Issue 3 (205), article 5


Cybernetics and Computer Engineering, 2021, 3(205)

YERMAKOVA I.I., Full Professor, DSc (Biology),
Leading Researcher of Complex Research
of Information Technologies Department
ORCID: 0000-0002-9417-1120

BOGATONKOVA A.I., Ph.D. (Engineering),
Senior Researcher,
Complex Research of Information Technologies Department
ORCID: 0000-0002-7536-5958

NIKOLAENKO A.Yu., Ph.D. (Engineering),
Complex Research of Information Technologies Department
ORCID: 0000-0002-2402-2947

TADEEVA Yu.P., Ph.D. (Engineering), Senior Researcher,
Complex Research of Information Technologies Department
ORCID: 0000-0001-5418-2848

Junior Researcher,
Complex Research of Information Technologies Department
ORCID: 0000-0002-9019-4894

Complex Research of Information Technologies Department
ORCID: 0000-0001-9712-6045

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. Rapidly evolving Mobile health (m-health) includes mobile phones, patient monitors, personal digital assistants, and other wireless devices for tracking certain data, such as fitness level, heart rate, medication dosage, sleep cycles, and more. This helps patients control their health, which is important in the face of growing medical shortages. Devices and applications help healthcare providers make visits / appointments and collect patient data.

An important means of person-centered is the creation of combined information and computer systems that allow patients to independently monitor vital indicators of their own health, and doctors – to monitor the health of patients remotely and analyze the results of monitoring using mobile applications for timely and effective adjustment of treatment and prevention measures.

The purpose of the article is to develop m-health technology to assess the risk of deterioration of human health in extreme environmental conditions. To do this, a computer module for determining the impact of the environment on the thermal state of man has been developed.

Conclusions. The use of a complex method of modeling in combination with modern computer technology makes it possible to study the processes of heat exchange between humans and the environment, given the huge number of many regulatory reactions and physiological processes.

The technology takes into account more than 490 indicators of man and environment, including: anthropometric data, anatomical parameters, biophysical characteristics, basic physiological characteristics, human adaptive properties, environmental characteristics and duration of human stay in selected conditions.

The developed m-health technology for forecasting the human condition in extreme environmental conditions is a program based on a set of mathematical models of human thermoregulation, which makes it possible to determine a number of important physiological factors. The main task of the program is to prevent damage to human health in extreme environmental conditions during exercise. The application makes it possible to predict the dynamics of thermoregulatory physiological reactions of a person during heat.

The mobile application issues a conclusion about the danger or safety of the planned physical activity under the given environmental conditions.

Keywords: simulation, computer module, extreme impact, environment

Download full text!

1. Abdulaev V.G., Askerov T.K., Chuba I.V. Mobilnyie prilozheniya dlya zdorovya [Tekst]. Radioelektronika i informatika. – 2014. – T. 1, No 64.

2. M. McCarthy, P. Spachos, “Wellness assessment through environmental sensors and smartphones”, Communications (ICC) 2017 IEEE International Conference on, pp. 1-6, 2017.

3. I. Khudetskyy., Yu. Antonova-Rafi “Development of the module for data processing psycho-physiological indicators based on the Android OS” Materials of the XII International Scientific and Methodical Conference “Physical Education in the Context of Modern Education,” Kyiv, pp.115-116, 2016.

4. O. Hotra; O. Boyko; T. Zyska “Improvement of the operation rate of medical temperature measuring devices” SPIE Proceedings, – Vol. 92914, pp. 92910A, 2014.

5. R.Holyaka, N.Kostiv “Energy-efficient signal converters of thermocouple, temperature sensors” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska, pp. 26-28, 2011.

6. Yermakova I., Nikolaienko A., Tadeieva J., Bogatonkova A., Solopchuk Y., Gandhi O., Computer model for heat stress prediction during physical activity. Proceedings of the 40th International scientific conference electronics and nanotechnology. Institute of Electrical and Electronics Engineers, Kyiv, Ukraine, 22-24 April 2020. – P. 569-573

7. Hrytsenko V., Nikolaienko A., Solopchuk Y., Yermakova I., Regan M. Dynamics of Physiological Responses during Long Distance Run: Modelling. Proceedings of the 38th International scientific conference electronics and nanotechnology. Institute of Electrical and Electronics Engineers, Kyiv, Ukraine, 24-26 April 2018, – pp. 439-442. ISBN 978-1-5386-6382-0. doi:10.1109/ELNANO.2018.8477470.

8. Dorosh N., Ilkanych K., Kuchmiy H., Boyko I., Yermakova I, Dorosh O., Voloshyn D. Measurement modules of digital biometrie medical systems based on sensory electronics and mobile-health applications. Proceedings of the 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). IEEE, Slavske, Ukraine, 20-24 February 2018, – pp. 687-691. doi:10.1109/TCSET.2018.8336294.

