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A MODEL OF RESOURCES ALLOCATION INFORMATION PROCESS IN DYNAMIC DISTRIBUTED COMPUTING ENVIRONMENTS
А.B. Klimenko110-1202025-10-01Abstract ▼The article considers the issue of modeling the information process of distributing computing resources in geo-distributed heterogeneous dynamic computing environments. The relevance of the work is due to the fact that by now "cloud" data processing systems are becoming insufficient due to the need to process large volumes of data in real time regime. In this regard, the "fog" and "edge" computing are in use. This implies localization of data processing in order to reduce the time required for this, on the one hand, and on the other hand, limitations on the computing power of devices leads to the need for a distributed solution of computing problems in a heterogeneous, dynamic and geographically distributed environment. This entails the need to develop new methods and algorithms for computing resources allocation, since previously developed methods did not take into account the properties of geographic distribution and dynamics of computing environments. The model of the information process of computing resources allocation proposed in this work includes the parameters of the resource cost of data transfers over the network individually for the nodes participating in the data transfer route, as well as the process of distribution of computing resources, which is what distinguishes it from analogs. The conducted experimental studies confirm the feasibility of the proposed model usage for the computing resources allocation in geo-distributed heterogeneous dynamic computing environments. The practical significance lies in reducing the resource intensity of the process of distribution of computing resources and the process of solving a computing problem
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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. -
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. -
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. -
POPULATION ALGORITHM FOR CONSTRUCTING A TREE OF SOLUTIONS BY METHOD OF CRYSTALLIZATION OF ALTERNATIVES FIELD
B.K. Lebedev , O.B. Lebedev , V. B. Lebedev2020-11-22Abstract ▼In some cases, it becomes necessary to establish a correspondence between the declared
and actual value of a categorical variable on the basis of a set of object characteristics. In this
case, there is a need for a classifier with an optimal sequence of the considered attributes with agiven value of the objective function. The target variable can be: yes, no, variety number, class
number, etc. This paper solves the problem of constructing a classification model in the form of an
optimal sequence of the considered attributes and their values included in the route from the root
vertex to the terminal vertex with a given value of the target variable. If a classifier is required
that includes the possibility of alternative answers, then first, independently from each other, optimal
routes are built for each value of the target variable, and then these routes are combined
("glued") into a single binary decision tree. In the algorithm for constructing a classifier based on
the method of crystallization of a placer of alternatives, each solution Qk is interpreted as an oriented
route Mk on a binary decision tree. Let us call the ordinal number of an element in the directed
route Mk the position siS={si|i=1,2,…,nA}. An element of the route Mk is the pair (xi, ui-),
where xi corresponds to Ai. ui- in the route Mk is an edge outgoing from xi and corresponds to the
value Ai chosen together with Ai. The second index of the element ui- is determined after the choice
of Ai, placed in the position sj+1 adjacent to sj. The work of the decision tree construction algorithm
is based on the use of collective evolutionary memory, which is understood as information
reflecting the history of the search for a solution. The algorithm takes into account the tendency to
use alternatives from the best solutions found. The peculiarities are the presence of an indirect
exchange of information – stigmerges. The totality of data on alternatives and their assessments
constitutes a scattering of alternatives. The key points of the analysis of alternatives in the process
of evolutionary collective adaptation are considered. Experimental studies have shown that the
developed algorithm finds solutions that are not inferior in quality, and sometimes surpass their
counterparts by an average of 3–4 %. The time complexity of the algorithm, obtained experimentally,
lies within O(n2)-O(n3). -
SEARCH POPULATION ALGORITHM FOR VLSI ELEMENTS PLACEMENT
B.K. Lebedev , O. B. Lebedev , V.B. Lebedev2020-11-22Abstract ▼The paper considers a population search algorithm for the placement of VLSI components.
By analogy with the process of the emergence and formation of crystals from matter, the process
of generating a solution by sequential manifestation and concretization of the solution based on an
integral placer of alternatives is called the method of crystallization of a placer of alternatives.
