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Issue 1 (203), article 5

DOI:https://doi.org/10.15407/kvt203.01.077

Cybernetics and Computer Engineering, 2021, 1(203)

KRYVOVA O.A.,
Researcher, the Medical Information Systems Department
e-mail: ol.kryvova@gmail.com
ORCID: 0000-0002-4407-5990

KOZAK L.M., DSc (Biology), Senior Researcher,
Leading Researcher, the Medical Information Systems Department
e-mail: lmkozak52@gmail.com
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

INFORMATION TECHNOLOGY FOR CLASSIFICATION OF DONOSOLOGICAL AND PATHOLOGICAL STATES USING THE ENSEMBLE OF DATA MINING METHODS

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

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Issue 1 (203), article 4

DOI:https://doi.org/10.15407/kvt203.01.060

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,
e-mail: aralova@ukr.net

KLYUCHKO O.M.2, PhD (Biology), Associate Professor,
Associate Professor, Faculty of Air Navigation,
ORCID: 0000-0003-4982 7490
e-mail: kelenaXX@nau.edu.ua

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

MASHKINA I.V.3, PhD (Engineering), Associate Professor
Associate Professor, Faculty of Information Technology and Management
ORCID: 0000-0002-0667-5749,
e-mail: mashkina@kubg.edu.ua

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

MATHEMATICAL MODEL OF FUNCTIONAL RESPIRATORY SYSTEM FOR THE INVESTIGATION OF HARMFUL ORGANIC COMPOUNDS INFLUENCES IN INDUSTRIAL REGIONS

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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REFERENCES

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6. 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. Kiev. 2001. P. 59-84 (In Russian)

7. 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. Kiev. 2001. P. 85-104 (In Russian)

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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10. Aralova N.I. Information Technologies of Decision Making Support for Rehabilitation of Sportsmen Engaged in Combat Sports DOI: 10.1615/J Automat Inf Scien.v48.i6.70. pages 68-78
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15. 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. N 2. P. 69-79 (In Russian)

16. Aralova N.I., Shakhlina L.Ya.-G., Futornyi S.M., Kalytka S.V. Information Technologies for Substantiation of the Optimal Course of Interval Hypoxic Training in Practice of Sports Training of Highly Qualified Sportswomen. Journal of Automation and Information Sciences, V. 52. Iss 1, pp. 41-55 DOI:
https://doi.org/10.1615/JAutomatInfScien.v52.i1.50

Received 04.11.2020

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Issue 1 (203), article 3

DOI:https://doi.org/10.15407/kvt203.01.039

Cybernetics and Computer Engineering, 2021, 1(203)

MISHCHENKO M.D.1, Student
e-mail: mishenkomihailo@gmail.com

GUBAREV V.F.2, DSc. (Engineering), Corresponding Member of NAS of Ukraine,
Head of the Dynamic Systems Control Department
e-mail: v.f.gubarev@gmail.com

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

HORIZON LENGTH TUNING FOR MODEL PREDICTIVE CONTROL IN LINEAR MULTI INPUT MULTI VARIABLE SYSTEMS

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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REFERENCES

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6. Gubarev V.F., Mishchenko M.D., Snizhko B.M. Model Predictive Control for Discrete MIMO Linear Systems. In: Kondratenko Y., Chikrii A., Gubarev V., Kacprzyk J. (eds) Advanced Control Techniques in Complex Engineering Theory and Applications. Studies in Systems, Decision and Control. 2019, vol. 203, pp. 63-81 Springer, Cham. https://doi.org/10.1007/978-3-030-21927-7_4

7. Mishchenko M.D., Gubarev V.F. Methods of Model Predictive Control for Discrete Multi-Variable Systems with Input. Cybernetics and Computer Engineering. 2020, 1(199), pp. 39-58.
https://doi.org/10.15407/kvt199.01.039

8. Vandenberghe, L. The cvxopt linear and quadratic cone program solvers. March 2010 http://www.ee.ucla.edu/~vandenbe/publications/coneprog.pdf, (Last accessed: 20.12.2020)

Received 24.12.2020

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Issue 1 (203), article 2

DOI:https://doi.org/10.15407/kvt203.01.026

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
e-mail: anatoly.v.anisimov@gmail.com