9. Dorosh N., Ilkanych K., Hrytsenko V., Yermakova I, Bogatonkova A., Dorosh O. Mobile Infocommunication System for Adaptive Analysing of the Biomedical Indicators and Signals. International Scientific and Practical Conference “Problems of Infocommunications. Science and Technology”, Kharkiv, Ukraine, 9-12 October 2018.

Received 03.06.2021

Issue 3 (205), article 4


Cybernetics and Computer Engineering, 2021, 3(205)

Senior Teacher of the Biomedical Engineering Department
ORCID: 0000-0003-3009-6421

SHLYKOV V.V.1, DSc (Engineering), Associate Professor,
Нead of the Biomedical Engineering Department
ORCID: 0000-0001-8836-4658

DUBKO A.G.2, PhD (Engineering), Associate Professor,
Researcher of Department of Welding and Related
Technologies in Medicine and Ecology
ORCID: 0000-0001-6070-3945

1National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” 37, . Peremogy av., Kyiv, 03056, Ukraine

“E.O. Paton Electric Welding Institute” 11, Kazimir Malevich str., Kyiv, 03150, Ukraine


Introduction. High-frequency electric welding of biological tissues is an effective method of treatment in surgery. This is an electrosurgical method that minimizes the possibility of the destructive effect of electric current on soft living tissues. The welding method is widely used in general surgery for joining soft tissues where a weld is created when a high frequency electric current is passed through the tissue. With this method, it is possible to carry out serious operations, such as welding of liver tissue, retina, resection of tumor tissue and many other operations. For operations in surgery, it is important to know the optimal parameters of HF- welding, such as welding temperature, mechanical stress on tissues, welding time and voltage.

The purpose of the article is to determinate the optimal conditions for high-frequency welding of living tissues, such as welding temperature, mechanical stress on tissues, welding time and voltage. To determine these parameters, the liver tissue fusion was simulated in the Sinda and Comsol software.

Results. As a result of modeling and research, model dependencies were obtained that determine the optimal parameters of high-frequency welding for performing surgical operations for resection and welding of liver tissue. In the place of direct contact of the electrodes with the tissue, the temperature does not exceed +70 ° C, and at a distance of 2 mm in the adjacent tissues does not exceed +50 °C, which provides a tissue-preserving electrosurgical effect.

Conclusions. The studies have shown that mathematical modeling of heating biological tissue by a split electrode, through which a high-frequency current passes, practically coincides with a real experiment. The optimal conditions for high-frequency welding of living tissues obtained as a result of modeling, such as welding temperature and welding time make it possible to reduce the recovery period after applying the HF-welding method by choosing the optimal coagulation modes.

Keywords: welding of biological tissues, mathematical modeling, temperature, liver, surgery, modeling in Sinda, modeling in Comsol

Download full text!

1. Lebedev, A.V., Dubko, A.G. Use of Electric Welding of Living Tissues in Surgery (review). Biomed Eng.2020, 54, pp.73-78.

2. Molotkovets, V.Y., Medvediev, V.V., Korsak, A.V. et al. Restoration of the Integrity of a Transected Peripheral Nerve with the Use of an Electric Welding Technology. Neurophysiology. 2020, 52, pp. 31-42 (2020).

3. Vazina, A.A., Vasilieva, A.A., Lanina, N.F. et al. Study of molecular and nanostructural dynamics of biological tissues under the influence of high-frequency electrosurgical welding. Bull. Russ. Acad. Sci. Phys. 2013, 77, pp.146-150.

4. Paton B.E., Lebedev V.K., Lebedev A.V. et al. Method for welding soft tissues of animals and humans: RU229417. Application number: 2003135514/14. Publication date: 2007.02.27

5. Shlykov Vladyslav, Kotovskyi Vitalii, Dubko Andrey, Visniakov, Nikolaj, Sesok Andzela. Temperature monitoring for high frequency welding of soft biological tissues: A prospective study. Technology and Health Care, 2019, vol. Pre-press, no. April, pp. 1-7.

6. Astrium. SINDA User Manual, ver. 3.2., 2003, 895 p.

7. COMSOL Multiphysics Reference Manual, ver.5.5, 2019, 1742 p.

8. Sydorets, V., Dubko, A. The current distribution in the electrodes of electrosurgical instruments during welding of biological tissues. Eastern-European Journal of Enterprise Technologies. 2015, 3(5), pp. 24-28.

9. Sydorets, V., Lebedev, A., Dubko, A. Mathematical modeling of the current density distribution in a high-frequency electrosurgery. Proceedings – 2015 16th International Conference on Computational Problems of Electrical Engineering, CPEE , 2015, pp. 215-217.

10. Dubko, A., Sydorets, V., Bondarenko, O. Simulation of the Temperature Distribution with High-Frequency Electrosurgical Heating. 38th International Conference on Electronics and Nanotechnology (ELNANO – 2018), Kyiv, Ukraine. 2018, p. 394-397.

