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• USING FAST PROTOTYPING FACILITIES FOR IMPLEMENTATION OF A CONVOLUTION NEURAL NETWORK ON A FPGA

  V. V. Bakhchevnikov , V. A. Derkachev , A. N. Bakumenko

  2020-10-11

  Abstract ▼

  Research in the field of artificial intelligence is carried out with increasing interest every
  year. The fields of application of artificial intelligence are quite extensive: automation, analysis of
  a large amount of data, smart home technology, machine vision, etc. Artificial intelligence technologies
  are based on the use of artificial neural networks, which are based on the principles of
  the animal nervous system. In this case, the actual issue is the implementation of artificial neural
  networks on various software and hardware platforms: programmable logic integrated circuits of
  the FPGA type (Field Programmable Gate Array), on special purpose integrated circuits (Application-
  Specific Integrated Circuit, ASIC), GPU, CPU etc. FPGA performs best in low-power mobile
  systems. ASIC demonstrates the highest performance at a fairly high development cost.
  The problem of rapid prototyping of projects based on the use of artificial neural networks for
  FPGAs using conventional methods (using HDL languages, HDL encoders, graphic programming)
  is that either such a project is complex and time-consuming to debug (HDL languages), or
  the resulting code is not optimal (HDL encoders), or the duration of the project development and
  the complexity of reconfiguring the neural network (graphical programming) are high. Therefore,
  in the framework of this work, an effective method for designing fully connected and convolutional
  neural networks for their implementation on FPGAs using the Xilinx System Generator for DSP
  and Matlab / Simulink package is considered. Artificial neural networks generated in this way are
  easily reconfigurable and allow solving the following problems: image recognition, optimal filtering
  (for example, for problems of subsurface radar).

• DEVELOPMENT OF AN UNDERWATER VEHICLE ROBOTIC SIMULATOR TO STUDY METHODS OF RESIDENT AUVS AUTONOMOUS INTERVENTION WITH UNDERWATER INFRASTRUCTURE OBJECTS

  А.М. Maevsky, I.А. Pechayko, М. А. Alekseev, N. М. Burov

  2025-04-27

  Abstract ▼

  The article presents the process of developing an underwater vehicle simulator (USV) with an installed
  5-degree underwater manipulator complex (MC). The simulator is designed for complex testing of
  autonomous interaction of a marine robotic complex (MRC) with underwater infrastructure objects. In
  particular, an example of solving the problems of simulator operation with a model of an underwater panel
  of an underwater production complex (UPC) and solving the problem of determining concretions and
  their autonomous collection using the simulator and MC are considered. Modern trends in the development
  of underwater robotics are focused on the creation of resident autonomous systems capable of operating
  in remote and hard-to-reach areas of the World Ocean all year round. The development of resident
  technologies is associated with the need to reduce operating costs, minimize risks to personnel and increase
  the autonomous functioning time of underwater complexes. The use of such technologies is especially
  relevant in the conditions of offshore shelf development, where traditional methods of operating
  underwater vehicles encounter technical and economic limitations. The need to carry out work on the distant shelf is due to the increasing demand for hydrocarbon resources and the depletion of easily accessible
  deposits on the continental shelf. According to forecasts, promising deep-water areas located at
  depths greater than 1000 m have significant potential for oil and gas production. According to experts, the
  volume of recoverable reserves in such areas can amount to hundreds of billions of barrels of hydrocarbon
  raw materials, which makes the development of effective autonomous solutions a strategically important
  task for the oil and gas industry. The paper presents software and hardware solutions used in the
  implementation of the USV. A structural diagram of the design is provided; the software architecture and
  features of the use of artificial neural network (ANN) systems as part of the technical vision system (TVS)
  of the USV are described. The use of TVS allows to significantly increasing the autonomy of underwater
  manipulators when performing complex technological operations, such as capturing objects from the
  ground, working with bottom infrastructure objects, etc. In conclusion, the obtained results are demonstrated,
  confirming the operability of the adopted design, software and hardware solutions when performing
  real work in autonomous mode with mock-ups of hot-stab and torque-tool working tools and mating
  parts located on the mock-up of the UPC panel.

• RESEARCH OF MACHINE LEARNING METHODS FOR DETECTING SPOOFING ATTACKS IN DECENTRALIZED NETWORKS

  М.А. Lapina , R.А. Dymuha , N.N. Kucherov , Е.S. Basan

  16-31

  2025-07-24

  Abstract ▼

  Unmanned aerial vehicles are appearing more and more in our lives and are used for various purposes such as cargo delivery, monitoring, household management, exploration and entertainment. But along with their growing popularity, the number of people who intentionally want to interfere with the operation of UAVs and use them for their own interests and purposes is also increasing. They use various types of attacks to eliminate or intercept the drone by any means. Spoofing attacks are one of the most common and dangerous types of attacks, as they allow attackers to act unnoticed, faking the identifiers of autonomous aircraft or operators, posing as legitimate participants in the system. The purpose of such attacks may be to intercept control, steal data, sabotage, or use UAVs to perform malicious actions such as espionage, damage, or malfunction operations. But every year it becomes more difficult to prevent attacks, as they are difficult to detect and can lead to serious consequences, which is why such a solution as detecting spoofing attacks on an unmanned vehicle using machine learning was invented. The article discusses spoofing attacks on UAVs, analyzes spoofing on autonomous aircraft, and studies machine learning methods for detecting spoofing attacks based on a dataset using the Knime platform. The results of the study demonstrate that the method of detecting attacks using machine learning based on the ensemble method, the Tree Ensemble Learner and Random Forest Learner models, which showed results of 97.110% and 97.039%, respectively, is the best among other methods, which will improve the security of unmanned aerial vehicles, reduce the burden on operators and increase the reliability of the system as a whole. In the future, the proposed approach can be expanded to detect other types of cyberattacks, which will make it a universal method of protection against intruders

