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ANALYSIS OF TRADITIONAL AND NEURAL NETWORK-BASED CONTROL METHODS FOR ELECTRIC DRIVES IN ROBOTICS AND PERSPECTIVES OF HYBRID APPROACHES
А. I. Tataurov , V.Е. Vavilov287-2982025-12-30Abstract ▼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.
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MODERN APPROACHES TO SOLVING THE 3D BIN PACKING PROBLEM
М.М. Sorokin , L. А. Gladkov , N. V. Gladkova131-1502026-09-10Abstract ▼The article is devoted to the consideration of current trends and approaches to solving the urgent optimization problem of three-dimensional bin packing problem. The importance of building effective methods for solving this problem is due to the rapid growth of e-commerce, where achieving even incremental improvements in container filling density can lead to significant reductions in freight transportation and storage costs. The article provides an analysis of various types of problems and suggests a classification of bin packing problems according to various criteria, including: offline and online packing, by dimension, by type and quantity of containers and cargo. The formulation of the classical optimization knapsack problem is given and various options for constraints due to the specifics of the tasks being solved are considered. A brief overview of the main approaches to solving the problem is given. The analysis and generalization of the characteristic features of the application of metaheuristic approaches based on the use of evolutionary and bioinspired algorithms and machine learning methods is carried out, their advantages and disadvantages are noted. Due to the complexity of the problem under consideration, it is proposed to actively use known and develop new modifications of metaheuristic algorithms that make it possible to find quasi-optimal solutions in polynomial time. The analysis of known machine learning methods and bioinspired algorithms is given, the principles of their operation are described, their main features, advantages and disadvantages are highlighted, and the prospects for their development and application to solve NP-complete combinatorial optimization problems are noted. A generalized principle of operation of metaheuristic algorithms is given. A comparative analysis of the application of various optimization methods has shown the effectiveness of using metaheuristic methods to solve the problem of three-dimensional packaging.
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THE USE OF DISTRIBUTIVE SEMANTICS IN THE IDENTIFICATION OF SIGNIFICANT COMBINATIONS OF TITLES OF SEVERAL TEXT COLLECTIONS IN THE FORMALIZATION OF LINGUISTIC EXPERT INFORMATION
V.I. Danilchenko, V.M. Kureichik2022-08-09Abstract ▼The paper discusses methods of forming special models for the representation of various sets
of knowledge in various information systems. The work is devoted to the application of distributive
semantics in the identification of significant combinations in one subject area (PRO) within the
framework of the formalization of linguistic expert information (LEI). The paper applies an approach
to the formalization of LEI based on a set of analytical methods, where linear algebra is used as
models. This approach makes it possible to initialize the procedure for the automatic formation of
hierarchical architectures of LEI or dendrograms when identifying significant combinations of titles
of several collections of texts. The scientific novelty lies in the proposed analytical approach using
distributive semantics in identifying significant combinations of titles of several collections of texts,
which allows for the analysis and processing of linguistic expert information. A distinctive characteristic
of the proposed approach is the ability to formalize the ABM "Global Optimization Methods"
based on the synthesis of various already existing hierarchies of the ABM under consideration. The
paper aims to create conditions for the formalization of the LEI by applying distributive semantics
when identifying significant combinations of titles of several collections. The practical value of the
work lies in the development of a new approach to the formalization of LEI, taking into account distributive
semantics when identifying significant combinations of titles of several collections of texts.
The ontology in owl format "Methods of global optimization" in the program "Protege" is also built
in the work. The ontology is built on the basis of related data about. The ontology constructed in this
work complements the search structure within the framework of the considered PRO and can be
supplemented and developed in the future. -
DEVELOPMENT OF INTELLIGENT MOBILE APPLICATIONS
Т. А. Kramarenko, E. V. Feshina , T. V. Lukyanenko2022-06-03Abstract ▼The article presents the development results of a module for the retail network mobile application
modernization. A feature of the presented mobile application module is the display of personalized
messages with advertising and promotions of the retail network. A mathematical model
of machine learning is used to collect and analyze data in a mobile application. The process ofchoosing a mathematical model, the operation algorithm and the model training stages on training
data are described in detail. The quality of the classifier's work was evaluated on a test and training
sample. Test sample objects classification and the real value of the class comparison with the resulting
classification were performed. The authors in the article presented the main stages of the algorithms
development for processing statistical data from customer receipts. The program codes for the
receipt analysis module implementation and display the mobile application personalized advertising
are presented. To implement the database as a tool, the authors used the relational data management
system MS SQL Server. The modules of the mobile application are developed in the Android Studio
environment for the Android operating system family. The authors presented the algorithm main
stages and testing the implemented modules operability in the paper. Based on the data on purchases
made by the buyer, information about preferred products is collected based on the fixation of product
groups and product items from the receipt. The loyalty card of the retail network is linked to the mobile
application, and receipts for purchases are linked to loyalty cards, in turn. Previously, the application
displayed ads for all products participating in promotions. The actual task is to display personalized
advertising, which has proven its effectiveness. The mobile application is distributed for
free through the Play Market and is designed for smartphones running the Android OS line.
