No. 2 (2023)

To improve the quality of manufactured products, it is necessary to improve all technological
processes, which requires increasing the accuracy of the entire measuring path as a whole.
For this it is necessary to carefully analyze systematic, random and fluctuating errors in the measurement
channel and take all measures to reduce them. Digital filtering or averaging of intermediate
measurements (observations) according to certain rules is a radical means of improving the
accuracy of measurements performed. The aim of this work is to compare the quality of suppression
of near-real noise interference using the eight most well-known averaging methods. A model of the measurement path and a general block diagram for modeling the measurement process on a
computer under the influence of random interference are proposed for eight averaging algorithms.
As a criterion for evaluating the quality of averaging methods, the ratios of absolute error variances
and mean square deviations before the computing device and after applying the specified averaging
algorithm are taken. Based on the simulation results, the following conclusions are made. 1. All averaging
algorithms provide suppression of random error components of complex interference to the
level of 40–60 dB. Three algorithms are the best: arithmetic mean AR, a-truncated mean AU5 and atenderized
mean AB5, which provide for the suppression of 5 % of anomalous results. With an increase
in the number of observations, the suppression coefficients increase proportionally. 2. The
sampling time must be a multiple of the duration of the 50 Hz AC mains period (20 ms). The optimal
number of observations (measurements) is 100–128; with 128 measurements, the division operation
is reduced to a simple shift, and the averaging result can be obtained in 1–2 μs. 3. When experimentally
applying the AR averaging method for filtering a highly noisy measurement signal in a communication
line with a length of 800 m, a decrease in the spread of ADC output codes was observed
from ± 3.5 % to ± 0.1 % after filtering (AR, 64 measurements in 40 ms).

A bionic search architecture has been developed to solve the problem of placing VLSI elements
based on the hybridization of the algorithms of a bee colony and a swarm of chromosomes,
which allows you to get out of "local holes" and increases the convergence of the placement algorithm. The initial iterations are implemented by the bee algorithm to provide a broad overview of
the search area, and the final iterations are implemented by the chromosome swarm algorithm,
which ensures the exact localization of the extremum found by the bee algorithm. Agents are represented
as a population of chromosomes, which are genotypes for solving the placement problem.
The paper describes a modified paradigm of a swarm of chromosomes, which, in contrast to the
canonical method, provides the possibility of searching for solutions in the affine space of positions
with integer values of the parameters. In the search population method of optimization by a
swarm of chromosomes, the agents of the population are chromosomes. The chromosome is the
genotype of the optimization object. The essence of the search procedure is the successive change
of the states of the object of optimization (chromosome) by the directed mutation operator and the
search for the optimal state. An affine-relaxation model (ARM) of a swarm of chromosomes is
proposed - this is a graph whose vertices correspond to chromosomes, and arcs correspond to
affine bonds between them. The transition of the chromosome to a new state is carried out using a
relaxation procedure. In the work, the directed mutation operator acts as a means of changing the
solution, the essence of which is to change the integer values of genes in the chromosome. The
purpose of the transition is to reduce the weight of the affine bond between chromosomes. The
mechanisms of the directed mutation operator are described. A modified structure of the bee algorithm
is proposed. For each base chromosome, a probabilistic choice of a set of chromosomes
located in the vicinity of the base chromosome is implemented. It is possible to improve the quality
of the developed algorithm by adjusting the values of the control parameters. The time complexity
of the algorithm for fixed values of the population size and the number of generations is O(n). In
general, the dependence of the running time of the hybrid algorithm is O(n2) – O(n3).

Currently, the physics of clouds and active impacts on them is gradually moving from the
stage of studying "elementary" cloud processes to the stage of studying clouds as a whole, taking
into account their systemic properties. One of the directions of research at this stage is the study of
the role of the system properties of clouds in the formation of their macro- and microstructural
characteristics. These properties are the main factors influencing the formation of the cloud structure.
The article presents some results of research on the role of one of these properties of clouds,
namely the interaction of clouds with their surrounding atmosphere (the property of the hierarchy
of systems). The mechanism caused by the aerosol composition of the atmosphere is considered as
a method of their interaction. The research methodology is based on the assumption that the intensity
of crystal formation in clouds is influenced by the content of aerosol particles of sufficient
concentration in the atmosphere with ice-forming properties (sublimation nuclei). A threedimensional unsteady model of convective clouds was used for calculations. The intensity of crystal
formation in the cloud was changed by varying the value of the parameter in the expression for
the crystal source in the model equations for the cloud environment. The paper also discusses the
current state of the problem of active impacts on convective clouds in order to control precipitation
processes. In order to carry out successful active exposure, it is necessary to determine the
local area in the cloud in which conditions are favorable for exposure and the concentration of
reagent particles that should be provided in this area at any given time. Model calculations
showed that a slight increase in the content of aerosol particles in the atmosphere led to an increase
in the values of maximum ice content and total water content, as well as ice content, while
simultaneously reducing the maximum water content in the cloud. At the same time, its macrostructural
characteristics have practically not changed. Further solving the problems requires the
development of effective methodologies for modeling active exposure to convective clouds.

