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The solution of the scientific and practical problem of determining the effect of factor influence on the result of the work of the information measuring system for the technological process of manufacturing processed cheese is considered through the use of a factor influence model that takes into a...
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| Date: | 2026 |
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The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2026
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System research and information technologies| _version_ | 1869472190648287232 |
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| author | Hryhorenko, Ihor Hryhorenko, Svitlana Khoroshailo, Iurii Biletskyy, Pavlo |
| author_facet | Hryhorenko, Ihor Hryhorenko, Svitlana Khoroshailo, Iurii Biletskyy, Pavlo |
| author_institution_txt_mv | [
{
"author": "Ihor Hryhorenko",
"institution": "National Technical University “Kharkiv Polytechnic Institute”, Kharkiv"
},
{
"author": "Svitlana Hryhorenko",
"institution": "National Technical University “Kharkiv Polytechnic Institute”, Kharkiv"
},
{
"author": "Iurii Khoroshailo",
"institution": "Kharkiv National University of Radioelectronics, Kharkiv"
},
{
"author": "Pavlo Biletskyy",
"institution": "Kharkiv National University of Radioelectronics, Kharkiv"
}
] |
| author_sort | Hryhorenko, Ihor |
| baseUrl_str | http://journal.iasa.kpi.ua/oai |
| collection | OJS |
| datestamp_date | 2026-06-30T06:14:59Z |
| description | The solution of the scientific and practical problem of determining the effect of factor influence on the result of the work of the information measuring system for the technological process of manufacturing processed cheese is considered through the use of a factor influence model that takes into account the simultaneous effect of five factors and their cross-interactions on the control indicator. The task of the study is to implement for use a simplified cross-classification model that makes it possible to estimate the amount of expected information about the levels of the control parameter when taking into account the levels of both influencing factors and their mutual interactions. An electrical schematic diagram of the control system has been developed and its practical implementation has been carried out, thanks to which statistical data on the main parameters of the technological process have been obtained. Conclusions have been drawn about the possibility of further use of the proposed cross-classification model for various information measuring systems regardless of their purpose. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2026.2.05 |
| first_indexed | 2026-07-01T01:00:13Z |
| format | Article |
| fulltext |
I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy, 2026
Системні дослідження та інформаційні технології, 2026, № 2 71
ПРОБЛЕМИ ПРИЙНЯТТЯ РІШЕНЬ
І УПРАВЛІННЯ В ЕКОНОМІЧНИХ, ТЕХНІЧНИХ,
ЕКОЛОГІЧНИХ І СОЦІАЛЬНИХ СИСТЕМАХ
UDC 681.518.22
DOI: 10.20535/SRIT.2308-8893.2026.2.05
MODEL OF FACTOR INFLUENCE ON THE OPERATION
OF THE INFORMATION AND MEASURING SYSTEM
I.V. HRYHORENKO, S.M. HRYHORENKO,
Iu.Y. KHOROSHAILO, P.M. BILETSKYY
Abstract. The solution of the scientific and practical problem of determining the effect
of factor influence on the result of the work of the information measuring system for
the technological process of manufacturing processed cheese is considered through the
use of a factor influence model that takes into account the simultaneous effect of five
factors and their cross-interactions on the control indicator. The task of the study is to
implement for use a simplified cross-classification model that makes it possible to
estimate the amount of expected information about the levels of the control parameter
when taking into account the levels of both influencing factors and their mutual
interactions. An electrical schematic diagram of the control system has been developed
and its practical implementation has been carried out, thanks to which statistical data
on the main parameters of the technological process have been obtained. Conclusions
have been drawn about the possibility of further use of the proposed cross-classification
model for various information measuring systems regardless of their purpose.
Keywords: information and measurement system, quality control, factor influence,
mathematical model, variance analysis, measurement uncertainty, error.
