Issue 2 (204), article 5


Cybernetics and Computer Engineering, 2021, 2(204)

VOVK М.І., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department

KUTSIAK О.А., PhD (Engineering),
Senior Researcher of Bioelectrical Control & Medical Cybernetics Department

International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of MES of Ukraine,
40, Acad. Glushkov av. Kyiv, 03187, Ukraine


Introduction. Diagnostics of motor functions plays an important role in the motor functions restoration after stroke. Synthesis of effective technologies for personalized assessment of motor functions disorders at different rehabilitation stages is an urgent scientific and applied task.

The purpose of the paper is to develop information technology for diagnostics of motor functions deficit after stroke, that uses artificial intelligence tools to increase the effectiveness of the diagnostic process.

Results. The theoretical and practical foundations to synthesize AI-technology for personal diagnostics of motor functions deficit, and the assessment of their restoration as a result of rehabilitation measures after stroke have been developed. For informational assistance to the physician in the diagnostic process, artificial intelligence is used. A new class of mobile digital medicine tools – the specialized software modules for motor functions diagnostics “MovementTestStroke 1.1 (PC)” installed in the PC-structure, and “MovementTestStroke 1.1 (MD)” installed in mobile platforms running under Android operation system have been developed. Software implementation — Visual Studio 2019, C# programming language. Structural and functional models of interaction of physician with software modules, algorithms for motor function deficit diagnostics, and UML-diagrams of these modules are presented.

Functional features of the technology: an expanded range of evidence criteria for personalized quantitative assessment of limb movement deficit, storage in the Database and display on the interface the results of deficit assessment, as well as the deficit dynamics during the rehabilitation course in a convenient form (tables, graphs) make it possible to reduce the physician error, prevent complications, identify the disorders specifics, compare the rehabilitation effectiveness of the upper and lower limbs, their distal and proximal parts, including fine motor skills of the hand, restoration of which helps to restore speech in motor or motor and sensory aphasia.

Conclusions. The use of artificial intelligence tools to diagnose motor deficit will increase the diagnostic effectiveness, and, as a consequence, rehabilitation services for patients after stroke.

Keywords: diagnostics, motor functions, stroke, personal quantitative assessment, criteria, technology, artificial intelligence, software module, structural-functional model, algorithm, activity diagram

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Received 01.04.2021

Issue 2 (204), article 4


Cybernetics and Computer Engineering, 2021, 2(204)

FAINZILBERG L.S.1, DSc. (Engineering), Professor,
Chief Researcher of the Department of Automatic Systems
ORCID: 0000-0002-3092-0794

SOLOVEY S.R.2, Student Faculty of Biomedical Engineering,

1International 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. Glushkova av., Kyiv, 03187, Ukraine,

2The National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
37, Peremohy av., Kyiv, 03056, Ukraine


Introduction. In connection with the COVID-19 pandemic, it is important to start treatment promptly in case of a threat of developing viral pneumonia in a patient. The solution to this problem requires the creation of new means for detecting respiratory disorders with a minimum probability of “missing the target”. At the same time, it is equally important to minimize visits to medical institutions by healthy patients because of the danger of their contact with possible carriers of coronavirus infections, that is, to minimize the likelihood of a «false alarm».

Purpose of the article is to develop a method that allows a patient to signal at home about the advisability of contacting a medical institution for an in-depth examination of the respiratory system, and to assess the possibility of implementing this method on a smartphone using a built-in microphone.

Methods. A distinctive feature of the proposed approach lies in the construction of a personalized standard of normal respiratory respiration for a particular patient based on self-learning from a finite sample of observations at home and in comparison, based on original computational algorithms of phonospirograms of sound signals of the following observations with the standard.

Results. A prototype of information technology has been developed that will provide home alarms about possible respiratory disorders, requiring consultation with a doctor and the need for an in-depth medical examination.