The aim of the study is to evaluate the technical and economic efficiency of a robotic complex designed
to eliminate man-made emergencies associated with the need to eliminate fires. These emergencies
can occur, first of all, in radiation and chemical accidents, as well as in accidents at fire and explosion
hazardous facilities. The elimination of such emergencies, as a rule, is associated with an increased risk
for firefighters and rescuers and requires the use of heavy equipment. It is proposed to develop and use a
robotic complex for multi-mode fire extinguishing (RTK-PM) in the following composition; six specialized
robotic means with the possibility of crew and remote control, including: barriers (RTS-RZ), fire (RTS-P),
high-rise (RTS-VS), sleeve (RTS-RK), pumping (RTS-NS) and gas stations (RTS-ZV); mobile control
point; supply machines. At the same time, the actual problem is the development of approaches to assessing the technical and economic efficiency of robotic tools and complexes used to eliminate emergency
situations. A technique for conducting such studies is proposed on the basis of a two-level quantitativequalitative
comparative assessment in terms of technical and economic efficiency of the task. To assess
the technical efficiency, it is proposed to consider the deployment time, the time to extinguish the fire, the
time to complete the task, the amount of fire extinguishing agents as the main indicators. As criteria for
technical efficiency, it is proposed to evaluate the coefficient of efficiency of the task, the coefficient of
deployment mobility; coefficient of fire extinguishing efficiency; the degree of efficiency of RTC
robotization. Qualitative comparison consists in identifying the types of work available to one of the compared
complexes and inaccessible to another. The evaluation of the economic efficiency of the RTK is
carried out by the value of the annual economic effect from the production and use of a unit of the complex.
Like the assessment of technical efficiency, the assessment under consideration is also comparative -
it shows an advantage (or disadvantage) over the replaced (basic) complex. Also presented are the results
of applying this technique when calculating the effectiveness of a promising robotic complex for multimode
fire extinguishing, which show that the use of one robotic complex for multi-mode fire extinguishing
will provide an economic effect in the amount of 1.39 million rubles. in year.

The aim of the study is to improve the efficiency of high-level design of robotic systems in terms
of management of computing and diagnostics of on-board computer software. Three problems are
considered: assignment, scheduling and diagnostics. The first problem, the assignment task, is to
determine the necessary resources and build the distribution of program modules among the onboard
computer processors in accordance with a given criterion. The article presents a mathematical
formulation of the problem, algorithms are given, and the presence of areas of effective dominance of
algorithms depending on the selected criterion is shown. The second problem, the scheduling problem,
is to determine the sequence of tasks in multi-channel systems in accordance with a given criterion.
A mathematical formulation of the problem is given, algorithms and the results of their study
are given. A feature of the scheduling algorithms under consideration is the use of a unified approach,
namely the concept of the dominance relation between processors and the construction of
solvable classes of systems. The third problem is software diagnostics. The complexity of the task of
diagnosing computer systems is determined not only by their high dimensionality, but also by the
multiplicity of causes of violations. The source of violations of the computing process can be both
hardware failures and errors in the organization of calculations made by developers. The article uses
a hierarchical approach, in this case, the components of the system, connected by an inclusion relation,
are arranged in accordance with the level of complexity in such a way that the model of higherlevel
components is represented by a composition of lower-level models. For each level, own diagnostic
tools are synthesized, focused on failures of information links between the components of the
previous level. The article proposes an approach to test diagnostics using a network dynamic model,
which involves the introduction of redundancy in order to simplify the diagnostic experiment and
reduce the complexity of its preparation. This approach allows you to automatically synthesize diagnostic
tools and detect violations in the addressing of information exchanges when the software of
robotic systems works as intended. Approbation of the algorithms under consideration was carried
out using software developed by the author at the stage of designing on-board systems at JSC «Concern
«Central Research Institute «Elektropribor».

The purpose of this article is to substantiate the indicator for evaluating the effectiveness of
measures to ensure the security of personal data of the personnel body of an industrial complex
enterprise (hereinafter - the defense industry). To build a mathematical model of the probability of
a threat, the methodological apparatus for assessing current threats to information security,
formed on the basis of regulatory and methodological documents of the FSTEC of Russia, is used.
The article presents the author's interpretation of the main methodological provisions presented in
the documents under consideration in relation to the assessment of threats to the security of personal
data (hereinafter - PD) of the personnel body of the defense industry enterprise. The peculiarity
of identifying vulnerabilities of information resources of the personnel body of the defense
industry enterprise, through which it is possible to implement threats to the security of PD, is the
use of calculation methods that allow to establish the fact of the potential possibility of a threat. o
determine the vulnerabilities of the information resources of the personnel body of the defense
industry enterprise to the implementation of threats to the security of PD, an expert analysis of the
information environment of the IP is carried out. As a result, a set is formed, the elements of which
determine vulnerabilities. Thus, it is necessary to consider a mathematical model of the probabilistic
characteristics of the occurrence of a threat to the security of the PD of the personnel body of
the defense industry enterprise as a product of the probabilities of preventing unauthorized copying,
unauthorized modification and blocking access to the information resources of the IS of the
personnel body of the defense industry enterprise