The solution Qk of the placement problem is represented as a bijective mapping Fk = A → P, each
element of the set A corresponds to one single element of the set P and vice versa. The
metaheuristic of crystallization of a placer of alternatives underlying the algorithm searches for
solutions taking into account collective evolutionary memory, which means information reflecting
the history of the search for a solution and the memory of the search procedure. A distinctive feature
of the metaheuristic used is that it takes into account the tendency to use alternatives from the
best found solutions. Compact data structures for storing solution interpretations and memory are
proposed. An algorithm associated with evolutionary memory seeks to memorize and reuse ways
to achieve better results. The developed algorithm belongs to the class of population. The iterative
process of finding solutions includes three stages. At the first stage of each iteration, the constructive
algorithm generates nq solutions Qk. The work of the constructive algorithm is based on the
indicators of the main integral placer of alternatives – the matrix R, which stores the integral indicators
of the solutions obtained at the previous iterations. The process of assigning an item to a
position involves two stages. In the first stage, the element is selected, and in the second stage, the
position pj. In this case, the restriction must be fulfilled: each element corresponds to one position
pj. The estimate ξk of the solution Qk and the estimate of the utility δk of the set of positions Pk selected
by the agents are calculated. The work uses a cyclical method of forming decisions.
In this case, the accumulation of estimates of the integral utility δk in the main integral placer of
alternatives R is performed after the complete formation of the set of solutions Q. At the second
stage of the iteration, the estimates of the integral utility δk are increased in the main integral
placer of alternatives − the matrix R. At the third stage of the iteration, the estimates of the utility
δk of the integral placer of alternatives R are reduced by a priori a given value δ*. The algorithm
ends after the specified number of iterations has been completed. Comparative analysis with other
solution algorithms was carried out on standard test examples (benchmarks) of the IBM corporation,
while the solutions synthesized by the CAF algorithm exceed the solution efficiency of the
known methods by an average of 6%. The time complexity of the algorithm is O(n2)-O(n3) -
MULTICHANNEL SYSTEM DESIGN OPTIMIZATION USING LOGICAL SYNTHESIS FOR QUALITY IMPROVEMENT OF VOLUME VISUALIZATION
N.I. Vitiska, N.A. Gulyaev, V.V. Selyankin2021-02-25Abstract ▼The paper reviewes a problem of optimization and quality improvement of development and
design of multi-channel systems which perform direct volume visualization. Volume visualization
is widely used in modern computer graphics and visualization systems. Volume visualization is
well-know for its requrements - it demands large amounts of data to be processed to produce a
high quality result. The optimization problem is considered as a quality-cost dependence, where
the target is to achieve is the required quality level at minimal cost. The paper proposes a method
for logical synthesis of such systems, which allows to obtain optimal quality-cost ratios depending
on the required parameters. The proposed method allows to achieve a quality level, that is close to
results of a full-search solutions, but it requires a significantly smaller amount of calculations. For
each channel of the system, a set of variables is defined, the optimization of which will ensure the
quality of the resulting images. Based on the optimization parameters, a switching function is constructed
using a Veitch diagram. This approach is implemented programmatically in each channel
of the distributed system in real time, what sets the general scheme of the method. In described
study, experimental research of relatationship between the accuracy of the solution and the
amount of calculations of direct volume visualization in each channel of a distributed system was
performed. A method for optimal image synthesis based equalizing the playback quality in a small
group of channels in a distributed system was developed. -
A NEW ALGORITHM FOR CONSTRUCTING THE SHORTEST TOUR OF A FINITE SET OF DISJOINT CONTOURS ON A PLANE
А. А. Petunin, E.G. Polishchuk, S.S. Ukolov2021-04-04Abstract ▼The problem of tool path routing for the CNC thermal cutting machines is considered.