The purpose of the development is to display in the application on the buyer's device first advertising
frequently purchased goods, and then the rest of the promotional goods. The mobile application has
passed load testing in real use by customers conditions of the retail network. -
ANALYSIS OF ARTIFICIAL INTELLIGENCE METHODS APPLIED TO SOLVING PSYCHIATRY PROBLEMS
E.S. Podoplelova2022-05-26Abstract ▼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 -
ASSESSMENT OF INFLUENCING FACTORS AND FORECASTING OF POWER CONSUMPTION IN THE REGIONAL POWER SYSTEM, TAKING INTO ACCOUNT ITS OPERATING MODE
N.K. Poluyanovich, М. N. Dubyago2022-05-26Abstract ▼The article is devoted to the research of the assessment of influencing factors and forecasting
of power consumption in the regional power system, taking into account its operating modes.
The analysis of existing methods of forecasting energy consumption is carried out. The choice of a
forecasting method using an artificial neural network is justified. An algorithm for creating a neural
network for short-term prediction of electrical load is considered. The relevance of the work is
due to the requirements of the current legislation for forecasting electricity consumption in order
to solve the problem of maintaining a balance of power between the generating side and the consumption
of electric energy. At the same time, one of the main tasks related to the generation of
electric energy and its consumption is the task of maintaining a balance of capacities. On the one
hand, with an increase in the planned load, interruptions in the supply of electricity may occur, on
the other hand, a decrease in electricity consumption will also lead to a decrease in the efficiency
of power plants, and ultimately to an increase in the cost of electricity both for the wholesale electricity
market and for the end user. The developed neural network model reduces the task of shortterm
forecasting of power consumption to the search for a matrix of free coefficients by training
on available statistical data (active and re-active power, ambient temperature, date and index of
the day). The received NS model of short-term forecasting of power consumption of a section of
the district 10 kV electric grid takes into account the factors: – time, - meteorological conditions,
– disconnections of individual power supply lines of cottages, – operating mode of electricity consumers.
Predictive estimates of the power consumption of the power system have been obtained
based on the data of the electricity consumed by the outdoor temperature, the type of day, etc. The
model for predicting the magnitude of the consumed active and reactive power is quite workable,
but at this stage still has a fairly high level of forecasting error. To improve the accuracy of forecasting,
it is necessary to increase the database that makes up the training sample, because at the
moment the available data cover a time period of only 3–4 months. The results of the analysis
showed that forecasting reactive power consumption causes the greatest difficulties. -
METAHEURISTIC OPTIMIZATION METHOD BASED ON THE STEM CELL BEHAVIOR MODEL
Y. V. Danilchenko , V.I. Danilchenko, V.M. Kureichik2022-05-26Abstract ▼The paper discusses optimization methods that are based on processes occurring in nature. Such
methods have become increasingly used to solve complex problems. However, such methods have some
drawbacks, which stimulates the development of new and more advanced optimization methods. Solving
NP complete problems requires optimal methods that will meet all design requirements, so there is a
need to develop new and more advanced methods for solving this class of problems. As such a method,
the authors propose an optimization method based on a model of the behavior of stem cells in the natural
environment. The conducted studies of the proposed method provide solutions that can overcome
many of the shortcomings of standard optimization approaches, such as getting into the local optimum
or low convergence rate of the algorithm based on the method under consideration. The purpose of this
work is to develop an optimization method and an algorithm based on it for solving a complex objective
function. The scientific novelty lies in the development of an optimization method based on the stem cell
behavior model for solving NP complete problems. The aim of the work is to create conditions for theoptimal search for a solution to complex functions by applying the search method and, based on it, an
algorithm for the behavior of stem cells. The practical value of the work lies in the development of a new
metaheuristic optimization method for the efficient solution of NP complete problems. Also in the work,
a comparative analysis with well-known competitors was carried out. The main difference of the proposed
method from other known methods is the use of a new approach of bioinspired search based on
the behavior of stem cells, which, as shown by practical comparison, has an advantage over known
analogues. The results of a practical comparison of methods and algorithms based on them showed the
advantages of the approach proposed in the work on known test functions. After analyzing the problem
of creating methods, algorithms and software for solving NP complete problems, we can conclude that
the development of such approaches is currently an urgent task. -
BIOINSPIRED METHOD FOR CLASSIFICATION OF DISTRIBUTED RESOURCES FOR DISPATCHING IN GRID-COMPUTING
D.Y. Kravchenko , Y.A. Kravchenko, V.V. Markov , A. E. Saak2021-11-14Abstract ▼The article is devoted to solving the problem of scheduling distributed computing resources
based on their classification by the bioinspired search method to improve the efficiency of gridcomputing
functioning. The relevance of the problem is justified by a significant increase in the
demand for the paradigm of distributed computing in conditions of information overflow and uncertainty.