• A SYSTEM FOR AUTOMATING DOCUMENT FLOW AND MONITORING ECONOMIC SECURITY INCIDENTS BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGIES

  А.Е. Anpilogova , V.А. Anpilogov

  31-41

  2025-07-24

  Abstract ▼

  Automation of document flow is a key element of process optimization and efficiency improvement. Automation of document flow based on artificial intelligence improves the management of economic security incidents by optimizing work processes and reducing costs. The transition to automated document flow in Russia is associated with a complex regulatory framework and large-scale implementation costs at  enterprises. Automation helps to comply with legal requirements and reduces the risks of legal and financial consequences. Integration of digital signatures increases the efficiency of document approval.
  The implementation of automation systems supports national digital transformation goals. Automation of document flow reduces dependence on paper processes and facilitates the creation of centralized digital repositories. The implementation of document automation systems requires a strategic approach and careful planning. Document automation provides time savings, reduced errors and increased compliance with regulatory standards. The article discusses the theoretical foundations of BPM, integration of digital technologies and regulatory aspects specific to Russia. The proposed system combines monitoring with AI and IoT, provides real-time data processing, automates the creation of legal documents and reports. The workflow automation system is based on data integration, artificial intelligence technologies and seamless solutions. The system combines monitoring technologies, facial recognition and behavior analysis algorithms, a centralized database and a communication module. The system generates reports and legal documents certified by QES and ensures interaction with law enforcement agencies and security services. Implementation results: a 30–40% reduction in operating costs and a 50% reduction in losses. The system complies with digital transformation standards and supports the modernization of the national economy.

• MULTI-AGENT SYSTEM USING ARTIFICIAL INTELLIGENCE TO PROCESS IMAGES FROM THE DRONE'S TECHNICAL VISION CAMERAS

  А. L. Verevkin , I.E. Josephs , V.V. Misyura , L.S. Verevkina

  198-212

  2025-07-24

  Abstract ▼

  Multi-agent technology with drones, modern sensors, precise GPS and artificial intelligence, have led to a breakthrough in the field of cyber-physical systems. This article presents a multi-agent system using artificial intelligence to process images from technical vision cameras installed on a drone. A block diagram of a multi-agent system on a drone was developed based on an effective and simple platform taken from the ARRISE 410 octocopter – an agricultural sprayer drone with: intelligent control system; omnidirectional digital microwave radar; 6-axis high-precision accelerometer; electronic level for measuring tilt; real-time optical camera 1 with a first-person view; control panel equipped with the latest Light Bridge 2 signal transmission system; remote control has a design protected from dust and water. The kit must be supplemented with: hyperspectral HS - camera for scanning, its power module and the ability to interface with the ARRISE 410 drone systems, an information compression module. Model for studying the throughput on the DJI Agras T20 hexacopter DJI Agras T20, MikrotikRB411 5G network card, Raspberry Pi 3 microcomputer, 1 Mpix RGB camera, built-in on-board computer Raspberry Pi OV5647 v1.3 and hyperspectral HS - camera 2 Resonon Pika L shoots hyperspectral data with 281 spectral bands with spectral wavelengths from 400 to 1000 nm and a spatial resolution of 900 hyperspectral pixels per image line. The article solves the problem of experimentally and computationally determining the required compression of information obtained from hyperspectral and optical range cameras with transmission through a telecom operator and the Internet for image processing by an artificial Internet

• MACHINE LEARNING AND DEEP LEARNING MODELS FOR ELECTRONIC INFORMATION SECURITY IN MOBILE NETWORKS

  Aussi Rim Mohammed Hedhair, E.V. Zargaryan, Y.A. Zargaryan

  2022-08-09

  Abstract ▼

  Recent advances in wireless communication technologies have led to the creation of a huge
  amount of data that is transmitted everywhere. Most of this information is part of an extensive and
  publicly accessible network that connects various stationary and mobile devices around the world.
  The capabilities of electronic devices are also increasing day by day, which leads to more data
  generation and information exchange through networks. Similarly, with the increasing diversity
  and complexity of mobile network structures, the frequency of security breaches in it has increased.
  This hinders the introduction of intelligent mobile applications and services, as evidenced
  by the wide variety of platforms that provide data storage, data computing and application services to end users. In such scenarios, it becomes necessary to protect data and check their use in
  the network and applications, as well as check their incorrect use in order to protect private information.
  According to this study, a security model based on artificial intelligence should ensure
  the confidentiality, integrity and reliability of the system, its equipment and protocols that control
  the network, regardless of its creation, in order to manage such a complex network as a mobile
  one. The open difficulties that mobile networks still face, such as unauthorized network scanning,
  fraudulent links, etc., have been thoroughly studied in this article. This article also discusses several
  ML and DL technologies that can be used to create a secure environment, as well as many
  cybersecurity threats. It is necessary to address the need to develop new approaches to ensure a
  high level of electronic data security in mobile networks, since the possibilities for improving the
  security of mobile networks are limitless.

