various thin films on silicon substrates, it was demonstrated that the analytical data pipeline with high accuracy finds the minimum of the film, which is very close to the set by the researcher using physical knowledge and carefully selected boundary conditions.

Keywords: neural network, radiography, thin films, data pipeline, machine learning

Modern automated process control systems include software complexes for monitoring, dispatching, data processing, and controlling industrial equipment. The correct operation of these systems depends on the predictable and stable deployment of software components, which requires a deterministic approach to configuration management. This paper proposes a mathematical model for software configuration management in APCS, based on difference equations of discrete systems. Numerical modeling was conducted in the Octave environment, confirming the correctness of the proposed model and allowing for the analysis of the impact of control parameters on process dynamics. The obtained results can be used to optimize strategies for automated deployment of software complexes in industrial systems.

Keywords: automated control, software complexes, discrete systems, difference equations, stability, dispatching, monitoring

This paper is devoted to the application of the Winograd method to perform the wavelet transform in the problem of image compression. The application of this method reduces the computational complexity and also increases the speed of computation due to group processing of pixels. In this paper, the minimum number of bits at which high quality of processed images is achieved as a result of performing discrete wavelet transform in fixed-point computation format is determined. The experimental results showed that for processing fragments of 2 and 3 pixels without loss of accuracy using the Winograd method it is enough to use 2 binary decimal places for calculations. To obtain a high-quality image when processing groups of 4 and 5 pixels, it is sufficient to use 4 and 7 binary decimal places, respectively. Development of hardware accelerators of the proposed method of image compression is a promising direction for further research.

Keywords: wavelet transform, Winograd method, image processing, digital filtering, convolution with step

This paper presents the results of a study aimed at developing a method for semantic segmentation of thermal images using a modified neural network algorithm that differs from the original neural network algorithm by a higher speed or processing graphic information. As part of the study, a modification of the DeepLabv3+ semantic segmentation neural network algorithm was carried out by reducing the number of parameters of the neural network model, which made it possible to increase the speed of processing graphic information by 48% – from 27 to 40 frames per second. A training method is also presented that allows to increase the accuracy of the modified neural network algorithm; the accuracy value obtained was 5% lower than the accuracy of the original neural network algorithm.

Keywords: neural network algorithms, semantic segmentation, machine learning, data augmentation