Search

Search Results

• SIMULATION OF A HYBRID CONTROLLER FOR CONTROLLING PLASMA WELDING PARAMETERS

  Al-Shamki Amir Abdulkadim Ouda, V.V. Shadrina, V.G. Galalu

  2022-08-09

  Abstract ▼

  One of the most common technological operations is welding of individual parts and blocks.
  Welding is widely used in shipbuilding, aviation, defense and chemical industries, in the construction
  of oil and gas pipelines. At the same time, very strict requirements are imposed on the quality of the
  weld in terms of strength, absence of voids and cavities, operability at high pressures (up to 100 kGf /
  cm2) and in a wide temperature range (± 50 ° C). Plasma (argon) welding meets these requirements
  most fully. A brief analytical review on the research topic was carried out. It is shown that a promising
  direction for the development of plasma welding control systems is the use of hybrid regulators
  created on the basis of classical automatic control methods and fuzzy control, formalizing the average
  knowledge of experts. The fuzzy component (expert knowledge) should be available for quick and
  easy input into the controller. A block diagram and a model of a single channel of a hybrid controller
  was developed in the Matlab Simulink environment. The current control channel was modeled using
  a fuzzy controller from the Fuzzy Logic library, using the Mamdani fuzzy output algorithm. 19 variants
  of linguistic and fuzzy variables were set, the surface of the variable membership function was
  obtained. It should be noted that it is possible to quickly enter linguistic assessments of experts into
  the memory of the hybrid controller. The behavior of hybrid controller models and standard PI and
  PID controllers under a single step action was analyzed. The hybrid regulator provides significantly
  better quality indicators (2.5-3 times) than standard regulators. The hybrid controller enters the
  steady-state mode after 6s, the PID controller – after 13s, the PI controller - after 15s, and the standard
  regulators have an overshoot (first emission) of up to 50%. Thus, the real possibility of constructing
  a fuzzy hybrid controller with specified characteristics is shown. It is possible to implement a
  hybrid controller in the form of an FPGA.

• 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

• ALGORITHMS OF ELECTRIC NETWORK CONTROL OF A HYBRID POWER SUPPLY SYSTEM OF AUV

  N.K. Kiselev, L.A. Martynova

  2022-03-02

  Abstract ▼

  The aim of the research was to control the electrical network of a hybrid power supply system
  for an autonomous underwater vehicle designed to travel over ultra-long distances over tens
  of thousands of kilometers. To overcome ultra-long distances, the urgent task is to minimize the
  specific consumption of electricity, provided that all consumers are provided with electricity. The
  relevance of the work is determined by the novelty of using a hybrid power supply system in autonomous
  unmanned underwater vehicles, consisting of heterogeneous sources of electricity operating
  on different physical principles. Due to the lack of research to date, related to the control of
  the hybrid power supply system, coordinated with the modes of motion of the vehicle in a wide range of speeds, the problem arose of developing control algorithms for the hybrid power supply
  system. To solve the problem, the reasons for the change in current consumption during the
  movement of the device were analyzed, the necessary conditions for connecting consumers to the
  bus ducts were formed, including providing all consumers with electricity in full, excluding the
  excess of the rated currents of each bus duct with consumption currents, minimizing electricity
  losses when passing through the conductor and through the equipment. In this regard, the possible
  configurations of the construction of the electrical network using conductors and equipment were
  analyzed, and losses on the current conductors and on the equipment used were estimated. Based
  on the results of the research, a graph of consumers' connections to the conductors was formed,
  and to determine the way of connecting each consumer to the energy source through the power
  grid, a connection path was determined that minimizes losses. The problem was formalized as
  finding the shortest path in a graph, and Dijkstra's algorithm was used as a basis to solve it. Based
  on the research results, algorithms were formed for the formation of ways to connect consumers to
  electricity sources through the power grid and an algorithm for controlling the switching of keys
  in the power grid when the consumption currents change. The developed algorithms were implemented
  in software, and a numerical experiment was carried out using a simulation model. The
  results of the experiment showed the correctness of the developed algorithms, and can be further
  used for implementation in the devices under development for moving over ultra-long distances.

