Author: admin

Issue 2 (208), article 5

DOI:https://doi.org/10.15407/kvt208.02.082

Cybernetics and Computer Engineering, 2022, 2(208)

PANAGIOTIS KATRAKAZAS, Ph.D., Research Area Manager
ORCID: 0000-0001-7433-786X
e-mail: p.katrakazas@zelus.gr

ILIAS SPAIS, Ph.D., Senior Project Manager
Researcher ID: https://www.semanticscholar.org/author/I.-Spais/1885927
e-mail: ilias.spais@zelus.gr

Zelus,
Tatoiou 92,
14452, Metamorfosi, Athens, GR

BLUEPRINTS ELICITATION FRAMEWORK FOR AN OPEN ACCESS
PAN-EUROPEAN NEURO-IMAGING ONLINE CENTRE

Introduction. Recent infrastructural endeavours in the field of neuroscience aimed at data integration and sharing and availability of research output. This approach recognized that opening experimental results produces significant gains for science advancement. Nonetheless, this leaves a large part of the grassroots neuroscience community underutilized: access to neuroimaging infrastructures remains locally restricted, obstructing data acquisition and the means to investigate novel hypotheses.

Purpose. Within our paper we seek to address this gap by providing the blueprints for a delocalized e-neuroscience centre, opening the access to functional neuroimaging acquisition systems at a pan-European level. This aim will be achieved by building operational interoperability, standardizing, and integrating the services of neuroscience centres across Europe and the development of a virtual environment allowing all European researchers to acquire state-of-the-art neuroimaging data, exploiting the principles of the European Charter for Access to Research Infrastructures

Results. The implementation of all necessary actions for the harmonization and interoperability of the experimental procedures of the labs entail standardization of protocols, procedures in the form of consensus-based guidelines, harmonization of hardware and software set-up and availability across laboratories, as well as adopting of common standards and formats for acquired data and metadata structures.

Conclusion. Consistent and streamlined mobility processes aim to become a blueprint for networking of the overall neuroscience community. The harmonized process framework presented in this paper can facilitate better use from current and future neuroscience projects. Data economies of scale and recruitment streamlining will put local EU and international funds to better use than the now dispersed efforts. This will lead to more successful projects and better pacing for EU neuroscientific communities in the international stage.

Keywords: multi-centre interoperability, operational harmonisation, neuroimaging, sharing infrastructures, open access framework.

Download full text!

REFERENCES

1. Ravindranath V. et al. Regional research priorities in brain and nervous system disorders. Nature, vol. 527, no. 7578, Art. no. 7578, Nov. 2015.
https://doi.org/10.1038/nature16036

2. GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol., vol. 16, no. 11, pp. 877-897, Nov. 2017.

3. Vos T. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, vol. 386, no. 9995, pp. 743-800, Aug. 2015.

4. De Domenico M., Granell C., Porter M.A., and Arenas A. The physics of spreading processes in multilayer networks. Nat. Phys., vol. 12, no. 10, pp. 901-906, Oct. 2016.
https://doi.org/10.1038/nphys3865

5. Battiston F., Nicosia V., Latora V. The new challenges of multiplex networks: Measures and models. Eur. Phys. J. Spec. Top., vol. 226, no. 3, pp. 401-416, Feb. 2017.
https://doi.org/10.1140/epjst/e2016-60274-8

6. Paraskevopoulos E., Chalas N., Anagnostopoulou A., Bamidis P.D. Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise. Sci. Rep., vol. 12, no. 1, Art. no. 1, May 2022.
https://doi.org/10.1038/s41598-022-12158-9

7. Mantzavinos V. and Alexiou A. Biomarkers for Alzheimer’s Disease Diagnosis. Curr. Alzheimer Res., vol. 14, no. 11, pp. 1149-1154, 2017.
https://doi.org/10.2174/1567205014666170203125942

8. Larson-Prior L.J. et al. Adding dynamics to the Human Connectome Project with MEG. NeuroImage, vol. 80, pp. 190-201, Oct. 2013.
https://doi.org/10.1016/j.neuroimage.2013.05.056

9. Dottori M. et al. Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing. Sci. Rep., vol. 7, no. 1, Art. no. 1, Jun. 2017.
https://doi.org/10.1038/s41598-017-04204-8

10. Iakovidou N.D. Graph Theory at the Service of Electroencephalograms. Brain Connect., vol. 7, no. 3, pp. 137-151, Apr. 2017.
https://doi.org/10.1089/brain.2016.0426

11. Shabir M.Y., Iqbal A., Mahmood Z., and Ghafoor A. Analysis of classical encryption techniques in cloud computing. Tsinghua Sci. Technol., vol. 21, no. 1, pp. 102-113, Feb. 2016.
https://doi.org/10.1109/TST.2016.7399287

12. Lingwei S., Fang Y., Ru Z., and Xinxin N. Method of secure, scalable, and fine-grained data access control with efficient revocation in untrusted cloud. J. China Univ. Posts Telecommun., vol. 22, no. 2, pp. 38-43, Apr. 2015.
https://doi.org/10.1016/S1005-8885(15)60637-9

13. Lee K., Lee D.H., Park J.H .Efficient revocable identity-based encryption via subset difference methods. Des. Codes Cryptogr., vol. 85, no. 1, pp. 39-76, Oct. 2017.
https://doi.org/10.1007/s10623-016-0287-3

14. Pasupuleti S.K. and Varma D. Chapter 5 – Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT. in Real-Time Data Analytics for Large Scale Sensor Data, vol. 6, H. Das, N. Dey, and V. Emilia Balas, Eds. Academic Press, 2020, pp. 97-114.
https://doi.org/10.1016/B978-0-12-818014-3.00005-X

15. Warsinske J. et al. The Official (ISC)2 Guide to the CISSP CBK Reference. John Wiley & Sons, 2019.

16. Zhou L., Fu A., Yu S., Su M., and Kuang B., Data integrity verification of the outsourced big data in the cloud environment: A survey. J. Netw. Comput. Appl., vol. 122, pp. 1-15, Nov. 2018.
https://doi.org/10.1016/j.jnca.2018.08.003

17. Khraisat A., Gondal I., Vamplew P., and Kamruzzaman J. Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, vol. 2, no. 1, p. 20, Jul. 2019, doi: 10.1186/s42400-019-0038-7.
https://doi.org/10.1186/s42400-019-0038-7

18. Nor’a M.N.A. and Ismail A.W., Integrating Virtual Reality and Augmented Reality in a Collaborative User Interface. Int. J. Innov. Comput., vol. 9, no. 2, Art. no. 2, Nov. 2019.
https://doi.org/10.11113/ijic.v9n2.242

19. Sarasvuo S., Rindell A., and Kovalchuk M.. Toward a conceptual understanding of co-creation in branding. J. Bus. Res., vol. 139, pp. 543-563, Feb. 2022.
https://doi.org/10.1016/j.jbusres.2021.09.051

20. Saarijärvi H. The mechanisms of value co-creation. J. Strateg. Mark., vol. 20, no. 5, pp. 381-391, Aug. 2012.
https://doi.org/10.1080/0965254X.2012.671339

21. Rizzo F., Deserti A., and Komatsu T. Implementing social innovation in real contexts. Int. J. Knowl.-Based Dev., vol. 11, no. 1, pp. 45-67, 2020.
https://doi.org/10.1504/IJKBD.2020.106840

22. Frow P., Nenonen S., Payne A., and Storbacka K. Managing Co-creation Design:
A Strategic Approach to Innovation. Br. J. Manag., vol. 26, no. 3, pp. 463-483, 2015.
https://doi.org/10.1111/1467-8551.12087

23. Martínez-Cañas R., Ruiz-Palomino P., Linuesa-Langreo J., Blázquez-Resino J.J. Consumer Participation in Co-creation: An Enlightening Model of Causes and Effects Based on Ethical Values and Transcendent Motives. Front. Psychol., vol. 7, 2016. Last accessed: May 21, 2022. URL: https://www.frontiersin.org/article/10.3389/fpsyg.2016.00793
https://doi.org/10.3389/fpsyg.2016.00793

24. Jansma S.R., Dijkstra A.M., and de Jong M.D.T. Co-creation in support of responsible research and innovation: an analysis of three stakeholder workshops on nanotechnology for health. J. Responsible Innov., vol. 9, no. 1, pp. 28-48, Jan. 2022,
https://doi.org/10.1080/23299460.2021.1994195

Received 23.05.2022

Issue 2 (208), article 4

DOI:https://doi.org/10.15407/kvt208.02.060

Cybernetics and Computer Engineering, 2022, 2(208)