The purpose of this work is to develop a model of keyboard monitoring of information system operators,
based on the use of a chain method of accounting keyboard handwriting parameters. The specified
method provides estimation of operator's keyboard handwriting on chains of characters of given
length, reflecting linguistically related parameters of keyboard set, characteristic for the given operator.
The keyboard typing of such chains by the operator with "good" keyboard handwriting has significantly
higher individuality due to correlation dependences between the time parameters of successive characters
and pauses. As a result, the chain method allows to provide higher accuracy of operator's identity
verification. Keyboard monitoring based on the chain method is proposed to be implemented in the
basis of artificial immune systems using an immunological model of clonal selection, in which the detectors
are represented by identifying parameters of the distribution area of the keyboard parameters of
"friend". In the tasks of keyboard monitoring the area of distribution of keyboard parameters of the
verified operator is always significantly less than the cumulative area of distribution of keyboard parameters
of other possible operators. The choice of the specified model allows to significantly reduce the
required volume of the detector population, and as a consequence - to significantly reduce the verification
time of the working operator. The decision to replace "friend" operator with "stranger" is proposed
to be considered reasonable when the frequency of operation of detectors exceeds the established
threshold value. The proposed immunological model has a number of advantages. The use of the chain
method of keyboard parameters accounting allows to verify the operator with greater accuracy in comparison
with traditional methods. The clonal selection model in combination with vector representation
of the keyboard data allows to significantly speed up the learning process and reduce the time required
to make a timely decision on the presence of a "stranger" operator. An important advantage of the model
is the ability to learn solely from the examples of keyboard handwriting operationally available
"friend" operators. The use of the clonal selection model also makes it possible to significantly reduce
the required volume of the population of detectors capable of effectively "covering" the distribution area
of the keyboard parameters of "friend" operator

This article discusses a classifier of radar signals reflected from unmanned aerial vehicles
(UAVs), based on neural networks. In the proposed classifier, for the formation of training data, a model
of scattering of radar signals from UAVs is used. Recently, the demand for UAV classification has
been quite high due to a significant increase in the number of models and sales of these devices. Increasing
the computing power of processors and the development of the theory of neural networks allows you
to create new types of classifiers. When using models, it is possible to create a set of training data that is
acceptable for training a classifier neural network. The convolutional neural network of the classifier is
trained using radar images obtained using the proposed model of scattering of radar signals from
UAVs. The resulting radar images are modeled taking into account the UAV orientation angles relative
to the UAV normal coordinate system, flight speed, and various propeller parameters of the simulated
UAV. To form training data, in addition to the signal structure, white noise of a certain configuration is
added, which helps to increase the diversity of training samples to improve the learning ability of the
convolutional neural network. The use of data obtained using the model for training a neural network is
due to the need to use a large number of training samples with various UAV movement parameters,
such as height, speed, direction, orientation in space, as well as a wide variety of possible configurations
of unmanned aerial vehicles: tricopter (three propellers), quadcopter (four propellers), hexacopter
(six propellers), or octocopter (eight propellers). which complicates the use of experimental data to
create classifiers of this type.

The key problems of ensuring the conditions for the development of society are now becoming the
adaptation of various fields of activity to climate change and the reduction of risks associated with dangerous
weather events. The article discusses possible approaches to reducing the risks in agriculture
associated with hail damage, touches upon the features of their information support. One method for
solving this problem and a model for its implementation, developed in the framework of decision theory,
are proposed. The method for solving the problem is classified by us as "passive" methods, which do not
imply interference in the processes of formation of hail precipitation in clouds. As a risk mitigation
mechanism, the proposed method uses the fact that the vulnerability of crops to hail is different for different
crops. Accordingly, risk management is carried out by selecting the structure of agricultural production,
taking into account the peculiarities of their vulnerability to this weather event, as well as the
conditions imposed on the volume of agricultural production. The article discusses the main tasks that
arise in the way of the practical use of this method. To analyze the effectiveness of the method for the
production and economic conditions of the steppe climatic zone of the Kabardino-Balkarian Republic,
model calculations were carried out. At the same time, to determine the possible states of hail processes,
the frequency of hail fall (the number of days with hail in the territory under consideration per year) was
used. Using the time series of this indicator for the period 1958-2018, the frequency of hail in the considered
climatic zone was presented as a discrete random variable with a known distribution law. This
made it possible to consider the problem of reducing losses in agriculture as a problem of decision making under risk. The results of model calculations showed the high efficiency of the method for reducing
the losses of agriculture from hail damage. An important advantage of the method is that its practical
use will be associated with insignificant costs