• EVOLUTIONARY DESIGN AS A TOOL FOR DEVELOPING MULTI-AGENT SYSTEMS

  L. A. Gladkov, N. V. Gladkova

  2021-11-14

  Abstract ▼

  The article is devoted to the discussion of the problems of constructing evolving multi -
  agent systems. Possible methodologies for designing multi-agent systems are considered. The
  relevance of developing new principles for constructing multi -agent systems based on evolutionary
  design methods is noted. The correspondences between the terms of the theory of
  agents and the theory of evolution are highlighted. The prospects of using hybrid approaches
  to the design of multi-agent systems are noted. The principles of construction and the poss ibility
  of using fuzzy genetic algorithms in the design of multi -agent systems are considered.
  It is suggested that the models and methods of the theory of evolutionary modeling can be
  successfully applied in the design of multi-agent systems. An evolving multi-agent system is
  proposed. The procedure for the formation of new agents in the process of evolution is described.
  The set of parameters for assessing the state of each agent in the population has
  been determined. The resource parameters are proposed to be used to assess the current state
  of the agent and the possibilities of its interaction with other agents. The definitions of an
  agency and a family, the minimum elements of an evolving multi -agent system are given. An
  evolutionary strategy for constructing a model of an evolving multi -agent system is proposed.
  The procedures for the execution of the original evolutionary operators for processing the
  population of agents are described. Based on the proposed methodology, a software system
  for supporting the evolutionary design of agents and multi-agent systems was developed. Atpresent, computational experiments are being carried out to study the proposed design model
  for multi-agent systems, as well as to evaluate the effectiveness of various operators and
  schemes for the formation of descendant agents, the necessary conditions for survival.

• HYBRID METHOD FOR SOLVING THE PROBLEM OF PLACEMENT OF DIGITAL COMPUTER DEVICES

  L. A. Gladkov , N. V. Gladkova , M.J. Yasir

  2021-11-14

  Abstract ▼

  The problem of placing elements of digital computing technology is considered in the article.
  The analysis of the current state of research on this topic is carried out, the relevance of the
  problem under consideration is noted. The importance of developing new effective methods for
  solving such problems are highlighted. The place of the placement problem in the general cycle ofthe design stage is shown. The importance of a high-quality solution to the placement problem
  from the point of view of the successful implementation of subsequent design stages is noted. The
  importance of minimizing connection delays in the design process of large-scale devices is noted.
  A review and analysis of various models and criteria for evaluating the solution to the placement
  problem is carried out. It was emphasized that the most important criterion is the length of the
  joints, it has a significant impact on the technologies used in the design. A complex mathematical
  formulation of the problem of placing elements of digital computing equipment has been completed.
  Perspective approaches to solving design problems are analyzed, hybrid methods and models
  for solving complex multicriteria optimization and design problems are described. The principles
  of operation and the model of a fuzzy logic controller are described. The description of the used
  fuzzy control scheme is given. The functions of various blocks of a fuzzy logic controller are determined.
  The structure of a multilayer neural network that implements the Gaussian function is
  proposed. The interaction of blocks of a fuzzy genetic algorithm is described. A model of a hybrid
  algorithm for solving the placement problem is proposed. The control parameters of the fuzzy
  logic controller are determined. The proposed hybrid algorithm is implemented as an application
  program. A series of computational experiments to determine the effectiveness of the developed
  algorithm and select the optimal values of the control parameters were carried out.

• HYBRID ENCRYPTION BASED ON SYMMETRIC AND HOMOMORPHIC CIPHERS

  L. K. Babenko , Е.А. Tolomanenko

  6-18

  2021-07-18

  Abstract ▼

  The purpose of this work is to develop and research a hybrid encryption algorithm based on the joint application of the symmetric encryption algorithm Kuznyechik and homomorphic encryp-tion (Gentry scheme or BGV scheme). Such an encryption algorithm can be useful in situations with limited computing resources. The point is that with the correct expression of the basic operations of the symmetric encryption algorithm through Boolean functions, it becomes possible on the transmitting side to encrypt the data with a symmetric cipher, and the secret encryption key - with a homomorphic one. In this case, manipulations can be carried out on the receiving side so that the original encrypted message is also encrypted only with a homomorphic cipher. In this case, symmetric encryption is removed, but the information remains inaccessible to the node that pro-cesses it. This property of secrecy makes it possible to carry out resource-intensive operations on a powerful computing node, providing homomorphically encrypted data for a low-resource node for the purpose of their subsequent processing in encrypted form. The article presents the developed hybrid algorithm. As a symmetric encryption algorithm, Kuznyechik encryption algorithm is used, which is part of the GOST R34.12 - 2015 standard. In order to be able to apply homomorphic encryption to data encrypted with the Kuznyechik cipher, the Kuznyechik algorithm S-boxes is presented in a boolean form using the Zhegalkin polynomial. Also, the linear transformation L is presented in the sequence form of performing the simplest operations of addition and multiplication on the transformeddata. The primary modeling of the developed algorithm was carried out on a simplified version of the KuzchyechikS-KN1 algorithm.