11. Zoya Popovic, Branko D. Popovic. Introductory Engineering Electromagnetics. Prentice Hall, 1999, 548 p.

12. Vazina, A.A., Lanina, G. S. Marinsky et al. Influence of high-frequency electrosurgical welding on the functional stability of the structure of biological tissues. Welding of soft living tissues. Current state and development prospects: materials of the Sixth International Seminar: edited by O. N. Ivanova. Kyiv: E.O. Paton Electric Welding Institute, 2011. p. 53.

13. MSC Sinda 2017 User’s Guide: ID DOC11364. MSC Software Corporation. 2017, 451 p.

14. Vitaliy B., Maksymenko V., Danilova A., Shlykov V. The Discrete Model for the System of the Myocardium and Coronary Vessels. KPI Science News.2017, No 1, pp. 54-60.

Received 05.04.2021

Issue 3 (205), article 3


Cybernetics and Computer Engineering, 2021, 3(205)

ARALOVA N.I.1, DSc (Engineering), Senior Researcher,
Senior Researcher of Optimization of Controlled Processes Department
ORCID: 0000-0002-7246-2736

KLYUCHKO O.M.2, PhD (Biology), Associate Professor,
Associate Professor of Air Navigation Faculty
ORCID: 0000-0003-4982 7490

MASHKIN V.I.1, PhD (Engineering),  Senior Researcher,
Senior Researcher of Optimization of Controlled Processes Department
ORCID: 0000-0002-4479-6498

MASHKINA I.V. 3, PhD (Engineering), Associate Professor,
Associate Professor of Information Technology and Management Faculty
ORCID: 0000-0002-0667-5749

RADZIEJOWSKI P.A. 4, DSc (Biology), Professor,
Professor of Management Faculty, Innovations
and Safety Management Systems Department
ORCID: 0000-0001-8232-2705

RADZIEJOWSKA M.P. 4, DSc (Biology), Professor,
Professor of Management Faculty, Innovations
and Safety Management Systems Department
ORCID: 0000-0002-9845-390X

1 V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, 40, Acad.Glushkov av., Kyiv, 03680, Ukraine

2 Electronics and Telecommunications National Aviation University, 1, Lubomyr Huzar av., Kyiv, 03058, Ukraine

3 Borys Grinchenko Kyiv University, 18/2, Bulvarno-Kudriavska str., Kyiv, 04053, Ukraine,

4 Czestochowa University of Technology 19b, Armii Krajowej str., 42-200, Częstochowa, Poland


Introduction. Various processes going in surrounding environment are controlled, i. e. their states are determined depending on the specific influence of controlling party. At the same time, it is natural to try to choose the optimal controlling influence that would be the best in comparison with other possible controlling methods. Intensive development of the theory of optimal solutions with computers use has obtained the ability to perform complex calculations and realize the rules of control due to the development of computational technology.

The problem of identifying and studying of the nature of self-organization mechanisms of processes going in organism, the disclosure of the laws of control that operate in it actually arises during the investigation of living systems. Problem solution of self-organization process knowing for these controlled objects should be carried out using the methods of mathematical modeling. Peculiarities of setting problems of control for functionally-organized systems can be conveniently examined on the example of processes going in living organism when the achievement of certain goals is ensured.

The purpose of the article is to create the mathematical model of functional respiratory system for the investigation of self-organization mechanisms in human organism in response to extreme disturbances.

Methods. The usual nonlinear differential equations are used for process description; they describe the mass transfer and mass exchange of respiratory gases flowing along all their ways in organism.

Results. Mathematical model of functional respiratory system has been developed to study the current functional state and to predict the mechanisms of self-organization of respiratory system in adapting to the disturbing influences of external and internal environment based on the problem of optimal control and taking into account the conflict situation between the self-regulating organs – controlling and executing.

Conclusions. Mathematical model of functional self-organization of respiratory and blood circulatory systems is proposed, which takes into account the interaction and inter-influence of organism functional systems, conflict situations between controlling and executive elements of self-regulation; it is based on the assumption of optimal regulation of oxygen regimes. The model may be useful for solving a number of applied problems of physiology and medicine.

Keywords: Functional respiratory system, controlled dynamic system, self-organization of respiratory system, operators of continuous interaction system, disturbing influence of environment.

Download full text!

1. Keener J., Sneyd J. Mathematical physiology. Springer-Verlag New York, 1998.

2. Anokhin P.K. Fundamental questions of general theory of functional systems. The principles of systemic organization of the functions. Moscow: Nauka, 1973, 258 p. [In Russian]

3. Balanter B.I. Introduction to mathematical modeling of pathological processes. M .: Medicine, 1980, 262 p. [In Russian]

4. Fursova I.V. Extreme principles in mathematical biology. Advances in contemporary biology. 2003,123, 2, pp. 115-117. [In Russian]

5. Mesentseva L.V., Pertsov S.S. Mathematical modeling in biomedicine. Journal of new medical technologies. 2013, ХХ, 1, pp.11-14.