Today pressure transmitters have high requirements such as reliability, quality, measurement
accuracy, the ability to work in extreme conditions and resistance to aggressive environments.
The main problems in achieving these target indicators are: the high cost of the original
products, the laboriousness of the technological process in serial production, and the limitations
that affect the accuracy of the indicators of the original devices. Solving these problems is the
subject of this article. To solve these problems and improve the physical and mechanical properties
of pressure sensor’s sensitive elements, the following tasks are considered in the article: development
of a pressure-sensing element design based on the silicon-on-sapphire (SOS) structureresearch on the method of its connection with a ceramic body element and development of a technological
route for manufacturing the structure based on “sapphire – vitreous dielectric – ceramic”
junction. As a result, the pressure sensor based on the SOS structure has high sensitivity,
stability, practically no mechanical hysteresis, and can operate in a wide temperature range from
-60 to +350°C when exposed to radiation. In turn, the use of a ceramic base makes it possible to
reduce the temperature error of the sensor due to better matching of the coefficient of linear thermal
expansion (CLTE) of ceramic (85–100×10-7 K-1) and sapphire substrate (60–75×10-7 K-1), as
well as reduce the cost of the technological process due to the use of ceramics instead of expensive
titanium alloys and complex metalworking. Thus, the structure "sapphire – vitreous dielectric –
ceramic" shows the possibility of increasing the sensitivity of the sensor and reducing the error
while expanding its functionality, simplifying the design and improving manufacturability

The purpose of the article is to analyze the combined effect of amplitude white Gaussian noise
(AWGN) present in the communication channel and frequency noise (FN) resulting from fluctuations in
the frequency of the signal in the communication channel on the probability of bit error when processing
QAM signals. Research tasks to be solved: 1. Development of a mathematical model for processing the
QAM signal, taking into account the combined effects of AWGN and FN in the communication channel.
2. Numerical study of the combined effect of AWGN and FN on the probability of bit error when processing
QAM signals. A mathematical model is proposed that establishes the relationship between the
signal-to-noise ratio in the channel and the mean square deviation of the signal frequency, on the one
hand, and the probability of bit error during A mathematical model is proposed that establishes the
relationship between the signal-to-noise ratio in the channel and the mean square deviation of the signal
frequency, on the one hand, and the probability of bit error during QAM signal demodulation, on the
other. The visualization of the effects associated with the presence of AWGN and FN in the channel on
the signal constellation of the received QAM signal is given. The main patterns associated with joint
action AWGN and FN in the communication channel are: - the appearance of FN in the communication
channel leads to a decrease in the signal level in the channel during the correlation processing of the
received signal and a corresponding decrease in SNR; - In addition to the blurring of the signal constellation
in the azimuthal direction, associated with the appearance of an integral phase fluctuation due to
frequency fluctuations during the pulse, an increase in the blurring of the signal constellation in the
radial direction causes a decrease in the SNR. Based on the results obtained, it is concluded that it is
necessary to take into account more fully the deviations of the signal parameters in the channel due to
both the presence of AWGN and FN.

In the 80s of the last century, integrated injection logic (I2L) was widely used as an element
base. Somewhat later, in the development of I2L, injection-field logic (IPL) appeared for the construction
of VLSI. Both element bases are close in the degree of integration on a chip. An increase in
the degree of integration into VLSI can be achieved using self-displacement of regions, in which the
introduction of impurities of different types is carried out using a single boundary of the masking
material. In this paper, this principle is used to create a vertical channel of a key field-effect transistor
of IPL logic. In a p-type epitaxial film deposited on an n+-type substrate, an n-type region with a
depth greater than the thickness of the epitaxial film is sequentially created first, and then impurity
diffusion is performed in the same window with the creation of a region p-type. The gap between
these n-type regions is the channel of the field-effect transistor being formed. Next, a shallow n+-type
region is created that overlaps the channel, which is the drain region of a key field-effect transistor
with a vertical channel, the p-type diffusion region is a gate, and the uniformly alloyed region of the
epitaxial film performs the function of an injector. Branching along the outlet in this IPL structure is
provided by placing several drains along the perimeter of the channel. Due to this geometry, the
structure has a greater reproducibility of parameters compared to the basic design of the IPL. Topological
variants of the implementation of the IPL cell and schemes based on it are considered:
schemes 6 OR-NOT and Dt-trigger. The proposed design and technological version of the IPL cell
can be recommended for creating VLSI of a high degree of integration

The effectiveness of the use of autonomous mobile robots largely depends on the motion control
system. For a crew car, the question of selecting the optimal driving speed is decided by the driver. The
speed of movement of autonomous robots, especially over very rough terrain, is significantly lower and
this is caused by the operation of an autonomous control system. In tracked chassis, one of the components
of speed is known to have such a property as agility, which characterizes the controllability of a
vehicle under specified conditions. The aim of the study is to increase the efficiency of automatic control
systems (ACS) for the movement of ground-based robotic systems for military purposes (RTK VN) on the
course based on the application of the method of two-circuit systems equivalent to combined. The use of
automatic control systems equivalent to combined systems makes it possible to increase the accuracy of
automatic control systems by reducing the value of the dynamic error, that is, achieving error invariance
without violating the stability of the system. The objective of the study is to experimentally determine
the dependences of the steady-state value of the error of reproducing the angle of the course with
constant and linear input influences in single-circuit and double-circuit automatic motion control
systems of the RTK. In the course of the work, it was proposed to draw up structural diagrams of
automatic traffic control systems of the RTK VN along the course angle, based on structural diagrams
to develop models for conducting experimental studies of the proposed approach by computer
modeling methods. In the course of the study, it is proposed to analyze the accuracy of reproducing
the angle of the course based on the data obtained as a result of modeling. As part of the ongoing
work, the task of building motion control systems for autonomous mobile tracked vehicles and robots
is considered. A model of the automatic motion control system of tracked RTCs is proposed based on
the application of the method of two-circuit systems equivalent to combined ones. The simulation
results confirm the efficiency of the proposed approach and show that it is possible to obtain improved
indicators of the functioning of the control system in terms of accuracy and efficiency.
The method proposed in the article allows us to solve the problem of improving the efficiency of the
RTK motion control system along the course angle in offline mode.