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

BELOSHITSKIY P.V.², DSc (Medicine)
Professor of Biological Faculty
ORCID 0000-0002-6058-3602
e-mail: bilosh827@ukr.net

ZUBIETA-CALLEJA G.³ M.D. Professor
Director
ORCID: 0000-0002-4283-6514
e-mail: zubieta@altitudeclinic.com

ARALOVA A.A.¹ PhD (Mathematics)
Researcher of the Department of Methods for Discrete Optimization,
Mathematical Modelling and Analyses of Complex Systems
ORCID 0000-0001-7282-2036
email: aaaralova@gmail.com

¹Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine,
40, Acad.Glushkov av., 03187, Kyiv, Ukraine

²Pavlo Tychyna Uman State Pedagogical University,
2, Sadova str, 20300, Uman, Chercassy distr., Ukraine

³High Altitude Pulmonary and Pathology Institute
HAPPI-IPPA La Paz, Bolivia

AUTOMATED INFORMATION SYSTEM FOR THE EVALUATION OF CLIMBERS’ PERFORMANCE UNDER CONDITIONS OF EXTREMELY LOW pO₂ OF INHALED AIR

Introduction. Currently, as a result of ever-increasing intensity of human activity, unfavorable environment, the need to perform work in various extreme disturbances, significantly increase physical, mental and emotional stress on the human body, leading to pronounced changes in functional systems. Therefore, the task of studying the adaptation of the human body to work in extreme environments is urgent. The work of climbers is a fairly adequate model for studying the combined effects of hypobaric hypoxia and exercise hypoxia. The need to process large amounts of information necessitates the use of modern computer technology that allows the training process in the training of climbers, which would repeatedly, almost in real time to speed up the processing of survey data and accumulate for further use in determining current status and forecasting regulatory reactions of the body to external and internal disturbances

The purpose of the paper is to develop an automated information system of functional diagnostics using the model of regulation of oxygen regimes of the body and its practical application in the study of highly qualified climbers

Methods. Programming methods for creating an automated information system and methods of functional diagnostics.

Results. On the basis of the model of regulation of oxygen regimes of the organism the automated information system for functional diagnostics of the persons who are in the conditions of extreme disturbances is constructed. The results of approbation of the offered software for research of group of highly skilled climbers are resulted.

Conclusions. The proposed software allows you to use a model of oxygen regimes of the body in real time, i.e. repeatedly accelerates the processing of data obtained during the survey of athletes, allows centralized collection of information for its pre-processing, storage and collective use, allows you to compare the basic parameters characterizing the functional respiratory system during natural sports activities and obtained during ergometric loading,

Key words: methods of functional diagnostics, highly qualified climbers, mathematical model of regulation of oxygen regimes of the organism, human adaptation to work in extreme environment, hypoxibritic hypoxia

Download full text!

REFERENCES

1. Sirotinin N.N. Living on the heights and ailments on the heels. Kiev: Edition of the Academy of Sciences of the URS,1939, 222 p. (In Ukrainian)

2. Biloshytsky P.V. Hypoxia. Encyclopedia of Successful Ukraine Kyiv: Institute of Encyclopedic Research NAS of Ukraine. 2006, 5, pp. 625-626. (In Ukrainian)

3. Beloshitskiy P.V. Study of the problems of sports medicine at the Elbrus medical and biological station. Sports medicine. 2008, 1, pp. 83-94. (In Russian)

4. Kolchinskaya A.Z. On the classification of hypoxic conditions. Patol. physiology and experimental therapy. 1981, no. 4, pp. 3-10. (In Russian)

5. Beloshitskiy P.V. Chronicle of biomedical research in the Elbrus region (1929-2006). Kyiv. 2014. Ukrainian Academy of Sciences. 550 p. (In Russian)

6. Seredenko M.M., Nazarenko A.I, Rozova. K.V. The capital of hypoxia. Kyiv, 2002. Print line. 24 p (In Ukrainian)

7. First world congress of high altitude medicine and physiology.La Paz-Bolivia. 1994, p.

8. Second world congress of high altitude medicine and physiology. Acta Andina. 1996, Cusco-Peru.

9. Materials to the II Congress of Pathophysiologists of Ukraine, dedicated to the 100th anniversary of the day of the Birth Day of Ac. M.M. Syrotinin. Physiol. journal. v.42,
no 3-4. (In Ukrainian)

10. The 3rd World Congress on Mountain Medicine and High Altitude Physiology and the 18th Japanese Symposium on Mountain Medicine, May 20th-24th. 1998. Matsumoto, Japan.

11. IV World Congress on Mountain Medicine and High Altitude Physiology. High Altitude medicine and biology. 2000, 1, N3, Arica, Chile.
https://doi.org/10.1089/15270290050144235

12. V World Congress on Mountain Medicine and High Altitude Physiology. High Altitude medicine and biology. 2002, 3, N1, Barcelona, Spain.
https://doi.org/10.1089/152702902753639478

13. Milledge J.S. VI World Congress on Mountain Medicine & High Altitude Physiology, Xining, Qinghai, and Lhasa, Tibet, August 12-18, 2004. High Alt Med Biol. 2004 Winter;5(4):457-64. doi: 10.1089/ham.2004.5.457.
https://doi.org/10.1089/ham.2004.5.457

14. VI World Congress on Mountain Medicine and High Altitude Physiology, Xining, Qinghai, and Lhasa, Tibet, August 12-18, 2004. High Altitude medicine and biology. 2004. 5, N2
https://doi.org/10.1089/ham.2004.5.457

15. Anholm J.D. The VIIIth World Congress of High Altitude Medicine and Physiology, Arequipa, Peru, August 8-12, 2010. High Alt Med Biol. 2010 Winter;11(4):381-4.
doi: 10.1089/ham.2010.1061.
https://doi.org/10.1089/ham.2010.1061

16. West J.B. World Congresses of Mountain Medicine and High Altitude Physiology, 1994-2002 High Altitude Medicine & Biology. V. 3, No. 1. Published Online: 6 Jul 2004 https://doi.org/10.1089/152702902753639478
https://doi.org/10.1089/152702902753639478

17. 7th Chronic Hypoxia Symposium, Feb 23 – Mar 2. 2019 La Paz-Bolivia. Dedicated to the Late Danish Prof. PoulErikPaulev p. 24. URL:https://zuniv.net/ symposium7/Abstracts7CHS.pdf

18. Catron T.F., Powell F.L., West J.B. A strategy for determining arterial blood gases on the summit of Mt. Everest. BMC Physiol. 2006 Mar 8;6:3. doi: 10.1186/1472-6793-6-3.
https://doi.org/10.1186/1472-6793-6-3

19. West J.B., Hackett P.H., Maret K.H. Milledge J.S., Peters R.M. Jr., Pizzo C.J., Winslow R.M. Pulmonary gas exchange on the summit of Mount Everest. J Appl Physiol Respir Environ Exerc Physiol. 1983 Sep;55(3):678-87. doi: 10.1152/jappl.1983.55.3.678.
https://doi.org/10.1152/jappl.1983.55.3.678

20. Malconian M.K., Rock P.B., Reeves J.T., Cymerman A., Houston C.S. Operation Everest II: gas tensions in expired air and arterial blood at extreme altitude. Aviat Space Environ Med. 1993 Jan;64(1):37-42.

21. West J.B. Lactate during exercise at extreme altitude. Fed Proc. 1986 Dec;45(13):2953-7.

22. West J.B. Tolerance to severe hypoxia: lessons from Mt. Everest. Acta Anaesthesiol Scand Suppl. 1990;94:18-23. doi: 10.1111/j.1399-6576.1990.tb03216.x.
https://doi.org/10.1111/j.1399-6576.1990.tb03216.x

23. West J.B. Acclimatization and tolerance to extreme altitude. J Wilderness Med. 1993 Feb;4(1):17-26. doi: 10.1580/0953-9859-4.1.17.
https://doi.org/10.1580/0953-9859-4.1.17

24. West J.B. Limiting factors for exercise at extreme altitudes. Clin Physiol. 1990 May;10(3):265-72. doi: 10.1111/j.1475-097x.1990.tb00095.x.
https://doi.org/10.1111/j.1475-097X.1990.tb00095.x

25. West J.B. Climbing Mt. Everest without oxygen: an analysis of maximal exercise during extreme hypoxia. Respir Physiol. 1983 Jun;52(3):265-79. doi: 10.1016/0034-5687(83)90085-3.
https://doi.org/10.1016/0034-5687(83)90085-3