6. Diekman C.O., Thomas P.J., Wilson, C.G. Eupnea, tachypnea, and autoresuscitation in a closed-loop respiratory control model. J. Neurophysiol. 2017, 118, pp. 2194-2215. doi:10.1152/jn.00170.2017

7. Duffin, J. Model validation and control issues in the respiratory system. Mathematical Modeling and Validation in Physiology Lecture Notes in Mathematics, eds J. J. Batzel, M. Bachar, F. Kappel (Berlin; Heidelberg; Springer), 2013, pp.133-162.

8. Fincham W. F., Tehrani F. T. A mathematical model of the human respiratory system.
J. Biomed. Eng. 1983, 5, pp. 125-133. doi: 10.1016/0141-5425(83)90030-4

9. Serna Higuita L.Y., Mañanas M.A., Hernández A.M., Marína Sánchez J., Benito S. Novel modeling of work of breathing for its optimization during increased respiratory efforts. IEEE Syst. J. 2014, 10, pp. 1003-1013. doi: 10.1109/JSYST.2014.2323114

10. Serna L.Y., Marín J., Hernández A.M., Mañanas, M.Á. Optimization techniques applied to parameter estimation in respiratory control system models. Appl. Soft Comput. 2016, 48, pp. 431-443. doi: 10.1016/j.asoc.2016.07.033

11. Tsai N. C., Lee R. M. Interaction between cardiovascular system and respiration. Appl. Math. Model. 2011, 35, pp. 5460-5469. doi: 10.1016/j.apm.2011.04.033

12. Serna L.Y., Mañanas M.A., Hernández A.M., Rabinovich R.A. An Improved Dynamic Model for the Respiratory Response to Exercise. Front. Physiol. 2018, 9.69. doi: 10.3389/fphys.2018.00069

13. Ellwein Fix L., Khoury J., Moores R.R.Jr., Linkous L., Brandes M., Rozycki H.J. Theoretical open-loop model of respiratory mechanics in the extremely preterm infant. PLoS ONE. 2018, 13(6): e0198425.

14. Ebrahimi Nejad S., Carey J.P., McMurtry M.S., Hahn J.-O. Model-based cardiovascular disease diagnosis: a preliminary in-silico study. Biomech Model Mechanobiol. 2017, 16, pp. 549-560 (2017).

15. Quarteroni A, Formaggia L. Mathematical modelling and numerical simulation of the cardiovascular system. Handb Numer Anal. 2004, 12:7-9. DOI: 10.1016/S1570-8659(03)12001-7

16. Quarteroni A., Manzoni A., Vergara C. The cardiovascular system: mathematical modelling, numerical algorithms and clinical applications. Acta Numer. 2017.26.365-590.

17. Capoccia M., Marconi S., Singh S.A., Pisanelly D.M., De Lazzari C. Simulation as a preoperative planning approach in advanced heart failure patients. A retrospective clinical analysis. BioMed Eng OnLine. 2018, 17, 52 .

18. Korobov A.A., Frolov S.V., Aliyev N.E., Rodionova I.E. Dual-contoured model of cardiovascular system regulation. J. Phys.: Conf. Ser. 2020, 1553 012006 DOI:10.1088/1742-6596/1553/1/012006

19. Albanese A., Cheng L., Ursino M., Chbat N.W. An integrated mathematical model of the human cardiopulmonary system: model development. Am J Physiol Heart Circ Physiol. 2016, Apr 1;310(7):H899-921. doi: 10.1152/ajpheart.00230.2014.

20. Cheng L., Albanese A., Ursino M., Chbat N.W. An integrated mathematical model of the human cardiopulmonary system: model validation under hypercapnia and hypoxia. Am J Physiol Heart Circ Physiol. 2016, Apr 1;310(7):H922-37. doi: 10.1152/ajpheart.00923.2014

21. Sarmiento C.A., Hernández A.M., Serna L.Y., Mañanas M.Á. An integrated mathematical model of the cardiovascular and respiratory response to exercise: model-building and comparison with reported models. Am J Physiol Heart Circ Physiol. 2021, Apr, 1;320(4):H1235-H1260. doi: 10.1152/ajpheart.00074.2020.

22. Trenhago P.R., Fernandes L.G., Müller L.O., Blanco P.J., Feijóo R.A. An integrated mathematical model of the cardiovascular and respiratory systems. Int. J. Numer. Methods Biomed. Eng. 32 2016, no. 1, e02736, 25 pp.

23. Zhou S, Xu L, Hao L, Xiao H, Yao Y, Qi L, Yao Y. A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure. Biomed Eng Online. 2019, Apr 2;18(1):41. doi: 10.1186/s12938-019-0660-3.

24. Maury B. The Respiratory System in Equations. Springer. 2013, 278 p. DOI: 10.1007/978-88-470-5214-7

25. Fouchet-Incaux, J. Artificial boundaries and formulations for the incompressible Navier-Stokes equations: applications to air and blood flows. SeMA. 2014, 64, pp. 1-40.