INTRODUCTION
A large number of problems that arise during control and technical diagnostics of
information and measuring systems could be avoided if obtaining primary
information about the state of the control object were not associated with the a priori
uncertainty of its output values. This is most critical when the output value cannot
be reduced to a normalized level, since the complexity of the information and
measuring system (IMS) and its dynamic properties lead to the randomness of the
relationship between the measured output values and the levels of the controlled
parameters. Thus, the problem of increasing the reliability of control and
diagnostics arises in conditions when the uncertainty of measurements is added to
the much greater uncertainty of the output values. IMS used in production for
metrological control of technological processes are objects with stochastic
parameters. For such systems, it is impossible to create deterministic models of
controlled values, and the use of only existing structural and algorithmic methods
to increase the reliability of control does not always make sense. In [1] it is stated
that processed cheese is a popular dairy product, which is produced by
thermomechanical processing of one or more cheeses and cheeses, in the presence
of melting salts or structuring agents, with the addition of products derived from
TIÄC
I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy
ISSN 1681–6048 System Research & Information Technologies, 2026, № 2 72
milk and food products, and biologically active additives, flavorings or without
them. In Ukraine, the quality of processed cheese is regulated by the State Standard
of Ukraine [2]. Analysis of works [3–7] showed the interest of the world scientific
community in the problems of maintaining high quality and competitiveness of
processed cheese produced in their countries, and also proved the need for constant
search for sources of improving the taste and usefulness of this product. One of
such ways is the method proposed in [5] for improving the quality of processed
cheese using ultrasound. This study investigated the effect of different ultrasonic
energy densities on the functional properties of processed cheese with different
levels of emulsifying salt (0 %, 0.5 %, 1 %, 2 % or 3 % disodium phosphate).
However, all of these studies agree on the need to ensure automated control of
process parameters taking into account the effect of factor influences.
A number of works [8–11] are devoted to the development of IMS for production
and research tasks, in which the emphasis is on the significant miniaturization of all
components of measuring and computing equipment, especially electronic
components. In such systems, the entire measuring and processing part can occupy an
area of several square millimeters, and the measuring equipment can be completely
placed in one or two structures with connectors for connecting sensors. Due to
miniaturization, the effect of factor influences on the quality of IMS functioning
becomes especially significant and its consideration is a necessary component of
ensuring the metrological reliability of IMS regardless of their purpose.
ANALYSIS OF THE LAST ACHIEVEMENTS AND PUBLICATIONS
The work [12] is devoted to the creation of theoretical foundations of probabilistic
mathematical modeling and synthesis of information procedures for control and
diagnostics under conditions of parametric uncertainty.
In [13], the task of assessing the effect of random factor influence was solved
by using analysis of variance as a method of organizing sample data according to
possible sources of dispersion. The approach chosen in [13] allowed us to
decompose the total dispersion into components that are due to the influence of
factor levels. The task of assessing the informativeness of colorimetric control
indicators in [14] was solved by using discriminant analysis models.
However, the problem of developing such a generalized method for quality
control of the functioning of the IMS, which would be able to be used for systems
regardless of their purpose, remained unsolved. In the work [15], such a control
method was proposed. One of the main stages of the method for quality control of
the functioning of the IMS proposed in [15] is the analysis of the effect of factor
influence on the result of measuring the control indicator. In the sense of the quality
of the IMS operation, compliance with the established metrological characteristics
of the IMS is, and through this, the maintenance of the established norms for the
parameters of the product to ensure the release of which the IMS was developed.
The method proposed in [15] combines the advantages of statistical analysis
methods, test control methods, fuzzy set theory and the theory of calculation of
measurement uncertainty, therefore it is used in the presented work to analyze the
effect of factor influence on the operation of the IMS for controlling the parameters
of the technological process of manufacturing processed cheese. The method
allows for ranking control indicators by decreasing the amount of information, i.e.
determining the level of their influence on the control parameter.