Pierce points are located at the parts bounding contours, consisting of straight-line segments and
circular arcs. Continuous cutting technique is used, each contour is cut out entirely, and no presampling
occurs, so cutting can start from any point on the contour. General problem of minimizing
the route length is reduced to minimizing the air move length. It is shown to be equivalent to
finding the shortest polyline with vertices on the contours. New algorithm for constructing such a
broken line for fixed order of contour traversing is proposed. The resulting solution is shown to be
a local minimum. Some sufficient conditions are described for the it to be also a global minimum,
which can be easily verified numerically, and some even visually. A technique is described for
automatically taking into account precedence constraints for the practically important case of
nested contours. This also decreases the size of the problem, which has a positive effect on the
optimization time. A heuristic routing algorithm based on the variable neighborhood search (VNS)
is proposed. Alternative approaches to the use of other discrete optimization methods along with
the proposed algorithm for constructing the shortest polyline for solving the complete problem of
continuous cutting, and the resulting difficulties of both theoretical and practical nature are described.
The generalization of the problem of continuous cutting to a wider class of problems of
(generalized) segment cutting is described, which makes it possible to advance in solving the problem
of intermittent cutting. The scheme of application of the proposed algorithm for solving problems
of generalized segment cutting is described. The results of numerical experiments are considered
in comparison with the exact solution of the GTSP problem. -
HYBRID EXECUTION OF QUERIES TO ANALYTICAL DATABASES
P. A. Kurapov2021-07-18Abstract ▼Analytical database engines should benefit from evolving heterogeneous distributed architectures
and utilize their resources efficiently: various accelerators, complex memory hierarchy,
and distributed nature of systems bring performance improvement opportunities. The article reviews
existing approaches for in-memory DBMS query executor implementation using hardware
accelerators, especially GPUs. Massive parallelism and high on-device memory bandwidth make
graphics processors a promising alternative as a core query evaluation device. Existing methods
do not utilize all modern hardware capabilities and usually are bound, performance-wise, by relatively
slow PCIe data transfer in a GPU-as-a-co-processor model for each kernel representing a
single relational algebra operator. Another existing approaches’ issue is explicit code base separation
for relational algebra operators code generation (for CPU and GPU) that significantly
limits possibilities of joint device usage for performance increase and make it less feasible. The
article presents an efficient query execution method using an example of two device classes (CPU
and GPU) by compiling queries into a single, device agnostic, intermediate representation (SPIRV)
and an approach for corresponding hybrid physical query plan optimization based on extended
classical “Exchange” operator with explicit control over heterogeneous resources and parallelism
level available. A cost model composition process using basic compute DBMS patterns benchmarking
and buses bandwidth data for both relational and auxiliary operators is proposed. Potential
processing speedup from holistic query optimization is estimated empirically with a commercial
open source DBMS OmniSci DB. Preliminary results show significant (3-8x, depending on
device choice) possible speedup even with just using the right device for the job. -
OPTIMIZATION-BASED CALIBRATION OF MEMS NAVIGATION SYSTEM
D.E. Chickrin , S.V. Golousov2021-08-11Abstract ▼Technologies of autonomous wheeled robotic systems are becoming more and more in demand
lately. A separate type of application of such technology is an autonomous unmanned
ground vehicle. Unlike other types of transport (air, water), ground vehicles need to periodically
operate in full autonomy - when external communication with the infrastructure and other agents
of the transport network is inaccessible. In such circumstances, the issue of autonomous navigation
comes out on top, and increased requirements are imposed on positioning accuracy, especially
in an anthropogenic environment, for example, when driving in an urban environment, along
narrow mountain roads, and tunnels. One of the components of autonomous navigation is often an
inertial assembly consisting of several accelerometers, gyroscopes, and magnetometers. To obtain
a high-precision navigation solution based on an inertial assembly, it is required to properly calibrate
it. A separate issue is automation and its cost for further scaling necessary for mass production.