It is shown that the construction of a personalized standard of normal breathing can be carried out based on the use of a set of original computational procedures for a finite sample of realizations, independently registered by the user using a microphone built into a smartphone. The algorithm for constructing a standard is based on digital processing of a matrix of paired distances between phonospirograms of the final training sample of observations.

Findings. A software application that provides the implementation of the proposed computational procedures can be implemented on a smartphone of average performance running the Android operating system.

Keywords: respiratory noises, intelligent IT, computational procedures, smartphone.

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Received 02.03.2021

Issue 2 (204), article 3


Cybernetics and Computer Engineering, 2021, 2(204)

SHEPETUKHA Y.M., PhD (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department
ORCID: 0000-0002-6256-5248

Head of the Intelligent Control Department
ORCID: 0000-0002-5418-6723

Senior Researcher of Intelligent Control Department
ORCID: 0000-0002-0119-0964

International Research and Training Center for Information Technologies and Systems of NAS of Ukraine and MES of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. Scientific-technical level of any country in a modern world is mainly determined by a current state and development rate of informational technologies. At the same time, the main avenue of information technologies’ improvement is their intellectualization. Due to intellectualization, it became possible to create advanced systems with principally novel functional capabilities, in particular, high-speed computer systems able to autonomous actions in a complex and dynamic environment. Control means for complex objects and processes play an important role in the operation of autonomous systems. Therefore, the study of theoretical as well as applied issues of such systems’ construction is an important scientific and engineering problem.

The purpose of the paper is to examine both current state and development prospects of a new direction in the area of intelligent information technologies – the elaboration of autonomous control systems for complex objects and processes in a dynamic environment; to formulate a well-grounded approach for the increase in intellectualization level of decision processes in such systems.

Methods. The development of autonomous control systems, as well as the increase in decision making processes’ intellectualization level in such systems, is based on the usage of the following conceptual, theoretical and methodological instruments: the theory of informational technologies’ intellectualization, the methodology of intelligent control, the theoretical fundamentals of artificial intelligence systems’ construction, decision making methods, the methodology of image-based reasoning, methods for simulation of image-based comprehension of environment.

Results. An approach for the consistent usage of methods of artificial intelligence, decision making and intelligent control aimed at the development of autonomous means for the control of complex objects and processes has been examined. Appropriateness of creation of the systems profiled for operations in designated problem domains has been grounded. Both specific features and components of the framework for decision making in intelligent control systems have been determined. Both necessity of the creation of intelligent environment and important role of sensor networks have been stressed. Methodology for the construction of informational images, which represent the most important components of a current situation, has been proposed. Examples of the usage of informational images for performing both dynamic and evolutional re-planning have been considered.

Conclusions. A reasonable way for the development of intelligent control systems is the one that provides a consistent usage of different types of models. Image-based representation of a current situation’s essential interconnections is an efficient instrument for the intellectualization at different stages of decision making processes – alternative generation, understanding of inconsistencies among different data sources, execution of choice procedure, evaluation of results. The application of artificial intelligence elements for decision making in autonomous systems is especially well-grounded in cases of time shortage as well as availability of a great number of existing alternatives.

Keywords: intellectualization of information technologies, intelligent control, decision making, autonomy, artificial intelligence, image, uncertainty, adaptation.

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Received 04.04.2021

Issue 2 (204), article 2


Cybernetics and Computer Engineering, 2021, 2(204)

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

Junior Researcher of the Cartography Department, Institute of Geography,
ORCID: 0000-0001-5164-6272

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

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


Introduction. The revolutionary changes in information technology of the last two decades allow the construction of electronic atlases (EA), the capabilities of which are fundamentally richer than the capabilities of “classic” EA. This is achieved through the use of the systemic properties of the new generation of EA, which are therefore named systemic. Systemic EA remain the simplest and most effective spatial information models of territorial systems allowing applying them for the decision of many practical problems.

