For authors
Search
Search Results
##search.searchResults.foundPlural##
-
DISCRETE-EVENT METHOD COMPUTATIONS ORGANIZING FOR PROCESSING LARGE SPARSE UNSTRUCTURED MATRIXES ON RCS
А.v. Podoprigora189-1972021-10-05Abstract ▼Increasing models complexity objects and processes study, in different sphere of science and technology, set up plenty issues to necessary to use high-performance computing systems. Arrays matrix processing by cluster multiprocessor computing systems in conjunction special methods aimed at organizing parallel computations, basically obtain computing performance system is quite high. However, that computational efficiency is not observed for all types of matrices. Matrix structure be in a position contain large amount of insignificant elements, large dimension and unstructured portrait. Calculation execute for described kind of matrices on cluster multiprocessor computing system couldn't achieve close peak performance. Considering that processing methods leave out the complex structure of the matrix being processed. As a result, the performance of the system is significantly reduced. The development of cluster MCS methods doesn't allow for full ensure high performance for class of problems processing of large sparse unstructured matrices. Rigid architecture of processor commutation net doesn’t take into account the peculiarities of such matrices, and lead to non-uniformity loading processor. To achieve performance close the peak for tasks large sparse unstructured matrices processing necessary to use reconfigurable compu-ting systems. RCS architecture allows adapting computation structure to the problem solved. This makes it possible to organize pipeline processing, such a way that computational resource RCS used only for informational significant operations. In addition using generally accepted methods for structural organization of high-performance computing for RCS, it is necessary to develop a format for storing and transferring large sparse unstructured matrices, to determine the principles of constructing basic matrix macro-operations and the possibility of organizing composite dis-crete-event matrix functions for solving applied problems. Сconsequently method founding laid allows organizing computations operands, which are large sparse unstructured matrices. The application this method for organizing computations can significantly increase productivity, and provide an increase in the efficiency of such a system
-
TRANSPORT FLOW FORECASTING MODEL BASED ON NEURAL NETWORKS FOR TRAFFIC PREDICTION ON ROADS
Alamir Haider Sagban Hussein, Е.V. Zargaryan, Y. А. Zargaryan124-1322021-08-11Abstract ▼In connection with the industrialization of modern society, the growth of the transport sys-tems of our country, an increase in certain necessary for the development of the needs of the citi-zens of our country, the number of vehicles of various types continues to increase every year very fast, causing huge traffic jams on transport roads, especially in large cities and megacities. Thus, forecasting traffic flows is an important and necessary component of optimal traffic control in modern conditions of transport network development. As a solution to this problem, this article aims to analyze and describe the application of artificial intelligence methods, in particular neural networks, which represent a modern approach to modeling in complex and nonlinear situations that arise when predicting a traffic flow model. The shown accuracy method is based on the devel-opment of a neural network to predict the daily traffic flow. The expected traffic flow is then com-pared with the actual dataset recorded on the road section and provided by the infrastructure manager. In fact, neural networks are able to learn from past situations and predict future situa-tions on the transport network. In this study, various neural network structures were examined,and the simulation results showed that the best predictions were obtained using the multilayer perceptron architecture, which has a good generalization system with a root mean square error of 0.00927 with the current set of vehicles. The first part of the article is devoted to defining various concepts related to the current research area, including a review of the literature on traffic predic-tion and neural networks. The second part is devoted to describing the problem of traffic conges-tion using forecasting problems and presenting the proposed solution method with an emphasis on artificial neural networks as a means of forecasting demand and its various structures. Then, nu-merical experiments are illustrated by analyzing the forecast results after the formation and test-ing of various neural network architectures.
-
SOFTWARE SUBSYSTEM FOR SOLVING NP-COMPLEX COMBINATORIAL LOGIC PROBLEMS ON GRAPHS
V.V. Kureichik, Vl. Vl. Kureichik2021-07-18Abstract ▼The paper is devoted to the development of the software for solving NP-hard and NP-hard
combinatorial-logical problems on graphs. The paper contains a description of graphs combinatorial-
logical problems. New multilevel search architectures such as simple combo, parallel combo,
two-levels, integrated, and hybrid are proposed to effectively address them. These architectures
are based on methods inspired by natural systems. The key difference between these architectures
is the division of search into two or three levels and the use of various algorithms for evolutionary
modeling and bioinspired search on them. This allows obtaining sets of quasi-optimal solutions to
perform parallel processing and partially eliminate the problem of premature convergence. The article
provides a detailed description of the developed software subsystem and its modules. As modules
in the subsystem, there are five developed architectures and a set of developed algorithms for evolutionary
modeling and bioinspired search, such as evolutionary, genetic, bee, ant, firefly and monkey.