• ORGANIZATION OF THE ELECTRIC NETWORK OF THE HYBRID POWER SUPPLY SYSTEM OF AUTONOMOUS UNDERWATER VEHICLE

  N.K. Kiselev, L.A. Martynova, I.V. Pashkevich

  2021-04-04

  Abstract ▼

  The aim of the study is to organize the power grid of a hybrid power supply system for an autonomous
  underwater vehicle capable of moving in a wide range of speeds. The need to move the
  autonomous underwater vehicle in a wide range of speeds requires the use of heterogeneous sources
  of electricity operating on different physical principles - storage batteries and electrochemical generators
  using reagents from the reagent storage. In addition, in order to provide consumers with
  electricity with the required parameters (currents, voltages, volumes of electricity), it is necessary to
  use additional switchboards, voltage converters, protective switching equipment, keys. The use of
  additional equipment in the power grid allows you to flexibly configure the power grid in order to
  generate energy in an amount consistent with the amount of electricity consumed. On the other hand,
  additional equipment causes losses of electricity in the network, and, accordingly, additional electricity.
  In this regard, the task of determining the option for organizing the power grid, at which the loss
  of electricity would be minimal, is relevant. To solve this problem, the features of the use of additional
  equipment in the power grid were analyzed, the consumption of electricity by an autonomous underwater
  vehicle at different stages of a route assignment was analyzed, the minimum and maximum
  volumes of consumption were determined when an autonomous underwater vehicle moved in different
  speed modes. This made it possible to determine the degree of involvement of heterogeneous
  sources of electricity in the process of performing a route assignment. Based on the results of the
  analysis, alternative options for the power grid were formed. To select the option of the organization
  that ensures the minimum losses of electricity, a target graph of the effect of losses on individual
  devices of the power grid was formed - on the losses of the entire power grid, and using the method of
  distributing tags, quantitative estimates of each of the alternative options were obtained. Teaching
  quantitative assessments made it possible to determine the option of organizing an electrical network
  that minimizes losses. This allows, in turn, to formulate the requirements for the functioning of the
  elements of the hybrid power supply system, to develop control algorithms. In general, the result
  obtained makes it possible to minimize the consumption of energy resources during the movement of
  an autonomous underwater vehicle throughout the entire duration of the route assignment.

• EVOLUTIONARY DESIGN AS A TOOL FOR DEVELOPING MULTI-AGENT SYSTEMS

  L.A. Gladkov , N.V. Gladkova

  2020-11-22

  Abstract ▼

  The article is devoted to the discussion of the problems of constructing evolving multi-agent systems
  based on the use of the principles of evolutionary design and hybrid models. The concept of an
  agent is considered. A set of basic properties of the agent is presented. The analogies between multiagent
  and evolutionary systems are considered. The principles of construction and organization of multi-
  agent systems are considered. The similarities between the main definitions of the theory of agents
  and the theory of evolution are noted. It that the main evolution models and evolutionary algorithms can
  be successfully used in the design of multi-agent systems is noted. The analysis of existing methods andmethodologies for designing agents and multi-agent systems is carried out. The existing differences in
  approaches to the design of multi-agent systems are noted. The main types of models are described and
  their most important characteristics are given. A model of agent interaction, including a description of
  services (services), relationships and obligations existing between agents is presented. The model of
  relations (contacts), which defines communication links between agents is described. The importance
  and prospects of using the agent-based approach to the design of multi-agent systems are noted. The
  concept of designing agents and multi-agent systems, according to which the design process includes the
  basic components of self-organization, including the processes of interaction, crossing, adaptation to the
  environment, etc is proposed. Various approaches to the evolutionary design of artificial systems are
  considered. An evolutionary model of the formation of agents and agencies as the main component of
  evolutionary design is proposed. Modified evolutionary crossing-over operators to implement the agent
  design process are proposed.