Bevza M.V., PhD student
ORCID: 0000-0002-2697-4968
e-mail: maksymbevza@gmail.com

Bobyl B.V., PhD student
ORCID: 0000-0002-9612-1071
e-mail: bobylbohdan@gmail.com

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

PREDICTION OF AUDIENCE REACTION ON TEXT-VISUAL CONTENT USING NEURAL NETWORKS

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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REFERENCES
1. De Fina, A. Storytelling and audience reactions in social media. Language in Society, 2016, 45, 473-498.
https://doi.org/10.1017/S0047404516000051

2. Gaspar, R., Pedro, C., Panagiotopoulos, P., Seibt, B. Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput. Human Behav. 2016, 56, 179-191.
https://doi.org/10.1016/j.chb.2015.11.040

3. Cliche, M. BB_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs. Proceedings of the 11th international workshop on semantic evaluations (SemEval-2017), pp. 573-580.
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4. Vaswani A., Shazeer N., Parmar N., Uszkoreit Ja., Jones L., Gomez A.N., Kaiser K., Polosukhin I. Attention is all you need. In Advances in Neural Information Processing Systems, 2017, pp. 6000-6010.

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7. Russakovsky O. ImageNet Large Scale Visual Recognition Challenge, arXiv e-prints, 2014.

8. He K., Zhang X., Ren S., Sun S. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. IEEE International Conference on Computer Vision (ICCV), 2015. pp. 1026-1034.
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9. Glorot X., Bengio Y. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249-256, 2010.

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https://doi.org/10.1109/CVPR.2016.90

Received 30.11.2020

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Issue 1 (203), article 1

DOI:https://doi.org/10.15407/kvt203.01.005

Cybernetics and Computer Engineering, 2021, 1(203)

I.V. SUROVTSEV1, DSc (Engineering), Senior Researcher,
Head of the Ecological Digital Systems Department
e-mail: dep115@irtc.org.ua, igorsur52@gmail.com

GALIMOV S.K.1, Leading Engineer, Ecological Digital Systems Department
e-mail: dep115@irtc.org.ua

V.M. GALIMOVA2, PhD (Chemistry), Associate Professor,
Senior Lecturer,
Analytical and Inorganic
Chemistry and Water Quality Department
e-mail: galimova2201@gmail.com

M.V. SARKISOVA2 Student
Veterinary Faculty
e-mail: mari.doga2014@gmail.com

1International Research and Training Center of Information Technologies and Systems of the NAS of Ukraine and MES of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine
2National University of Life and Environmental Sciences of Ukraine,
17, str. Heroes of Defense, 17, bldg. № 2, of. 18, Kyiv, 03041, Ukraine

METHOD OF CHRONOIONOMETRIC DETERMINATION OF CONCENTRATIONS OF FLUORINE, NITRATE, AMMONIUM IN DRINKING WATER

Introduction. Using method of chronoionometry and ion-selective electrodes makes it possible to determine quickly the concentrations of chemical elements, which allows you to assess the quality of drinking water and the ecological condition of the environment.

The purpose of the paper is to apply the developed method of chronoionometry to measure the concentrations of fluoride, nitrate, ammonium in drinking water and to assess the accuracy of measuring concentrations.

Methods. Chronoionometric method of chemical analysis uses the principles of direct potentiometry to measure the concentrations of chemical elements.

Results. Methods for detection the concentrations of fluorine, nitrates, ammonium in drinking water were obtained and tests were performed in model aqueous solutions using the device of inversion chronopotentiometry “Analyzer SCP”, which testify to the compliance of measurement errors with metrological normative values.

Conclusions. Improved analytical system “Analyzer SCP” to determine the concentration of 20 chemical elements (Hg, As, Pb, Cd, Cu, Zn, Sn, Ni, Co, Se, Mn, I, Cr, Fe, K, Na, Ca, F, NO3, NH4) in aqueous solutions by inversion chronopotentiometry and chronoionometry, which is sufficient for ecological assessment of drinking water quality and environmental objects. The use of a new method of chronoionometry significantly expands the functionality of the device of inversion chronopotentiometry, increases the reliability and accuracy of measuring the concentrations of chemical elements.

Keywords: chronoionometry method, concentration, of fluoride, nitrate, ammonium, ion-selective electrode, inversion chronopotentiometry, drinking water.

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Received 03.12.2020