Thanks to its modular structure, the subsystem has the ability to design more than 50 different search
combinations. This makes it possible to use all the advantages of bioinspired optimization methods
for efficiently solving NP-complex combinatorial-logical problems on graphs. To confirm the effectiveness
of the developed software subsystem, a computational experiment was carried out on test
examples. The series of tests and experiments carried out have shown the advantage of using a software
product for solving combinatorial-logical problems on graphs of large dimension, in comparison
with known algorithms, which indicates the prospects of using this approach. The time
complexity of the developed algorithms is O(nlogn)) at best, and O (n3) at worst. -
MODELING OF THE VACUUM INFUSION PROCESSES IN THE MANUFACTURING OF THE LARGE POLYMERIC COMPOSITE STRUCTURES
Huang Jyun-Ping2021-08-11Abstract ▼The article presents the technology of computer simulation of the vacuum infusion process
in the production of large-sized polymeric composite structures, which is attracting more and
more attention in the aircraft industry, due to the ease of implementation and the relatively low
cost of production preparation. The difficulty of industrial implementation of the process and ensuring
the required quality is due to its high sensitivity to modes - temperature, vacuum pressure
and the layout of the vacuum ports and resin injection. The purpose of the developed methodology
for computer modeling of the process with the possibility of its subsequent optimization is to exclude
the currently used lengthy and very expensive trial and error method when working out the
technology. The proposed mathematical model of the process linking the equation of the phase
field, which reconstructs the interface between the resin and the void region of the preform, the
Richards equation for the propagating viscous fluid in an unsaturated porous medium, the thermal
kinetics of the resin and thermal conductivity, is implemented in the environment of a finite element
package. Computer implementation of the model provides an accurate reconstruction of the
dynamics of the front of the propagating resin in a porous preform, the possibility of the emergence
and localization of non-impregnated zones of the molded structure, thereby eliminating the
formation of irreparable defects. The results obtained demonstrate the ability of the developed
technique to ensure the stability of the quality of the produced composite structures with increased
requirements for the continuity of its microstructure and its structural strength. -
DEVELOPMENT OF METHODS OF OPTIMIZATION AND PARALLELIZATION OF COMPUTATIONAL PROCESSES IN QUANTUM ACCELERATORS
S. M. Gushanskiy, V. S. Potapov, V.I. Bozhich2021-08-11Abstract ▼Recently, there has been a rapid increase in interest in quantum computers. Their work is
based on the use of quantum-mechanical phenomena such as superposition and entanglement for
computing to transform input data into outputs that can actually provide effective performance
3–4 orders of magnitude higher than any modern computing devices, which will allow solving theabove and others. tasks in real- and accelerated-time scale. This article is devoted to solving the
problem of research and development of methods for optimizing quantum computing within the
framework of the application of quantum accelerators. A block diagram of a hardware accelerator
is proposed to increase the performance of simulated quantum computing. The development of the
structural diagram of the communication module of the hardware accelerator and the software
model was carried out. The relevance of these studies lies in mathematical and software modeling
and implementation of correction codes for correcting several types of quantum errors in the development
and implementation of quantum algorithms for solving classes of problems of a classical
nature. The scientific novelty of this direction is expressed in the elimination of one of the disadvantages
of the quantum computational process. The scientific novelty of this area is primarily
expressed in the constant updating and supplementation of the field of quantum research in a
number of areas, and the computer simulation of quantum physical phenomena and features is
poorly covered in the world. -
DEVELOPMENT OF BIOHEURISTICS FOR CREATING AN INTELLECTUAL SUBSYSTEM FOR MAKING EFFECTIVE DECISIONS OF NP-HARD AND NP-DIFFICULT COMBINATORY-LOGICAL PROBLEMS ON GRAPHS
D. V. Zaruba , E. V. Kuliev , D.Y. Zaporozhets , M. M. Semenova2021-11-14Abstract ▼The article is devoted to the solution of new topical problems that have arisen in the conditions
of the modern development of information and nanometer technologies in the field of design,
as well as the development of new innovative methods that provide effective solutions in polynomial
time. The article deals with the problem of solving NP-hard problems. The description of the
procedure for measuring the complexity of the problem is presented the features of NP-hard and
NP-difficult combinatorial logic problems are described. The main differences between the tasks
are presented, as well as the problems that one has to face when solving this type of task. The general
decision-making scheme is presented, consisting of the problem formulation; decisionmaking;
signal in automatic systems and feedback. At the second stage (formation and selection of
solutions), the solution is based on a bioinspired algorithm for finding solutions to the traveling
salesman problem. To solve this problem, a modified bioinspired algorithm based on the behaviorof an ant colony was developed. Unlike other optimization methods, metaheuristic algorithms can
find global optimal solutions for problems where there are many local solutions due to their random
nature. These reasons have led to the widespread use of such algorithms in solving various
optimization problems. Bioinspired algorithms are becoming a new revolution in the field of solving
optimization problems. The statement of the traveling salesman problem is presented, as well
as the solution of the problem on the basis of the ant algorithm. Algorithms such as genetic algorithms
and PSO can be very useful, but they still have some disadvantages in solving multimodal
optimization problems. These algorithms can find optimal solutions regardless of the physical
nature of the problem. In the framework of experimental studies, the analysis of the work of
bioinspired algorithms was carried out: the algorithm of a flock of bats, the bacterial algorithm
and the ant algorithm. -
ESTIMATION OF THE PROBABILITY OF DETECTING A FALSE RESULT OF DISTRIBUTED CALCULATIONS PERFORMED BY A CENTRALIZED MULTI-AGENT SYSTEM
V. A. Litvinenko, S.A. Khovanskov , V. S. Khovanskovа2021-11-14Abstract ▼We consider the issues of protection of distributed computing organized on the basis of a multiagent
system for solving problems of multivariate modeling. When modeling, choosing one of the many
options may require going through a huge set of parameters that are not available for a high-speed
computer. Distributed computing is used to reduce the time needed to solve such problems. There are
many different approaches for organizing distributed computing in a computer network: grid technology,
metacomputing (BOINC, PVM, and others). All of them are intended for creating centralized distributed