26. Wagner P.D., Wagner H.E., Groves B.M., Cymerman A., Houston C.S. Hemoglobin P(50) during a simulated ascent of Mt. Everest, Operation Everest II. High Alt Med Biol. 2007 Spring;8(1):32-42. doi: 10.1089/ham.2006.1049.
https://doi.org/10.1089/ham.2006.1049

27. Wagner P.D. Operation Everest II. High Alt Med Biol. 2010 Summer;11(2):111-9. doi: 10.1089/ham.2009.1084.
https://doi.org/10.1089/ham.2009.1084

28. Samaja M., Mariani C., Prestini A., Cerretelli P. Acid-base balance and O2 transport at high altitude. Acta Physiol Scand. 1997 Mar;159(3):249-56. doi: 10.1046/j.1365-201X.1997.574342000.x.
https://doi.org/10.1046/j.1365-201X.1997.574342000.x

29. Hoiland R.L., Howe C.A., Coombs G.B., Ainslie P.N. Ventilatory and cerebrovascular regulation and integration at high-altitude. Clin Auton Res. 2018 Aug;28(4):423-435. doi: 10.1007/s10286-018-0522-2.
https://doi.org/10.1007/s10286-018-0522-2

30. Bärtsch P., Saltin B. General introduction to altitude adaptation and mountain sickness. Scand J Med Sci Sports. 2008 Aug;18 Suppl 1:1-10. doi: 10.1111/j.1600-0838.2008.00827.x.
https://doi.org/10.1111/j.1600-0838.2008.00827.x

31. Grocott M.P., Martin D.S., Levett D.Z., McMorrow R., Windsor J., Montgomery H.E.; Caudwell Xtreme Everest Research Group. Arterial blood gases and oxygen content in climbers on Mount Everest. N Engl J Med. 2009 Jan 8;360(2):140-9. doi: 10.1056/NEJMoa0801581.
https://doi.org/10.1056/NEJMoa0801581

32. Zubieta-Calleja G.R., Paulev P.E., Zubieta-Calleja L., Zubieta-Castillo G. Altitude adaptation through hematocrit changes. J Physiol Pharmacol. 2007 Nov;58 Suppl 5(Pt 2):811-8. PMID: 18204195.

33. Zubieta-Calleja G.R., Ardaya G., Zubieta N., Paulev P.E., Zubieta-Castillo G. Tolerance to Hypoxia [Internet]. Vol. 59, J Fisiol. 2013. p. 65-71. URL: https://zuniv.net/pub/TolerancetoHypoxiaFiziol.pdf

34. Paulev P.E., Zubieta-Calleja G.R. Essentials in the diagnosis of acid-base disorders and their high altitude application. J Physiol Pharmacol. 2005 Sep; 56 Suppl 4:155-70. PMID: 16204789.

35. Zubieta-Calleja G., Zubieta-DeUrioste N. The Oxygen Transport Triad in High-Altitude Pulmonary Edema: A Perspective from the High Andes. Int J Environ Res Public Health. 2021 Jul 17;18(14):7619. doi: 10.3390/ijerph18147619. PMID: 34300070; PMCID: PMC8305285.
https://doi.org/10.3390/ijerph18147619

36. Zubieta-Castillo G., Zubieta-Calleja G.R., Zubieta-Calleja L., Zubieta-Calleja Nancy. Adaptation to life at the altitude of the summit of Everest. Physiol. Journal, 1994.

37. Schoene R.B. Limits of human lung function at high altitude. J Exp Biol. 2001 Sep;204(Pt 18):3121-7.
https://doi.org/10.1242/jeb.204.18.3121

38. Schoene R.B. Limits of respiration at high altitude. Clin Chest Med. 2005 Sep;26(3):405-14, vi. doi: 10.1016/j.ccm.2005.06.015.
https://doi.org/10.1016/j.ccm.2005.06.015

39. Hawkins M.N. Raven P.B., Snell P.G., Stray-Gundersen J., Levine B.D. Maximal oxygen uptake as a parametric measure of cardiorespiratory capacity. Med Sci Sports Exerc. 2007 Jan;39(1):103-7. doi: 10.1249/01.mss.0000241641.75101.64.
https://doi.org/10.1249/01.mss.0000241641.75101.64

40. Noakes T.D. Maximal oxygen uptake as a parametric measure of cardiorespiratory capacity: comment. Med Sci Sports Exerc. 2008 Mar;40(3):585; author reply 586.
doi: 10.1249/MSS.0b013e3181617350.
https://doi.org/10.1249/MSS.0b013e3181617350

41. Bassett D.R. Jr., Howley E.T. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med Sci Sports Exerc. 2000 Jan;32(1):70-84. doi: 10.1097/00005768-200001000-00012
https://doi.org/10.1097/00005768-200001000-00012

42. Feitosa W.G., Barbosa T.M., Correia R. De A., Castro F.A. De S. Maximal oxygen uptake, total metabolic energy expenditure, and energy cost in swimmers with physical disabilities. International Journal of Performance Analysis in Sport Volume 19, 2019
Issue 4, pp. 503-516
https://doi.org/10.1080/24748668.2019.1631053

43. Kang M.-Y., Sapoval B. Prediction of maximal oxygen uptake at high altitude. European Respiratory Journal 2016 48: PA1583; DOI: 10.1183/13993003.congress-2016.PA1583
https://doi.org/10.1183/13993003.congress-2016.PA1583

44. Bernardi L., Schneider A., Pomidori. L., Paolucci E., Cogo A. Hypoxic ventilatory response in successful extreme altitude climbers. European Respiratory Journal 2006 27: 165-171; DOI: 10.1183/09031936.06.00015805
https://doi.org/10.1183/09031936.06.00015805

45. Biloshitsky P.V., Onopchyk Yu.M., Marchenko D.I., Aralova N.I. Mathematic methods for investigation of reliability problem of organism functioning in extreme high mountain conditions Physiol. Journal, 2003, pp. 139-143 (In Ukrain)

46. Aralova A.A., Aralova N.I., Beloshitsky P.V., Onopchuk Yu.N. Automated Information System for Functional Diagnostics of Mountaineers. Sports Medicine 2008. 1: 163-169.

47. Kolchinskaya. A.Z., Monogarov V.D., Radzievsky P.A., Molchanova N.I. Complex control in mountaineering and its role in the management of the training process. Сontrol of the process of adaptation of the organism of sports-shifts of high qualification: Collection of scientific works. works / Ed. board: D.A. Polishchuk and others Kyiv.KSIPT 1992, pp.122-132 (in Russian)

48. Kolchinskaya A.Z., Monogarov V.D., Radzievsky P.A. Scientific and methodological support for the preparation of Soviet climbers for the traverse of the Kanchenjunga massif. Theory and practice of physical culture. 1991.4.10-14. (in Russian)

49. Kolchinskaya A.Z., Beloshitsky P.V., Monogarov V.D., Pivnutel R.V., Radzievsky P.A., Krasyuk A.N., Ivashkevich A.A., Borisov A.N. Physiological performance of climbers in conditions of extremely low pO2 in inhaled air. Physiological journal. 1989, 35, 2,
pp. 68-74. (in Russian)

50. Lauer N.V., Kolchinskaya A.Z. About the oxygen organism regime and its regulation. K.: Nauk.Dumka, 1966, pp. 157-200/ (In Russian)

51. Klyuchko O.M., Aralova N.I., Aralova A.A. Electronic automated work places for biological investigations Biotechnologia Acta, K, 2019, V.12, №2, pp. 5-26. https://doi.org/10.15407
/biotech12/02/005
https://doi.org/10.15407/biotech12.02.005

52. Aralova A.A., Aralova N.I., Kovalchuk-Khimyuk L.A., Onopchuk Yu.N., Automated information system for athletes functional diagnostics. Control systems and machines. 2008 3: 73-78. (in Russian).

53. Aralova N.I. Mathematical model of the mechanism short- and medium-functional adaptation of breath of persons work in extreme conditions high. Kibernetika i vyčislitelnaâ tehnika. 2015, 182, pp. 15-25. (in Russian).
https://doi.org/10.15407/kvt182.02.045

Received 19.01.2022

Issue 2 (208), article 3

DOI:https://doi.org/10.15407/kvt208.02.044

Cybernetics and Computer Engineering, 2022, 2(208)

BONDAR S.O., PhD student,
Researcher of the Intelligent Control Department
ORCID: 0000-0003-4140-7985
e-mail: orangearrows@bigmir.net

SHEPETUKHA Yu.M., PhD (Engineering), Senior Researcher
Acting Head of the Intelligent Control Department
ORCID: 0000-0002-6256-5248
e-mail: shepetukha@irtc.org.ua

VOLOSHENYUK D.O., PhD (Engineering),
Senior Researcher of the Intelligent Control Department
ORCID: 0000-0003-3793-7801
e-mail: p-h-o-e-n-i-x@ukr.net

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, Akad. Glushkov av., Kyiv, 03680, Ukraine.