• ANALYSIS OF REQUIREMENTS AND DEVELOPMENT OF ALGORITHMS FOR INTELLIGENT MONITORING SERVICES

  М.S. Anferova, А.М. Belevtsev

  2022-08-09

  Abstract ▼

  The paper considers the problems of strategic analysis and the choice of directions for the development
  of innovative enterprises in the conditions of transition to the 6th technological order and industry
  4.0. The main levels of analysis are determined. The objectives of the strategic analysis are outlined
  based on the scale of the research being conducted. The analysis tasks are highlighted, the solution of
  which will allow achieving the set goals. The complexity of solving global monitoring tasks, which are
  caused by a large volume of heterogeneous and unstructured information, is shown. In these conditions,
  thematic search and analytical processing of information cannot be performed without the use of automated information and analytical systems and the creation of search services based on artificial intelligence.
  A general monitoring procedure is proposed. The main stages of monitoring technological trends
  are defined, the tasks to be solved within a specific stage and the planned result are shown. Based on the
  general monitoring procedure, the main priority functions that the developed services should have are
  determined. As well as the problems of their development and structuring of the received information in
  the form of information objects and clustering of documents. In contrast to the well-known global monitoring
  systems, in which the search is based on indicators: an increase in the use of keywords, an increase
  in the number of new authors, quoting works from related fields. Algorithms are proposed that
  provide the definition of reference topics, assessment of ranking and relevance of information. The description
  of the algorithms is given on the example of creating a summary information table, with the
  help of which the interrelationships of documents of scientific and technological development in each
  direction of monitoring and the search for specific documents in the database are formed. The construction
  of search services based on the presented algorithms will ensure the allocation of reference topics
  of documents, provide more reliable results of clustering of unstructured information and the formation
  of scientific and technological trends in information and analytical complexes. To implement the algorithm,
  it is proposed to use the Python programming language. The implementation of these algorithms
  will improve the quality and efficiency of information retrieval in conditions of a large volume of unstructured
  information.

• ANALYSIS OF ARTIFICIAL INTELLIGENCE METHODS APPLIED TO SOLVING PSYCHIATRY PROBLEMS

  E.S. Podoplelova

  2022-05-26

  Abstract ▼

  The use of artificial intelligence methods in the field of medicine has become widespread,
  helping to diagnose, analyze and make recommendations for treatment. Psychiatry is a branch of
  medicine that studies mental disorders, methods for their diagnosis and treatment. Her range of
  tasks includes not only diagnosis and treatment, but also observation, monitoring and subsequent
  rehabilitation of patients. This subject area has significant problems, such as objectivity, inconsistency
  in the diagnosis, the complexity of the classification of diseases, and the unpredictability
  of the course of the disease. With a number of these problems, the use of machine learning methods
  and artificial intelligence algorithms helps to cope. This paper is devoted to a review of research
  on artificial intelligence methods used to solve problems in the field of psychiatry.
  The relevance of the topic is due to the high need for improvements in this subject area. Specific
  issues are presented in this article. Among them, the main directions were identified: data deidentification,
  classification of symptom severity, accuracy of condition prediction. To solve them,
  the authors used such methods as latent semantic analysis for natural language processing, classification
  methods, convolutional neural networks for prediction, and cognitive modeling. Separately,
  the effectiveness of hybrid systems, including the implementation of several machine learning
  methods at once, is noted. The aim of the study was to highlight the main directions of development
  of research in the scientific community, which demonstrate the successful integration of artificial intelligence into psychiatry, as well as to compare them with each other according to the
  obtained estimates of the accuracy of the models. Which, in turn, implies the analysis and analysis
  of specific algorithms, their performance for specific tasks

• INTELLIGENT SUBSYSTEM FOR DECISION SUPPORT BASED ON BIOLOGICALLY PLAUSIBLE ALGORITHMS FOR SELF-ORGANIZATION

  E.V. Kuliev , M.P. Krivenko, М.М. Semenova, S. V. Ignatieva

  2021-11-14

  Abstract ▼

  The article discusses the basic concepts and definitions of decision support systems based
  on self-organization. Decision Support Systems refers to a range of interactive computer systems
  that help to use data, models, and knowledge to solve semi-structured, unstructured, or unstructured
  problems. The diagram of the basic structure of the decision support system is shown and
  described. Three main components of Decision Support Systems are considered, and a case is
  described when the fourth component of a decision support system - a knowledge-based management
  system - can be applied. The article offers a description of an intelligent decision support
  system. Examples of specialized intelligent decision support systems include intelligent marketing
  decision support systems and medical diagnostics systems, flexible manufacturing systems. The
  problems associated with making optimal decisions occupy an important place in computer-aided
  design and require improving methods and means of supporting optimal design processes at various
  stages. Self-organization algorithms inspired by wildlife are considered. Bioinspired algorithms
  are a representative class of self-organization algorithms. Bio-inspired computing mimics
  nature and uses the underlying concepts and behavior of these systems to solve complex problems.
  The article describes the algorithm for bats. An experimental analysis of the process of applying
  the self-organization algorithm in decision-making systems is carried out.