computing systems. Distributed computing is organized on the basis of a multi-agent system on
the computing nodes of any computer network. When using a large-scale computer network as a computing
environment, there may be security threats to distributed computing. One of these threats is getting
a false result from hackers during calculations. A false result may lead to making an inappropriate or
incorrect decision during the simulation process. Managing agents of a centralized distributed computing
system, in addition to managing a distributed system, are forced to detect false results of the calculation
process. A method has been developed for calculating the probability of detecting a false result
depending on the total number of agents in a multi-agent system and the number of control agents. Examples
of calculating the number of control agents that provide the required probability of detecting
false results in a multi-agent system are given. -
IMPLEMENTATION OF A PROBABLE DEEP NEURAL NETWORK DECODER FOR STABILIZER CODES
S.M. Gushanskiy, V.N. Pukhovsky, V.S. Potapov2021-12-24Abstract ▼Recently, there has been a rapid increase in interest in quantum computers. Their work is
based on the use of quantum-mechanical phenomena such as superposition and entanglement for
computing to transform input data into outputs that can actually provide effective performance
3–4 orders of magnitude higher than any modern computing devices, which will allow solving the
above and other tasks in real and accelerated time scale. This work is a study of the influence of
the environment on a quantum system of qubits and the results of its implementation. A probabilistic
deep neural network decoder for stabilizer codes has been developed. The issues of error correction
for a three-bit code without state decoding are analyzed and considered. The relevance of
these studies lies in mathematical and software modeling and implementation of correction codes
for correcting several types of quantum errors in the development and implementation of quantum
algorithms for solving classes of problems of a classical nature. The scientific novelty of this direction
is expressed in the elimination of one of the disadvantages of the quantum computational
process. The scientific novelty of this area is primarily expressed in the constant updating and
supplementation of the field of quantum research in a number of areas. -
COMPUTATIONAL ASPECTS OF SOLVING GRID EQUATIONS ON GRAPHICS ACCELERATORS
N.N. Gracheva, V.N. Litvinov, N.B. Rudenko, A.V. Nikitina, А. Е. Chistyakov2021-12-24Abstract ▼To predict emergencies and irreversible consequences of human activities, scientists use
mathematical modeling. When an emergency occurs, it is very important to minimize the decisionmaking
time. The development of the project solution can be based on the forecast of changes in
the modeled process. In the numerical solution of problems of hydrophysics and biological kinetics,
it becomes necessary to develop effective methods for solving systemic equations of large dimension
with a non-self-adjoint operator. The large volume of processed information and the
complexity of computations necessitate the use of computational clusters, which include video
adapters to increase the performance of the computing system and the speed of information processing.
The aim of the research is to develop a solution for a module that implements the algorithm
of the system of linear algebraic equations (SLAE) by the modified alternative triangular
iterative method (MATM) (self-adjoint and non-self-adjoint case) using NVIDIA CUDA technology.
A method for decomposition of the computational domain in a three-dimensional case is described.
A graph model of a parallel pipeline computational process is proposed, focused on the
GPU (Graphics Processing Unit). To determine the two-dimensional configuration of flows in the
computational unit, when performing one step of one step, the MATM is minimal. The studies have
shown that the choice of the method of decomposition of the computational domain in the form of
parallelepipeds must be performed taking into account the architecture of the video adapter. The
developed algorithm and software module make it possible to more effectively use the computational
resources of the GPU used to solve computationally laborious problems of hydrophysics. -
DEVELOPMENT AND ANALYSE VISUAL NAVIGATION SYSTEM FOR AIR AND GROUND-BASED ROBOTS
V. P. Noskov, Y. S. Barichev, О.P. Goydin, А.N. Kuryanov2025-04-27Abstract ▼The work is devoted to solving urgent problems of joint autonomous visual navigation for air and
ground-based robots in urbanized environments. These environments are highly demanded for special
operations, including dense urban areas and buildings, where the use of traditional remote control devices
is limited due to the presence of shielded areas. The proposed solution addresses group navigation tasks
based on data from onboard vision systems during operational reconnaissance of the working area by an
unmanned aerial vehicle (UAV). The results of this reconnaissance enable autonomous movement and
flight, both for individual heterogeneous robotic systems and for groups.The navigation algorithms are
based on methods for extracting a horizontal reference surface and horizontal sections of the external
environment from a volumetric point cloud generated by an onboard lidar. These methods allow for the
precise and rapid determination of all six coordinates of the control object. Cases where the navigation
task cannot be fully solved due to specific environmental characteristics are also considered. To address these challenges, methods are proposed to enhance lidar rangefinding data by integrating video camera
data. An accuracy assessment of the video navigation solutions is provided, obtained through mathematical
modeling of the external environment and the generation of video data. To ensure safe autonomous
flight and movement of robotic systems in urban environments, methods for reducing video navigation
errors are proposed. These methods utilize a specially designed bank of reference images with known
coordinates of their formation. The effectiveness of the applied methods and the proposed video navigation
algorithms is confirmed by experimental studies of the corresponding software and hardware in real
urbanized environments -
DETERMINING THE RELIABILITY OF THE INSTRUMENT SPEED PARAMETER BASED ON THE DYNAMIC CHARACTERISTICS OF THE OBJECT OBTAINED DURING FLIGHT TESTS
А.А. Zadorozhniy2022-03-02Abstract ▼The article describes about of the typical methods of air data parametric quorum control,
and an analysis of their capabilities to determine parametric failures that occur in the air data
system. To perform the calculations, the most common types of failures of the path of perception
and measurement of air pressure of the air signal system, causing catastrophic consequences,
were selected, the physical principles of their occurrence were described, the implementation of
which made it possible to build mathematical models of signal distortion. Based on the results of
modeling the operation of typical quorum control methods, and their response to failures artificially
introduced into the system, the advantages and disadvantages of the methods used are determined.