USING OF HIGH-QUALITY POSITIONING TOOLS FOR HYBRID UNMANNED AERIAL VEHICLES AUTOMATIC CORRECTION UNDER THE LIMITED SPACE CONDITION

Introduction. Original class of hybrid unmanned aerial vehicles is considered for multitask mission accomplishment at this article. Advantages of such vehicles usage for purposes that are always done by several different agents are considered. Perspective of the position precisioning for different tasks that could be done by unmanned aircrafts is analyzed.

The purpose of the paper is to universalize the process of surveillance, photo and video data collection and other missions that is provided by unmanned aerial vehicles today. The action of data precision during some periods of the misson accomplishment and increased specification for main targets of the mission could demonstrate brand new vector of the unmanned aerial vehicle usage and creation of the brand new domains for the unmanned aerial vehicles. Complex data gathering could help to avoid extra mediators and could simplify data processing on the next stages and also could do such data much more precise.

Results. The usable scenario of route for hybrid unmanned aerial vehicle and the model of it could be a proof of universal multitask unmanned aerial vehicle utilization. Such scenario unites several information missions of different scale and could provide data for several data centers that can use it for defferent problem solving just from one flight. Also it proves that utilization of such aircraft with an additional onboard precision block could be the next step at the mapping and digitalizing domains. Financial analysis of the market is provided for demonstration of the fact that such hybrid aircraft complex system would provide such scale as well as attention to the object details but be much cheaper then mapping and surveillance systems that are already existing.

Conclusion. A need for optimization of some problems that could be achieved by unmanned aerial vehicles leaded to the usage of hybrid vehicles that were represented at the article. Complex design of such an aircraft could be a collateral disadvantage but the whole influence of the hybrid UAV usage for different tasks would optimize a lot more processes, devices and unnecessary equipment that would be needed for a large list of tasks at each domain UAVs are using right now from surveillance to agricultural tasks. Model of different scale purpose universal hybrid unmanned aerial system is a proof of the possibility to use just one single aircraft for a complex mission that needs different set of capabilities, features and equipment. Also such aircraft could provide much more certain results of missions and do it at lower price. Further developments could provide information about the most effective hybrid UAV type for such type of missions and provide game changing rules to the digitalizing and surveillance processes because of the new information gathering way.

Keywords: unmanned aerial vehicle, hybrid vehicle, positioning, multipurpose flight.

Download full text!

REFERENCES
1. FAS Intelligence Resource Program. Eagle Eye UAV. URL: https://irp.fas.org/ program/collect/eagle-eye.htm

2. Flores Reyes A., Flores Colunga G.R. Design and Development of an UAV with Hybrid Flight Capabilities. Leon, Guanajuato, Mexico, 2018. URL: https://cio.repositorioinstitucional.mx/jspui/bitstream/1002/800/1/17444.pdf

3. Saeed A.S., BaniYounes A., Cai Ch., Cai G. A Survey of Hybrid Unmanned Aerial Vehicles. Progress in Aerospace Sciences Journal. Oxford, United Kingdom, 2018. Vol.98, pp. 98-105.
https://doi.org/10.1016/j.paerosci.2018.03.007

4. delftAcopter: innovative single-propeller hybrid drone URL: https://tudelftrobotics-institute.nl/robots/delftacopter

5. Douglas A. Airial Robotics develops “new type” of UAV for global commercial drone market. Commercial Drone Professional website. 2020. URL: https://www.commercialdroneprofessional.com/breaking-news-airial-robotics-develops-new-type-of-uav-for-global-commercial-drone-market/

6. Barskiy R. Gyrotrak: brand new hybrid UAV concept. Science and technic. 2020. URL:
https://naukatehnika.com/gyrotrak-novaya-gibridnaya-koncepcii-bespilotnikov.html

7. Makeflyeasy Freeman 2300 Tilt VTOL Aerial Survey Carrier Span Fpv Rc Fix-wing Model drone Wing 2300mm UAV mapping Long range pryce. URL: https://www.uavmodel.com/products/makeflyeasy-freeman-2300-tilt-vtol-aerial-survey-carrier-span-fpv-rc-fix-wing-model-drone-wing-2300mm-uav-mapping-long-range

8. Grytsenko V.I., Volkov O.Ye., Komar.M.M. et al. Modern unmanned aerial vehicle automatic control systems intellectualization. Cybernetics and Computer Engineering, 2018, № 1(191), pp. 45-59. URL: http://kvt-journal.org.ua/834/ (in Ukrainian)
https://doi.org/10.15407/kvt191.01.045

9. Volkov O.Ye., Grytsenko V.I., Komar M.M. et al. Integral Adaptive Autopilot for an Unmanned Aerial Vehicle. AVIATION: Scientific journal: scientific article. Vilnius, Lithuania, 2018, Vol. No 22, pp. 129-195.
https://doi.org/10.3846/aviation.2018.6413

10. Makeflyeasy Freeman 2300 Specification & Options URL: https://aliexpress.com/ item/10000223137957.html

 

Received 06.04.2022

Issue 2 (208), article 2

DOI:https://doi.org/10.15407/kvt208.02.030

Cybernetics and Computer Engineering, 2022, 2(208)

Savchenko-Synyakova Ye.A., PhD (Engineering),
Senior Researcher of the Department for Information
Technologies of Inductive Modeling
https://orcid.org/0000-0003-4851-9664
e-mail: savchenko_e@meta.ua

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

COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS AND OTHER DIRECTIONS OF ARTIFICIAL INTELLIGENCE

Introduction. Nowadays, the application of machine learning methods and tools is developing very rapidly, given the overall automation and digitalization. The use of machine learning methods and tools for modeling complex processes makes it possible to solve problems that were previously difficult or impossible to solve.

However other methods of mathematical modeling also make it possible to solve the problem of constructing a model based on a sample of experimental data. The task was to compare various scientific areas of artificial intelligence, such as machine learning, mathematical modeling, statistics, data mining and inductive modeling in terms of building mathematical models, to find out what common and distinctive features they have.

The purpose of the paper is a comparative analysis of the areas of mathematical modeling, statistics and machine learning. And also compare the methods of inductive modeling and inductive generation of models.

Results. A comparative analysis of machine learning and other approaches to solving artificial intelligence problems was carried out.

Conclusion. The conducted analysis showed that the machine learning tasks of mathematical (statistical) modeling are close, but not the same, and it is difficult to draw a hard line between them. They can be distinguished by the purpose, the ability to check or interpret the obtained results.

Keywords: machine learning, mathematical modeling, statistics, inductive approach, inductive generation of models.

Download full text!

REFERENCES
1. Ponyattya modeli ta modelyuvannya. Vlastyvosti ta klasyfikatsiya modeley. URL: [Last accessed 2 May 2022] (In Ukrainian).

2. Srivastava, T. Difference between Machine Learning & Statistical Modeling. URL: [Last accessed 25 Apr. 2022].

3. Sharma, D., & Kumar, N. (2017). A review on machine learning algorithms, tasks and applications. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 6(10), pp. 2278-1323.

4. Machine Learning vs Statistical Modeling. URL: [Last accessed 2 Apr. 2022].

5. Most Common Machine Learning Tasks. URL: [Last accessed 25 May 2022].

6. Mitchell, T. (1997). Machine Learning. New York: McGraw Hill. ISBN 0-07-042807-7. OCLC 36417892.

7. Difference Between Algorithm and Model in Machine Learning. URL: [Last accessed 2 Apr. 2022].

8. The Actual Difference Between Statistics and Machine Learning. URL: https://towardsdatascience.com/the-actual-difference-between-statistics-and-machine-learning-64b49f07ea3> [Last accessed 5 May 2022].

9. Traditional Modeling vs. Machine Learning. URL: [Last accessed 12 Apr. 2022].

10. Breiman L. (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199-231. doi: 10.1214/ss/1009213726.
https://doi.org/10.1214/ss/1009213726

11. Ij H. (2018). Statistics versus machine learning. Nat Methods, 15(4), 233. https://www.nature.com/articles/nmeth.4642.
https://doi.org/10.1038/nmeth.4642

12. Dangeti P. (2017). Statistics for machine learning. Packt Publishing Ltd.

13. Statistical Modeling vs Machine Learning. URL: [Last accessed 2 Dec. 2021].

14. Mannila, H. (1996). Data mining: machine learning, statistics, and databases,” Proceedings of 8th International Conference on Scientific and Statistical Data Base Management, pp. 2-9, doi: 10.1109/SSDM.1996.505910.
https://doi.org/10.1109/SSDM.1996.505910

15. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17 (3), pp. 37-37.