• HYBRID APPROACH THE JOINT SOLUTION OF PLACEMENT AND TRACING PROBLEMS

  L.A. Gladkov , N. V. Gladkova , Dzhabbar Yasir Yasir Mukhanad

  2020-11-22

  Abstract ▼

  The article proposes an integrated approach to solving the problems of placing and tracing elements
  of circuits of electronic computing equipment. The approach is based on the joint solution of
  placement and tracing problems using fuzzy genetic methods. A description of the problem under
  consideration is given and a brief analysis of existing approaches to its solution is performed. The
  article discusses integrated approaches to solving optimization problems of computer-aided design of
  digital electronic computing equipment circuits. The urgency and importance of developing new
  effective methods for solving such problems is emphasized. It is noted that an important direction in
  the development of optimization methods is the development of hybrid methods and approaches that
  combine the advantages of various methods of computational intelligence. The article describes the
  following main points: the structure of the proposed algorithm and its main stages; modified genetic
  crossover operators; models for the formation of the current population are proposed; modified heuristics,
  operators and strategies for finding optimal solutions. The results of computational experiments
  are presented. The experiments carried out confirm the effectiveness of the proposed approach.
  In conclusion, a brief analysis of the results obtained is given.

• METHOD OF AUTOMATIC OPTIMIZATION OF THE FUZZY RULE BASE OF AN INTELLIGENT CONTROLLER BASED ON SUBTRACTIVE CLUSTERING

  А.S. Ignatyeva , V.V. Shadrina , D.S. Ignatyev , А.V. Maksimov

  181-197

  2025-07-24

  Abstract ▼

  The aim of the work is to develop a method for optimizing the fuzzy rule base of an intelligent controller for controlling a technical object using subtractive clustering. The article provides an overview and a brief analysis of the state of affairs in the field of optimizing the operation of intelligent control systems. To achieve the goal of the study, a hybrid model has been developed in which the technical object is controlled using a classical PI controller and a fuzzy PI controller with a generated structure of a Cygeno-type fuzzy inference system and a developed model of an adaptive neuro-fuzzy inference system. This configuration of the model allows you to form a fuzzy rule base that does not depend on the expert's knowledge in the subject area. The article proposes a new method for optimizing the fuzzy controller rule base based on clustering methods, in particular subtractive clustering, which allows you to reduce the number of fuzzy logical inference rules and increase the performance of the technical object control system. First, a hybrid model synthesized on the basis of the values of the fuzzy and classical controllers before applying subtractive clustering was simulated. The application of subtractive clustering according to the method developed in the study for the values of the classical and fuzzy controllers allowed us to achieve their quantitative reduction by 1.7 and 5.25 times, respectively. Then, the hybrid model synthesized on the basis of the values of the fuzzy and classical controllers after applying subtractive clustering was simulated. The results obtained in the process of simulation showed high efficiency of the proposed method for optimizing the fuzzy controller rule base. Due to the application of subtractive clustering in the hybrid model for the intelligent controller, it was possible to significantly reduce the number of membership functions required to describe the input linguistic variables (from five to four) and reduce the number of fuzzy logical inference rules (from twenty-five to sixteen). The analysis of the resulting graphs of transient processes obtained for the hybrid models before and after applying subtractive clustering showed that the main indicators of the quality of the control process remain unchanged with a significant reduction in the calculations performed.

• HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT

  D.Y. Kravchenko, Y.A. Kravchenko, V. V. Markov

  2020-07-20

  Abstract ▼

  The article is devoted to solving the problem of mapping ontological models in the processes
  of extracting and knowledge management. The relevance and significance of this task are due to
  the need to maintain reliability and eliminate redundancy of knowledge during the integration
  (unification) of various origins structured information sources. The proximity and consistency of
  the conceptual semantics of the combined resource during the mapping is the main criterion for
  the effectiveness of the proposed solutions. The article considers the problems of choosing appropriate
  solution approaches that preserve semantics when displaying concepts. The strategy of
  choosing bio-inspired modeling is substantiated. The aspects of the effectiveness of various decentralized
  bio-inspired methods are analyzed. The reasons for the need for hybridization are identified.
  The paper proposes to solve the problem of mapping ontological models using a bio-inspired
  algorithm based on hybridization of bacterial and cuckoo search algorithms optimization mechanisms.
  The hybridization of these algorithms allowed us to combine their main advantages: a consistent
  bacterial search that provides a detailed study of local areas, and a significant number of
  the cuckoo agent during the implementation global movements of Levy flights. To evaluate the
  effectiveness of the proposed hybrid bio-inspired algorithm, a software product was developed and
  experiments were performed on the mapping of different sizes ontologies. Each concept of any
  ontology has a certain set of attributes, which is a semantic vector of attributes. The degree of the
  semantic vectors similarity for the compared concepts of displayed ontologies is a criterion for
  their integration. To improve the quality of the display process, a new encoding of solutions has
  been introduced. The quantitative estimates obtained demonstrate time savings in solving problems
  of relatively large dimension (from 500,000 ontograph vertices) of at least 13 %. The time
  complexity of the developed hybrid algorithm is O (n 2). The described studies have a high level of
  theoretical and practical significance and are directly related to the solution of classical problems
  of artificial intelligence aimed at finding hidden dependencies and patterns on a multitude of
  knowledge elements.