Three circuit techniques are proposed that provide (with simultaneous use) an increase by
more than two orders of magnitude of the maximum output voltage slew rate (SR) of microelectronic
operational amplifiers (op-amps) based on bipolar transistors with a classical architecture,
designed to operate in automatic control systems, radio engineering and communications, for
example, as drivers for ultra-high-speed analog-to-digital converters (EVIOAS150, EVIOAS350,
AD9208, AD9691, 1273PV14, etc.). The considered op-amps contain a cascode input stage with a
non-linear correction of the pass-through characteristic and a tracking circuit that increases the
attenuation coefficient of the input common-mode signals and the noise suppression coefficient on
the power buses, as well as an intermediate stage based on a “folded” cascode. The use of a
"folded" cascode makes it possible to increase the efficiency of using power supply voltages, as
well as to increase the unity gain frequency of the corrected op-amp. However, such an intermediate
stage is an essential non-linear link that limits the maximum output currents that recharge the
op-amp correction capacitor. The results of computer simulation of two modifications of the
AmpSR1, AmpSR2 op amps, which differ from each other in the structure of a nonlinear parallel
channel, which eliminates the dynamic overload of a "folded" cascode, are presented. The relevance
of the research performed is related to the problems of import substitution in the class of
high-speed op-amps and the lack of new and promising ideas for increasing the SR of the op-amp
based on the simultaneous use of non-linear and differentiating transient correction circuits in the
large signal mode among analog circuit designers. The considered circuit techniques are also
effective when using CMOS technological processes.

The use of filters matched to radio signals is quite common in radar, which helps to improve
range resolution, as well as in communication systems and many other radio engineering systems,
allowing you to increase the output signal-to-noise ratio (SNR). Designing digital devices on field
programmable gate array (FPGA) allows us to configure them quite flexibly and create prototypes
of radio engineering systems for further implementation of DSP algorithms, on applicationspecific
integrated circuits (ASIC ), GPU, CPU, etc. FPGA digital devices are most used in low
power mobile systems, while ASICs show the highest performance with high development costs.
In this work, special attention is paid to the design and implementation of a filter matched to a
complex chirp signal in the frequency domain on an FPGA using the Xilinx System Generator for
DSP library of Matlab/Simulink. The results of the hardware-software model operation are presented
in paper both for a single point object and for three point objects with different sampled
delays. The dependence of the output on the input SNR for a linear and quadratic envelope detector
is shown. The analytical curve SNROUT(SNRIN) is compared with the curve obtained using the
developed hardware-software model implemented on the FPGA. The paper shows the benefits of
using Xilinx System Generator for rapid prototyping of DSPs on FPGAs, and it provides an analysis
of the used FPGA resources for the developed matched filter.

In the framework of the drift-diffusion model of carrier transport in the bulk of III–V type
semiconductors, under external action of strong constant electric and magnetic fields orthogonally
oriented relative to each other, a new principle for applying the discovered effects is proposed, which
take into account the nonlinearities of the output parameters of the working chip, which leads to the
possibility creation of new semiconductor structures controlled by a magnetic field (SSCMF). Previously,
the diffusion component of the output current density was not taken into account as a separate
effect arising under the orthogonal action of strong electric and magnetic components, which was at
first time considered in this paper. It is shown that this component is a part of the inductive transverse
output current and can be considered as an independent effect. The proposed practical application
is based on the classical relations that describe the component spatial representation of the effective
mass energy dependence and the parameters of the kinetic equations for the carriers drift and
heating in the bulk of highly mobile III–V type semiconductors structures. (The energy dependence of
the reciprocal effective mass value was obtained under the assumption that this parameter becomes
heavier in the framework of the two-valley representation. However, the mechanism of such increasing
is not considered in detail, but is taken into account as a result of expansion in a Taylor series.)
At the same time, some new phenomena were also hypothetically discovered: a diffusion detector
effect and a transverse induction effect controlled by a magnetic field, similar in its manifestation to
the Gunn effect observed in this direction. The results obtained open the prospect for creating fundamentally
new frequency-converting devices based on the above SSCMF, such as autodyne-type converters
(mixers), one of the designs of which is also proposed in this work in a waveguide version. In
the case of experimental confirmation of the discovered effects, which can be investigated using the
block diagram of the measuring setup proposed in the work, we can conclude that there are promising
new applications of magnetically controlled semiconductor structures. In addition, equipment
developers will be interested in the possibility of using the magnetic field orientation angle to control
the output parameters of such structures as part of converters