Model of factor influence on the operation of the information and measuring system
Системні дослідження та інформаційні технології, 2026, № 2 73
FORMULATION OF THE ARTICLE PURPOSE
The aim of the article is to implement the developed model of factor influence on
the result of determining the control indicator for the IMS of the technological
process of manufacturing processed cheese. This model takes into account the
effects of the simultaneous interaction of five factors, such as: temperature during
melting of the cheese mass, pH level of the cheese mass, steam pressure level
during melting of the cheese mass, temperature during sterilization of the cheese
mass, noise of the analog part of the steam pressure measurement channel. Based
on the presented model, to estimate the amount of expected information about the
levels of the control parameter (quality of processed cheese) for the information
control indicator, taking into account the levels of both influencing factors and their
mutual interactions. To rank the control indicators by the level of their influence on
the control result. To establish the factor that has the greatest influence on the
quality of processed cheese.
STATEMENT OF THE MAIN MATERIAL
Maintaining high quality and taste properties of processed cheese directly depends
on the control of the main parameters of the technological process at the stages of
production. This becomes possible due to the implementation of the IMS, which
receives information from the technological process. Based on the comparison of
the current values of the parameters with the pre-set values, the IMS generates
(if necessary) control influences for the actuators in order to actively influence the
technological process. A simplified structural diagram of the IMS (without actuators),
used to control the production of processed cheese, is presented in Fig. 1.
Fig. 1. Simplified structural diagram of the IMS for controlling the process of making
processed cheese
I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy
ISSN 1681–6048 System Research & Information Technologies, 2026, № 2 74
The IMS includes the following elements: Sensor 1, Sensor 2, Sensor 3 – primary
measuring transducers designed to control the temperature of the cheese mass in
the vat (three sensors are required to be able to obtain an integral value of the
temperature of the cheese mass over the entire volume); Sensor 4 – designed to
control the pH level of the melted cheese mass; Sensor 5 – to measure the pressure
of the steam that melts the cheese mass; Sensor 6 – to measure the temperature during
sterilization; SMT 1 – SMT 4 – secondary measuring transducers, which use digital
voltage converters with built-in precision amplifiers and analog-to-digital
converters (ADC); microcontroller – processes measurement information and
generates control commands; GSP – additional to the built-in microcontroller
generator of synchronizing pulses; CP – control panel; IF – interface, provides data
exchange with a personal electronic computer (PC); DRD is a digital reading device
that provides information on the status of control parameters; PS is a power supply.
Based on the structural diagram, an electrical schematic diagram of the IMS
was developed, which is presented in Fig. 2.
Fig. 2. Electrical schematic diagram of the IMS
Model of factor influence on the operation of the information and measuring system
Системні дослідження та інформаційні технології, 2026, № 2 75
To measure the temperature during cheese melting and sterilization,
TXA-1090 temperature sensors were used, connected to the disconnections X1, X2,
X3, X4, respectively, which are connected to the microcontroller via high-precision
digital converters MAX31855 (DD1 – DD4). The sensor for measuring the vapor
pressure during cheese melting – OVEN PD100-DG1,0-137-0,5.100 is connected
to the disconnector X6. The pH level control sensor Orbisint CPS11D, which
includes the DD5 circuit, is directly connected to the DD6 microcontroller bus.
ATmega16 was used as the microcontroller. The measurement results are displayed
on the DRD, represented by the HD44780 (HG1) chip. The measurement results
are transmitted to the PC via the X7 disconnector, thanks to the RS485 serial
interface (DD7). The microcontroller is reset by the SB1 button. Capacitors C6 and
C7 set the operating mode of the quartz resonator ZQ1. Power is supplied to the
control system via the X5 disconnector.