The article presents the theory and methodology for automated calibration of an inertial
navigation system based on MEMS sensors by solving an optimization problem. The proposed
technique does not require high-precision calibration equipment. The aim of the presented work is
to develop methods and theory for the calibration of inertial navigation units. The article formulates
general measurement models of sensors included in the inertial assembly, and proposes
methods for calibrating the parameters of accelerometers and gyroscopes fixed relative to each
other. The method of automation of the calibration process is presented, which does not require
high-precision equipment. The results of the application of the developed methods for the calibration
of a real inertial assembly are presented. A stand for automated calibration is presented. -
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. -
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. -
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. -
DEVELOPMENT OF MODIFIED METHODS AND MODELS OF SEARCH ADAPTATION FOR SOLVING THE PROBLEM OF PLANNING VLSI
O.B. Lebedev, А.А. Zhiglatiy, Е.О. Lebedevа2021-12-24Abstract ▼In this work, to solve the VLSI planning problem, a search algorithm has been developed
based on a modified ant colony method. The task of forming a VLSI plan is reduced to the task of
forming the corresponding Polish expression. The developed method for the synthesis of the Polish
expression includes the construction of a tree of cuts, the choice of the types of cuts (H or V), identification
and orientation of modules. The evolving population is split into pairs of agents. Each
member of the population is a pair of agents working together. In this case, the constructive algorithms
A1 and A2 used by the agents of the pair are different. The problem solved by Algorithm A1
is formulated as the problem of finding a one-to-one mapping Fk=M*→P of the set of modules M
with selected orientations, |M*|=|M| to the set P of positions of the template Sh. In fact, the solution
consists in choosing on the graph G1 a subset of edges E*1E1 included in the corresponding
mapping Fk. In Algorithm A2, the graph G2=(X, E2) is developed as a model of the search space
for solutions for choosing the type, sequence and location of cuts in the pattern Sh.
X={(x1i,x2i)|i=1,2,…,n} the set of vertices of the graph G2, corresponds to the set P of potential
positions of the template Sh for the possible placement of the names of the cut symbols in them.
Each potential position piP of the template Sh is modeled by two alternative vertices (x1i,x2i).
The choice of the vertex x1i when placing the cuts indicates that a cut of type V is placed in position
pi, the choice of vertex x2i indicates that a cut of type H is placed in position pi. Each iteration
l of the general algorithm includes an initial and three main stages. The initial stage is as follows.
Co-evolutionary memory matrices are nullified CEM*1 and CEM*2 are reset to zero. At the first
stage, each pair of agents dk=(a1k,a2k): – with constructive algorithms A1 and A2 he synthesizes
his solution Wk=(E1k
*,Sk); – the Polish expression Shk is formed, corresponding to the solution Wk;
– on the basis of Shk, a tree of sections Tk is formed; – on the basis of Tk, the plan Rk is formed and
the estimate of the solution Fk is calculated; – agents deposit (add) the pheromone to the cells of
the collective evolutionary memory (CEM) matrices CEM*1 and CEM*2 corresponding to the
solution edges Wk=(E1k
*,Sk) in the solution search graphs G1 and G2 in an amount proportional
to the solution estimate Fk. At the second stage, the pheromone accumulated in CEM*1 and
CEM*2 by agents of the population at iteration l is added to CEM 1 and CEM2. At the third stage,the pheromone is evaporated on the edges of the graphs G1 and G2. Tests have confirmed the
effectiveness of the proposed method. The time complexity of the algorithm, obtained experimentally,
coincides with theoretical studies and it is O(n2) for the considered test problems. -
SOLUTION OF THE PROBLEM OF INTELLECTUAL DATA ANALYSIS BASED ON BIOINSPIRED ALGORITHM
E.V. Kuliev, D.Y. Zaporozhets, Y.A. Kravchenko, М.М. Semenova2022-01-31Abstract ▼The article discusses a bioinspired algorithm for solving the problems of intellectual analysis.