The phenomenon of congestion on the roads occurs when the demand rate on the road or on
a transport facility exceeds the available capacity, and there are two types: either routine, i.e.
occurs at certain times that are peak, for example, on the road, walking or returning from work or
educational institutions of people; or another type – sudden traffic jams that have appeared as a
result of a traffic accident, that is, in the event of an accident on the road, or due to other force
majeure reasons. In this regard, in order to reduce the increase in congestion in cities, it is possible
and necessary to use the concept of smart systems in modern conditions of life and technology
development. It is distinguished by a variety of algorithms used in the world of machine learning(ML) and the Internet of Things (IoT) to more accurately predict the flow of traffic in the short
term and identify opportunities to prevent congestion. In modern cities, many different sensors can
be used to collect information to predict short-term traffic in the city and accurately capture the
spatial and temporal evolution (change) of traffic flow. Algorithms embedded in machine learning
improve the capabilities of the system being developed. The quality of the decisions made by the
developed artificial intelligence increases with a simultaneous increase in the volume of data collected.
This article proposes a model of the TCC-SVM system for analyzing traffic jams in a smart
city environment. The proposed model includes an Internet of Things (IoT) traffic management
system that reports congestion at a certain point. Existing traffic management systems are becoming
ineffective due to the increase in the number of vehicles on the roads. In urban areas, traffic
jams and accidents are a serious problem. An intelligent transport system is necessary to solve the
problems caused by congestion on the roads.

The purpose of this article is to analyze the current state of research in the field of fuzzy
multicriteria optimization methods, as well as the development of aggregation operators and algorithms
using fuzzy multicriteria group decision-making using an intuitionistic attitude of linguistic
preferences. The most well-known fuzzy methods of multicriteria optimization are presented:
ELECTRE, PROMETHEE, VIKOR, TOPSIS, AHP, ANP, MACBETH, DEMATEL, Shoke integral
and DEA, their features, applications and the most cited articles are considered. Most real optimization
problems may have conflicting goals. The method of fuzzy multi-purpose decision-making
FMODM is also presented for situations where there are inaccuracies and uncertainty in some
goals and variables on which they depend; methods of fuzzy multi-purpose linear programming
FMOLP, fuzzy multi-objective target programming FMOGP and fuzzy heuristic decision-making
methods. The problem of fuzzy multicriteria group decision-making during evacuation with an
intuitive relation of linguistic preferences is considered. It is noted that fuzzy logic methods are
particularly suitable for making evacuation decisions when there is little data, knowledge of
cause-and-effect relationships is inaccurate, and observations and criteria can be expressed in
linguistic qualitative terms. The main stages of group making the best decision among alternatives
in a fuzzy environment are presented: combining expert assessments; obtaining a final assessment
for each alternative represented by a linguistic variable; ranking alternatives; group making the
most preferred decision. An approach to group decision-making with an intuitive preference relationship
based on aggregation procedures is proposed. The group model of decision-making and
the concept of fuzzy group decision and linguistic variables used in predicting an emergency situation
and planning evacuation are considered. It is noted that the well-known operators of ordered
weighted averaging OWA, LOWA do not take into account the weights of experts. The Low operator
is defined, which allows taking into account the weight values of experts, as well as an approach
to determining a fuzzy group solution of aFCS as a type 2 set. Algorithms for determining
a fuzzy group multicriteria solution based on aFCS are presented

The article is devoted to solving the scientific problem of decision support for the prevention
and elimination of emergencies’ consequences based on solving the problem of structuring information.
The relevance of this task is due to the need to develop theoretical foundations for optimizing
the risk of adverse effects on human health and the environment in connection with emergencies.
The authors give definitions to the main terms of the studied subject area. A formalized
statement of the problem to be solved is presented. A detailed emergencies’ classification with a
description of the presented classes’ features is given. The system of rules for decision support in
emergencies should have a multi-level hierarchy, which allows for the construction of variousdecision-making trajectories on a top-down basis. The most suitable model for building such an information
space is an ontological structure that provides the creation of the necessary multi-level
hierarchy, taking into account all the parameters and criteria that affect the development of the situation.
The main elements of this ontological model are entities and relationships between them, the
presence of which at the upper level of decomposition will indicate the risk of an emergency, and at
each lower level it will expand the taxonomy of a detailed description of emergencies’ possible situations
and the necessary actions to prevent or eliminate them consequences. The processing of this
ontological model of rules is implemented on the basis of the structuring information fuzzy method
proposed by the authors in emergencies, which differs from known analogs by the use of a new generalized
criterion for optimizing the choice of decision support alternatives. The originality of the
optimization formulation of the structuring problem lies in the assessment of the information elements
contextual binding to a certain class of emergency situations, interdisciplinary, taking into account
the presence of many links between subject areas, as well as taking into account the decrease in the
level of information efficiency about the course of emergencies over time.