• DETERMINING A SET OF CONDITIONS FOR AUTOMATICALLY FINDING THE BEST OPTION FOR HYBRID MACHINE TRANSLATION OF TEXT AT THE LEVELOF GRAPHEMES

  V. S. Kornilov, V. M. Glushan, A.Y. Lozovoy

  2020-07-20

  Abstract ▼

  The article is devoted to the Algorithmic Search for Optimal Solutions for evaluating and
  improving the Quality of Hybrid Machine Translation of Text. The Object of the Research is Texts
  on any Alphabetical Languages with different Bases (Alphabets), as well as their Translations into
  other Alphabetical Languages.Currently, existing Methods and Means of Hybrid Machine Translation
  are characterized by a wide variety of Quality Assessment Algorithms, but the Disadvantage
  of these Methods is that most of them do not have Clear Criteria, Limitations and Schemes of Assessments,
  eventually, the Result of the Translation in most cases does not correspond to the Level
  of Publication. The Aim of the Work is to determine a Set of Conditions for automatic search for
  the Best Option of Hybrid Machine Translation of Text at the Level of Graphemes.The Main Tasks
  to be solved during the Research are the Search for Qualitative and Quantitative Conditions, including
  the maximum, minimum and average values of the Lengths of Translations, Reverse Translations
  and Editorial Distances between Pairs of Texts that have the Same Meaning. The Scientific
  Novelty lies in use the Graphical Representation of the Model of Alphabetic Languages at the
  Level of Graphemes in the Form of a Cartesian Coordinate System with a Dimension equal to a
  Unit Editorial Distance (by Levenstein). When solving the de Goui’s Theorem, the current Rules ofStandardization PR 50.1.027–2014 "Rules for the Provision of Translation and Special Types of
  Linguistic Services", the Method of Decanonicalization and the Model "Original Text – Translation
  – Reverse Translation" were used. As a Result, Actual and Practically Applicable Solutions
  for the Problems under consideration are obtained. In this regard; this Work may be interest to a
  wide range of Specialists engaged in Machine Translation and Translation Studies.

• THE MATHEMATICAL MODEL OF THE FUNCTIONING OF A HYBRID ENERGY SUPPLY SYSTEM AS PART OF A DEBUGGING AND MAINTENANCE STAND AUV

  N.K. Kiselev, L.A. Martynova, I.V. Pashkevich

  2020-07-10

  Abstract ▼

  The aim of the research is to develop a complex of mathematical models that provide initial data for a mathematical model of the hybrid energy supply system for subsequent integration into the stand for debugging and maintenance. The work is a development of the previously published mathematical model of the functioning of the hybrid energy supply system of an autonomous un-derwater vehicle. In the work, based on the results of the analysis of the goals and objectives of modeling, mathematical models of electric power sources — a storage battery and an electro-chemical generator — are developed. Since control over the operating parameters of the battery and the electrochemical generator depends on the parameters of the vehicle’s movement, addi-tional mathematical models of the marching propulsion engine and the integrated control system of the vehicle have been developed. The external conditions for the functioning of the vehicle and the route task were set in a specially developed tactical situation simulator. Based on the theory of integrated hierarchical modeling with variable resolution, the most appropriate degree of detail of the developed mathematical models was determined. In view of the need to take into account the non-uniformity of gas blowing of fuel elements in an electrochemical generator, the mathematical model is based on solving a non-linear system of equations, including the Navier-Stokes equation, equations of conservation of momentum, energy and charge. When developing a mathematical model of the battery, the uneven charge of individual batteries was taken into account; The math-ematical model took into account the parameters of individual batteries according to their manu-facturer. The simulation results were the charge-discharge characteristics of the battery. In the mathematical model of the main consumer of electricity - the marching propulsion - the depend-ence of the generated thrust on the required speed of the vehicle is implemented, which allowed to obtain the amount of electricity consumed by the marching propulsion. In the mathematical model of an integrated control system, depending on the current position of the vehicle, motion control-lers are implemented to form control elements of the propulsion system, providing typical modes of maneuvering the vehicle. In addition, the control of the functioning parameters of the hybrid ener-gy supply system was implemented - switching of electric power sources, switching of battery charge processes. In the mathematical model of a tactical situation simulator, the possibilities of defining a route and external conditions are realized. In addition, a model of the vehicle movement was implemented taking into account the forces and moments acting on the vehicle. The developed complex of mathematical models, which provides the data with a mathematical model of the func-tioning of the hybrid energy supply system, can be used as a part of the stand for debugging and maintenance of an autonomous underwater vehicle.