The article deals with the problems of scheduling heterogeneous computing resources
when solving complex professional and scientific problems arriving at different points in time,
based on the classification according to significant signs of resource compliance and readiness. A
comparative review of existing analogues is carried out. The formulation of the problem to be
solved in the context of the selected research topic is formulated. The strategy of choosing
bioinspired modeling for solving the problem has been substantiated. The aspects of various decentralized
bioinspired methods effectiveness of the use are analyzed. It is proposed to solve the
problem of scheduling computational resources based on determining the correspondence of the
resource to the required class. The classification is carried out on the basis of the bioinspired
optimization method application, built on the basis of the Fish School Search algorithm. The use of
the population bioinspired method allows us to provide unprecedented parallelism in obtaining
alternative solutions and to optimize the distribution of available computing resources depending
on the sets of significant features. The object of the research is the processes of data classification,
which include ordered sequences of actions aimed at the distribution of computing resources by
classes of problems to be solved. The subject of the research is bioinspired methods for solving the
problem of data classification in grid-computing. To evaluate the effectiveness of the proposed
method, a software application was developed and a computational experiment was carried outwith a different number of computing resources generated classes. Each computing resource has a
certain set of attributes, which is a vector of its features. The cosine measure of the similarity between
a resource attributes vector and a certain class attributes vector is a classification criterion.
To improve the quality of the dispatching process, the task of classifying computing resources is
solved for a variety of options for organizing the flows of complex tasks to be solved in gridcomputing.
The obtained quantitative estimates demonstrate the time savings in solving the problems
of scheduling distributed computing resources based on their classification by the bioinspired
search method at least 7 %. The time complexity in the considered examples was . The described
studies have a high level of theoretical and practical significance and are directly related
to the solution of artificial intelligence classical problems. -
EVOLUTIONARY DESIGN AS A TOOL FOR DEVELOPING MULTI-AGENT SYSTEMS
L. A. Gladkov, N. V. Gladkova2021-11-14Abstract ▼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. -
ALGORITHM OF EFFECTIVE CONTROLS FOR NONSTOCHASTIC CAUSAL MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES FOR SYSTEMS OF DECISION MAKING CONTROL
A.N. Tselykh, V.S. Vasilev , L.A. Tselykh2021-11-14Abstract ▼The paper deals with the problem of reproducing the decision-making process by a person under
conditions of uncertainty and incompleteness of the initial data. The decision-maker relies on his
belief system, which includes a shared vision of the system in relation to which the decision is being
made. The system is presented in the form of a causal model created on the basis of human mental
representations. These models are directed graphs, on the arcs of which the causal relationship is
expressed in the form of labels with a sign that determines the direction of change in the state of the
system. The vertices of this directed graph are high-level abstraction concepts. This graph simulates
the functioning of a real system. Thus, we investigate the problem of predicting and controlling human
actions based on non-stochastic causal models in the absence of observable variables for use in
decision support systems and expert systems. Decision-making is considered from the point of view of
the choice of objects of application of managerial influences - the factors of the model. In this study,
we show that the application of the proposed algorithm can facilitate decision-making regarding the
choice of control actions that support the achievement of the tactical and strategic goals of the decision
maker. It should be noted that the algorithm implements an automatic selection of the regularization
parameter, which makes the development and application of the proposed algorithm available
to users who do not have sufficient mathematical training. The convergence of the sequence of Lagrange
multipliers of an effective control algorithm is proved. The theorem on resonance in a nonstochastic
causal mod-el, represented by a directed graph, which is determined by the range of admissible
values of the damping coefficient in the control model, is proved. It is expected that the introduction
of this tool into decision support systems will in-crease the reliability of decisions regarding
the operation of the system as a whole. The choice of control actions using the proposed algorithm
has high efficiency and productivity. Thus, the results presented in the study can be useful for
developing applications in intelligent systems. -
THE ADJACENCY MATRIX RECONSTRUCTION ALGORITHM FOR CAUSAL GRAPH MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES
A. N. Tselykh , V.S. Vasilev, L. A. Tselykh2021-11-14Abstract ▼The paper deals with the problem of modeling complex systems in the absence of observable
variables. To solve this problem, it is proposed to use causal graph models. The class of causal
models considered here is defined as non-stochastic causal models with unobservable variables.