The presented publication is devoted to an overview of the problem of presenting textual information
for the subsequent implementation of cluster analysis in the framework of processing
and managing high-dimensional information. Modern requirements for analytical, search and
recommendation information systems demonstrate the weak formation of a holistic solution that
can provide a sufficient level of speed and quality of the results obtained within the framework of
the current information technology market. The search for a solution to the presented problem
entails the need to conduct an objective analysis of existing solutions for representing textual information
in vector space, in order to form a holistic view of the advantages and disadvantages of
the analyzed approaches, as well as the formation of criteria that allow one to implement their
own approach, devoid of identified weaknesses. The presented work is analytical, and allows you
to get an idea of the current state and elaboration of the identified problem within a limited subject
area. Clustering of text data is the automatic formation of subsets, the elements of which are instances
of documents of some researched, unstructured sample of a fixed dimension. This process
can be classified as unsupervised learning, which implies the absence of an expert who personally
assigns class indices to the original sample of documents. However, the implementation of cluster
analysis of text data without any pre-processing is impossible. To do this, it is necessary to ensure
standardization and reduction of input data to a single format and form. Within the framework of
this stage of the implementation of cluster analysis, the presented publication discusses methods
for preprocessing text data. The novelty of the presented publication lies in the formation of the
theoretical basis of the main methods of text data vectorization, by systematizing and objectifying
the proposed assumptions, by conducting a series of experimental studies. The main difference of
this work from the already published scientific works is the systematization and analysis of modern
solutions, as well as the hypotheses about the relevance and effectiveness of our own hybridized
approach designed for text data vectorization.

In recent years, machine learning algorithms, or rather deep learning algorithms, have been
widely used in many fields, including cybersecurity. However, machine learning systems are vulnerable
to attacks by attackers, and this limits the use of machine learning, especially in nonstationary
environments with hostile actions, such as the cybersecurity field, where real attackers
exist (for example, malware developers). With the rapid development of artificial intelligence (AI)
and deep learning (GO) methods, it is important to ensure the safety and reliability of the implemented
algorithms. Recently, the vulnerability of deep learning algorithms to conflicting patterns
has been widely recognized. Fabricated samples for analysis can lead to various violations of the
behavior of deep learning models, while people will consider them safe to use. The successful implementation
of enemy attacks in real physical situations and scenarios of the real physical world
once again proves their practicality. As a result, methods of adversarial attack and defense are
attracting increasing attention from the security and machine learning communities and have
become a hot topic of research in recent years not only in Russia, but also in other countries.
Sberbank, Yandex, T1 Group, Atlas Medical Center and many others are developing competitive
solutions, including on the international market. Unfortunately, in the list of the 10 largest IT
companies, the direction of Big Data, in particular, and protection against attacks is represented
only by the T1 Group company, but the market growth potential is huge. In this paper, the theoretical
foundations, algorithms and application of methods of adversarial attacks of the enemy arepresented. Then a number of research papers on protection methods are described, covering a
wide range of research in this area. This article explores and summarizes adversarial attacks and
defenses, which represent the most up-to-date research in this field and meet the latest requirements
for information security.

The conditions of ever-growing competition require saving not only material or financial resources,
but also intellectual, informational and temporary ones. Information technologies used at
all stages of the product life cycle, from strategic analysis and production to modernization and
disposal, play a special role in solving this problem. The development of modern information
technologies, characterized by the desire to combine information resources, cooperation in the
creation of information systems, information sharing, automation and regulation of enterprise
processes, has played an important role in solving a group of tasks to save resources. The production
of complex products today is unthinkable without providing them with information support at
all stages of the life cycle. Information support is a whole range of issues, including automation of
design processes, provision of technological processes of production, automation of management
activities of enterprises, creation of electronic design and operational documentation, implementation
of automated systems for ordering spare parts, etc. This article discusses the development of a
general algorithm and structure of an automated information support system for a radar profile
enterprise, which is an important step towards improving the efficiency and effectiveness of these
enterprises. The development and implementation of such a system requires careful planning,
design and testing, as well as continuous maintenance and support. However, the potential benefits of such a system are significant and can help enterprises working with radar profiles to remain
competitive and successful in an increasingly complex and complex business environment. The
document discusses the implementation of the system, including the various stages of design and
development, testing and verification procedures.