• A TIME SERIES FORECASTING METHOD BASED ON COGNITIVE FUZZY MODELING AND REGRESSION ANALYSIS

  А.I. Guseva , R.М. Romanov

  157-178

  2025-12-30

  Abstract ▼

  The relevance of the study stems from the low effectiveness of traditional time series forecasting methods under conditions of high uncertainty and limited data, which are typical of weakly formalized systems. The aim of the work is to develop and substantiate a time series forecasting method based on a hybrid approach that integrates cognitive fuzzy modelling, regression analysis, and the analytic network process. Within the study, a systematic review and comparative analysis of existing forecasting methods was carried out, including approaches based on fuzzy logic, neural network and cognitive modelling, as well as ensemble and hybrid methods, and their limitations were identified when dealing with small samples, nonlinear dependencies, and uncertainty. The proposed method includes: the construction of fuzzy cognitive maps, defuzzification of linguistic assessments, clustering of factors, application of the analytic network process to determine priorities, and the formation of a weighted regression model. The model undergoes statistical validation using the , , , and  metrics, as well as diagnostic checks of the assumptions underlying regression analysis, including tests for multicollinearity and autocorrelation. Application of the method reduced  from 0.38 to 0.22,  from 0.30 to 0.18, and  from 11.65 % to 7.12 %, thereby confirming an improvement in the accuracy and robustness of forecasts under limited data compared with classical multiple regression. The novelty of the proposed method lies in the integration of cognitive modelling, regression analysis, and the analytic network process, whereby the strengths of each component compensate for their individual limitations, providing more accurate and robust forecasting under the uncertainty inherent in the system under study. The practical significance of the work consists in the possibility of applying the proposed method to support decision-making and to enhance the validity of forecasts in various subject domains and situations characterized by a limited number of observations, a substantial role of expert judgments, and a complex structure of causal relationships between indicators over time

• ANALYSIS OF TRADITIONAL AND NEURAL NETWORK-BASED CONTROL METHODS FOR ELECTRIC DRIVES IN ROBOTICS AND PERSPECTIVES OF HYBRID APPROACHES

  А. I. Tataurov , V.Е. Vavilov

  287-298

  2025-12-30

  Abstract ▼

  The objective of this study is to conduct a comparative analysis of traditional and neural network-based control methods for electric drives in robotics, with an emphasis on identifying their strengths and weaknesses, determining their areas of application, and assessing the prospects for the development of hybrid approaches. Effective control of electric drives is critically important for modern robotic systems, which must demonstrate high performance, reliability, and versatility in various application domains. Specifically, key challenges include high-precision trajectory tracking, energy-efficient control, robust control under uncertainties and disturbances, constraint-aware control, as well as synchronized and coordinated control of multiple electric drives. In this regard, optimizing the control of electric drives to ensure motion accuracy, energy efficiency, and adaptation to changing conditions becomes a top priority. To achieve this goal, the study systematizes and analyzes the characteristics and applications of traditional electric drive control methods, such as PID controllers, Kalman filters, sliding mode control, and model predictive control. It also examines key neural network-based approaches to electric drive control, including feedforward neural networks, recurrent neural networks, radial basis functions, neuro-fuzzy systems, and reinforcement learning. A comparative analysis of these methods is conducted to identify their advantages and limitations based on key parameters such as trajectory tracking accuracy, robustness to disturbances and uncertainties, adaptability to changing operating conditions, and computational complexity. Additionally, the study investigates and assesses the prospects for hybrid electric drive control methods that combine the reliability and control quality of traditional methods in linear and structured environments with the flexibility and adaptability of neural network-based methods in complex and dynamic robotic systems. The study’s key findings indicate that traditional electric drive control methods, such as PID controllers and sliding mode control, remain effective and preferable in linear and well-defined systems due to their simplicity and reliability. At the same time, neural network-based approaches demonstrate significant advantages in controlling complex nonlinear systems, as well as in uncertain conditions requiring adaptation to changing environments. Special attention is given to hybrid control methods, which integrate the strengths of both traditional and neural network-based approaches. These methods are regarded as the most promising and advanced direction, enabling the development of intelligent and robust electric drive control systems capable of operating efficiently in complex and dynamic environments.