16. Van Calster, B., Verbakel, J. Y., Christodoulou, E., Steyerberg E. W. & Collins G. S. (2019). Statistics versus machine learning: definitions are interesting (but understanding, methodology, and reporting are more important). Journal of clinical epidemiology, 116.
https://doi.org/10.1016/j.jclinepi.2019.08.002

17. Mitchell, Tom M. (1999). Machine learning and data mining. Communications of the ACM 42.11, pp. 30-36.
https://doi.org/10.1145/319382.319388

18. Hripcsak, G., Duke, J. D., Shah, N. H., et all. (2015). Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Studies in health technology and informatics, 216, 574 p.

19. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. 800 p. ISBN: 0262035618. DOI:10.1007/s10710-017-9314-z.
https://doi.org/10.1007/s10710-017-9314-z

20. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, pp. 85-117.
https://doi.org/10.1016/j.neunet.2014.09.003

21. Stepashko V.S. (2016). Conceptual Fundamentals of Intelligent Modeling. Upravlausie sistemy i masiny. №4. pp. 3-15 (In Russian).
https://doi.org/10.15407/usim.2016.04.003

22. Kelleher, J. D. (2019). Deep learning. MIT press. DOI: https://doi.org/10.7551/
mitpress/11171.001.0001.
https://doi.org/10.7551/mitpress/11171.001.0001

23. Ivakhnenko, O.H.; Lapa, V.H. (1965). Kibernetychni prorochi prystroyi. Kyiv: Naukova dumka (In Russian).

24. Ivakhnenko, A. (1971). Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics (4): 364-37.
https://doi.org/10.1109/TSMC.1971.4308320

25. Schmidhuber, J. My First Deep Learning System of 1991. Deep Learning Timeline 1960-2013.

26. Bzdok, D., Altman, N. & Krzywinski, M., (2018). Statistics versus machine learning. Nature Methods. 15, pp. 233-234. https://doi.org/10.1038/nmeth.4642.
https://doi.org/10.1038/nmeth.4642

27. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: Springer.
https://doi.org/10.1007/978-1-4614-7138-7

28. Statistical Modeling vs Machine Learning. URL: [Last accessed 2 Apr. 2022].

Received 22.04.2022

Issue 2 (208), article 1

DOI:https://doi.org/10.15407/kvt208.02.005

Cybernetics and Computer Engineering, 2022, 2(208)

RACHKOVSKIJ D.A.^1,2, DSc (Engineering),
Chief Researcher, Dept. of Neural Information Processing Technologies,
Visiting Professor, Dept. of Computer Science, Electrical and Space Engineering,
https://orcid.org/0000-0002-3414-5334
e-mail: dar@infrm.kiev.ua

GRITSENKO V.I.¹, Corresponding Member of NAS of Ukraine,
 Directorate Adviser,
ORCID ID 0000-0002-6250-3987
e-mail: vig@irtc.org.ua

VOLKOV O.E.¹, PhD (Engineering), 
Director,
https://orcid.org/0000-0002-5418-6723
e-mail: alexvolk@ukr.net

GOLTSEV A.D.¹, PhD (Engineering), Senior Researcher,
Acting Head of the Dept. of Neural Information Processing Technologies,
https://orcid.org/0000-0002-2961-0908
e-mail: root@adg.kiev.ua

REVUNOVA E.G.¹, DSc (Engineering),
Senior Researcher, Dept. of Neural Information Processing Technologies,
https://orcid.org/0000-0002-3053-7090
e-mail: egrevunova@gmail.com

KLEYKO D.³, PhD (Computer Science), Researcher,
https://orcid.org/0000-0002-6032-6155
e-mail: denis.kleyko@ri.se

LUKOVICH V.V.¹ Researcher of the Dept. of Neural Information Processing Technologies
ORCID ID 0000-0002-3848-4712
e-mail: vvl97@ukr.net

OSIPOV E.², PhD (Computer Science), Professor,
https://orcid.org/0000-0003-0069-640X
e-mail: evgeny.osipov@ltu.se

¹ International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of the MES of Ukraine, 40, Acad. Glushkova av., Kyiv, 03680, Ukraine

² Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 971 87 Lulea, Sweden

³ RISE Research Institutes of Sweden AB, 164 40 Kista, Sweden

NEURAL DISTRIBUTED REPRESENTATIONS FOR ARTIFICIAL INTELLIGENCE AND MODELING OF THINKING

Introduction.Current progress in the field of specialized Artificial Intelligence is associated with the use of Deep Neural Networks. However, they have a number of disadvantages: the need for huge data sets for learning, the complexity of learning procedures, excessive specialization for the training set, instability to adversarial attacks, lack of integration with knowledge of the world, problems of operating with structures known as binding or composition problem. Overcoming these shortcomings is a necessary condition for advancing from specialized Artificial Intelligence to general one, which requires the development of alternative approaches.

The purpose of the paper is to present an overview of research in this direction, which has been carried out at the International Center for 25 years. The approach being developed stems from the ideas of N. M. Amosov and his scientific school. Connections to the Hyperdimensional Computing (HDC) and Vector Symbolic Architectures (VSA) field as well as to current brain research are also provided.

Results. The concept of distributed data representation is outlined, including HDC/VSA that are capable of representing various data structures. The developed paradigm of Associative-Projective Neural Networks is considered: codevector representation of data, superposition and binding operations, general architecture, transformation of data of various types into codevectors, methods for solving problems and applications.

Conclusion. An adequate representation of data is one of the key issues within the Artificial Intelligence. The main area of research reviewed in this article is the problem of representing heterogeneous data in Artificial Intelligence systems in a unified format based on modeling the neural organization of the brain and the mechanisms of thinking. The approach under development is based on the hypothesis of distributed representation of information in the brain and allows representing various types of data, from numeric values to graphs, as vectors of large but fixed dimensionality.

The most important advantages of the developed approach are the possibility of natural integration and efficient processing of various types of data and knowledge, a high degree of parallel computing, reliability and resistance to noise, the possibility of hardware implementation with high performance and energy efficiency, data processing based on associative similarity search — similar to how human memory works. This allows one to unify the methods, algorithms, and software and hardware for Artificial Intelligence systems, increase their scalability in terms of speed and memory with an increase in data volume and complexity.

The research creates the basis for overcoming the shortcomings of current approaches to the specialized Artificial Intelligence based on Deep Neural Networks and paves the way for the creation of Artificial General Intelligence.

Keywords: distributed data representation, associative-projective neural networks, codevectors, hyperdimensional computing, vector symbolic architectures, artificial intelligence.

Download full text!

REFERENCES
1. Amosov N.M. Modelling of thinking and the mind. New York: Spartan Books, 1967, 192 p.
https://doi.org/10.1007/978-1-349-00640-3

2. Amosov N.M., Kasatkin A.M., Kasatkina L.M., Kussul E.M., Talaev S.A. Intelligent behaviour systems based on semantic networks. Kybernetes. 1973, Vol. 2, N 4, pp. 211-216.
https://doi.org/10.1108/eb005340

3. Amosov N.M., Kussul E.M., Fomenko V.D. Transport robot with a neural network control system. Advance papers of 4th Intern. Joint Conf. on Artif. Intelligence. 1975, Vol 9, pp. 1-10.

4. Amosov N.M. Algorithms of the Mind. Kiev: Naukova Dumka, 1979, 223 p. (in Russian)

5. Amosov N.M., Baidyk T.N., Goltsev A.D., Kasatkin A.M., Kasatkina L.M., Rachkovskij D.A. Neurocomputers and intelligent robots. Kiev: Naukova Dumka. 1991, 269 p. (in Russian)

6. Kussul E.M. Associative neuron-like structures. Kiev: Naukova Dumka. 1992, 144 p.
(in Russian)

7. Kleyko D., Rachkovskij D.A., Osipov E., Rahimi A. A survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and data transformations. ACM Computing Surveys. 2022. https://doi.org/10.1145/3538531
https://doi.org/10.1145/3538531

8. Kleyko D., Rachkovskij D.A., Osipov E., Rahimi A. A survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges. Accepted, ACM Computing Surveys. 2022. Available online: arXiv:2112.15424.
https://doi.org/10.1145/3558000

9. Thorpe S. Localized versus distributed representations The Handbook of Brain Theory and Neural Networks. Edited by M.A. Arbib.Cambridge, MA: The MIT Press. 2003, pp. 643-646.