In [16] it is stated that when developing a model of factor influence for each
of the industrial IMS, it is advisable to use a model that takes into account the
simultaneous action of at least five factors from the following factor groups: noise
of the analog part of the measuring channel, the effect of electromagnetic
interference, noise of the signal switching device, error of digital signal conversion,
changes in the set temperature regime of operation. The indicated factor groups are
difficult to stabilize to reduce the impact on the control parameter. In such
conditions, it is advisable to carry out a procedure for randomizing factors in order
to translate the factor influence into a stochastic process with the possibility of
using statistical analysis methods. At the same time, each technological process has
its own characteristics in determining the sources of factor influence. In [16],
a model of multifactor influence on the result of measuring the control indicator
is proposed for use. Since the hierarchy in the system of factors' action is unknown
in advance, a model with full cross-classification is promising for use. Such a model
will have the form
,
i
i
abcde a b c d e ab ac ad ae
bc bd be cd ce de abc
abd abe bcd bce cde
ade acd ace abcd abce
abcdebcde acde abde
E E R S T U V RS RT RU RV
ST SU SV TU TV UV RST
RSU RSV STU STV TUV
RUV RTU RTV RSTU RSTV
STUV RTUV RSUV ε
(1)
where
iabcdeE – control indicator determined during the measurement process (for
example, noise of the analog part of the measurement channel), and ,a,b, c, d e –
numbers indicating the levels of factors; E – average value of the control
indicator; aR – deviation of the measurement result of the control indicator E from
its average value ,E which is due to the influence of the control parameter
(processed cheese quality); , , ,b c d eS T U V – deviation of the measurement
result abcdeE from E , caused by the action of five factors;
, , , , , , , ,
ab ac ad ae bc bd be cd ce
RS RT RU RV ST SU SV TU TV – deviations
that are caused by pairwise interactions of factors;
, , , , , , , , –abc abd abe bcd bce cde ade acd aceRST RSU RSV STU STV TUV RUV RTU RTV
deviations that are caused by cross-interactions of three factors;
I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy
ISSN 1681–6048 System Research & Information Technologies, 2026, № 2 76
, , , ,
abcd abce bcde acde abde
RSTU RSTV STUV RTUV RSUV – deviation, which
is caused by the cross-interactions of four factors;
iabcdeε – random remainder;
і – number of multiple measurements at fixed levels edc,b,a, , [16].
When using a full factorial influence model, it is very difficult to technically
ensure the homogeneity of the measurement experiment with a sufficiently large
sample size, therefore it is advisable to simplify model (1) by leaving only the main
deviations , , , ,a b c d eR S T U V , as well as deviations due to pairwise interactions of
factors , , , ,
ab ac ad ae
RS RT RU RV assuming that three-factor and four-
factor interactions are equal to zero [16]. The choice of these pairwise interactions
is due to the fact that they are aR a random variable, since they reflect the effect of
a priori uncertain levels of the control parameter and must be taken into account in
pairwise interactions to increase the reliability of the model.
A simplified model would look like this
j jabcde a b c d e abcdeab ac ad ae
E E R S T U V RS RT RU RV γ , (2)
where
jabcdeγ – random residue greater than
iabcdeε .
The advantage of the simplified model proposed in [16] is that it makes it
possible to estimate the amount of expected information about the parameter levels
for the information indicator E, taking into account the levels of both influencing
factors and their compatible interactions [16]
2
2
σlog 1 ,
σ
E
E
I
(3)
where 2 2
1
1 ( )
( 1)
n
E i
i
σ E E
N
, (4)
1
1 n
i
i
E E
N
, 2
Eσ – is a function of the sums of squares of deviations.
For the technological process of making processed cheese, the factors that
affect the quality of the final product are five main factors: the temperature during
melting of the cheese mass, the pH level of the cheese mass, the vapor pressure
during melting of the cheese mass, the temperature during sterilization of the cheese
mass, and the noise of the analog part of the measuring channels.
For the research, the measurement results obtained during the production
process with multiple observations of the change in: temperature during melting of
the cheese mass and during its sterilization, pH level, vapor pressure during melting
of the cheese mass were used. 35 observations were obtained in each series. The
observation results are considered independent and equally accurate (under the
experimental conditions). There is no systematic error component. Under the
production conditions, a confidence probability of P = 0.95 (significance level
= 0.05) of the measurement results was established. A graphical representation
of the process of measuring the temperature during melting of the cheese mass is
presented in Fig. 3.
Model of factor influence on the operation of the information and measuring system
Системні дослідження та інформаційні технології, 2026, № 2 77
Fig. 3. Results of observations of temperature changes during cheese melting
A graphical representation of the process of measuring vapor pressure during
cheese melting is presented in Fig. 4.