These models are presented in the form of a directed graph, created on the basis of human mental
representations. In this case, on the arcs, causality is expressed in the form of some marks with a
sign that determines the direction of change in the state of the system. The considered causal models
include heterogeneous, complex and qualitative types of variables that illustrate the nonnumerical
nature of nodes and links and, as a consequence, the absence and impossibility of obtaining
time series data. In the absence of observable variables and the impossibility of conducting
experiments, the problem of reconstructing the adjacency matrix of the causal graph model becomes
much more complicated. It is required to obtain a model with a certain spectral decomposition
that implements the main function of the modeled system. Based on this concept, a new method
for reconstructing the adjacency matrix is proposed, implemented on the basis of the corresponding
causal propagation matrix or transmission matrix. The idea is to use combinatorial optimization
based on spectral graph theory to generate data from a qualitative non-stochastic causal
model and reconstruct an adjacency matrix using that data. In this case, the eigenvectors are
identified as key objectives of the matrix reconstruction process, which postulates a fundamental
approach based on the spectral properties of the graph. The results of computational experiments
on solving the problem of reconstructing the adjacency matrix for causal graph models in the absence
of observable variables using the developed algorithm have shown that the algorithm effectively
reconstructs matrices from the given parameters with admissible similarity indices. The
convergence of the approximation to the solution of the matrix reconstruction algorithm is proved
no slower than with the speed of a geometric progression. From a technical point of view, the
advantage of the algorithm is the implementation of a tool for automatic adjustment of the regularization
parameter, suitable for users without prior mathematical knowledge. -
TRANSFORMATION AND ANALYSIS OF INFORMATION WHEN CREATING A DATABASE OF PARTICIPANTS OF THE GREAT PATRIOTIC WAR 1941-1945 IN THE MEMORIAL COMPLEX «ROAD OF MEMORY» IN THE MAIN RUSSIAN ARMED FORCES CATHEDRAL ON THE BASIS OF COMPUTER METHODS OF INFORMATION PR
S. A. Botsvin , V.A. Khvatkov2021-11-14Abstract ▼Preserving the historical memory of the participants of the Great Patriotic War
1941–1945 is a world-class task that should preserve the truth about the most terrible war and the
feat of our people. In modern conditions, attracting interest in history, traditions and finally
recognition of one's duty to the past generations requires modern methods. One of these methods
is the transformation of information, which allows you to present this information in such a way
that it can be used most effectively. At the same time, the main goal in the transformation of historical
data is to optimize their representations and formats and not change the information content.
The presented algorithms of transformation and analysis of information when creating a database
of participants of the Great Patriotic War were aimed at maximizing the preservation of historical
value and reliability of information. To achieve this goal, computer methods of information processing
for normalization and consolidation of personal data obtained from various sources are
considered. The analysis of the content of information in archival documents with the presentation
of statistical data on the number of documents (records) from various sources (archives, databases,
information resources, etc.) is carried out and the procedure for translating information
from archival documents into electronic form, which has been applied in practice, is described.
Based on the analysis of the information, diagrams of the content of personal information in archival
sources are constructed, the stages of systematization and bringing the generalized information
array records to a single format are presented, as well as the procedure for combining and
deleting duplicate records. For the possibility of using in other projects, an algorithm for consolidating
data obtained from various sources is described in detail, and its block diagram is constructed.