26. 2 Bobryakova I.L., “Sensitivity of mathematical model and optimality of regulation of the functional respiratory system” diss. Candidate of Physical and Mathematical Sciences Sciences, Kyiv, 2000, 179 p. (In Russian)

27. Onopchuk Yu.N. About one scheme of regulation of external respiration modes, minute blood volume and tissue blood flow at oxygen request. Cybernetics. 1980, No. 6, pp. 110-115 [In Russian]

28. Onopchuk Yu.N. On the construction of model for optimal distribution of blood flow in human tissues and organs – In the book: Theory of optimal solutions. Kyiv, 1979, pp. 80-86 [In Russian]

29. Onopchuk Yu.N. Controlled models of organism gas dynamics and their numerical analysis. Dis … ..doct. phys.-mat. sciences. Kyiv, 1984, 208 p. (In Russian)

30. 3 Onopchuk Yu.N., “Equilibrium states and transients in the systems of external respiration and blood circulation. Research on a mathematical model,” Cybernetics. 1981, no. 1, pp. 136-139. (In Russian)

31. 3 Onopchuk Yu.N. Homeostasis of functional respiratory system as a result of intersystem and system-medium informational interaction. Bioecomedicine. Uniform information space. Ed. by V. I. Gritsenko. Kyiv, 2001, pp. 59-84. (In Russian)

32. 3 Onopchuk Yu.N. Homeostasis of the functional circulatory system as a result of intersystem and system-medium informational interaction. Bioecomedicine. Uniform information space. Ed. by V. I. Gritsenko. Kyiv, 2001, pp. 85-104. (In Russian)

33. 3 Aralova N. I. Mathematical models of functional respiratory system for solving the applied problems in occupational medicine and sports. Saarbrücken: LAP LAMBERT Academic Publishing GmbH&Co, KG. 2019, 368 p. (In Russian). ISBN 978-613-4-97998-6

34. 3 Pontryagin L.S., Boltyansky V.G., Gamkrelidze R.V., Mishchenko E.F., Mathematical theory of optimal processes. Moscow: Nauka. 1983, 392 p.

35. 3 Aralova N.I. Mathematical model of the mechanism short- and medium-functional adaptation of breath of persons work in extreme conditions high. Kibernetika i vychislitelnaya tekhnika. 2015, V. 182, pp. 15-25.

36. 3 Onopchuck Y. N., Beloshitsky P. V., Aralova N. I. To problem of reliability of functional systems in organism. Kibernetika i vychislitelnaya tekhnika.1999, V. 122, pp. 72-82. ISSN – 0454-9910. (In Russian)

37. 37. Beloshitsky P.V., Onopchuk Yu.M., Aralova N.I. Mathematical methods for the investigation of the problem of organism functioning reliability at extreme high mountains conditions. Journal.2003,V. 49, № 3, pp. 47-54(In Russian)

38. 3 Aralova N.I. Information technologies of decision-making support for rehabilitation of sportsmen engaged in combat sport. J. Automation Information Sci. 2016, V. 3, pp. 160-170. https://doi:10.1615 / JAutomatInfScien.v48. i6.70

39. Onopchuk Yu.N., Aralova N.I., Beloshitsky P.V., Podlivaev B.A., Mastucash Yu.I. Forecasting of wrestler’ state in the combat on the base of mathematic model of functional respiratory system. Computer mathematics. 2005, № 2, pp. 69-79 (In Russian)

Received 27.05.2021

Issue 3 (205), article 2


Cybernetics and Computer Engineering, 2021, 3(205)

CHABANIUK V.S.1, 2, PhD (Phys.-Math.),
Senior Researcher of the Cartography Department, Institute of Geography,
Director of “Intelligence systems-GEO” LLC
ORCID: 0000-0002-4731-7895

Head of Production of “Intelligence systems-GEO” LLC
ORCID: 0000-0002-4927-4200

1 Institute of Geography, National Academy of Sciences of Ukraine 44, Volodymyrska str., Kyiv, 01054, Ukraine

2 “Intelligence systems-GEO” LLC, 6/44, Mykilsko-Slobidska str., Kyiv, 02002, Ukraine


Introduction. Part 2 discusses three critical systemic properties (CSP) of electronic atlases (EA) new generation. With their help fundamentally new, systemic EA are determined. Compared with classic systems, new EA have much more opportunities to model spatial systems of actuality.

The purpose of the article is to describe and prove the criticality of three CSP for a new generation of EA — systemic EA.

Results. Three CSP are described: CSP.System, CSP.Tree, CSP.View. CSP.System means that systemic EA should be models of spatial systems of actuality. These models are primary in contrast to the classic EA models, which are secondary. CSP.Tree means that the contents/solutions tree of the systemic EA must classify the modeled spatial system of actuality. CSP.View should model the visualization needs of users, in particular through interactivity. The methods of Conceptual Frameworks and Solutions Frameworks of Relational cartography, as well as facts from Model-Based Engineering were used for proof.