In order to eliminate the shortcomings found as a result of the analysis, an alternative
method for determining failures of the sensor group of the air data system is proposed by implementing
cross-checking of the parameters obtained from the pneumatic and vane sensor groups of
the system. For the proposed method, the results of modeling based on real flight data of a mainline
aircraft with parametric failures artificially introduced into them are presented. The possibility
of using the cross-checking algorithm in single-channel systems of air signals of small-sized
aircraft is evaluated. The statement of the research problem is formulated as follows: in order to
ensure the flight safety of an aircraft when using information in the control loop from a singlechannel
air data system, it is necessary to ensure the detection and exclusion of unreliable data
from the array of information issued by the system to consumers of information. At the same time,
the task of detecting and excluding data must be solved by the air data system itself, without using
additional data from other aircraft systems. Mathematical analysis, numerical modeling, determination
of correction factors and preparation of initial data were carried out in the MathCAD software
and mathematical complex. Analysis of the results of the studying implemented in the
MathCAD PMC cross-checking algorithm showed that the problem of determining the reliability
of information can be solved autonomously when it implemented a single-channel system of air
signals in an aircraft. -
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. -
METHOD AND ALGORITHM FOR OPERATION PLANNING BASED ON FUZZY FINITE AUTOMATA MODEL
М. V. Knyazeva, А. V. Bozhenyuk, I.N. Rozenberg2022-05-26Abstract ▼In this paper the planning and scheduling problem as an important optimization problem in
many transportation and robotic applications is discussed. To solve planning problems, the main
approaches are based on optimization methods, sampling-based methods, and usually such kinds
of problems are NP-hard and high dimensional. In this work, the method for planning and scheduling
based on the fuzzy finite state machine model is developed. Fuzzy graph presentation of the
scheduling problem and operation planning is given. The paper presents two approaches to the
formulation of the planning problem with limited resources and temporal variables: state-oriented
(with transitions between states), temporal ordering-oriented (on a time scale). Temporal modeling
for planning problems implies a qualitative approach to managing the distribution of operations
or topological ordering, as well as a quantitative approach to handling imprecise durationsrelationships between operations in multiple parameters. The concepts of fuzzy intervals and fuzzy
relations are introduced for planning operations on a graph. A planning algorithm based on the
theory of automata and temporal modeling under uncertainty has been developed. Using this formalism,
a path planning problem is solved by successively altering a state using various operations
until a solution is found. The idea of temporal-ordered partial schedule associated with the
planning state of the system is discussed. A model of a finite automaton for a planning system under
conditions of uncertainty is proposed. A method and algorithm for scheduling operations
based on a non-deterministic finite automaton and an enumeration scheme have been developed.
The non-deterministic computation for a scheduling problem is a decision tree whose root corresponds
to the beginning of the scheduling process, and each branch point in the tree corresponds
to a computation point at which the machine has multiple choices. And the finite state machine
model (automata) for the planning system under uncertainty is suggested. -
EVALUATION OF THE STATE OF DYNAMIC WEIGHING BY THE KALMAN FILTER METHOD
Е.V. Zargaryan, Y.A. Zargaryan, А. Y. Nomerchuk2022-05-26Abstract ▼Currently, due to widespread computerization, the development of automated control systems
is relevant. Due to the development of small businesses, the purchase of commercially available
systems is a very expensive solution. It is possible to create similar control systems based on
inexpensive microprocessor kits (in this particular case, the K1816VE35 microprocessor kit is
used). In the future, such a system will not be difficult to improve, and it is also easy to implementinterfacing with various electronic computers (control from a personal computer). A system for
measuring and regulating bulk raw materials (an automated weighing system) is to be developed,
which provides control of the pneumatic transport automation with a 2-speed rotary dispenser,
through which bulk raw materials are fed to a weighing hopper suspended on a load-bearing device.