• DEVELOPMENT OF ALGORITHMS OF INTELLIGENT SERVICE FOR INFORMATION SEARCH AND MONITORING

  M. S. Anferova, A. M. Belevtsev

  2021-08-11

  Abstract ▼

  This paper describes the problem of strategic analysis and the choice of directions for the development
  of an innovative enterprise in the conditions of transition to the 6th technological order and
  industry 4.0. In these conditions, search and analytical processing of information cannot be fully performed
  without the use of automated information and analytical systems, including those based on artificial
  intelligence. During the analysis, the main priority functions that the developed services should
  provide were identified. The main difficulties in the development of these services are identified, such as:
  pre-processing of data and automated checking of the relevance of databases. To effectively solve thetasks set, the intelligent monitoring and information retrieval service should use an integrated approach,
  taking into account the effectiveness of applying methods for individual subtasks, and ensure high efficiency
  of implementing all stages of the intelligent monitoring procedure. In this regard, this paper describes
  not only the development of a general intelligent search algorithm, but also individual block
  algorithms necessary to ensure the priority functions of the service being developed. The paper presents
  the following algorithms: an information search algorithm necessary to solve the problem of full-text
  search of documents within the database of information resources of the information and analytical
  complex; an algorithm for the procedure for entering new documents; an algorithm for pre-processing
  data that includes stemming and removing punctuation marks for subsequent text analysis; an algorithm
  for evaluating the ranking and relevance of information, including vectorization of documents; an algorithm
  for clustering information search results based on the Kohonen neural network; the algorithm for
  checking the relevance of information is to check whether the local copy of the document corresponds to
  the current version on the source's web resource. The Python programming language for the implementation
  of the presented algorithm is proposed and justified. The system provides automated continuous
  monitoring with a high frequency of sending a request without the participation of an operator, which
  will increase the quality and efficiency of information search in conditions of a large volume of unstructured
  information.

• WIRELESS SENSOR NETWORKS IN PROTECTED AREAS

  G.P. Vinogradov, A.S. Emtsev, I.S. Fedotov

  2021-04-04

  Abstract ▼

  For military purposes, wireless sensor networks allow you to "link autonomous systems" into a
  complex that has the property of self-organization, when objects "know" how to find each other and
  form a network, and in the event of a failure of any of the nodes can establish new routes for transmitting
  messages. It is possible to achieve the desired efficiency of such complexes, mainly by improving
  the intellectual component of their control system in general and individual node in particular.
  However, it should be noted that the vast majority of research in this area remains at the theoretical
  level. The goal is to: 1) the study and development of algorithms for network design with mobile
  nodes and their possible failures due to the combat mission; 2) the study and development of site use
  sensor network to collect, analyze, and transmit data about the situation and decision-making in the
  area of responsibility; 3) to offer relatively simple algorithms for giving the network node the property of intelligent behavior under the conditions of restrictions on power consumption and speed.
  It is shown that the required algorithms can be developed if the classes of typical situations and
  successful methods of action in real conditions are identified. On this basis, it becomes possible to
  develop formal models (patterns) for implementation in the node management system. A two-level
  structure of an intelligent network management system is proposed. The upper level, implemented
  by the operator, corresponds to such properties as survival, security, fulfillment of mission obligations,
  accumulation and adjustment of the knowledge base in the form of effective behavior patterns.
  The object of control for it is the network, considered as a functional system.

• TRANSPORT FLOW FORECASTING MODEL BASED ON NEURAL NETWORKS FOR TRAFFIC PREDICTION ON ROADS

  Alamir Haider Sagban Hussein, Е.V. Zargaryan, Y. А. Zargaryan

  124-132

  2021-08-11

  Abstract ▼

  In connection with the industrialization of modern society, the growth of the transport sys-tems of our country, an increase in certain necessary for the development of the needs of the citi-zens of our country, the number of vehicles of various types continues to increase every year very fast, causing huge traffic jams on transport roads, especially in large cities and megacities. Thus, forecasting traffic flows is an important and necessary component of optimal traffic control in modern conditions of transport network development. As a solution to this problem, this article aims to analyze and describe the application of artificial intelligence methods, in particular neural networks, which represent a modern approach to modeling in complex and nonlinear situations that arise when predicting a traffic flow model. The shown accuracy method is based on the devel-opment of a neural network to predict the daily traffic flow. The expected traffic flow is then com-pared with the actual dataset recorded on the road section and provided by the infrastructure manager. In fact, neural networks are able to learn from past situations and predict future situa-tions on the transport network. In this study, various neural network structures were examined,and the simulation results showed that the best predictions were obtained using the multilayer perceptron architecture, which has a good generalization system with a root mean square error of 0.00927 with the current set of vehicles. The first part of the article is devoted to defining various concepts related to the current research area, including a review of the literature on traffic predic-tion and neural networks. The second part is devoted to describing the problem of traffic conges-tion using forecasting problems and presenting the proposed solution method with an emphasis on artificial neural networks as a means of forecasting demand and its various structures. Then, nu-merical experiments are illustrated by analyzing the forecast results after the formation and test-ing of various neural network architectures.