The purpose of the paper is to formulate the need for systemic EA and describe methods for studying their systemic properties. These methods will be used to find and describe critical systemic properties without which EA cannot be systemic. The methods are founded on Relational Cartography and Model-Based Engineering.

Results. The evolution of “classic” EA is considered: from paper atlases and their images to analytical atlases. It is shown that on the imaginary border of classic and nonclassic EA there are already new generation EA — systemic EA. Both the theory and practice of such systemic EA have many unresolved problems. Some of them are described in the article. The authors believe that many problems can be solved by implementing the critical systemic properties of EA. Two methods are used to study the problems and to prove the results: Conceptual frameworks and Solutions frameworks. Both the methods themselves and the possibility of their application to find the critical systemic properties of the new generation of EA are described.

Conclusions. The main problems of electronic atlases of the new generation are described and their solution is offered by a method of Conceptual frameworks and a method of Solutions framework.

Keywords: systemic electronic atlas, Conceptual framework, Solutions framework, critical system property.

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Received 22.03.2021

Issue 2 (204), article 1


Cybernetics and Computer Engineering, 2021, 2(204)

GRITSENKO V.I., 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

BABAK O.V., PhD (Engineering), Senior Researcher,
Ecological Digital Systems Department
ORCID: 0000-0002-7451-3314

SUROVTSEV I.V., DSc (Engineering), Senior Researcher,
Head of the Ecological Digital Systems Department
ORCID: 0000-0003-1133-6207

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. The 5G, 6G mobile technologies, which are actively developing in the world, and the Internet of Things (IoT), Big Data (BD), artificial intelligence (AI) are closely intertwined. It is important to understand the features of the relationship to effectively use them in new intelligent information technologies.

The purpose of the article is to highlight the most important features of the relationship, which are viewed on the basis of experience in implementing 5G and 6G technologies.

Results. the Internet of Things, industrial (IIoT), the Internet in total (IoE) use 5G, 6G technologies, as well as cloud, fog and boundary computing for high-speed communication with devices. Machine learning (ML), Date Mining, neural networks and simulation are used to analyze BD. AI algorithms are an integral part of all technologies, they allow you to intelligently connect and control 5G / 6G + IoT + BD + AI.

Conclusions. 5G and 6G high-speed networks, Internet of Things technology, cloud computing, big data analysis and artificial intelligence are necessary conditions for the further development of the digital economy.

Keywords: communication networks, big data, Internet of Things, artificial intelligence.

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Received 20.03.2021

Issue 2 (204)


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Informatics and Information Technologies:

Gritsenko V.I., Babak O.V., Surovtsev I.V.
Peculiarities of Interconnection 5G, 6G Networks with Big Data, Internet of Things and Artificial Intelligence

Chabaniuk V.S., Kolimasov I.M., Krakovskyi S.P.
Critical Systemic Properties of Electronic Atlases of New Generation. Part 1: Problem and Research Methods

Intelligent Control and Systems:

Shepetukha Yu.M., Volkov O.Ye., Komar M.M.
Intellectualization of Decision Making Processes in Autonomous Control Systems

Medical and Biological Cybernetics:

Fainzilberg L.S., Solovey S.R.
Self-learning Information Technology for Detecting Respiratory Disorders in Home Conditions

Vovk М.І., Kutsiak О.А.
AI-Technology of Motor Functions Diagnostics after a Stroke

Issue 1 (203), article 5


Cybernetics and Computer Engineering, 2021, 1(203)

Researcher, the Medical Information Systems Department
ORCID: 0000-0002-4407-5990

KOZAK L.M., DSc (Biology), Senior Researcher,
Leading Researcher, the Medical Information Systems Department
ORCID: 0000-0002-7412-3041

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. The digital technologies implementation provides registration of large amounts of bio-medical data (ECG, EEG, electronic medical records) as a basis for assessing and predicting the patients` condition. Data Mining methods allow to identify the most informative indicators and typological groups, to classify the person` functional state and the patients` disease stages to predict their changes.