Fig. 4. Results of observations of changes in the vapor pressure level during cheese melting
A graphical representation of the process of measuring the pH level of cheese
mass is presented in Fig. 5.
Fig. 5. Results of observations of changes in the pH level of the cheese mass
I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy
ISSN 1681–6048 System Research & Information Technologies, 2026, № 2 78
A graphical representation of the temperature measurement process during
cheese mass sterilization is presented in Fig. 6.
Fig. 6. Results of observations of temperature changes during cheese mass sterilization
The next factor to consider in the production of processed cheese is the noise
of the analog part of the measuring channel ρnoiseΔ . The largest amplitude value
among the measuring channels is the noise of the analog part of the pressure
measuring channel, which does not exceed the value 3105,3 V. This value is
taken for further calculations.
A graphical representation of the noise of the analog part of the steam pressure
measuring channel is presented in Fig. 7.
Fig. 7. Graphical representation of the noise of the analog part of the steam pressure
measurement channel
The values 2
Eσ are determined by formula (4) for each of the factors.
The results of the calculated values 2
Eσ for the five factors affecting the quality
of processed cheese are presented in Table 1.
Model of factor influence on the operation of the information and measuring system
Системні дослідження та інформаційні технології, 2026, № 2 79
T a b l e 1 . Results of calculated values 2
Eσ
Denotation
of factors Factor name E
n
i
i EE
1
2 2
Eσ
1E Melting temperature
of cheese mass, 0С 82,2 0С 13,1 (0С)2 0,38 (0С)2
2E Vapor pressure during cheese
melting, МPа 0,49 МPа 51043,8 (МPа)2 61045,2 (МPа)2
3E pH level of the curd mass,
units. рН
5,45
units. рН
0,006
(units. рН)2
41076,1
(units. рН)2
4E Temperature during cheese
mass sterilization, 0С 148,5 0С 9,65 (0С)2 0,28 (0С)2
5E
Noise of the analog part
of the pressure measuring
channel, V
31075,1 V 61098,7 V2 71035,2 V2
Table 2 shows the calculations of the amount of expected information I by the
levels of control indicators. The residual mean squares 2
Eσ are determined from
the analysis of variance of the simplified model (2). The indicators in Table 2
are arranged in decreasing order of the value of I. The calculation of the amount
of expected measurement information I was carried out according to equation (3).
Fisher’s F-statistic was used to test one of the two hypotheses [16]:
0 1:σ ... σ mH E E , (5)
1 1:σ ... σ mH E E . (6)
If the statistical conclusions indicate the validity of the main hypothesis (5),
then the control parameter does not affect the change in the control indicator E.
If the alternative hypothesis (6) is valid, then the indicator E is informative to the
control parameter. Decisions about the validity of the hypotheses are made based
on the result of comparing Fisher’s F-statistic with the critical value of the
F-statistic – Fcr [16].
In Table 2, Fisher’s F-statistic has the form 4;35F , where 4 is the number
of degrees of freedom corresponding to the largest sample variance
(N – 1 = 5 – 1 = =4); 35 is the number of measurements in the series 4;35F is the test
statistic of the dispersion relation
2
E
4;35 2
ΔE
σF
σ
, (7)
where 2
Eσ – factorial variance per one degree of freedom; 2
Eσ – residual variance
per one degree of freedom.
Since the percentage point of the F-distribution 4;35;0,05 2,61F for the 5 %
level of significance, the hypothesis (5) is rejected for all control indicators.
Based on the obtained values of the amount of information on the control
indicators, it is obvious that the greatest influence on the quality of processed
cheese is the maintenance of the temperature regime during the melting of the
cheese mass (І = 1,56).