Measuring the weight of the bulk mass in the hopper of the scales, followed by automatic
control of unloading of bulk raw materials from the hopper. The profitability of any industrial
operation involving the weighing of raw materials, work in progress and finished products directly
depends on the accuracy of the weight data. However, even when using high-precision weighing
equipment, the method of collecting, recording and processing weight data for the micro ingredients
system may be subject to errors and inaccuracies. This can cause a potential revenue drain
that is difficult to detect and verify. In many cases, it is assumed that the cause of the problem is
related to the weighing equipment, whereas in fact it is related to the traditional data collection
and management system. In many factories where bulk products are mixed in batches, dosing
scales is a manual, time-consuming operation in which the ingredients are weighed separately
before loading into a blender or other technological container. A significant number of such plants
can benefit from the installation of an automated weighing and dosing system. -
MODELING OF THE NON-TURBULENT SURFACE LAYER ELECTRODYNAMIC STRUCTURE
G.V. Kupovkh, A.G. Klovo, V.V. Grivtsov, О. V. Belousova2022-08-09Abstract ▼The article presents an electrodynamic model of the atmospheric surface layer caused by
the action of the electrode effect near the earth's surface, and an analysis of its equations by methods
of similarity theory. Mathematical models of the surface layer electrical state in the approximations
of the classical and turbulent electrode effect are considered separately. In the mathematical
formulation of modeling problems, a number of well-founded physical assumptions were created
that made it possible to obtain analytical solutions to the equations. Analytical formulas have
been obtained for calculating the profiles of aeroion concentrations, the density of the space electric
charge and the electric field in the electrode layer. As a result of mathematical modeling, the
dependences of the electrical characteristics distribution in the surface layer on the values of the
electric field, the degree of air ionization and aerosol pollution of the atmosphere are investigated.
It is shown that the ratio of the electric field values on the earth's surface and at the upper boundary
of the electrode layer is almost constant. The increasing of the electric field, the rate of air
ionization and the presence of sufficient concentration aerosol particles leads to a decrease in the
thickness of the electrode layer and, as a consequence, the scale of distribution of its parameters.
An amplification in the degree of ionization increases, and an increase in the concentration of
aerosol particles in the atmosphere decreases the values of the electric charge density in the surface
layer. Theoretical calculations are in good agreement with experimental data and the results
of numerical modeling of the surface layer electrical structure. The analytical formulas obtained
in the work for calculating the electrical characteristics of the surface layer and the results of
calculations can be useful in solving a number of applied problems of geophysics, in particular for
monitoring the electrical state of the atmosphere. -
OVERVIEW OF 3D PRINTING SLICERS
V.V. Lisyak60-742025-07-31Abstract ▼The article presents an overview of software for preparing three-dimensional models
(3D models) of objects for various purposes for transfer to a 3D printer for printing. It is noted that
recently 3D printing is an integral part of the additive manufacturing process. Specifies that in order to
perform the 3D printing process, created or downloaded 3D models stored in STL files must be translated
into the printer control language. Such a language is called G-code, and the programs that make it
possible are called slicers. It is noted that the main function of the slicer is cutting the object model into
separate layers. The article discusses an overview of slicer programs from various manufacturers. For
each slicer, characteristics are given that reflect its functional content, focus on a certain category of
users, affordability, support for other software, and other characteristics. It is noted that two types of
slicers are known - universal and specialized. Specialized slicers are usually focused on one technology
or one model line of printers, while universal slicers are focused on a wide range of printers. It is indicated
that in recent years, manufacturers have begun to create software that combines the processes of
model development and its translation into G-code. It is noted that the 3D printing process requires the
preliminary setting of many parameter settings involved in the process of printing software and hardware.
The above review shows that almost all slicers, except for the core of the program that calculates
geometric shapes and converts the model to G-code, have six standard settings blocks: for slicer, model,
printer, material, additional services and cutting. The tuning parameters for each of the tuning blocks
are given. The slicer programs considered in the article are selected taking into account the orientation
towards different user groups, their modern functional content, popularity and availability on the Russian
market. -
MODELING RESULTS OF THE TURBULENT SURFACE LAYER ELECTRODYNAMIC STRUCTURE
О.V. Belousova, G.V. Kupovkh, А.G. Klovo, V.V. Grivtsov2022-11-01Abstract ▼The article presents the results of mathematical modeling of turbulent surface layer
electrodynamic structure. A model of a stationary turbulent electrode effect operating near the
earth's surface is used. The analysis of equations by methods of similarity theory allowed us to
make a number of reasonable physical assumptions that allowed us to obtain analytical solutions.