In addition, the applied fuzzy search algorithms are described, which made it possible to
minimize errors in records, as well as image comparison algorithms for searching for duplicates
from photographs. All of these algorithms have made it possible to bring together information
contained on various media, having different structures and geographical location. The created
information resource allows you to enormously reduce the resources needed to find the necessary
information, including access to which was limited or not at all. Further improvement of algorithms
for normalization and consolidation of information can serve as a basis for data migration
from outdated to promising systems, as well as for the formation of information resources from
existing heterogeneous archival funds. -
HYBRID METHOD FOR SOLVING THE PROBLEM OF PLACEMENT OF DIGITAL COMPUTER DEVICES
L. A. Gladkov , N. V. Gladkova , M.J. Yasir2021-11-14Abstract ▼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. -
ALGORITHM FOR OPTIMAL CONTROL THE DIGITAL TWIN OF THE ENTERPRISE
S.N. Masaev2021-08-11Abstract ▼The volume of processed information increases when analyzing and control the activities of an
enterprise as a system. The amount of processed information directly depends on the dimension of
this system. In the work, the activity of the enterprise is formalized as a digital twin of the enterprise.
The digital twin of the enterprise is analyzed as a dynamic system. The enterprise was identified as a
dynamic system. The digital twin of the enterprise is formalized as V. Leontiev's balance model. An
algorithm for optimal control of the digital twin of the enterprise has been created.
The following functions are considered as parameters of optimal control: the trajectory of the system,
the execution time of the algorithm and the indicator of the state of the system. In the algorithm for
enterprise control, the following methods were used: Bloom's taxonomy, the competence of graduates
in the SFU specialties and the National Qualifications Framework of the Russian Federation. The
identification of the enterprise processes is carried out by the method for which the patent has been
obtained. The algorithm is implemented in the author's software package for analyzing a system with
a dimension of 1.2 million values. The study showed significant changes in the values of the optimal
control functions characterizing the states of a dynamic object, depending on the selected techniques.
Calculations have shown how the choice of control method affects the optimality of decisions. The
state of the enterprise is displayed through the competencies of the personnel: psychomotor, cognitive
and affective. It was found that with low cognitive and affective abilities of the staff, psychomotor
activity begins to prevail, which leads to little result. With the growth of the cognitive abilities of thepersonnel, psychomotor activity becomes more adequate to the internal tasks and the influence of the
parameters of the external environment. An integral indicator was used to assess the implementation
of methods in enterprise control. The estimation of the optimality of the solution for control the digital
twin of the enterprise as a dynamic system is carried out. -
IMPLEMENTATION OF CONVENTIONAL NEURAL NETWORKS ON EMBEDDED DEVICES WITH A LIMITED COMPUTING RESOURCE
V.V. Kovalev, N.E. Sergeev2022-01-31Abstract ▼Large amounts of video data captured by sensor sensors in various spectral ranges, the significant
size of convolutional neural network architectures create problems with the implementation of
neural network algorithms on peripheral devices due to significant limitations of computing resources
on embedded computing devices. The article discusses the use of algorithms for automatic search and
pattern recognition based on machine learning methods, implemented on embedded devices with a
computing resource Graphics Processing Unit. Detection convolutional neural networks «You Only
Look Once V3» and «You Only Look Once V3-Tiny» are used as a search and pattern recognition algorithm,
which are implemented on embedded computing devices of the NVIDIA Jetson line, located in
different price ranges and with different computing resources ... Also, in the work, the estimates ofalgorithms on embedded devices are experimentally calculated for such indicators as power consumption,
forward passage time of a convolutional neural network, and detection accuracy.
On the basis of solutions implemented, both at the hardware level and in software, presented by
NVIDIA, it becomes possible to use deep neural network algorithms based on the convolution
operation in real time. Computational optimization methods offered by NVIDIA are considered.
Experimental studies of the influence of computations with reduced accuracy on the speed and
accuracy of object detection in images of the investigated architectures of convolutional neural
networks, which were previously trained on a sample of images consisting of the PASCAL VOC
2007 and PASCAL VOC 2012 datasets, have been carried out. -
STUDY OF PARALLEL SOLUTION ORGANIZATION FOR EXTERNAL AERODYNAMICS PROBLEMS BASED ON SPLITTING SCHEMES
V.V. Semenistyy , I. E. Gamolina2021-01-19Abstract ▼The aim of this work is to study the ways to organize parallel solutions of external aerodynamics
problems. A hybrid parallel-conveyor method for numerical solution of two-dimensional
problems is considered. It allows to simulate the flow of viscous compressible fluids around objects
of complex shape. A parabolized system of Navier-Stokes equations is considered, for the
numerical solution a finite-difference algorithm is chosen. Due to its features (cost-effectiveness
and stability in the study of boundary layers of moving bodies) this algorithm was preferred. To
implement a nonlinear finite-difference scheme, the internal iterations are used in each main section.