• CONVOLUTIONAL NEURAL NETWORK HYBRID ARCHITECTURE DEVELOPMENT USING SPECTRAL TRANSFORMATIONS

  B. V. Kostrov , S.I. Babaev , А.I. Efimov , V. Y. Tarasova

  2026-02-27

  Abstract ▼

  The hybrid convolutional neural network architecture with combining spectral and spatial layers, as well as new methods of subsampling (WalsPooling) and convolution (ConvWals) are proposed. The developed system is used to geographical proximity assess of images pair based on their visual similarity. A pair of different sensors obtained images visual similarity determination is complicated by different scales and sensor tilt angles shooting conditions. Based on the low-altitude image fragment, a search in the database of underlying surface images is performed. The search is performed in the surrounding area of a given route based on the vector of image features, which is formed on the last layer of the convolutional neural network. The system uses the Siamese architecture, since a pair of images must be submitted to the input. The relevance of this problem stems from the need to ensure UAV navigation in the absence or unreliability of a GPS signal. The approach to data set formation and its preprocessing is also considered. The database search is performed in the surrounding area of the route, which reduces computational costs. The experiments include an analysis of the applicability of the proposed layers (WalsPooling, ConvWals) and a comparison with traditional pooling and convolution methods. The paper also presents a linear approximation method with trainable parameters for reducing the dimensionality of the convolutional layer. The main advantage of the approach is its resistance to changes in the scale and angle of shooting due to a combination of spectral and spatial features. The results demonstrate the applicability of the method for UAV navigation in conditions of loss of GPS signal is lost or unreliable. The experiment demonstrated that using images reconstructed after spectral transformation yields the best neural network convergence and mean square error. The developed architecture demonstrates robustness to geometric and brightness distortions, and its quality metrics (Precision = 0.728, Recall = 0.800, F1 = 0.872) confirm the effectiveness of the approach for visual localization tasks based on images from a surface database.

• MODERN APPROACHES TO FACE RECOGNITION IN LOW-LIGHT CONDITIONS: A REVIEW AND THE CONCEPT OF A HYBRID END-TO-END ARCHITECTURE

  D. А. Morozov , V.V. Gilka , А. S. Kuznetsova

  113-133

  2026-07-07

  Abstract ▼

  The article addresses the problem of reliable face recognition in critical areas such as video surveillance and biometric authentication under low-light conditions. Existing approaches typically separate the tasks of image enhancement and face identification, which leads to error accumulation and loss of informative features. The aim of this work is to overcome this limitation by developing and theoretically substantiating a hybrid end-to-end architecture in which image enhancement and face recognition are solved jointly. The study provides a systematic review of modern methods, including classical algorithms (such as histogram equalization and noise suppression) and advanced deep neural networks (including EnlightenGAN, Zero-DCE, ArcFace, and RetinaFace). The main contribution is the integration of generative and identification modules into a single computational graph. The key result of the study is the demonstration that joint optimization of all processing stages within a unified model, unlike fragmented solutions, fundamentally changes the approach to the problem. Theoretical analysis and comparative evaluation of existing concepts show that the proposed architecture ensures a more efficient gradient flow during training, leading to the formation of higher-quality and noise-robust identity features. It is shown that this approach prevents error accumulation between stages and minimizes information loss. The novelty of the work lies in the holistic, end-to-end view of the face recognition problem under low-light conditions. The practical significance is confirmed by the applicability of the architecture in real systems, where its implementation can potentially improve reliability and processing speed by combining heterogeneous tasks into a single optimizable framework.

  The article addresses the problem of reliable face recognition in critical areas such as video surveillance and biometric authentication under low-light conditions. Existing approaches typically separate the tasks of image enhancement and face identification, which leads to error accumulation and loss of informative features. The aim of this work is to overcome this limitation by developing and theoretically substantiating a hybrid end-to-end architecture in which image enhancement and face recognition are solved jointly. The study provides a systematic review of modern methods, including classical algorithms (such as histogram equalization and noise suppression) and advanced deep neural networks (including EnlightenGAN, Zero-DCE, ArcFace, and RetinaFace). The main contribution is the integration of generative and identification modules into a single computational graph. The key result of the study is the demonstration that joint optimization of all processing stages within a unified model, unlike fragmented solutions, fundamentally changes the approach to the problem. Theoretical analysis and comparative evaluation of existing concepts show that the proposed architecture ensures a more efficient gradient flow during training, leading to the formation of higher-quality and noise-robust identity features. It is shown that this approach prevents error accumulation between stages and minimizes information loss. The novelty of the work lies in the holistic, end-to-end view of the face recognition problem under low-light conditions. The practical significance is confirmed by the applicability of the architecture in real systems, where its implementation can potentially improve reliability and processing speed by combining heterogeneous tasks into a single optimizable framework.