Analytical formulas have been obtained for calculating the profiles of concentrations of small ions
(aeroions), the density of the space electric charge and the electric field strength in a turbulent
electrode layer. As a result of mathematical modeling, the dependences electrical characteristics
in surface layer on the values of the electric field, the turbulent mixing degree and aerosol pollution
of the atmosphere are investigated. It is shown that the parameter of the electrode effect (the
ratio of the values of the electric field strength on the earth's surface and at the upper boundary of
the electrode layer) practically does not depend on atmospheric conditions, whereas the height of
the electrode layer and, accordingly, the scale of the distribution of the electrical characteristics
of the surface layer vary significantly. The intensification of turbulent mixing in the surface layer
leads to an increase in the height of the electrode layer and, as a consequence, the scale of distribution
of its parameters. The strengthening of the electric field or air pollution by aerosol particles of sufficient concentration leads to a decrease in its height. An increase in the concentration
of aerosol particles in the atmosphere reduces the values of the electric charge density at the
earth's surface. Theoretical calculations are in good agreement with experimental data and the
results of numerical modeling of the surface layer electrical structure. The analytical formulas
obtained in the work for calculating the electrical characteristics of the surface layer and the results
of calculations can be useful in solving a number of applied problems of geophysics, in particular
for monitoring the electrical state of the atmosphere. -
STOCHASTIC DYNAMIC MODEL OF UNDERWATER WIRELESS SENSOR NETWORK BASED ON LOUVAIN CLUSTERING ALGORITHM
А.М. Maevsky , V.А. Ryzhov , Т. А. Fedorova , I. V. Kozhemyakin , N.М. Burov62-812025-07-24Abstract ▼Underwater wireless sensor networks (UWSNs) play an important role in monitoring ocean processes, underwater navigation, environmental control and security. However, underwater environment features such as high signal attenuation, limited energy resources and changing network topology create significant challenges in organizing efficient data transmission. To optimize network operation and extend its service life, a clustering method is used to group nodes, reduce the load on communication channels and improve energy efficiency. However, in the event of network node failure, static clustering becomes ineffective, which requires the implementation of dynamic reclustering. The procedure of redistributing node roles and rebuilding the network topology allows maintaining communication stability and minimizing data losses, taking into account the energy balance of the entire network as a whole. This paper examines modern approaches to clustering and reclustering in UWSNs taking into account the energy balance, node failure probability and interference in the transmission medium. The development of adaptive UWSN control methods is an urgent task aimed at increasing the reliability, energy efficiency and durability of underwater communication networks. The article presents a stochastic cross-level model for dynamic three-dimensional PBSNs of arbitrary topology. The model uses a new clustering/reclustering technique based on the Louvain algorithm, a routing protocol built on the Dijkstra method, and a time-domain management (TDMA) method. The proposed PBSN operating model is the basis for the developed simulation complex, which allows assessing the efficiency and reliability of the network, taking into account the loss of connectivity and vulnerabilities for PBSNs of various scales and purposes. As part of the research, a parametric analysis of systematic calculations of the PBSN functional characteristics was performed. The results of the analysis showed that the proposed simulation model provides an increase in the autonomous network operation time and a decrease in the number of lost messages compared to the models of other authors
-
STUDY OF THE APPLICATION OF THE SPIKING NEURAL NETWORK AND FINITE ELEMENT METHOD FOR DIAGNOSTICS OF ROBOT ASSEMBLIES
А. Y. Tamm, Е. А. Barymova, М. I. Kuzmin2025-04-27Abstract ▼One of the key parameters of any modern mechanical system is its vibration and acoustic characteristics,
which have a direct impact on the environment and humans during operation. In this connection, the
task of diagnosing the vibration characteristics of various complex mechanical objects, to which industrial
robotic complexes can be referred, remains relevant. Due to the difficulty in carrying out diagnostics and
experimental debugging of newly developed mechanisms, it is interesting to apply modern approaches to
solving the problem of diagnostics, in particular, with the use of neural networks and numerical methods.
The purpose of this work was to investigate the possibility of joint application of spike neural network and
finite element method for estimation of vibration characteristics on the example of wave gearbox bearing.
The paper describes in detail the algorithm of diagnostics, which includes the stages of development of both
the finite element model of the investigated mechanical system and the development of the neural network
architecture. At the same time, the generation of training and control datasets for the neural network is carried
out on a simplified finite element model having characteristics similar to the detailed one, which is ensured
by the coincidence of the first ten eigenforms of the assembly. The data sets were generated on the
basis of numerical calculations using an explicit scheme of integration in time of a simplified model of a
gearbox with several types of artificially introduced defects similar to those appearing during operation of a
real bearing. To analyze the frequency characteristics, a spike neural network architecture was developed
and further improved on a training set of single defects. As a result of the study it was determined that the
developed spike neural network provides classification of data on the control dataset with 85% accuracy,
which allows us to conclude about the applicability of the proposed method of determining the vibration state
of mechanical systems with the joint use of neural networks and finite element method. -
OBJECT IDENTIFICATION METHOD FOR INTEGRATION WITH ROBOTIC SYSTEMS
N.М. Chernyshov, I. К. Romanova-Bolshakova2025-04-27Abstract ▼The aim of the research is to develop a methodology for identifying and determining the location of objects
under conditions of low visibility and potential changes in their shape, with a focus on extracting parts
created using selective laser sintering (SLS) from a powder medium. The study examines two fundamentally
different approaches to forming control algorithms for a robotic manipulator. The first approach, trust-based, is
based on the assumption of minimal displacement of the object during manipulation. The manipulator moves
along a trajectory calculated from a preliminary three-dimensional model without correction until the moment
of capture. This method is characterized by high operational speed and minimal computational costs. However,
it carries risks such as object deformation due to environmental resistance, displacement of the part upon contact
with the tool, and the inability to capture the object if it deviates significantly from its nominal position.