Conclusions. Three CSP of systemic EAs are described and it is proved that each of them is a necessary property of EA new generation.

Keywords: electronic atlases new generation, critical systemic property, Conceptual Framework, Solutions Framework, Relational cartography.

Download full text!


1. Chabaniuk V., Kolimasov I., Krakovskyi S. Critical systemic properties of Electronic atlases new generation. Part 1: Problem and research methods. Cybernetics and Computer Engineering. 2021, No 2(204), pp. 20-49.

2. Kuhne Thomas (2006). Matters of (meta-)modeling. Softw. Syst. Modeling. 5, pp. 369-385.

3. Chabaniuk V., Polyvach K. Critical properties of modern geographic information systems for territory management. Cybernetics and Computer Engineering. 2020, No 3(201), pp. 5-32.

4. Klir G.J. (1985) Architecture of Systems Problem Solving. New York: Springer.

5. Gaines Brian R. General systems research: quo vadis? General Systems: Yearbook of the Society for General Systems Research. 1979,Vol. 24, pp. 1-9.

6. Cresswell Tim. Geographic Thought: A Critical Introduction (Critical Introductions to Geography). Wiley-Blackwell, 2013. 298 p. (290 (300) p.)

7. Aslanikashvili A.F. Metacartography. Main problems. Tbilisi: Metsniereba, 1974.126 p. (in Russian)

8. Chabaniuk Viktor. Relational cartography: Theory and practice. Kyiv: Institute of Geography of the NAS of Ukraine, 2018, 525 p. (in Ukrainian)

9. URL: [Last accessed 10.07.2021].

10. Brambilla Marco, Cabot Jordi, Wimmer Manuel. Model-driven Software Engineering in Practice (Synthesis Lectures on Software Engineering). Morgan & Claypool Publishers, 2nd Ed., 2017, 209 p.

11. Large Encyclopedic Dictionary. Ch. editor Prokhorov A.M. Soviet Encyclopedia, 1993, 1628 p.

12. URL: deutschland-autobahnnetz.htm [Last accessed 10.07.2021].

13. URL: [Last accessed 10.07.2021].

14. URL:!в!!т!o!м!a!г!!и!c!т!pa!л!!и!_!Г!!е!p!м!a!н!!и!!и! [Last accessed 10.07.2021].

15. URL:,9.6836355,8z?hl=ru [Last accessed 10.07.2021].

16. Salichtchev K.A., Ed. Complex Regional Atlases. M.: MSU Publishing, 638 p. (in Russian)

17. Salichtchev K.A. Cartography. Textbook. M.: MSU Publishing, 3rd Ed, 400 p. (in Russian)

18. Svatkova T.G. Atlas cartography. Textbook. M.: Aspect Press, 2002, 203 (210) p. (in Russian)

19.URL:,9.6836355,258690m/data=!3m1!1e3?hl=ru[Last accessed 10.07.2021].

20. URL:!л!o!в!ap!ь!/a!н!!г!!л!!и!!й!c!к!!и!!й!/systematization [Last accessed 27.08.2020].

21. Pedagogical Encyclopedic Dictionary. / Ch. ed. B.M. Bim-Bad. M., Large Russian Encyclopedia, 2002, 528 p. URL:!и!c!т!!е!!м!a!т!!и!!з!a!ц!!и!!я! [Last accessed 30.04.2021] (in Russian).

22. Philosophical encyclopedic dictionary, 2010. URL: [Last accessed 11.07.2021]

23. URL: [Last accessed 10.07.2021].

24. Chabaniuk V., Dyshlyk O. Atlas Basemaps in Web 2.0 Epoch. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,!
!V. XLI-B4, 2016 XXIII ISPRS Congress, (12-19 July 2016, Prague), Prague, Czech Republic, pp. 611-618.

25. URL:!и!c!т!!е!!м!a!т!!и!!к!a [Last accessed 03.07.2020].

26. New Philosophical Encyclopedia: In 4 volumes / Institute of Philosophy RAS, Nat. general scientific fund; Scientific ed. Council: Chairman V.S. Stepin, Deputy Chairmens: A.A. Guseinov, G.Yu. Semigin, Scientific Secretary A.P. Ogurtsov. M.: Thought, 2010. ISBN 978-2-244-01115-9. V.I. 2010 744 p. ISBN 978-2-244-01116-6. V. II. 2010 634, [2] p. ISBN 978-2-244-01117-3. V. III. 2010 692, [2] p. ISBN 978-2-244-01118-0. V. IV. 2010 736 p. ISBN 978-2-244-01119-7. (in Russian)

27. Chabaniuk V.S, Rudenko L.G. Software solutions in the processes of creating and using atlas interactive maps. Journal of the Belarusian State University. Geography. Geology. 2019, No. 2, pp. 25-39. Russian.