The purpose of the paper is to develop information technology for the classification of human health states using a set of Data Mining methods and to carry out its validation on examples of an operators` functional state and patient’s disease severity.

Results. The developed IT unites several stages: I — data pre-processing; II — clustering, selecting the homogeneous groups (data segmentation); III — predictors` identification; IV — classifying the studied states, development of predictive models using machine learning algorithms (Decision trees, Support vector machines, neural networks) and the method cross-validation. The proposed IT was used to classify the operators` functional statе and the patients` severity in case of disease progression.

Conclusions. The IT use to assess the operators` activity successes made it possible to identify the most informative HRV indicators, changes in which can predict the operators` reliability, taking into account the type of vegetative regulation. Assessing the disease activity of children with dysplasia with IT use made it possible to identify diagnostic markers of CCC and develop diagnostic rules for determining the stages of the disease by ECG parameters (T wave symmetry, an integral indicator of the ST_T segment shape).

Keywords: information technology, Data Mining, machine learning models, severity of the patient.

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Received 31.11.2020

Issue 1 (203), article 4


Cybernetics and Computer Engineering, 2021, 1(203)

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, Faculty of Air Navigation,
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, Faculty of Information Technology and Management
ORCID: 0000-0002-0667-5749,

1V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine.
40, Acad.Glushkov av., Kyiv, 03680, Ukraine
2Electronics and Telecommunications National Aviation University,
1, Lubomyr Huzar av., Kyiv, 03058, Ukraine
3Borys Grinchenko Kyiv University,
18/2, Bulvarno-Kudriavska str., Kyiv, 04053 Ukraine, 04053


Introduction. The areas around industrial objects, and now in regions of military actions are characterized by a high content of pollutants. Qualitative spectrum of these pollutants is extremely broad and contains both inorganic and organic elements and compounds. In particular, environmental pollution is caused by hydrocarbons with wide range of chemical structures, the study of which is very important due to their harmful and toxic influences on living organisms. The methods, currently used in medicine, give only a “thin slice” of current pathological state of organism, but they cannot predict the long-term consequences of such lesions. That is why it seems appropriate to use mathematical models that simulate the movement of organic compounds in the respiratory and circulatory systems and thus to predict possible pathologies in organs and tissues caused by hypoxic states that occur when these organs and tissues are affected.

Purpose of the paper is to create a mathematical model of functional respiratory system, which simulates the influence of external environment on the parameters of self-organization of human respiratory system in the dynamics of respiratory cycle; and thus to predict hypoxic conditions during tissue damage by hydrocarbons.

Results. The mathematical model for respiratory gases transport and mass transfer in human organism is represented as a system of differential equations, which is a controlled dynamic system, and the states of which are determined by oxygen and carbon dioxide stresses in each structural link of the respiratory system (alveoli, blood, and tissues) at each moment of time. The model is supplemented by the equations of transport of the substances in each structural link as well as by the mathematical model of organism oxygen regimes regulation. The model includes seven groups of tissues – brain, heart, liver and gastrointestinal tissues, kidneys, muscle tissue etc. The algorithm of the work and iterative procedure of research with application of suggested complex are given.

Conclusion. The proposed mathematical model for studying of the transport of organic substances in human organism which consists of differential equations of respiratory gases transport and mass transfer in it, and for the transport of organic compounds is theoretical only for today. However, in the presence of appropriate array of experimental data, it will be able to monitor the state of functional respiratory system after the pathogenic organic compounds inquiry, which may be useful in choosing of strategies and tactics for the treatment of particular lesion.

Keywords: functional respiratory system, regulation of organism oxygen regimes, harmful organic substances, hypoxic state, mathematical model of respiratory system, transport of gases by blood, self-regulation of respiratory system.

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8. Aralova N.I. Mathematical models of functional respiratory system for solving the applied problems in occupational medicine and sports. Saarbrucken: LAP LAMBERT Academic Publishing GmbH&Co, KG. 2019. 368 p.