The second approach, cautious, involves the gradual removal of powder layers to visualize the object and adjust
the trajectory before capture. This method includes several stages: removing the top layer of the medium to
partially expose the part, analyzing data to refine the object's position, and constructing an adaptive trajectory
considering possible displacement. Special attention in the article is given to data generation for training neural
networks, which are used for object identification under noisy conditions. Two methods of artificial modeling of
powder coatings are considered. The primitive method involves expanding the vertices of a three-dimensional
model along their normals with the addition of random noise. The improved method proposes differentiated
powder distribution considering local surface curvature. Subsequent experimental results showed that training a
neural network using real data has low efficiency. Recognition accuracy ranged from 60% to 75%, which is
attributed to the small sample size and the influence of external factors such as lighting and interference. At the
same time, the use of synthetic data, prepared according to the methodology presented in the study, increased
recognition accuracy to 92%. The practical significance of the work lies in the development of a methodology
for searching, detecting, and identifying a part immersed in powder, which can be used to automate postprocessing
processes in industries utilizing selective laser sintering. The developed solutions are adapted for
integration into robotic systems operating under conditions of limited visibility. The proposed methods can be
scaled to a wide range of tasks in additive manufacturing and robotics, making them promising for implementation
in industrial processes. -
DEVELOPMENT OF A DISTRIBUTED CONTROL SYSTEM OF THERMAL PROCESSES IN A HYDRAULIC PRESS
A.L. Liashenko2021-12-24Abstract ▼The necessity of regulating the temperature of the coolant in hydraulic presses, providing
hot gluing of plywood, regulating the pressure in the press channels and maintaining technological
parameters at a given level, is considered. A column hydraulic press P-714-B for hot gluing of
plywood, installed at the Ust-Izhora plywood mill, is considered as a control object. The article
provides a description of the column hydraulic press. To monitor the parameters of the presented
installation of plywood production, it is proposed to consider the heating press plates and plywood
packages as an object with distributed parameters. To develop a mathematical model of the control
object, a functional diagram of this device with the main equipment and technological flows of
the coolant was considered. A technique has been developed for modeling objects of this class as
objects with distributed parameters. Consideration of the processes occurring in the channels of
the heating plates made it possible to formulate differential equations of motion that describe the
flow of the working medium in the system of channels. The developed method of mathematical
modeling of heat propagation in the heating plates of the press and plywood packages made it
possible to draw up a mathematical model for the object under consideration. This mathematical
model turned out to be quite complex, and it is not possible to solve the resulting system of partial
differential equations analytically (to isolate the transfer function). For a numerical analysis of the
considered control object, a discrete model of equations and a computational algorithm were
compiled. In the process of compiling discrete models, the problems of “joining” the boundary
conditions were solved, the stability of the computational scheme was ensured, and the steps of
discretization with respect to spatial variables were selected. Software was specially developed for
computer modeling. With its help, the temperature values at the control points were calculated.
The presented mathematical model made it possible to carry out a numerical experiment, as a
result of which the frequency characteristics of the object under study were obtained. These characteristics
were used in the synthesis of a distributed high-precision controller. -
MODELING AND IDENTIFICATION OF A COGENERATION BOILER UNIT FOR KRAFT PULP PRODUCTION AS A CONTROL PLANT
D.А. Kovalev , D.H. Imaev , S.Е. Dushin226-2412026-09-10Abstract ▼The key unit of kraft pulp production – the recovery boiler – ensures the regeneration of black liquor chemicals. The task of increasing the liquor reduction degree without changing the process technology or equipment design, i.e., through information and algorithmic means – automatic control is relevant. A higher guaranteed quality of recovery is provided by control algorithms synthesized on the basis of adequate mathematical models. The aim of this paper is a system analysis of the recovery boiler as a control plant and the development of an enhanced mathematical model suitable for synthesizing high-quality automatic stabilization systems. The methodology is based on the principle of sequential uncertainty removal for complex control system models (A.A. Vavilov). Unlike known approaches, the dynamics of the recovery boiler in the vicinity of a selected operating point are described as a multi-level LTI-class model. Combining analytical methods with experimental data processing for a specific operating mode made it possible to obtain mathematical models of an actual recovery boiler with parameters determined down to specific numerical values. The developed multilevel linear stationary model of the recovery boiler makes it possible to analyze the dynamics of subsystems and the unit as a whole, the stability of modes and the influence of disturbances, and justify the need to automatically maintain the required quality of technological processes in the vicinity of the selected mode. Symbolic models are applicable to most similar kraft process units. Parameters of fully defined models should be adjusted based on the results of processing data related to a specific unit and mode. The direction of further research is associated with the development of methods for analyzing the dynamics of complex control systems according to models of a hierarchical structure, as well as the synthesis of systems for stabilizing the regime and reducing the influence of perturbations.ls, as well as the synthesis of systems for regime stabilization and mitigation of the influence of disturbances
-
GLOBAL AND LOCAL VARIATIONS OF THE ELECTRIC FIELD IN THE SURFACE ELECTRODE LAYER
О. V. Belousova180-1902026-09-10Abstract ▼The paper presents a mathematical model of the atmospheric surface electrode layer structure, taking into account the combined action of global and local electric field generators. Spatiotemporal modeling is based on the conjugation of harmonic trigonometric functions of the diurnal cycle for local turbulent exchange and for the global total current density. When modeling the behavior of the electric field, analytical solutions of the electrode effect equations in the atmosphere are used. The proposed approach consists of a step-by-step substitution of the total electric current density and the turbulent diffusion coefficient instantaneous values, obtained from the equations of their periodic variation for a specific hour of the day, into analytical formulas for the stationary spatial distribution of the turbulent electrode layer characteristics. The validity of using the quasi-stationary approximation is strictly substantiated by the significant difference (by more than two orders of magnitude) between the time scales of establishing electrical equilibrium in the medium and the period of the global current generator diurnal variation. It was established that the combined modeling mode adequately reproduces the synergistic expansion of the electric field strength (potential gradient) diurnal curve range and the deformation of its profile, characteristic of the summer season in the high mountains. The reliability of the obtained theoretical results was confirmed by their comparison with experimental data from in-situ measurements at the alpine station of Peak Cheget (430 16' N and 420 30' E), located in the Elbrus region at altitudes of 3040 m above sea level.