10. Rachkovskij D.A., Kussul E.M. Binding and normalization of binary sparse distributed representations by context-dependent thinning. Neural Computation. 2001, Vol. 13, N 2, pp. 411-452.
https://doi.org/10.1162/089976601300014592

11. Plate T. Holographic Reduced Representation: Distributed Representation for Cognitive Structures. Stanford: CSLI Publications, 2003, 300 p.

12. Kanerva P. Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation. 2009, Vol. 1, N2, pp. 139-159.
https://doi.org/10.1007/s12559-009-9009-8

13. Gayler R.W. Multiplicative binding, representation operators, and analogy. Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences. Sofia, Bulgaria: New Bulgarian University, 1998, p. 405.

14. Kussul E.M., Rachkovskij D.A., Baidyk T.N. Associative-Projective Neural Networks: Architecture, Implementation, Applications. Proc. Neuro-Nimes’91. 1991, pp. 463-476.

15. Kussul E.M., Rachkovskij D.A., Baidyk T.N. On image texture recognition by associative-projective neurocomputer. Proc. ANNIE’91 Conference “Intelligent engineering systems through artificial neural networks”. St. Louis, MO: ASME Press, 1991, pp. 453-458.

16. Kussul E.M., Rachkovskij D.A. Multilevel assembly neural architecture and processing of sequences. In Neurocomputers and Attention: Connectionism and Neurocomputers, vol. 2. Manchester and New York: Manchester University Press, 1991, pp. 577-590.

17. Gritsenko V.I., Rachkovskij D.A., Goltsev A.D., Lukovych V.V., Misuno I.S., Revunova E.G., Slipchenko S.V., Sokolov A.M., Talayev S.A. Neural distributed representation for intelligent information technologies and modeling of thinking. Cybernetics and Computer Engineering. 2013, Vol. 173, pp. 7-24. (in Russian).

18. Rachkovskij D.A., Kussul E.M., Baidyk T.N. Building a world model with structure-sensitive sparse binary distributed representations. Biologically Inspired Cognitive Architectures. 2013, Vol. 3, pp. 64-86.
https://doi.org/10.1016/j.bica.2012.09.004

19. Gritsenko V.I., Rachkovskij D.A., Frolov A.A., Gayler R., Kleyko D., Osipov E. Neural distributed autoassociative memories: A survey. Cybernetics and Computer Engineering. 2017, N 2 (188), pp. 5-35
https://doi.org/10.15407/kvt188.02.005

20. Frolov A.A., Rachkovskij D.A., Husek D. On information characteristics of Willshaw-like auto-associative memory. Neural Network World. 2002. Vol. 12, No 2, pp. 141-158.

21. Frolov A.A., Husek D., Rachkovskij D.A. Time of searching for similar binary vectors in associative memory. Cybernetics and Systems Analysis. 2006, Vol. 42, N 5, pp. 615-623.
https://doi.org/10.1007/s10559-006-0098-z

22. Fusi S. Memory capacity of neural network models. arXiv:2108.07839. 21 Dec 2021.

23. Steinberg J., Sompolinsky H. Associative memory of structured knowledge. 2022. https://doi.org/10.1101/2022.02.22.481380
https://doi.org/10.1101/2022.02.22.481380

24. Liang J.C., Erez J., Zhang F., Cusack R., Barense M.D. Experience transforms conjunctive object representations: Neural evidence for unitization after visual expertise. Cerebral Cortex. 2020. Vol. 30, N 5, pp. 2721-2739.
https://doi.org/10.1093/cercor/bhz250

25. Li A.Y., Fukuda K., Barense M.D. Independent features form integrated objects: Using a novel shape-color “conjunction task” to reconstruct memory resolution for multiple object features simultaneously. Cognition. 2022, Vol. 223, Article 105024.
https://doi.org/10.1016/j.cognition.2022.105024

26. Andermane N., Joensen B.H., Horner A.J. Forgetting across a hierarchy of episodic representations. Current Opinion in Neurobiology. 2021, Vol. 67, pp. 50-57.
https://doi.org/10.1016/j.conb.2020.08.004

27. Michelmann S., Hasson U., Norman K.A. Event boundaries are steppingstones for memory retrieval. 2021. Preprint DOI 10.31234/osf.io/k8j94.
https://doi.org/10.31234/osf.io/k8j94

28. Schneegans S., McMaster J.M.V., Bays P.M. Role of time in binding features in visual working memory. Psychological Review. 2022. https://doi.org/10.1037/rev0000331
https://doi.org/10.1037/rev0000331

29. Geerligs L., van Gerven M., Campbell K.L., Güçlü U. A nested cortical hierarchy of neural states underlies event segmentation in the human brain. Neuroscience. 2021. https://doi.org/10.1101/2021.02.05.429165
https://doi.org/10.1101/2021.02.05.429165

30. Peer M., Brunec I.K., Newcombe N.S., Epstein R.A. Structuring knowledge with cognitive maps and cognitive graphs. Trends in Cognitive Sciences. 2021, Vol. 25, N 1, pp. 37-54.
https://doi.org/10.1016/j.tics.2020.10.004

31. Rachkovskij D.A. Shift-equivariant similarity-preserving hypervector representations of sequences. arXiv:2112.15475. 2021.
https://doi.org/10.1109/IJCNN55064.2022.9892462

32. Rachkovskij D.A. Representation and processing of structures with binary sparse distributed codes. IEEE Trans. Knowledge Data Engineering. 2001, Vol. 13, N 2, pp. 261-276.
https://doi.org/10.1109/69.917565

33. Rachkovskij D.A., Slipchenko S.V. Similarity-based retrieval with structure-sensitive sparse binary distributed representations. Comput. Intelligence. 2012,Vol. 28, N. 1, pp. 106-129.
https://doi.org/10.1111/j.1467-8640.2011.00423.x

34. Papadimitriou C.H., Vempala S.S., Mitropolsky D., Collins M.J., Maass W. Brain computation by assemblies of neurons. Proceedings of the National Academy of Sciences. 2020, Vol. 117, N 25, pp. 14464-14472.
https://doi.org/10.1073/pnas.2001893117

35. Müller M.G., Papadimitriou C.H., Maass W., Legenstein R. A model for structured information representation in neural networks of the brain. eNeuro. 2020, Vol. 7, N 3, pp. 1-17.
https://doi.org/10.1523/ENEURO.0533-19.2020

36. Mitropolsky D., Collins M.J., Papadimitriou C.H. A biologically plausible parser. Transactions of the Association for Computational Linguistics. 2021, Vol. 9, pp. 1374-1388.

37. Papadimitriou C.H., Friederici A.D. Bridging the gap between neurons and cognition through assemblies of neurons. Neural Computation. 2022, Vol. 34, N 2, pp. 291-306.
https://doi.org/10.1162/neco_a_01463

38. Rachkovskij D.A., Misuno I.S., Slipchenko S.V. Randomized projective methods for the construction of binary sparse vector representations. Cybernetics and Systems Analysis. 2012, Vol 48, N 1, pp. 146-156.
https://doi.org/10.1007/s10559-012-9384-0

39. Rachkovskij D.A. Vector data transformation using random binary matrices. Cybernetics and Systems Analysis. 2014, Vol. 50, N 6, pp. 960-968.
https://doi.org/10.1007/s10559-014-9687-4

40. Rachkovskij D.A. Formation of similarity-reflecting binary vectors with random binary projections. Cybernetics and Systems Analysis. 2015, Vol. 51, N 2, pp. 313-323.
https://doi.org/10.1007/s10559-015-9723-z

41. Rachkovskij D.A. Estimation of vectors similarity by their randomized binary projections. Cybernetics and Systems Analysis. 2015, Vol. 51, N 5, pp. 808-818.
https://doi.org/10.1007/s10559-015-9774-1

42. Dasgupta S., Stevens C.F., Navlakha S. A neural algorithm for a fundamental computing problem. Science. 2017, Vol. 358, pp. 793-796.
https://doi.org/10.1126/science.aam9868

43. Rachkovskij D.A., Gritsenko V.I. Distributed Representation of Vector Data Based on Random Projections. Kyiv: Interservice, 2018. ISBN: 978-617-696-837-5. (in Ukrainian)

44. Rachkovskij D.A. Codevectors: Sparse Binary Distributed Representations of Numerical Data. Kiev: Interservice, 2019. ISBN: 978-617-696-987-7. (in Russian)

45. Gritsenko V.I., Rachkovskij D.A. Methods for Vector Representation of Objects for Fast Similarity Estimation. Kyiv: Naukova Dumka, 2019. ISBN: 978-966-00-1741-2. (In Russian)

46. Rachkovskij D. A. Introduction to fast similarity search. Kyiv: Interservice, 2019, 294 p. ISBN: 978-617-696-904-4. (in Russian)

47. Ghazi B., Panigrahy R., Wang J. Recursive sketches for modular deep learning. Proceedings of the 36th International Conference on Machine Learning. PMLR. 2019, Vol. 97, pp. 2211-2220.