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Received 04.11.2020

Issue 1 (203), article 3


Cybernetics and Computer Engineering, 2021, 1(203)


GUBAREV V.F.2, DSc. (Engineering), Corresponding Member of NAS of Ukraine,
Head of the Dynamic Systems Control Department

1National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” 37, Peremohy av., 03056, Kyiv, Ukraine
2Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine
40, Acad. Glushkova, 03187, Kyiv, Ukraine


Introduction. There is a wide range of systems describable as multi input multi variable systems evolving in discrete time. This mathematical model is often used in engineering, but it can also be applied in many other fields. The problem of stabilization of this kind of system frequently arises. In this paper we consider the model predictive control approach to this problem. Its main principle is to generate control signals by optimizing consequent system’s future dynamics on limited prediction horizon. While it demonstrates some good results, in practice we are always limited in terms of computational resources. Thus, we can optimize outcomes of our future control sequence only for limited horizon lengths. That is why it is valuable to understand how this limit affects control quality.

The purpose of the paper is to propose a way to appraise drawbacks of limiting of the prediction horizon to certain length for a particular system, so that we can make informed choice of such limit and therefore choose controller’s microprocessor with sufficient computing power.

Methods. Several indexes which characterize the stabilization process are defined. Their heatmaps built against system’s initial state are used as a convenient visualization of how system’s stabilization dynamics changes depending on its initial state and of drawbacks induced by prediction horizon length limiting. Such heatmaps were built for several prominent example systems with different structures by performing corresponding series of computational experiments.

Results. Drawbacks of prediction horizon length limiting vary from severe to completely nonexistent depending on the system’s structure and representation. These drawbacks relax with increase of this limit. Simple future state’s norm minimizing objective function gives best results with systems whose natural response matrix is not defective and is represented in real Jordan form. Otherwise results worsen dramatically.

Conclusions. The stabilization dynamics depends largely on the system’s structure. Therefore, it is advised to take it into account and build heatmaps of aforementioned indexes to decide on prediction horizon length limit. A good system’s representation can improve stabilization time with limited prediction horizon length. Also, the function of minimum required stabilization time for initial state can be treated as an ideal objective function, but finding this function for a particular system is problematic.

Keywords: MPC, MIMV, heatmap, control synthesis, discrete controllable system, linear system.

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Received 24.12.2020

Issue 1 (203), article 2


Cybernetics and Computer Engineering, 2021, 1(203)

Anisimov A.V., DSc (Phys & Math), Corresponding Member
of National Academy of Sciences of Ukraine,
Dean of the Faculty of Computer Science and Cybernetics
ORCID: 0000-0002-1467-2006

Bevza M.V., PhD student
ORCID: 0000-0002-2697-4968

Bobyl B.V., PhD student
ORCID: 0000-0002-9612-1071

Taras Shevchenko National University of Kyiv
60, Volodymyrska st., Kiyv, 01033, Ukraine


Introduction. Social networks create highly-personalized experiences for their users, giving them an opportunity to follow pages of other users that publicize relevant and interesting content for them. Authors of the content create visual and text content that later receive feedback from their followers in the form of likes, shares and comments.

The purpose of the paper is to build a system that can predict the reaction of the audience on the post and account for all the specialties of the page itself, its audience, the author and variety of possible reactions. In our work we explain the process of the neural network training, that gives the ability to train the neural network for each particular page and audience to get better quality of the algorithms work.

Results. We have created a system that processes both visual and textual part of the content and gives the program the full context of the publication that algorithm will process. The features of the text and image part of the content has been received via processing the data with state-of-the-art neural networks such as BERT and VGG-16.

Conclusions. The result of the work is a state-of-the-art algorithm that can predict reactions of the audience on each publication of the personal page of a user of social media.

Keywords: artificial intelligence, natural language processing, computer vision, social networks.

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Received 30.11.2020