The integration of bioinspired algorithms for solving data mining problems is a promising
area of research. As a bioinspired algorithm, an algorithm based on the adaptive behavior of an
ant colony is considered. The ant colony algorithm allows for a high-quality search for promising
solutions to obtain optimal and quasi-optimal solutions. The algorithm has the ability to search for
suitable logical conditions. The ant colony algorithm is based on the example of the behavior of
living ants in nature. Ants are able to find the shortest solution by adapting to changes in the environment.
The authors proposed a modified ant colony algorithm for solving the problem of data
mining. The clustering problem was chosen as the task of data mining. Clustering is a combining
of similar objects into groups, is one of the fundamental tasks in the field of data analysis and
Data Mining. The list of application areas where it is applied is wide: image segmentation, marketing,
anti-fraud, forecasting, text analysis and many others. The solution to this problem is of particular relevance in the context of the constantly growing volume of generated, transmitted and
processed data. Classical clustering methods are optimized by combining with the proposed
bioinspired optimization algorithm - the ant algorithm. The proposed method is a model in which
ants are represented as agents that randomly move in the solution space with some restrictions
(for example, obstacles in their path). To determine the effectiveness of the developed modified ant
algorithm (ALA) with the clustering algorithm, the authors carried out a series of computational
experiments. For comparison, we took the genetic algorithm, the monkey algorithm and the wolf
algorithm. The simulation results prove that the clustering-based ant algorithm gives better results
than other proposed algorithms. -
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. -
THE USE OF HETEROGENEOUS COMPUTING NODES IN GRID SYSTEMS IN SOLVING COMBINATORIAL PROBLEMS
А.М. Albertian, I. I. Kurochkin, E.I. Vatutin142-1532021-10-05Abstract ▼The main goal of this work is to create a parallel application that performs computations using a multithreaded execution model, optimized to make the best utilization of all available hardware resources. One of the main implementation requirements is to optimize application per-formance on different computer architectures, and to enable parallel execution of the application on various computing devices that are part of a heterogeneous computing system. The possibility of applying various methods of software and algorithmic optimization on multiprocessor architec-tures of different generations was investigated as well as the effectiveness of their use for highly loaded multithreaded applications was estimated. The problem of quasi-optimal dynamic distribu-tion of computational tasks among all currently available computing devices of a heterogeneous computing system was also solved. Currently, not only multiprocessor computing systems are used to solve large computational problems, but also various types of distributed systems. Distributed computing systems have a number of features: possible failures of nodes and communication channels, unstable operating time of nodes, possible errors in calculations, heterogeneity of com-puting nodes. By heterogeneity of computing nodes, we will understand not only the different com-puting capacity and different architectures of central processors, but also the presence of other devices on the node capable of performing calculations. Such devices include video cards and mathematical coprocessors. A node of a distributed computing system will be called heterogene-ous if, in addition to one or more central processing units, it contains additional computing devic-es. When solving a computational problem on a distributed system, it is necessary to maximize the utilization of all available computing resources. To do this, it is necessary not only to distribute computing subtasks to nodes in accordance with their computing capacity, but also to take into account the features of additional computing devices. This work is devoted to the study of methods for maximizing the resources utilization of heterogeneous nodes.