28. Bochkovska, et al. Electronic version of the Pilot Project “The National Atlas of Ukraine”. Ukrainian Geographic Journal. 2000, No 1, pp. 48-61 (in Ukrainian)

29. Mikowski Michael, Powell Josh. Single Page Web Applications: JavaScript end-to-end.- Manning Publications. 2014, 407 (433) p.

30. URL: [Last accessed 07.07.2020].

31. Roth Robert E. Interacting with Maps: The science and practice of cartographic interaction. The Pennsylvania State University, Doctor of Philosophy (Geography) Dissertation. 215 (225) p. DOI: 10.1179/1743277412Y.0000000019.

32. Roth Robert E. Interactive Maps: What We Know And What We Need To Know. Journal of Spatial Information Science. No. 6, pp. 59-115. DOI: 10.5311/JOSIS.2013.6.105.

33. Interactive maps: essence and software solutions for creation and use, p. 21-35.!
!in Rudenko L., ed. Actual directions of cartography development in Ukraine. Kyiv, Institute of Geography of the National Academy of Sciences of Ukraine. 88 p. (in Ukrainian)

34. Donohue Richard G. Web Cartography with Web Standards: Teaching, Learning, and Using Open Source Web Mapping Technologies. University of Wisconsin-Madison, Doctor of Philosophy (Geography) Dissertation. 2014, 167 (173) p.

Received 22.03.2021

Issue 3 (205), article 1


Cybernetics and Computer Engineering, 2021, 3(205)

GRITSENKO V.I.1, Corresponding Member of NAS of Ukraine,
Director of International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine
ORCID: 0000-0003-4813-6153

GLADUN A.Ya.1, PhD (Engineering),
Senior Researcher of the Department of Complex Research of Information Technologies and Systems
ORCID: 0000-0002-4133-8169

Researcher of the Department of Complex Research of Information Technologies and Systems
ORCID: 0000-0002-9477-970X

Martínez-Béjar R.2, PhD (Informatics),
Professof at the Department of Information and Communication Engineering
ORCID: 0000-0002-9677-7396

1 International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, 40, Acad. Glushkov av., Kyiv, 03187, Ukraine

2 Department of Information and Communication Engineering and Artificial Intelligence University of Murcia CP 30180 Bullas, Spain


Introduction. The expediency of reapplication of ontology in applied intelligent information systems (IIS), which are focused on functioning in the open Web environment on the basis of Semantic Web technologies, is substantiated in the work. Features of ontology storage and management platforms and their metadata are analyzed. Possibilities of searching in ontology repositories and their reuse in IIS are considered. The mechanisms of ontology search based on semantic processing of their metadata, analysis of ontology structure using metrics of semantic similarity between their concepts related to the current user task are presented.

The purpose of the article is the development of algorithms and methods for evaluating semantic models, which consist in combining qualitative (ontological) representation of knowledge with quantitative (numerical) evaluation of ontologies and their parameters (semantic proximity, semantic distance, semantic affinity) aimed at finding similarities different ontologies

Methods. Methods of ontological analysis of objects of the subject area, theoretical and multiple approaches to determine the degree of closeness of two objects by comparing their properties (feature matching) and traditional methods of statistical analysis are used to solve the tasks set in the work.

Results. The proposed method of estimating semantic similarity allows on the basis of semantic analysis of natural annotations of metadata both ontologies and data (including Big Data) to perform the task of their interpretation and selection to the problem to be solved by the applied IIS or application. The obtained results allow to create original IIS for artificial intelligence in economics, medicine, national security, defense and social sphere.

Conclusion. We proposed an original approach to the evaluation and analysis of metadata (ontologies, data), based on semantic analysis of metadata and determining the semantic similarity of structural data models (ontologies, data) and the formation of a ranked set of related ontologies to solve problems of artificial intelligence. The application of methods for defining semantically similar concepts is presented as a tool for semantic comparison of the structure of ontologies, which were found in the repository under formal conditions, with a poorly structured PM-description. At present, there is no generally accepted standard for presenting metadata, so the proposed methods of analysis of PM annotations are the most adequate means of comparing the semantics of ontologies, data with the problems for which they can be used.

Keywords: semantic similarity, formal ontology model, metadata, metadata standards, intelligent information system, ontology repository.

Download full text!


1. Vasyukov V.L. Formal ontology and artificial intelligence. Moscow: IP RAS, 2006, 140 p.

2. Gladun A.Ya., Rogushina Yu.V. Ontology repositories as a means of reusing knowledge for recognizing information objects. Design Ontology. 2013, No. 1 (7), pp.35-50.

3. Torsten Hahmann Ontology repositories: A treasure trove for content ontology design patterns. Proceedigs of 8th Inter. Workshop on Modular Ontologies (WoMO-2014), Rio de Janeiro, Brazil, 2014.