The developed parallel algorithm consists constructively of nested iterative loops. The system
of equations is solved at each internal iteration. It is organized in two stages. At the first stage the
equations of motion are solved; at the second stage the density is determined. At each fractional
step of the internal iteration, one-dimensional data arrays are calculated. The paper uses the
method of splitting the operator by physical processes. For the numerical solution of the problem,
the factorization of the stabilizing operator is carried out. The scheme of the organization of the
process of problem solving is given in each internal iteration. The paper proposes the principle of
organizing parallel computing. The internal parallelism of the physical problem is used here.
To implement the parallel algorithm, a computing environment is specially selected. It contains a
decisive field of computing devices connected by switching connections, each of computing device
has its own RAM. Besides computing environment contains a control device. The parallel algorithm
uses a communication topology between worker processors. Reducing the dimension of the
problem (to 2d) allows to save time on data exchange between the processors. In this paper, time
estimates of the effectiveness of the developed parallel algorithm for each internal iteration are
carried out. The use of the parallel run method and the proposed principle of organizing parallel
calculations allow to increase the effectiveness of solving problems of such class. -
DEVELOPMENT OF HOMOMORPHIC DIVISION METHODS
I.D. Rusalovsky, L.K. Babenko, О.B. Makarevich2022-11-01Abstract ▼The article deals with the problems of homomorphic cryptography. Homomorphic cryptography
is one of the young areas of cryptography. Its distinguishing feature is that it is possible to
process encrypted data without decrypting it first, so that the result of operations on encrypted
data is equivalent to the result of operations on open data after decryption. Homomorphic encryption
can be effectively used to implement secure cloud computing. To solve various applied problems,
support for all mathematical operations, including the division operation, is required, but
this topic has not been sufficiently developed. The ability to perform the division operation
homomorphically will expand the application possibilities of homomorphic encryption and will
allow performing a homomorphic implementation of many algorithms. The paper considers the
existing homomorphic algorithms and the possibility of implementing the division operation within
the framework of these algorithms. The paper also proposes two methods of homomorphic division.
The first method is based on the representation of ciphertexts as simple fractions and the
expression of the division operation through the multiplication operation. As part of the second
method, it is proposed to represent ciphertexts as an array of homomorphically encrypted bits, and
all operations, including the division operation considered in this article, are implemented
through binary homomorphic operations. Possible approaches to the implementation of division
through binary operations are considered and an approach is chosen that is most suitable for a
homomorphic implementation. The proposed methods are analyzed and their advantages and disadvantages
are indicated. -
COMPARATIVE ANALYSIS OF MISSING DATA RECOVERY METHODS
A.A. Sorokin , A. V. Dagaev , I. M. Borodyansky2020-11-22Abstract ▼In recent decades, the methods of system analysis have been developing qualitatively. It is
associated with an increase in the rate of technical development, the densification of time processes,
the rapid growth of accumulated information and new capabilities of computer technology.
These include methods for analyzing large amounts of data, methods of data mining, methods of
analytical modeling, methods of parallel data processing, neural network methods, forecasting
methods, and others. The presented methods make it possible to quickly and efficiently process
heterogeneous clusters of information, accumulate and synthesize data, generalize and classify
information. The last of the presented methods are methods of interpolation and extrapolation of
lost, damaged or missing information. These methods allow to structure, restore and model information
based on statistical data, mathematical and algorithmic methods. Thus, the article deals
with the problem of recovering missing data in graphic and complex objects. Literary sources on
the problems under consideration are given. They provide extensive information on the topic under
consideration: present genetic algorithms used for spatial interpolation; the solution of problems
of heterogeneity of interpolation of seismic data is considered; it is described the use of
spline approximation to calculate the characteristics of nonlinear electronic components; the
method of constructing a model of three-dimensional parametric rational bodies using generalized
Bezier interpolation is analyzed, which allows modeling the shape of a body and anisotropic
space; methods using fuzzy linear equations are described, which are widespread in computer
vision; the method of adaptive interpolation based on the gradient and taking into account the
local gradient of the original image is investigated. It is made comparing several common methods
of interpolation and data restoration, in article, such as: bilinear interpolation, Bezier surface.