• DEVELOPMENT OF AN INTELLIGENT ROBOTIC HARVESTING SYSTEM

  Z.V. Nagoev, О.Z. Zagazezheva, К.C. Brzhikhatlov, I.А. Mambetov

  2025-04-27

  Abstract ▼

  In the context of the need to ensure food security, the tasks of optimizing production processes in the
  agricultural sector are becoming relevant. For example, given the shortage of labor in agriculture, it is
  necessary to develop and implement robotic systems to automate the processes of plant care, harvesting
  and processing. The article presents the results of the development of an autonomous robot for picking
  apples, created on the basis of a universal anthropomorphic robot developed at the Kabardino-BalkarianScientific Center of the Russian Academy of Sciences. The robot is equipped with two multi-link manipulators
  similar to human hands, which allows it to perform complex harvesting tasks. To ensure intelligent
  control of the entire system, a multi-agent neurocognitive architecture is used, which imitates the work of
  the human brain and allows the robot to adapt to changing environmental conditions. The robot is
  equipped with a set of sensors, including video cameras, ultrasonic and infrared rangefinders, lidar and
  encoders on the manipulator drives. This allows it to accurately determine the location of apples, assess
  their ripeness and plan the trajectory of the manipulators. Particular attention is paid to the development
  of a gripper that imitates a human hand and allows you to adjust the squeezing force, which minimizes the
  risk of damage to the fruit. A multi-agent neurocognitive architecture is used to control the robot, which
  provides autonomous decision-making based on sensor data. The system is able to build a map of the area,
  determine the position of the robot and plan a route, as well as recognize apples and assess their condition.
  The article also considers the problems associated with the automation of harvesting in agriculture,
  including a lack of labor and crop losses due to improper operation of equipment. The authors emphasize
  that automation and robotization of harvesting processes have great potential, especially for crops
  that require an individual approach, such as fruits and vegetables. The presented robot demonstrates high
  efficiency in solving these problems, which is confirmed by the results of field tests. The developed system
  can be adapted to work with other crops, which makes it a universal solution for the agricultural industry

• MODEL OF COOPERATIVE TRANSPORTATION TASK ALLOCATION FOR HETEROGENEOUS ROBOTIC SYSTEMS

  S. Gong

  220-232

  2026-07-07

  Abstract ▼

  The development of intelligent warehouse systems and the automation of logistics processes require effective solutions for task allocation in heterogeneous multi-robot complexes, particularly in the cooperative transportation of large and heavy cargo. The aim of this work is to develop and verify a hybrid model for cooperative transportation task (CTT) identification and allocation in a warehouse environment, taking into account multi-criteria optimization. A brief review of publications on the application of mivar technologies and machine learning methods in the mathematical modeling of complex robotic systems is provided. A two-level approach is proposed, including a mivar decision-making system for the automatic identification of CTT and a task allocation model based on a combined auction algorithm. The required number of transport robots (RT) is determined by the mivar decision-making system, considering the size and mass of the cargo. The developed mathematical model for CTT allocation aims to improve efficiency and reliability by accounting for key dynamic factors (heterogeneity, redundancy, and path-dependent costs). Simulation experiments with 30 transport robots and 100 tasks demonstrated the superiority of the proposed method over baseline strategies (Random, Nearest Neighbor, Greedy Capacity): when processing 6 CTT, the total cost reduction reached up to 40.7%, and with 12 tasks, an additional reduction of 10.8% was achieved while maintaining 100% success rate. The model’s ability to scale efficiently was established, manifesting in an additional cost reduction of 10.8% as the number of tasks increased. The results indicate the robustness, adaptability, and high practical applicability of the model for integration into modern intelligent warehouse systems that handle a diversified range of cargo

• FORMATION OF PARAMETERS OF INFORMATION SOURCES FOR NEURO-LINGUISTIC TEXT IDENTIFICATION

  К.Y. Rumyantsev , V. V. Kotenko , L.К. Khadzhieva

  173-188

  2026-07-07

  Abstract ▼

  This paper explores a method of neurolinguistic text identification aimed at analyzing and verifying information sources, including texts generated by artificial intelligence systems. Three versions of the information states of the text of Luo Guanzhong's Romance of the Three Kingdoms are analyzed: the original text and the text generated by the Gemini and GPT artificial intelligence systems. The study aims to formulate and substantiate parameters for use as identification factors in the generated text, as well as to create 3D images of neurolinguistic textual identification of information sources. The specialized software package "Neurolinguistic Text Identification Analyzer" is used, processing text data based on horizontal and vertical scanning of neurolinguistic information frames. As a result, information spectra, quantitative characteristics (information capacity, entropy, redundancy), and 3D neurolinguistic information images of neurolinguistic frames of textual information of the Chinese work are formed. A comparison of the identity levels of neurolinguistic 3D informational images of the textual information source and neurolinguistic information frames shows that the highest level of identity is observed when comparing the texts of neurolinguistic information frames with the original text, while the lowest level of identity is observed when comparing the original text with the text generated using neural networks. The obtained results demonstrate significant differences between the parameters of the neurolinguistic information frames of the original text and the parameters of the text generated by neural networks, both in terms of quantitative text characteristics and the characteristics of the neurolinguistic 3D informational images. It was found that the neurolinguistic 3D informational images of texts generated by neural networks have a smoother visual representation structure and an excellent color distribution compared to the neurolinguistic 3D images of the original text. The practical significance of this study lies in the application of an approach that allows for the identification of generated text and the verification of information sources. The obtained results open up prospects for further work and the possibility of creating programs capable of detecting the presence of text generation