I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy
ISSN 1681–6048 System Research & Information Technologies, 2026, № 2 80
T a b l e 2 . Results of determining the amount of expected information by control
indicators
Factors
Dispersions
4;35F І, bit 2
Eσ 2
Eσ
1E 0,38 (0С)2 0,049 (0С)2 7,72 1,56
2E 61045,2 (MPа)2 71029,7 (МPа)2 3,36 1,06
3E 41076,1 (units. рН)2 51088,2 (units. рН)2 6,12 1,42
4E 0,28 (0С)2 0,038 (0С)2 7,44 1,54
5E 71035,2 V2 81055,5 V2 4,48 1,23
Ranking the indicators by decreasing amount of information І makes
it possible to present them in the form of a series
25341 ,,,, EEEEEQ . (8)
For all calculated indicators, the F-statistic 4;35F is greater than critical
4;35;0,05 2,61F , which indicates a statistically significant effect of the indicators on
the control parameter Q (processed cheese quality).
CONCLUSIONS
1. For the developed information and measuring system for controlling the
parameters of the technological process of manufacturing processed cheese,
a model of factor influences on the result of determining the control indicator
(processed cheese quality) with five influencing factors is proposed. This model
takes into account the effects of the simultaneous interaction of such factors as:
temperature during cheese melting, pH level of cheese mass, vapor pressure level
during cheese melting, temperature during cheese sterilization, noise of the analog
part of the steam pressure measurement channel. Based on the presented model,
an assessment of the amount of expected information about the levels of the control
parameter (processed cheese quality) for the information control indicator was
carried out, taking into account the levels of both influencing factors and their
mutual interactions.
2. The presence and significance of the factor influence on the quality of
processed cheese was proven. The control indicators were ranked by the level of
their influence on the control result. It was established that the temperature regime
during cheese melting has the greatest influence on the quality of processed cheese.
3. It is promising for further research to implement the proposed factor
influence model to analyze the operation of various IMS, regardless of their scope.
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I. V. Hryhorenko, S. M. Hryhorenko, Iu. Y. Khoroshailo, P. M. Biletskyy
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Received 30.01.2025
INFORMATION ON THE ARTICLE
Ihor V. Hryhorenko, ORCID: 0000-0002-4905-3053, National Technical University
“Kharkiv Polytechnic Institute”, Ukraine, e-mail: grigmaestro@gmail.com
Svitlana M. Hryhorenko, ORCID: 0000-0003-0150-4844, National Technical University
“Kharkiv Polytechnic Institute”, Ukraine, e-mail: sngloba@gmail.com
Iurii Y. Khoroshailo, ORCID: 0000-0002-4239-4357, Kharkiv National University
of Radioelectronics, Ukraine, e-mail: yurii.khoroshailo@nure.ua
Pavlo М. Biletskyy, ORCID: 0009-0000-6644-9669, Kharkiv National University
of Radioelectronics, Ukraine, e-mail: pavlo.biletskyi@nure.ua
МОДЕЛЬ ФАКТОРНОГО ВПЛИВУ НА РОБОТУ ІНФОРМАЦІЙНО-
ВИМІРЮВАЛЬНОЇ СИСТЕМИ / І.В. Григоренко, С.М. Григоренко,
Ю.Є. Хорошайло, П.М. Білецький
Анотація. Розглянуто розв’язання науково-практичної задачі визначення дії
факторного впливу на результат роботи інформаційно-вимірювальної системи
для технологічного процесу виготовлення плавленого сиру завдяки
використанню моделі факторного впливу, яка враховує одночасну дію п’яти
факторів та їх перехресних взаємодій на показник контролю. Завдання
дослідження полягає у впровадженні для використання моделі перехресної
класифікації, яка дає змогу оцінити кількість очікуваної інформації про рівні
параметру контролю за урахування рівнів як факторів, що впливають, так і їх
сумісних взаємодій. Розроблено електричну принципову схему системи
контролю і виконано її практичну реалізацію, завдяки чому отримано
статистичні дані про основні параметри технологічного процесу. Зроблено
висновки про можливість подальшого використання запропонованої моделі
перехресної класифікації для різноманітних інформаційно-вимірювальних
систем незалежно від їх призначення.