Each method and features of its application within the framework of the experiment are briefly
described. The result of a series of experiments with the presented methods with different numbers
of tests is presented. In conclusion, summary is drawn about the rationality of choosing one of the
proposed methods without the use of a long field experiment in each case. -
HYBRID APPROACH THE JOINT SOLUTION OF PLACEMENT AND TRACING PROBLEMS
L.A. Gladkov , N. V. Gladkova , Dzhabbar Yasir Yasir Mukhanad2020-11-22Abstract ▼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 IMPLEMENTING HOMOMORPHIC DIVISION
L. K. Babenko, I. D. Rusalovsky2020-11-22Abstract ▼The article deals with the problems of homomorphic cryptography. Homomorphic cryptography
is one of the young directions of cryptography. Its peculiarity lies in the fact that it is possible
to process encrypted data without preliminary decryption in such a way that the result of operations
on encrypted data is equivalent, after decryption, to the result of operations on open data.
The article provides a brief overview of the areas of application of homomorphic encryption. To
solve various applied problems, support for all mathematical operations is required, including the
division operation, and the ability to perform this operation homomorphically will expand the
possibilities of using homomorphic encryption. The paper proposes a method of homomorphic
division based on an abstract representation of the ciphertext in the form of an ordinary fraction.
The paper describes in detail the proposed method. In addition, the article contains an example of
the practical implementation of the proposed method. It is proposed to divide the levels of data
processing into 2 levels – cryptographic and mathematical. At the cryptographic level, a completely homomorphic encryption algorithm is used and the basic homomorphic mathematical operations
are performed – addition, multiplication and difference. The mathematical level is a superstructure
on top of the cryptographic level and expands its capabilities. At the mathematical level,
the ciphertext is represented as a simple fraction and it becomes possible to perform the
homomorphic division operation. The paper also provides a practical example of applying the
homomorphic division method based on the Gentry algorithm for integers. Conclusions and possible
ways of further development are given. -
TWO-STAGE BOOSTING OF BINARY CLASSIFICATION BASED ON THE APPLICATION OF BIOINSPIRED ALGORITHMS
D. V. Balabanov , A. V. Kovtun , Y. A. Kravchenko2020-10-11Abstract ▼In the process of solving a wide range of applied problems, it becomes necessary to decompose
objects. As a result, the classification problem is an urgent problem in modern data mining
systems. Binary classification is one of the most important tasks, and has a number of unsolved
problems. One such problem is the effectiveness of automated classification. In the tasks of automated
classification, it is relevant to use the algorithmic apparatus of evolutionary computing.
Thus, it is advisable to use genetic and bio-inspired algorithms in the task of finding the optimalvalues of the classifier parameters. To solve this problem, it is proposed to apply the particle
swarm algorithm (PSO). This algorithm in the context of the task of finding suboptimal values of
the parameters of the classifier is able to provide high quality classification. A modification of the
algorithm is a dynamic change in the coordinate values that are responsible for the type of kernel
function. This revision can significantly reduce the time spent developing the classifier. To increase
the classification efficiency, it is advisable to use ensembles of algorithms. The paper presents
the structure of a two-level classifier. At the first level of this classifier, an ensemble of simple
classifiers is formed that form the training set, which is further used by the particle swarm
algorithm in the second stage. This approach can significantly reduce time costs, as well as improve
the quality of the resulting solutions. The particle swarm algorithm (PSO), in the context of
the task of finding suboptimal values of the parameters of the classifier, is able to provide high
quality classification. The proposed two-level algorithm has been experimentally tested. A comparison
is made with analogues, comparative charts are given. The described studies show that
the work is of high theoretical significance, and the conducted experimental studies prove high
practical significance. -
IMPLICIT THREATS IDENTIFICATION BASED ON ANALYSIS OF USER ACTIVITY ON THE INTERNET SPACE
V. V. Bova , D. Y. Zaporozhets, Y.A. Kravchenko , E. V. Kuliev , V. V. Kureichik , N. A. Lyz2020-10-11Abstract ▼The article is devoted to the problem of identifying implicit information threats of a user's
search activity in the internet space based on an analysis of his activity in the course of this interaction.
The use of knowledge stored in the Internet space for the implementation of criminal intentions
poses a threat to the whole society. Identifying malicious intent in the users’ actions of the
global information network is not always a trivial task. The proven technologies for analyzing the
context of user interests fail in the case of cautious and competent actions of attackers who do not
explicitly demonstrate the goal they are pursuing. The paper analyzes the threats associated with
certain scenarios for the implementation of search procedures that manifest themselves in search
activities. Criteria of inefficient and effective search scenarios estimation are described. Among
the signs indicating the possibility of a threat, the following main ones are highlighted: avoiding
solving the problem in aimless navigation or attractive resources, superficial search, lack of
meaningful immersion in solving the search problem, and chaotic actions during the search.