4. Kenneth Baclawski Todd Schneider The open ontology repository initiative: Requirements and research challenges. Proc. 8th International Semantic Web Conference, ISWC 2009, Chantilly, VA, USA, 2010.

5. Rachel Heery, Sheila Anderson. Digital repositories review, February 2005.

6. W. H. Inmon. Building the Data Warehouse, 3rd Ed. Wiley, New York, 2002.

7. Jens Hartmann, York Sure, Raul Palma, Peter Haase, Mari Carmen Suarez-Figueroa, Rudi Studer, Asuncion Gomez-Perez. Ontology metadata vocabulary and applications. Int Conf on Ontologies, Databases and Applications of Semantics. Workshop on Web Semantics (SWWS), Oct 2005.

8. Vekhorev M.N., Panteleev M.G. Construction of repositories of ontological knowledge bases. Software products and systems. 2011, No. 3 (3), pp.67-89.

9. Taylor C. An Introduction to Metadata. The University of Queensland, Australia. URL:

10. Corcho O. Ontology based document annotation: trends and open research problems. Journal of Metadata, Semantics and Ontologies. Vol. 1, Issue 1, January 2006. URL:

11. ISO 15489-1:2016 Information and documentation – Records management – Part 1: Concepts and principles.

12. ISO 15836-1:2017 Information and documentation – The Dublin Core metadata element set – Part 1: Core elements.

13. ISO 15836-2:2019 Information and documentation – The Dublin Core metadata element set – Part 2: DCMI Properties and

14. DSTU ISO 15489-1: 2018 Information and documentation. Records management. Part 1. Concepts and principles (ISO 15489-1: 2016, IDT).

15. DSTU ISO 15836-1: 2018 Information and documentation. Dublin Core Metadata Element Set. Part 1. Basic elements (ISO 15836-1: 2017, IDT).

16. Gladun A., Khala K., Subach I. Ontological Approach to Big Data Analytics in Cybersecurity Domain. Collection “Information Technology and Security”. 2020, Vol. 8, No. 2, pp.120-132. DOI:
URL: (Last accessed:

17. Obrst L., Ceusters W., Mani I., Ray S., Smith B. The evaluation of ontologies. Semantic web. 2020, pp. 139-158.

18. Gomez-Perez, A. Ontology evaluation. Handbook on ontologies. Springer, Berlin, Heidelberg, 2004, pp. 251-273

19. Leo Obrst, Werner Ceusters, Inderjeet Mani, Steve Ray, Barry Smith. The evaluation of ontologies. In Christopher J.O. Baker and Kei-Hoi Cheung, editors, Revolutionizing Knowledge Discovery in the Life Sciences, chapter 7. Springer, Berlin, 2007, pp.- 139-158.

20. Obrst, L. Ontologies for semantically interoperable systems. Proceedings of the 12th International Conference on Information and knowledge management, 2003, pp. 366-369.

21. Gangemi A., Catenacci C., Ciaramita M., Lehmann J. Modelling ontology evaluation and validation. European Semantic Web Conference. Springer, Berlin, Heidelberg, 2006, pp. 140-154.

22. Jens Hartmann, Raul Palma, and Asunci’on Gomez-Perez “Ontology Repositories”. Chapter in Handbook on Ontologies, Springer, 2009, pp 551-571.

23. Yimin Wang, Jie Bao, Peter Haase, and Guilin Qi. Evaluating formalisms for modular ontologies in distributed information systems. Proc. of the First Inter Conf on Web Reasoning and Rule Systems (RR2007), LNCS 4524, (June, 2007, Innsbruck). Innsbruck, Austria, p 178-182.

24. Jens Hartmann, Raul Palma. OMV – Ontology Metadata Vocabulary. Semantic Web. 2006, Vol. 2.0, URL:

25. Palma R., Hartmann J., Haase P. OMV Ontology Metadata Vocabulary. SemanticWeb. 2009, 76p. URL: (Last accessed:

26. Hartmann, Jens, Raul Palma, Peter Haase, Asuncion Gomez-Perez. “Ontology Metadata Vocabulary-OMV.” 2007.

27. Welcome to OMV – Ontology Metadata Vocabulary URL:

28. A.Y. Gladun, K.A. Khala Ontology-based semantic similarity to metadata analysis in the information security domain. Prombles in programming. 2021, N2, pp.34-41. DOI: htpps:// URL:

29. Tversky A. Features of Similarity. Psychological Rev. 1977, V. 84, p. 327.

30. Rogushina J. Use of Semantic Similarity Estimates for Unstructured Data Analysis. CEUR Vol-2577, Selected Papers of the XIX International Scientific and Practical Conference “Information Technologies and Security” (ITS 2019), Kyiv, 2019, pp. 246-258. URL:

31. A.Ya. Gladun, Yu.V. Rogushina. Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization. Prombles in programming. 2021, N2, pp. 3-15. DOI: htpps:// URL:

Received 11.01.2021