48. Panigrahy R., Wang X., Zaheer M. Sketch based memory for neural networks. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS’21). 2021, San Diego, California, USA. PMLR. 2021, Vol. 130, pp. 3169-3177.

49. Hiratani N., Sompolinsky H. Optimal quadratic binding for relational reasoning in vector symbolic neural architectures. arXiv:2204.07186. 2022, pp. 1-32.

50. Chaitanya R., Hopfield J., Grinberg L., Krotov D. Bio-inspired hashing for unsupervised similarity search. Proceedings of the 37th International Conference on Machine Learning. PMLR. 2020, Vol. 119, pp. 8295-8306.

51. Liang Y., Ryali C., Hoover B., Grinberg L., Navlakha S., Zaki M., Krotov D. Can a fruit fly learn word embeddings? Proc. ICLR’21. 2021.

52. Li W. Modeling winner-take-all competition in sparse binary projections. In: Machine Learning and Knowledge Discovery in Databases. Edited by: F. Hutter, K. Kersting,
J. Lijffijt, I. Valera. Lecture Notes in Computer Science. Vol. 12457. Cham: Springer, 2021, pp. 456-472. https://doi.org/10.1007/978-3-030-67658-2_26
https://doi.org/10.1007/978-3-030-67658-2_26

53. Li W.Y., Zhang S.Z. Binary random projections with controllable sparsity patterns. Journal of the Operations Research Society of China. 2022. https://doi.org/10.1007/s40305-021-00387-0
https://doi.org/10.1007/s40305-021-00387-0

54. Rachkovskij D.A., Slipchenko S.V., Misuno I.S., Kussul E.M., Baidyk T.N. Sparse binary distributed encoding of numeric vectors. Journal of Automation and Information Sciences. 2005, Vol. 37, N 11, pp. 47-61.

55. Izawa S., Kitai K., Tanaka S., Tamura R., Tsuda K. Continuous black-box optimization with quantum annealing and random subspace coding. Proc. Adiabatic Quantum Computing (AQC’21), June 22-25, 2021.

56. Izawa S., Kitai K., Tanaka S., Tamura R., Tsuda K. Continuous black-box optimization with an Ising machine and random subspace coding. Phys. Rev. Research. 2022, Vol. 4, N 2. Article 023062.
https://doi.org/10.1103/PhysRevResearch.4.023062

57. Barclay I. et al. Trustable service discovery for highly dynamic decentralized workflows. Future Generation Computer Systems. 2022. DOI: 10.1016/j.future.2022.03.035
https://doi.org/10.1016/j.future.2022.03.035

58. Wei Y., Xie P., Zhang L. Tikhonov regularization and randomized GSVD. SIAM J.
Matrix Analysis Appl. 2016, Vol. 37, pp. 649-675.
https://doi.org/10.1137/15M1030200

59. Zhang L., Wei Y. Randomized core reduction for discrete ill-posed problem. J. Comput. Appl. Math. 2020, Vol. 375, Article 112797.
https://doi.org/10.1016/j.cam.2020.112797

60. Wei W., Zhang H., Yang X., Chen X. Randomized generalized singular value decomposition. Commun. Appl. Math. Comput. 2021, Vol 3, pp. 137-156.
https://doi.org/10.1007/s42967-020-00061-x

61. Zuo Q., Wei Y., Xiang H. Quantum-inspired algorithm for truncated total least squares solution. arXiv:2205.00455. 2022

62. Revunova E.G., Rachkovskij D.A. Using randomized algorithms for solving discrete ill-posed problems. J. Information Theories and Applications. 2009, Vol. 16, N 2, pp. 176-192.

63. Rachkovskij D.A., Revunova E.G. Randomized method for solving discrete ill-posed problems. Cybernetics and Systems Analysis. 2012, Vol. 48, N 4, pp. 621-635.
https://doi.org/10.1007/s10559-012-9443-6

64. Revunova E.G. Analytical study of the error components for the solution of discrete
ill-posed problems using random projections. Cybernetics and Systems Analysis. 2015, Vol. 51, N 6, pp. 978-991.
https://doi.org/10.1007/s10559-015-9791-0

65. Revunova E.G. Model selection criteria for a linear model to solve discrete ill-posed problems on the basis of singular decomposition and random projection. Cybernetics and Systems Analysis. 2016, Vol. 52, N 4, pp. 647-664.
https://doi.org/10.1007/s10559-016-9868-4

66. Revunova E.G. Increasing the accuracy of solving discrete ill-posed problems by the random projection method. Cybernetics and Systems Analysis. 2018, Vol. 54, N 5, pp. 842-852.
https://doi.org/10.1007/s10559-018-0086-0

67. Revunova O.G., Tyshchuk O.V., Desiateryk О.О. On the generalization of the random projection method for problems of the recovery of object signal described by models of convolution type. Control Systems and Computers. 2021, N 5-6, pp. 25-34.
https://doi.org/10.15407/csc.2021.05-06.025

68. Sokolov A., Rachkovskij D. Approaches to sequence similarity representation. Int. J. Inf. Theor. Appl. 2006, Vol. 13, N 3, pp. 272-278.

69. Sokolov A. Vector representations for efficient comparison and search for similar strings. Cybernetics and Systems Analysis. 2007, Vol. 43, N 4, pp. 484-498.
https://doi.org/10.1007/s10559-007-0075-1

70. Rachkovskij D.A. Development and investigation of multilevel assembly neural networks. PhD thesis. Kiev, Ukrainian SSR: V.M. Glushkov Institute of Cybernetics, 1990. (in Russian)

71. Kussul E.M., Baidyk T.N. On information encoding in Associative-Projective Neural Networks. Technical Report 93-3. V.M. Glushkov Institute of Cybernetics, 1993. (in Russian)

72. Kleyko D., Osipov E., De Silva D., Wiklund U., Vyatkin V., Alahakoon D. Distributed representation of n-gram statistics for boosting self-organizing maps with hyperdimensional computing. Proc. PSI’19. 2019, pp. 64-79.
https://doi.org/10.1007/978-3-030-37487-7_6

73. Kussul E.M., Baidyk T.N., Wunsch D.C., Makeyev O., Martin A. Permutation coding technique for image recognition system,” IEEE Trans. Neural Networks. 2006, Vol. 17, N 6, pp. 1566-1579.
https://doi.org/10.1109/TNN.2006.880676

74. Rachkovskij D.A. Application of stochastic assembly neural networks in the problem
of interesting text selection. Neural network systems for information processing. 1996, pp. 52-64. (in Russian)

75. Rachkovskij D.A., Kleyko D. Recursive binding for similarity-preserving hypervector representations of sequences. Proc. IJCNN’22. 2022.
https://doi.org/10.1109/IJCNN55064.2022.9892462

76. Kussul E., Baidyk T. Improved method of handwritten digit recognition tested on MNIST database. Image and Vision Computing. 2004, Vol. 22, pp. 971-981.
https://doi.org/10.1016/j.imavis.2004.03.008

77. Makeyev O., Sazonov E., Baidyk T., Martin A. Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces. Neurocomputing. 2008, Vol. 71, N 7-9, pp. 1413-1421.
https://doi.org/10.1016/j.neucom.2007.05.004

78. Rachkovskij D.A. Distance-based index structures for fast similarity search. Cybernetics and Systems Analysis. 2017, Vol. 53, N 4, pp. 636-658.
https://doi.org/10.1007/s10559-017-9966-y

79. Rachkovskij D.A. Index structures for fast similarity search for binary vectors. Cybernetics and Systems Analysis. 2017, Vol. 53, N 5, pp. 799-820.
https://doi.org/10.1007/s10559-017-9983-x

80. Kussul E.M., Baidyk T.N., Lukovich V.V., Rachkovskij D.A. Adaptive neural network classifier with multifloat input coding. Proc. NeuroNimes’93, Nimes, France, Oct. 25-29, 1993, pp. 209-216.

81. Lukovich V.V., Goltsev A.D., Rachkovskij D.A. Neural network classifiers for micromechanical equipment diagnostics and micromechanical product quality inspection. Proc. EUFIT’97, 1997, pp. 534-536.