• MULTI-AGENT ARCHITECTURE OF AN ENVIRONMENT REPRESENTATION SYSTEM FOR AN AUTONOMOUS AGRICULTURAL ROBOT

  К.C. Bzhikhatlov , I. А. Pshenokova

  2026-04-29

  Abstract ▼

  The relevance of this research stems from the need to create effective control systems for autonomous robots capable of operating in uncertain and dynamically changing environments. An environment representation system must address the challenges of localization, mapping, object detection and classification, dynamic prediction, and semantic interpretation. For an autonomous robot to navigate and perform goal-directed actions in its environment, it must understand its environment—without this, it will be unable to effectively plan movements, avoid obstacles, and reach destinations. Existing environment representation methods have limitations when adapting to unfamiliar, unstructured environments. The aim of this study is to develop the concept and architecture of an environment representation system based on a multi-agent neurocognitive architecture for controlling autonomous robots within a heterogeneous human-machine team. The scientific novelty of this study lies in the development of a multi-agent neurocognitive architecture for representing the state of the environment. The proposed approach enables the creation of flexible world models capable of self-organization and scalability with increasing knowledge. The research methodology is based on the use of multi-agent technologies and neurocognitive models. A multi-agent neurocognitive architecture has been developed, including mechanisms for collecting data from sensors, representing objects and subjects in the environment, forming a mechanism for constructing cause-and-effect relationships, and sharing knowledge between members of a heterogeneous human-machine team. The developed architecture enables the scalability of decision-making systems and facilitates knowledge sharing between team members. A promising direction for further research is improving the system's safety mechanisms. The results of this study can be used in the development of next-generation autonomous robotic systems.

• A UNIVERSAL MODEL OF ADAPTIVE MANAGEMENT OF CLOSED AGRICULTURAL PRODUCTION USING AI TECHNOLOGIES

  А.А. Kochkarov , А.К. Kulikov , V.М. Matsakova

  2026-04-29

  Abstract ▼

  The relevance of this research stems from the contradiction between the need to increase food production in an urbanized environment and the fragmentation of existing high-tech solutions (hydroponics, aeroponics, IoT), which are being implemented in isolation, without a unified management methodology. The lack of unified approaches to data collection and adaptive control of environmental parameters limits the scalability of vertical farms. The goal of this research is to develop and theoretically substantiate the architecture of a universal adaptive management model for closed-loop agricultural production systems, integrating various cultivation methods based on machine learning algorithms. The methodology is based on a systematic analysis of scientific publications and experimental data on the use of embedded devices and machine learning algorithms in hydroponic, aeroponic, and soil-based vertical greenhouses. Based on this data synthesis, parametric matrices were constructed to standardize technological processes. The main results include the development of a structural diagram of a universal model that enables the integration of disparate systems into a single platform with the ability to continuously monitor and perform predictive analytics. The minimum required sensor set is substantiated: pH, EC, temperature, humidity, CO₂, PAR, pressure, and nutrient solution flow. The proposed architecture enables dynamic switching between hydroponic, aeroponic, and indoor modes within a single phytotron. The conclusions and significance of this work lie in creating a foundation for designing scalable vertical farms with predictable profitability and resource efficiency indicators, while enabling continuous further training of AI algorithms for predictive microclimate management and early plant disease detection in urban environments

• NEURAL NETWORK TECHNOLOGIES IN THE TASKS OF MONITORING THERMOFLUCTUATION PROCESSES OF A CABLE LINE TAKING INTO ACCOUNT THE INFLUENCE OF INTERFERENCE

  N.K. Poluyanovich, M.N. Dubyago

  2021-02-13

  Abstract ▼

  The article is devoted to the assessment of the influence of magnetic interference, in the
  study of thermal fluctuation processes in the dynamic current load mode of a power cable line
  (SCL). On the basis of such artificial intelligence methods as neural networks and fuzzy logic, the
  thermal resistance of SCL insulating materials determining the throughput of the cable line of
  electric power systems was investigated. A comparative review of the currently existing traditional
  non-destructive methods for predicting thermal processes in SCR showed that most of the methods
  have a low prediction accuracy, as well as have a high degree of complexity and a large number
  of necessary computational operations to obtain the necessary data for predicting thermal processes
  in SCR. Also, most forecasting methods are not able to work in real time, which is an extremely
  significant drawback. To solve this problem, it is necessary to resort to forecasting systems
  that are based on artificial intelligence using machine learning methods. The method of artificial neural networks (ANN) seems to be the most promising today. The need to develop a more
  perfect method for analyzing the aging of SCR insulating materials is shown. The urgency of the
  problem of creating neural networks (NN) for assessing the throughput, calculating and predicting
  the temperature of SCL cores in real time based on the data of the temperature monitoring system,
  taking into account the change in the current load of the line and the external conditions of heat
  removal, has been substantiated. A neural network has been developed to determine the temperature
  regime of the current-carrying conductor of a power cable. A comparative analysis of the
  experimental and calculated characteristics of temperature distributions was carried out, while
  various load operating modes and functions of changing the cable current were investigated. A
  neural network model was developed in Matlab Simulink for predicting the temperature of a cable
  core. The creation, training and modeling of the neural network was carried out using the Neural
  Network Toolbox. The model can be used in devices and systems for continuous diagnostics of
  power cables by temperature conditions.