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NEURAL NETWORK APPROXIMATION OF MODEL-PREDICTIVE CONTROL FOR A DYNAMIC OBJECT STABILIZATION SYSTEM
B.А. Komarov , S.V. Leonov , Т.Е. Mamonova276-2872025-12-30Abstract ▼Relevance. When solving problems of stabilization of dynamic objects, classical model predictive control is widely used. It provides high quality control by solving the optimization problem at each step, but it has significant computing costs, which limits its application in real-time systems with high requirements for update frequency. Therefore, the question of investigating the applicability of a neural network regulator trained on a model predictive regulator (MPC) when solving the problem of stabilizing the position of a dynamic object with a limited computational and time resource is relevant. Goal. The purpose of the presented work was to develop and study a neural network regulator trained on the basis of an MPC regulator to stabilize the position of a dynamic object on a mobile platform. Methods. When performing the work, methods of system analysis, simulation modeling, as well as experimental tests on the bench were used. Results and conclusions. As part of the study, a neural network regulator was developed and trained that approximates the behavior of MPC based on data obtained when controlling a real balancing platform. The training was conducted on the input and output data of the MPC without using the internal model of the system, which made it possible to reproduce the dynamics of the regulator at significantly lower computational costs. Experimental results showed that the neural network model provides a stabilization quality comparable to the original MPC, while the calculation time was reduced from 47 ms to
1.6 ms, which amounted to an acceleration value of 29 times. The proposed approach demonstrates the potential of neural network control methods in the problems of replacing complex optimization regulators for systems with limited computing resources. -
INTELLIGENT METHODS OF PARAMETRIC FORECASTING AND OPTIMIZATION OF UAV TRAJECTORIES
V.I. Danilchenko , V.V. Bova263-2762025-12-30Abstract ▼This paper examines the problem of intelligent parametric forecasting and trajectory optimization for unmanned aircraft systems (UAS) using evolutionary algorithms and machine learning methods. The relevance of the study stems from the multi-criteria and high complexity of UAS trajectory generation processes, as well as the need for accurate and timely assessment of its flight parameters. This is particularly important for ensuring the reliability, safety, and efficient performance of flight missions in UAS operating conditions, including scenarios related to the operation of critical infrastructure facilities. The objective of the study is to improve the accuracy of trajectory parameter diagnostics and the reliability of parametric forecasting of UAS trajectories under conditions of uncertainty and the multi-criteria nature of the problem. The paper proposes a hybrid approach incorporating a genetic algorithm (GA), a particle swarm algorithm (PSO), and an XGBoost machine learning model that provides adaptive assessment of the quality of the generated solutions. A computational software package has been implemented, including selection, recombination, mutation, and elite inheritance mechanisms, as well as a machine learning module for validating route trajectories and associated parameters. A computational experiment was conducted, which compared the effectiveness of GA and PSO under various operating scenarios. Testing was performed on industry-specific datasets with varying numbers of iterations. The computational experiment revealed the advantage of the genetic algorithm, namely, a 14–17% improvement in the quality of design solutions. The results of the study demonstrate high adaptability and practical applicability in modeling, parametric forecasting, and routing tasks, and also indicate the potential for integration with intelligent UAS navigation and monitoring systems. The article's materials are of practical interest to specialists in the field of UAS development and operation, as well as to researchers working on multi-criteria route planning, parametric forecasting, and improving the reliability of UAS operations.
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OPTIMIZATION OF THE COMPUTATIONAL SCHEME FOR THE INTERPOLATION OF DECADAL METEOROLOGICAL DATA BY INVERSE DISTANCE WEIGHTING WITH PARALLEL PROCESSING OF MULTIPLE TIME SLICES