Time series with seasonal variability is widely used to describe processes in various
fields, such as trade, analysis of financial markets, forecasting of passenger air transportation,
and description of climatic changes. Recently, this approach has been widely used to describe
technological processes as well. In this regard, applying predictive models in control systems of
complex technical objects has become possible. Machine learning methods can be effectively
used to build predictive models of series of this type. In this case, only historical data accumulated
over several periods of seasonal observation is used as input data for constructing the
forecast. Knowledge of other parameters, as a rule, is not required. The article considers creating
a predictive time series model with seasonal variability, describing a technological process,
the inlet flow of a wastewater treatment plant being chosen as a model. The general methodology
of model building, requirements for the input data sets, and algorithms of preprocessing to
form samples used for model training and testing are described. Classical methods (SARIMA,
Holt-Winters Exponential Smoothing, ETS), as well as new algorithms (Facebook Prophet,
XGBoost, Long Short Term Memory), were used to build the predictive model. The implementation
of the algorithms is done in the Python language, and recommendations for the use of existing
libraries and functions of this language are given in the work. The comparative analysis of
the accuracy of the obtained models is given on the calculation of a set of statistical metri cs.
Analysis of methods performance is also carried out since the time it takes to create a model
and get a forecast plays an important role when running the model in real production conditions.
The best method for solving the set task for application in real-time control systems was
chosen based on the sum of estimates. In conclusion, recommendations for improving forecast
accuracy were given, and future research directions were outlined.

Due to the rapid development of the sphere of trade, the means of automatic control of the
work of employees providing services to customers are gaining particular popularity. At the moment,
there are many modern approaches, methods and algorithms for automatically tracking
buyers and sellers in the store. Modern companies are trying to solve this problem in different
ways: counting visitors, monitoring devices, various neural network solutions, and so on. After
reviewing the solutions with the necessary functionality, the main disadvantages were identified,
such as, for example, high cost, inconvenience in use, and so on. As a result, the authors set a
goal: to improve the quality of tracking the movement of employees / customers through the development
of automated means and methods of movement control, inter-chamber tracking and identification
of the individual. The article describes a method for automatic recognition and tracking of
employees of stores and firms. The method is based on a cascade of neural networks and algorithms
that allow recognizing customers and employees in uniform, as well as evaluating the
quality of employees' work and customer satisfaction by voice. As the results of the research, this
article presents models and methods for classifying customers and sellers by uniform, methods for
determining the level of interaction between sellers and customers based on algorithms for determining
the satisfaction of visitors and customers by voice and face, and algorithms for determining
the quality of employees' work. The developed methods can improve the efficiency of employees, as
well as increase the quality of services provided. Based on the results of the work, testing was
carried out and a conclusion was made about the satisfactory performance of the presented methods
and algorithms.

Most of the first attempts to extraction of causal relationship were tied with complex and manual
linguistic patterns, syntactic rules and small datasets based on domain. This article examines the paradigm
of a non-statistical approach to the extraction of causal relationships, its basis, language constructs,
patterns, and classification of causal relationships. The aim was to study the methods of this
paradigm, to determine their disadvantages, advantages, and the possibility of their application.The article discusses various approaches given by the authors of well-known and highly cited research
papers and their impact on the success of the extraction of causal relationships. The analysis of these
scientific papers has unequivocally confirmed that the task of extracting CR is an extremely difficult task
of natural language processing. The presence of a variety of linguistic constructions of the language,
ambiguities of various kinds, as well as language features greatly affect the accuracy of CR extraction.
Almost all non-statistical methods have encountered the problem of highly specialized fields of
knowledge, where expert description is almost always required. Also, almost all non-statistical methods
are manual or semi-automatic, because assume the construction of templates for determining the CR in
the text. Even though non-static methods with sufficient accuracy (on average 70-80%) successfully
cope with the task under consideration, there is currently no universal method for extracting CR.
The proposed method should be universal with respect to languages, universal with respect to subject
areas and with the possibility of defining implicit CR.

The purpose of this article is to analyze the current state of research in the field of development
of algorithms inspired by nature, including categorization, classification, testing, citation,
and application areas. A new multi-level classification system based on the following features is
presented: the criterion of conformity to a natural metaphor, structural, behavioral, search, component,
and evaluation criteria. The classification of bio heuristics involves the systematic assignment
of each bio heuristics to one and only one class within a system of mutually exclusive and
non-overlapping classes. Categorization allows an objective approach to the choice of bio heuristics.
For each bio heuristics there are specific tasks with which it copes well. Knowing these relationships
is important for the purposeful application of bio heuristics. An example of classification
is considered. It is noted that the most informative classification criterion is the behavioral criterion,
the most cited class of bio heuristics are swarm intelligence algorithms, and the most cited bio
heuristics is the PSO particle swarm algorithm. Modern approaches to benchmarking of bio heuristics
are presented: discrete and continuous optimization problems, as well as optimization engineering
problems. There is a tendency to compare the performance of bio heuristics using statistical
hypothesis testing on benchmarks. The tasks successfully solved by bio heuristics in such areas
as engineering design, image processing and computer vision, computer networks and communications,
energy and energy management, data analysis and machine learning, robotics, medical
diagnostics are systematized. There is a tendency to hybridize bio heuristics in one optimizer.
However, convincing evidence is required that the results compensate for the increase in complexity
compared to individual algorithms. Optimization problems requiring further research are noted:
dynamic and stochastic optimization problems; multicriteria optimization problems; multimodal
optimization problems; multidimensional optimization problems; memetic optimization
problems in which a variety of search algorithms are combined; optimization problems and adaptation
of bio heuristics parameter settings to achieve a balance between the convergence rate and
the diversification of the solution search space.