• INVESTIGATION OF SYNAPTIC PLASTICITY IN MEMRISTIVE CROSS-POINT STRUCTURES FOR NEUROMORPHIC ROBOTIC SYSTEMS

  R.V. Tominov , Z. Е. Vakulov , V.I. Varganov , I.О. Ignatieva , V. А. Smirnov

  200-207

  2025-12-30

  Abstract ▼

  The results show multilevel resistive switching and synaptic plasticity of a memristive cross-point based on a nanocrystalline zinc oxide film. It is shown that with a decrease in the amplitude and duration of input pulses, the memristive cross-point demonstrates resistive states from 4.27 × 10⁵ Ohm to 8.34 × 10⁷ Ohm. It is shown that the switching energy of some synaptic levels is picojoules, which is promising for creating compact low-power neuromorphic systems. Thus, it is shown that nanocrystalline ZnO films have synaptic plasticity, i.e. When applying voltage pulses, large limits and duration can vary depending on the synaptic levels.
  The fabricated memristive cross-point demonstrates paired-pulse facilitation PPF at t_p from 1 ms to 10 ms and pair-pulse depression PPD at t_p from 50 ms to 100 ms. The analysis of the experimental results of the PPF and PPD study showed that an increase in the number of pulses from 10 to 90 leads to an increase in postsynaptic current EPSC from 32 μA to 73 μA for t_p = 1 ms, from 31 μA to 59 μA for t_p = 5 ms, from 31 μA to 48 μA for
  t_p = 10 ms, and a decrease in EPSC from 30 μA to 25 μA for t_p = 50 ms, from 30 μA to 17 μA for t_p = 70 ms, from 30 μA to 5 μA for t_p = 100 ms. From the obtained results it follows that the interval between pulses, the higher the PPF index, thus it can be concluded that the manufactured memristive cross-point based on ZnO nanocrystalline films imitates the crucial plasticity of the biological synapse, in which the plasticity of PPF and PPD is determined by the concentration of Ca+ ions and which plays a role in many biological functions of the brain, such as determining the key source of sound, pattern recognition, associative learning, filtering unnecessary. information. The obtained results can be used for hardware implementation of neural networks, neuromorphic structures of robotic complexes, prostheses and artificial intelligence systems

• DEVELOPMENT OF AN AUTONOMOUS ROBOT TO PERFORM THE FUNCTIONS OF A SALES - CONSULTANT IN RETAIL NETWORKS

  М.А. Khapova , К. C. Bzhikhatlov , L.B. Kokova

  262-272

  2025-10-01

  Abstract ▼

  The active increase in the share of large chain stores in the retail sector increases the demand for employees of such networks. At the same time, with the growth of the turnover of large stores, the requirements for timely display of goods on the shelves also grow. According to the estimates of the retailers themselves, losses from incorrect or untimely display of goods can reach 5% of the total annual turnover. Given the significant turnover of large chain retailers and noticeable staff turnover, the problem of automation of product display in chain stores can be considered relevant. This paper presents the results of the development of an autonomous robotic system that can ensure uninterrupted control of filling of shelves and timely display of goods. Based on the results of a survey of representatives of large retail chains, the requirements for an autonomous system for monitoring and placing goods in a store are determined.
  In particular, the requirements for the capabilities of an intelligent robot control system, design features and hardware implementation of robots, requirements for the capabilities of the system of interaction with employees and customers in the store and preferences for the appearance and user interface of the robot are determined. Based on the identified requirements of retailers, a prototype of an autonomous robot for work in sales areas has been developed. The basis of the robot is a transport module with two motor wheels and a pair of steering wheels, on which an anthropomorphic unit with two manipulators is installed. The manipulators are made in the form of human hands and have a full set of necessary degrees of freedom. In addition, the article presents the architecture of the autonomous robot control system.
  The robot is controlled by an intelligent decision-making and control system based on a multi-agent neurocognitive architecture that simulates the processes occurring in the human brain. The design and mechatronic part of the robot were tested in real conditions: in the sales areas of a retail store in Nalchik in the presence of sales consultants and customers. In the future, work is planned to refine and train the intelligent decision-making system.

• RESEARCH OF MACHINE LEARNING METHODS FOR DETECTING FRAUDULENT WEBSITES

  М.А. Lapina , D. А. Lukyanov , V.G. Lapin , N.N. Kucherov

  250-262

  2025-10-01

  Abstract ▼

  Every year our lives become more and more connected with large volumes of data that need to be analyzed. As the volume of information increases, its analysis becomes a more voluminous and complex task.
  In this situation, the problem of finding a tool that will help companies and institutions in collecting, analyzing and forecasting data arises. Machine learning is an area of artificial intelligence that finds patterns in a database and, based on them, tries to predict the result. Another area of application of machine learning is the detection of fraudulent sites. Currently, with the development of information technology, digital crimes have become a serious threat to confidential information and user data. Artificial intelligence is able to analyze site parameters and determine the presence of threats to information. The study is aimed at systematizing knowledge about phishing attacks and studying machine learning methods for detecting fraudulent sites. During the study, machine learning methods for detecting phishing sites were developed, schemes were built that allow machine learning models to correctly transform data for feeding them to models. The analysis of the data provided in the dataset made it possible to correctly transform the data for the correct operation of the models, which will avoid errors. The problem of retraining machine learning models was solved. A detailed study of the dataset made it possible to filter out data that could cause errors in the model and reduce the quality of artificial learning forecasting. As a result of the work, the developed methods for searching for phishing attacks using machine learning models were tested on test data, based on the results obtained, graphs of changes in the accuracy of detecting illegitimate sites from changing the model settings were constructed. An analysis of the study was carried out and the results of the work were summarized.