О.М. Golozubov , А.V. Kozlovskiy , E.V. Melnik , Y.E. Melnik , А.N. Samoylov22-322025-12-30Abstract ▼The present study is devoted to solving the problem of computational inefficiency in spatial interpolation of large arrays of decadal meteorological data using the inverse distance weighting method. Traditional approaches involving sequential and independent processing of each time slice demonstrate a linear increase in execution time and significant RAM consumption, which becomes a critical barrier to the rapid construction of detailed and geographically linked raster fields in GeoTIFF format. This significantly limits the use of the method in tasks requiring rapid processing of long-term data archives. The purpose of this work is to develop and validate an optimized computational scheme that can radically reduce time costs while maintaining the completeness and accuracy of the results. The key scientific novelty of the proposed approach lies in the fundamental rethinking of the computational process. Instead of repeating identical operations many times, a scheme is proposed based on a single calculation of the full vector of geodetic distances from each grid cell to all weather stations. This most resource-intensive operation is performed only once. Subsequently, the resulting distance vector is applied to all time slices (decades) to calculate the interpolated values, which eliminates the main computational redundancy and ensures a sublinear dependence of processing time on the number of decades. To further improve performance, a parallel processing mechanism is used at the CPU level, implemented by dynamically dividing the raster into independent computing units (batches). The size of the batches is adaptively adjusted taking into account the available RAM, which guarantees the stability and scalability of the solution on systems of various capacities. The testing of the method on real meteorological data for the period 2015-2024 demonstrated a radical reduction in the execution time. In particular, processing ten decade time slices on a standard laptop takes less than 3.5 minutes, and on a server platform it takes about 3 minutes, which represents a multiple acceleration compared to traditional implementations. Thus, the developed solution makes the operational processing of large spatial and temporal meteorological arrays a reality for a wide range of researchers, opening up new opportunities for climate monitoring, agrometeorology and geoinformation analysis without the need for specialized expensive equipment
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THE MODULE FOR PREDICTING CONVERTER PARAMETERS BASED ON SPECIFIED AMPLITUDE-FREQUENCY CHARACTERISTICS
V.I. Shlaev93-1032025-11-10Abstract ▼The article discusses the solution of the problem of developing converters based on specified amplitude-frequency characteristics. The main problem is to carry out a large number of measuring measures with changes in the parameters of the transducers to achieve the necessary amplitude-frequency characteristics, which leads to high time and resource costs for development. The analysis of the main parameters of the converters affecting the specified amplitude-frequency characteristics is carried out. The existing approaches, methods and algorithms for creating converters of the required characteristics are analyzed. The development of a module for predicting the parameters of electromechanical converters based on specified amplitude-frequency characteristics is described. The research objectives include the creation of structural-parametric and mathematical models for calculating the characteristics of converters at the design stage. An algorithm for training a model based on experimental data obtained during measurements is described. The use of machine learning methods to predict parameters minimizes the number of experiments performed and reduces the cost of developing converters. The proposed approach is based on the use of the relationship between the design parameters of the converters and their frequency characteristics. The gradient boosting algorithm is used to increase the accuracy of forecasting. The stages of data preparation for model training are presented. The learning process of the model is described. The results demonstrate a significant reduction in the modeling time of the converters: the use of the module makes it possible to speed up the process several times compared with the experimental approach. Predicting characteristics based on a model provides comparable accuracy with a larger amount of data. The findings of the study confirm the effectiveness of the proposed approach in the development of converters, reducing time and financial costs, increasing the accuracy of modeling and applicability in conditions of limited resources.
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MULTI-STAGE ANT ALGORITHM OF ONE-DIMENSIONAL PACKING BASED ON EFFICIENT DECISION ENCODING METHODS AND TWO-LEVEL EVOLUTIONARY MEMORY
М.А. Ganzhur , B.К. Lebedev , О.B. Lebedev21-372025-10-01Abstract ▼The aim of the work is to develop and study bioinspired search methods for solving problems of one-dimensional packaging in identical containers based on effective algorithms for encoding and decoding solutions, composite criteria and a two-level structure of evolutionary memory. The paper proposes the structure of an ordered code for packing one-dimensional elements into identical containers, the main advantage of which is that one packaging solution corresponds to one code and vice versa. The search procedure is based on the modified metaheuristics of the ant algorithm. At each iteration, the one-dimensional packing algorithm has a multistep structure. The stages are performed sequentially one after the other, starting from the first one. Each stage of the Сk includes procedures performed by the zk agent. The number of stages is equal to the number of agents in the population plus the final iteration stage.