Ключові слова: інформаційно-вимірювальна система, контроль якості,
факторний вплив, математична модель, дисперсійний аналіз, невизначеність
вимірювань, похибка.
|
| id | journaliasakpiua-article-365257 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-01T01:00:13Z |
| publishDate | 2026 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/3b/f5083933748d4626c15a7a33d2cb543b.pdf |
| spelling | journaliasakpiua-article-3652572026-06-30T06:14:59Z Model of factor influence on the operation of the information and measuring system Модель факторного впливу на роботу інформаційно-вимірювальної системи Hryhorenko, Ihor Hryhorenko, Svitlana Khoroshailo, Iurii Biletskyy, Pavlo інформаційно-вимірювальна система контроль якості факторний вплив математична модель дисперсійний аналіз невизначеність вимірювань похибка information and measurement system quality control factor influence mathematical model variance analysis measurement uncertainty error The solution of the scientific and practical problem of determining the effect of factor influence on the result of the work of the information measuring system for the technological process of manufacturing processed cheese is considered through the use of a factor influence model that takes into account the simultaneous effect of five factors and their cross-interactions on the control indicator. The task of the study is to implement for use a simplified cross-classification model that makes it possible to estimate the amount of expected information about the levels of the control parameter when taking into account the levels of both influencing factors and their mutual interactions. An electrical schematic diagram of the control system has been developed and its practical implementation has been carried out, thanks to which statistical data on the main parameters of the technological process have been obtained. Conclusions have been drawn about the possibility of further use of the proposed cross-classification model for various information measuring systems regardless of their purpose. Розглянуто розв’язання науково-практичної задачі визначення дії факторного впливу на результат роботи інформаційно-вимірювальної системи для технологічного процесу виготовлення плавленого сиру завдяки використанню моделі факторного впливу, яка враховує одночасну дію п’яти факторів та їх перехресних взаємодій на показник контролю. Завдання дослідження полягає у впровадженні для використання моделі перехресної класифікації, яка дає змогу оцінити кількість очікуваної інформації про рівні параметру контролю за урахування рівнів як факторів, що впливають, так і їх сумісних взаємодій. Розроблено електричну принципову схему системи контролю і виконано її практичну реалізацію, завдяки чому отримано статистичні дані про основні параметри технологічного процесу. Зроблено висновки про можливість подальшого використання запропонованої моделі перехресної класифікації для різноманітних інформаційно-вимірювальних систем незалежно від їх призначення. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2026-06-30 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/365257 10.20535/SRIT.2308-8893.2026.2.05 System research and information technologies; No. 2 (2026); 71-82 Системные исследования и информационные технологии; № 2 (2026); 71-82 Системні дослідження та інформаційні технології; № 2 (2026); 71-82 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/365257/350708 |
| spellingShingle | інформаційно-вимірювальна система контроль якості факторний вплив математична модель дисперсійний аналіз невизначеність вимірювань похибка Hryhorenko, Ihor Hryhorenko, Svitlana Khoroshailo, Iurii Biletskyy, Pavlo Модель факторного впливу на роботу інформаційно-вимірювальної системи |
| title | Модель факторного впливу на роботу інформаційно-вимірювальної системи |
| title_alt | Model of factor influence on the operation of the information and measuring system |
| title_full | Модель факторного впливу на роботу інформаційно-вимірювальної системи |
| title_fullStr | Модель факторного впливу на роботу інформаційно-вимірювальної системи |
| title_full_unstemmed | Модель факторного впливу на роботу інформаційно-вимірювальної системи |
| title_short | Модель факторного впливу на роботу інформаційно-вимірювальної системи |
| title_sort | модель факторного впливу на роботу інформаційно-вимірювальної системи |
| topic | інформаційно-вимірювальна система контроль якості факторний вплив математична модель дисперсійний аналіз невизначеність вимірювань похибка |
| topic_facet | інформаційно-вимірювальна система контроль якості факторний вплив математична модель дисперсійний аналіз невизначеність вимірювань похибка information and measurement system quality control factor influence mathematical model variance analysis measurement uncertainty error |
| url | https://journal.iasa.kpi.ua/article/view/365257 |
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