• ANALYSIS AND SELECTION OF METHODOLOGIES IN THE SOLUTION OF THE PROBLEMS OF INTELLECTUALIZATION IN SYSTEMS FOR PROGNOSIS OF THERMOFLUCTUATION PROCESSES IN CABLE NETWORKS

  N.K. Poluyanovich, M.N. Dubyago

  2020-07-20

  Abstract ▼

  The article is devoted to research on the creation of diagnostics and prediction of
  thermofluctuation processes of insulating materials of power cable lines (PCL) of electric power
  systems based on such methods of artificial intelligence as neural networks and fuzzy logic. The
  necessity of developing a better methodology for the analysis of thermal conditions in PCL is
  shown. The urgency of the task of creating neural networks (NS) for assessing the throughput,
  calculating and predicting the temperature of PCL conductors in real time based on the data of
  the temperature monitoring system, taking into account changes in the current load of the line and
  the external conditions of the heat sink, is substantiated. Based on the main criteria, traditional
  and neural network algorithms for forecasting are compared, and the advantage of NS methods is
  shown. The classification of NS methods and models for predicting the temperature conditions of
  cosmic rays has been carried out. To solve the problem of forecasting the PCL resource, a network
  was selected with direct data distribution and back propagation of the error, because networks of this type, together with an activation function in the form of a hyperbolic tangent, are to
  some extent a universal structure for many problems of approximation, approximation, and forecasting.
  A neural network has been developed to determine the temperature regime of a currentcarrying
  core of a power cable. A comparative analysis of the experimental and calculated characteristics
  of the temperature distributions was carried out, while various load modes and the
  functions of changing the cable current were investigated. When analyzing the data, it was determined
  that the maximum deviation of the data received from the neural network from the data of
  the training sample was less than 2.5 %, which is an acceptable result. To increase the accuracy, a
  large amount of input and output data was used when training the network, as well as some refinement
  of its structure. The model allows you to evaluate the current state of isolation and predict
  the residual life of PCL. The model can be used in devices and systems for continuous diagnosis
  of power cables by temperature conditions.

• MULTI-STAGE METHOD FOR SHORT-TERM FORECASTING OF TEMPERATURE CHANGES MODES IN THE POWER CABLE

  N.K. Poluyanovich, N.V. Azarov, A.V. Ogrenichev, M.N. Dubyago

  2020-07-20

  Abstract ▼

  The article is devoted to research on the creation of diagnostics and prediction of
  thermofluctuation processes of insulating materials of power cable lines (PCL) of electric power
  systems based on such methods of artificial intelligence as neural networks and fuzzy logic. The
  necessity of developing a better methodology for the analysis of thermal conditions in PCL is
  shown. The urgency of the task of creating neural networks (NS) for assessing the throughput,
  calculating and predicting the temperature of PCL conductors in real time based on the data of
  the temperature monitoring system, taking into account changes in the current load of the line and
  the external conditions of the heat sink, is substantiated. Based on the main criteria, traditional
  and neural network algorithms for forecasting are compared, and the advantage of NS methods is
  shown. The classification of NS methods and models for predicting the temperature conditions of
  cosmic rays has been carried out. The proposed neural network algorithm for predicting the characteristics
  of electrical isolation was tested on a control sample of experimental data on which
  training of an artificial neural network was not carried out. The forecast results showed the effectiveness
  of the selected model. To solve the problem of PCL resource prediction, a network was
  selected with direct data distribution and back propagation of the error, because Networks of thistype, together with the activation function in the form of a hyperbolic tangent, are to some extent a
  universal structure for many problems of approximation, approximation, and forecasting. A neural
  network was developed to determine the temperature regime of a current-carrying core of a power
  cable. A comparative analysis of the experimental and calculated characteristics of the temperature
  distributions was carried out, while various load modes and the functions of changing the
  cable current were investigated. When analyzing the data, it was determined that the maximum
  deviation of the data received from the neural network from the data of the training sample was
  less than 2.2 %, which is an acceptable result. The model can be used in devices and systems for
  continuous diagnosis of power cables by temperature conditions.

• A REVIEW OF METHODS FOR IMPROVING REASONING IN LARGE LANGUAGE MODELS

  V.B. Savinov , N.N. Shusharina

  2026-02-27

  Abstract ▼

  The emergence of large language models has become an important milestone in the field of natural language processing, as such models demonstrate impressive results in text generation, transformation, and analysis, as well as in solving a wide range of applied tasks. However, despite significant practical success, large language models possess limited reasoning capabilities. These limitations manifest in difficulties with generalizing knowledge beyond the training distribution, challenges in transferring knowledge to new contexts, and reduced accuracy when performing multi-step logical and mathematical operations. The goal of this work is to examine methods for improving the reasoning abilities of large language models, where reasoning is understood as the process of forming and evaluating inferences based on existing information. The paper discusses the main types of reasoning relevant to large language models: mathematical, logical, and commonsense reasoning. It provides a list of the most commonly used benchmarks applied to assess the reasoning quality of language models. An overview is presented of the methods used to enhance reasoning in large language models at 2025. Depending on the stage of application (during training or during model usage), the work examines approaches to training data preparation, architectural modifications of language models, training and finetuning procedures (including those using specially constructed synthetic datasets), reinforcement learning, various chain-of-thought construction techniques, mechanisms for integrating external tools, and multi-agent approaches. The paper also discusses existing limitations of large language models, which include the lack of conceptual understanding, poor out-of-distribution generalization, and reduced effectiveness as task complexity increases. Finally, the most promising methods aimed at improving the quality and reliability of reasoning in large language models are highlighted.