The main task solved by the constructive algorithm at the Сk stage is to construct the Rk code for packing a set of X elements into identical containers. The stage is divided into periods according to the number of lists Xjk generated by the agent zk. The period is divided into stages. In each period, the following tasks are solved sequentially in stages: agent zk constructively generates a set Rk of ordered lists Xjk of onedimensional packaging in identical containers; fjk estimates of the packaging of each container Oj by elements of the list <Xjk> are calculated; the amount of λjk pheromone proportional to the fjk estimate is calculated; the estimate Wk=∑i(fjk) is calculated one-dimensional packing of a set of elements X into H identical containers; pheromone is deposited on the edges of graph G corresponding to the list Xjk in the cells of the accumulative memory matrix E of the second level. After all agents of the zk population Z have formed ordered lists of Rk, the accumulated pheromone is added to the main memory matrix Φ of the first level. For each Rk, the total Fk indicator of the packaging quality of the set of X elements is calculated. The final operation in the iteration is pheromone evaporation on the edges of graph G and fixation of zk with the best Fk. Experimental studies have been conducted to determine the quality of the method's operation on large-dimensional test sets. To compare the developed algorithm with known methods and approximate algorithms, the authors selected several groups of benchmarks from various sources -
OPTIMIZATION OF PID PARAMETERS OF SERVO SYSTEMS USING A GENETIC ALGORITHM AND A NEURAL NETWORK CLASSIFIER
Ahmad Zoualfikar , Y.А. Kravchenko , А.М. Mansour237-2502025-10-01Abstract ▼Machine learning algorithms play a vital role in enhancing the performance of industrial systems, providing high precision and operational efficiency in real time. In servo motor control systems, these algorithms help reduce noise and vibration, improving efficiency and extending equipment lifespan. This article examines various types of noise that occur and their negative impact on industrial processes. The primary research objective is to optimize PID controller parameters in servo systems using a combined algorithm that combines neural networks and genetic algorithms. Unlike traditional methods such as genetic algorithms (GA) and particle swarm optimization (PSO), which suffer from slow convergence and risk of motor damage, the proposed solution is based on a control software platform. This platform ensures safe real-time interaction with the servo motor. A CAN Bus-based control system has been developed that enables developers to: read all servo motor parameters (speed, current, voltage, encoder position); modify PID coefficients with a single click, eliminating the need for manual tuning as in MOTO-MASTER. The implementation of the developed control system allowed the use of a trained neural classifier to constrain PID parameters within safe limits, reducing search space and accelerating the optimization process. Experimental results on SPH-S servo motors demonstrated significant reduction in noise and mechanical vibrations during real-time operation while maintaining stability across a wide speed range (0-1500 rpm).
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METAHEURISTICS BASED ON THE BEHAVIOR OF A COLONY OF WHITE MOLES
Y.V. Danilchenko, V. I. Danilchenko, V. М. Kureichik132-1402021-08-12Abstract ▼Optimization algorithms inspired by the natural world have turned into powerful tools for solv-ing complex problems. However, they still have some disadvantages that require the study of new and more advanced optimization algorithms. In this regard, when solving NP complete problems, there is a need to develop new methods for solving this class of problems. One of these methods can be metaheuristics based on the behavior of a colony of white moles. This paper proposes a new metaheuristic algorithm called the blind white moles algorithm. This algorithm was developed based on the social behavior of blind moles in search of food and protecting the colony from intruders. The proposed solution will be able to overcome many disadvantages of conventional optimization algo-rithms, including falling into the trap of local minima or a low convergence rate. The purpose of this work is to develop an algorithm for optimizing a complex objective function. The scientific novelty lies in the development of a genetic algorithm based on the behavior of a colony of white moles for solving NP complete problems. The problem statement in this paper is as follows: to optimize the search for solutions to complex functions by applying an algorithm based on the behavior of a colony of white moles. The practical value of the work lies in the creation of a new search architecture that allows using the developed algorithm for the effective solution of NP complete problems, as well as conducting a comparative analysis with existing analogues. The fundamental difference from the known approaches is in the application of a new bioinspired search structure based on the behavior of a colony of white moles, which will allow to exclude falling into a local minimum or a low conver-gence rate. The presented results of the computational experiment showed the advantages of the pro-posed multidimensional approach to solving the problems of placing VLSI elements in comparison with existing analogues. Thus, the problem of creating methods, algorithms and software for solving NP complete problems is currently of particular relevance