The analysis demonstrated good quantitative and chronological agreement between the calculated extremes values and the observed diurnal variations in electric field during the summer season. The results obtained can be directly used to improve the accuracy of geophysical monitoring data interpretation -
EVALUATING THE TIAGO BASE MOBILE ROBOT'S PERSON-FOLLOWING ALGORITHM USING REALISTIC DIGITAL HUMAN MODELS IN THE GAZEBO SIMULATOR
Т. R. Gamberov , R.N. Safin , E.V. Chebotareva , Т. G. Tsoy , Е.А. Magid109-1192026-09-10Abstract ▼Digital Human Models (DHMs) are becoming an important tool in robotics, enabling reproducible and controlled validation of computer vision algorithms. Such algorithms include human detection, tracking, and following, which play a key role in autonomous navigation, trajectory planning, and safe robot-human interaction in shared spaces. However, standard simulators are typically limited in the realism of virtual actors, the diversity of their appearance, and the variability of their behavior. These limitations reduce the reliability of the results and make it harder to transfer algorithms to real-world robots. This paper presents a set of customizable DHMs for the Gazebo simulator. The developed models feature variability in clothing, anthropometric parameters, and appearance, and include a library of walking animations simulating various human movement scenarios, including changes in speed and direction. To evaluate the effectiveness of the proposed approach, virtual experiments were conducted in a realistic office environment using the TIAGo Base mobile robot with differential drive, equipped with a 2D lidar and a monocular camera. The efficiency of the user-following algorithm was evaluated in terms of average distance traveled and the number of target loss events (the number of false positives). The results showed that the proposed DHMs make it possible to reproduce complex perception conditions, including occlusions and dynamic obstacles, while providing scalable and systematic validation of algorithms. The obtained data confirm the significance of the developed DHMs for testing robotic systems oriented toward human interaction in conditions close to real-world settings
-
ALGORITHM FOR CONTEXTUAL VALIDATION OF INDICATORS OF BEHAVIOR (IOB) FOR DETECTING STEALTHY ATTACKS IN ICS
Е. S. Abramov , N.Е. Belov , G. Е. Veselov6-202026-09-10Abstract ▼This article addresses the pressing issue of ensuring the information security of critical information infrastructure (CII) amidst the qualitative evolution of cyber threats and the massive shift of threat actors toward stealthy "Living-off-the-Land" (LotL) attacks. Because such attacks are executed using legitimate administration tools, classical indicators of compromise (IOCs) lose their effectiveness, and traditional monitoring systems generate an excessive number of false alarms, thereby provoking "alert fatigue". To solve this problem, the paper proposes a methodology for the contextual validation of indicators of behavior (IOB) in Industrial Control Systems (ICS) networks. The primary scientific result is the developed "reverse enrichment" algorithm, which utilizes the deterministic nature of the technological process as a strict a priori filter. The algorithm verifies every control action by predicting the next state of the system using a state-space mathematical model and checking its membership in a formalized set of safe values, $\Omega_{safe}$. Additionally, the organizational context, $S_{org}$, including shift schedules and maintenance windows, is taken into account. Unlike probabilistic machine learning approaches, this method provides a strict binary criterion for command admissibility. The effectiveness of the proposed approach is confirmed by simulation results based on the verified dataset of the SWaT (Secure Water Treatment) academic testbed. The implementation of physical and organizational filters achieved a 97.7% False Positive Reduction Rate (FPRR) and successfully detected 1,021 out of 1,035 injected stealthy destructive impacts. The average computational latency of the algorithm was 1.4 ms, which fully satisfies the stringent requirements of real-time systems. The proposed method does not require the instrumentation of legacy field equipment and ensures the precise attribution of cyber incidents based on the physical laws of the production cycle.