To determine the presence of adverse signs, a system of indicators is built. The features of an effective
scenario for organizing a search in the Internet space are formulated, options for the presence
of implicit threats for a similar situation are described.An approach for identification the
described threats is presented taking into account the specified criteria for evaluating various
scenarios of user behavior in the global information space. A machine learning algorithm has
been developed to identify problem scenarios by comparing with key behavioral patterns. The
software implementation of the subsystem for identifying information threats has been created,
experimental studies have been conducted to confirm the effectiveness of the subsystem. Experimental
studies were carried out on the basis of processing open data from social networks, as well
as using analysis of user search activity in the university corporate information environment. -
HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT
D.Y. Kravchenko, Y.A. Kravchenko, V. V. Markov2020-07-20Abstract ▼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. -
CLASSIFICATION AND ANALYSIS OF EVOLUTIONARY METHODS OF EVA BLOCK LAYOUT
Y.V. Danilchenko, V.I. Danilchenko, V. M. Kureichik2020-07-20Abstract ▼Currently, there is a large increase in the need for the design and development of radioelectronic
devices. This is due to increasing requirements for radio-electronic systems, as well as
the emergence of new generations of semiconductor devices. In this regard, there is a need to develop
new tools for automated layout of EVA blocks. There are a number of problems that complicate
the actual representation of knowledge in CAD and are probably solvable at the current level
of cognitive science development. The problem of stereotyping and the problem of coarsening are
interrelated and need to create hybrid models of representation. The paper deals with the problem
of solving the problem of EVA block layout in the design of radio-electronic equipment. The purpose
of this work is to find ways to optimize the planning of EVA block layout using a genetic
algorithm. The relevance of the work is that the genetic algorithm can improve the quality of layout
planning. These algorithms allow you to improve the quality and speed of layout planning. The
scientific novelty lies in the search and analysis of effective methods for composing EVA blocks
using genetic algorithms. The main difference from the known comparisons is in the analysis of
new promising algorithms for composing EVA blocks. Result of work. The paper shows the disadvantages
of traditional algorithms for searching for a suboptimal EVA plan. Descriptions of modern
models of evolutionary and other calculations are given. Genetic algorithms have a number of
important advantages – adaptability to a changing environment, the evolutionary approach makes
it possible to analyze, Supplement and change the knowledge base depending on changing conditions,
as well as quickly create optimal solutions. If you apply genetic algorithms and preprocessing
heuristics to provide optimal initial solutions, you can achieve more productive use of
algorithms. Known genetic algorithms converge quickly, but they lose population diversity, which
affects the quality of the solution. To balance data, the solution is corrected using efficient operators
or stable mutation. -
RESEARCH OF MARCHING PROPULSIONS THRUST CONTROL METHODS OF UNMANNED UNDERWATER VEHICLES
V.V. Kostenko, N.A. Naidenko, I.G. Mokeeva, A.Y. Tolstonogov2020-07-10Abstract ▼The aim of the study is to assess advantages and disadvantages of existing methods for con-trolling thrust of main propulsions (MP) of unmanned underwater vehicles (UUV). The mathemat-ical model of the MP developed by IMTP FEB RAS was adopted as the object of study. It’s consist-ing of a set of models of an electric motor, propeller and thruster control unit. During the research the following tasks were solved: development of the mathematical model of a brushless motor with refine parameters based on results of its load tests; development of the mathematical model of a propeller based on its action curves determined in accordance with the PROPS model test regres-sion base; development of the mathematical model of an thruster control unit (TCU); simulation of reaction of the thruster for stepwise change of desired thrust with the open-loop regulation of elec-tromotive torque, with feedback on the frequency of rotation and on measured thrust. As the result of simulation main propulsion reaction on stepwise change of desired thrust in bollard pull mode it has been established that different types of thrust control are only differed in transient response time and static control error is almost non-existent for all types of control. Herewith, twofold de-crease in transient response time with thrust and frequency control was found over torque control. This is due to increased power consumption of the motor in the transition process. Modeling of the MP control at the counter flow caused by the movement of the underwater vehicle showed that the control with thrust feedback has the minimum static error and transient response time is compara-ble with the speed control.