• PREDICTING BOND PRICE MOVEMENTS USING A HYBRID METHOD BASED ON XGBOOST AND A GENETIC ALGORITHM

  L. E. Khairullina , Z.N. Khakimov , D.I. Galiev , А.N. Khairullina

  208-219

  2026-07-07

  Abstract ▼

  The article presents a hybrid method for predicting the direction of bond price movements, combining the XGBoost machine learning method with hyperparameter optimization using a genetic algorithm. The research is aimed at solving the problem of binary classification of the direction of the price of the Russian Railways bond on the next trading day. The research methodology includes the formation of an expanded feature space of 18 technical indicators calculated on the basis of daily OHLCV data. To configure XGBoost hyperparameters, a genetic algorithm is implemented using the DEAP library. The study was conducted on three time horizons: 01.01.21-31.10.25, 01.01.23-31.10.25, from 01.01.24-31.10.25.
  As a result, a significant dependence of the effectiveness of the model on the time horizon of the training data is shown. The best quality was demonstrated by a model trained on data from 2024-2025, with an accuracy of 64.4% in the test sample, balanced precision and recall metrics, as well as high F1-score scores for both classes. Models trained over longer periods (2021-2025 and 2023-2025) showed a decrease in generalizing ability, which indicates that the relevance of the data prevails over its volume in the context of changes in Russia's monetary policy in 2021-2025. To maintain the predictive power of the model in changing market conditions, it is recommended to use a sliding learning window of 1.5–2 years. The comparison with the "Buy & Hold" strategy confirmed the effectiveness of the proposed hybrid approach

• APPLICATION OF SOFT SITUATIONAL-COGNITIVE MODELS FOR INTELLIGENT CONTROL OF COMPLEX SYSTEMS AND PROCESSES

  S. А. Fedulova

  244-254

  2026-07-07

  Abstract ▼

  Currently, it is in demand to design methods and technologies for intelligent control of complex systems and processes that take into account situational awareness of problems, various control strategies and scenarios for achieving target situations in conditions of uncertainty. Models and methods based on fuzzy situational and fuzzy cognitive approaches take into account the specifics of situational awareness when controlling such systems and processes. The limitations of fuzzy situational models in controlling complex systems and processes under conditions of uncertainty are: the difficulty of accounting for the mutual influence of situational features due to the ambiguity of transitions from one fuzzy situation to another; the difficulty of assessing the simultaneous impact of several control decisions on various interdependent situational features; insufficient consideration of the time factor and duration of the impact of situational decisions on situational features; the difficulty of modeling scenario dynamics taking into account various strategies. Due to this, the best sequence of control decisions is formed, depending on the chosen strategy, and the time of their application is justified. The paper discusses the application of a new proposed variety of Soft Situational-Cognitive Models for intelligent control of turbocharger air installations. The results of the comparative assessment make it possible to substantiate the improvement of the quality of intelligent control and the efficiency of turbocharger air installations in conditions of uncertainty using the proposed model for various control strategies and scenarios for achieving target situations.

• A HYBRID APPROACH FOR DEEP LEARNING BASED FINGER VEIN BIOMETRICS TEMPLATE SECURITY

  Shendre Shivam , Shubhangi Sapkal

  2020-10-11

  Abstract ▼

  We are living in the today’s society, where we have fairly-enough storage capacity and processing
  power, the only issue is with security. As, the technologies are evolving with faster rate, we
  are tend to grow the use of electronic devices rapidly in todays’ society, it started to flow or leakage
  of personal information around/across, which then leads to breach of this information. Now,
  personal or identical verification is key problem is being crucial. So whatever traditional methods
  we have for providing authentication or security those have proven inadequate to be unreliable
  and do not provide strong security. Biometric template protection is one of the most important
  issues in securing today’s biometric system. We have many algorithms which don’t give adequate
  solution for the same. So we tried to give a method which will reach to the expectations more satisfactorily
  and certainly to the extent required. In this paper we have discussed a hybrid method for
  finger vein biometric recognition based on deep learning approach using BDD and fuzzy commitment
  schemes. The proposed hybrid method consists of four parts, namely Finger vein feature
  extraction, BDD-based secure template generation, Fuzzy commitment scheme and ML based
  finger vein recognition and decision making. Thus it has four module and each module works efficiently
  and gives accurate results on all databases.