82. Rachkovskij D.A., Kussul E.M. DataGen: a generator of datasets for evaluation of classification algorithms. Pattern Recognition Letters. 1998, Vol.19, N 7, pp. 537-544.
https://doi.org/10.1016/S0167-8655(98)00053-1

83. Goltsev A.D. Neural networks with assembly organization. Kyiv: Naukova Dumka. 2005, 200 p. (in Russian)

84. Rachkovskij D. Linear classifiers based on binary distributed representations. J. Inf. Theories Appl. 2007, Vol. 14, N 3, pp. 270-274.

85. Goltsev A., Rachkovskij D. A recurrent neural network for partitioning of hand drawn characters into strokes of different orientations. International Journal of Neural Systems. 2001, Vol. 11, pp. 463-475.
https://doi.org/10.1142/S0129065701000862

86. Goltsev A., Gritsenko V., Húsek D. Segmentation of visual images by sequential extracting homogeneous texture areas. Journal of Signal and Information Processing. 2020, Vol. 11, N 4, pp. 75-102.
https://doi.org/10.4236/jsip.2020.114005

87. Goltsev A. D., Gritsenko V.I. Algorithm of sequential finding the textural features characterizing homogeneous texture segments for the image segmentation task. Cybernetics and Computer Engineering. 2013, N 173, pp. 25-34.

88. Goltsev A., Gritsenko V., Kussul E., Baidyk T. Finding the texture features characterizing the most homogeneous texture segment in the image. Proc. IWANN’15. 2015, pp. 287-300.
https://doi.org/10.1007/978-3-319-19258-1_25

89. Volkov O., Komar M., Volosheniuk D. Devising an image processing method for transport infrastructure monitoring systems. Eastern-European Journal of Enterprise Technologies. 2021, Vol. 4, N 2 (112), pp. 18-25. https://doi.org/10.15587/1729-4061.2021.239084
https://doi.org/10.15587/1729-4061.2021.239084

90. Gritsenko V., Volkov O., Komar M., Volosheniuk D. Integral adaptive autopilot for an unmanned aerial vehicle. Aviation. 2018, Vol. 22, N 4, pp. 129-135. http://dx.doi.org/10.3846/ aviation.2018.6413
https://doi.org/10.3846/aviation.2018.6413

91. Misuno I. S., Rachkovskij D. A., Slipchenko S. V., Sokolov A. M. Searching for text information with the help of vector representations. Probl. Programming. 2005, N 4, pp. 50-59.

92. Goltsev A., Rachkovskij D. Combination of the assembly neural network with a perceptron for recognition of handwritten digits arranged in numeral strings. Pattern Recognition. 2005, Vol. 38, N 3, pp. 315-322.
https://doi.org/10.1016/j.patcog.2004.09.001

93. Kasatkina L.M., Lukovich V.V., Pilipenko V.V. Recognition of the person’s voice by the classifier LIRA. Control Systems and Computers. 2006, N. 3, pp. 67-73.

94. Slipchenko S. V. Distributed representations for the processing of hierarchically structured numerical and symbolic information. System Technologies. 2005, N 6, pp. 134-141. (In Russian)

95. Rachkovskij D.A., Revunova E.G. Intelligent gamma-ray data processing for environmental monitoring. In: Intelligent Data Processing in Global Monitoring for Environment and Security. Kiev-Sofia: ITHEA, 2011, pp. 136-157.

96. Revunova E.G. Rachkovskij D.A. Training a linear neural network with a stable LSP solution for jamming cancellation. Intern. Journal Information Theories and Applications. 2005, Vol.12, N 3, pp. 224-230.

97. Revunova E.G., Rachkovskij D.A. Random projection and truncated SVD for estimating direction of arrival in antenna array. Cybernetics and Computer Engineering. 2018, N 3(193), pp. 5-26.
https://doi.org/10.15407/kvt192.03.005

98. Hinton G. How to represent part-whole hierarchies in a neural network. arXiv:2102.12627. 2021, pp. 1-44.

99. Goyal A., Bengio Y. Inductive biases for deep learning of higher-level cognition. arXiv:2011.15091. 2020, pp. 1-42.

100. Greff K., van Steenkiste S., Schmidhuber J. On the binding problem in artificial neural networks. arXiv:2012.05208. 2020, pp. 1-75.

Received 17.03.2022

Issue 2 (208)

DOI:https://doi.org/10.15407/kvt208.02

Download Issue 2 (208) as PDF
View web version

TABLE OF CONTENTS:

Informatics and Information Technologies:

Rachkovskij D.A., Gritsenko V.I., Volkov O.Ye., Goltsev A.D., Revunova E.G., Kleyko D., Lukovich V.V., Osipov E.
Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

Savchenko-Synyakova Ye.A.
Comparative Analysis of Machine Learning Methods and Other Directions of Artificial Intelligence

Intelligent Control and Systems:

Bondar S.O., Shepetukha Yu.M., Voloshenyuk D.O.
Using of High-Quality Positioning Tools for Hybrid Unmanned Aerial Vehicles Automatic Correction Under the Limited Space Condition

Medical and Biological Cybernetics:

Aralova N.I., Beloshitskiy P.V., Zubieta-Calleja G., Aralova A.A.
Automated Information System for the Evaluation of Climbers’ Performance Under Conditions of Extremely Low pO2 of Inhaled Air

Katrakazas P., Spais I.
Blueprints Elicitation Framework for an Open Access Pan-European Neuro-Imaging Online Centre

Issue 1 (207), article 7

DOI:https://doi.org/10.15407/kvt207.01.087

Cybernetics and Computer Engineering, 2022, 1(207)

Vovk М.І., PhD (Biology), Senior Researcher,
Head of the Department of Bioelectrical Control & Medical Cybernetics
ORCID: 0000-0003-4584-9553
e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com

Kutsiak О.А., PhD (Engineering),
Senior Researcher of the Department of Bioelectrical Control & Medical Cybernetics
ORCID: 0000-0003-2277-7411
e-mail: spirotech85@ukr.net

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

INFORMATION TECHNOLOGIES FOR MUSCLE FUNCTIONS CONTROL. RETROSPECTIVE ANALYSIS AND DEVELOPMENT PROSPECTS

Introduction. The research on muscle functions control is determined not only by scientific interest but also by practical necessity. 

The purpose of the paper is to conduct a retrospective analysis of the synthesis of information technologies for the control of human muscle functions for their recovery, correction or training.

Results. The evolution of the synthesis of science-intensive information technologies for muscle functions controlling with the purpose for recovering, correcting or training them on the basis of external control circuits is shown. The informative and energy myostimulation signals play the role of these loops. And the signals come from electronic software devices or information software and hardware complexes. The main classes of the first generation of such devices as open (“MIOTON”), adaptive (“MIOSTIMUL”), and modern — “TRENAR” are considered. The devices contain a set of basic software modules for activating the patient reserves to recover the muscle activity depending on the motor functions state and the patient general state. The new patented technology for oral speech recovery after stroke based on training the fine motor skills of the hand is considered. The new information technology of digital medicine “AI-REABILITOLOG” is presented. This technology for information assistance to user (physician) in making diagnostic and treatment decisions on rehabilitation of motor and speech functions uses artificial intelligence tools — specialized software modules for creating the personalized training plan of extremities, fine motor skill of the hand, in particular for oral speech recovery, and the gait on the results of their disorders quantitative assessment. The results of practical application, the advantages of the developed information technologies are presented. The prospects for their development are considered.

Conclusions. The main principles for synthesis of science-intensive information technologies for muscle functions controlling in order to recover, correct or train them on the basis of external control circuits are a combination of physical and cognitive influences, active participation of the subject in training procedures and their self-control.

Keywords: information technologies, digital medicine, control, myostimulation devices, muscle functions, movements, speech, diagnostics, rehabilitation, stroke, personalized quantitative assessment, criteria, artificial intelligence, software module, structural and functional model

Download full text!

REFERENCES
1. Inventor’s certificate. The method of motor control / L. Aleev, S. Bunimovich (USSR); No 1019769/31-16; published 29.12.66, Bull. No 2. (in Russian).

2. Aleev L.S. Bioelectrical system “Mioton” and motor functions of a person. Bull. of AS of USSR. 1969. Issue 4. pp. 70-80 (in Russian).

3. Aleev L.S., Vovk M.I., Gorbanev V., Shevchenko A.B. “Mioton” in motor control. Kiev: Nauk. dumka, 1980. 142 p. (in Russian).

4. Inventor’s certificate. The method of motor control of a person / L. Aleev, S. Bunimovich, M. Vovk, V. Gorbanev, A. Shevchenko (USSR); No 321 245; 03.09.1981. (in Russian).

5. Inventor’s certificate. Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko (USSR); No 929 054; 23.05.1982. (in Russian).

6. Inventor’s certificate. Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko (USSR); No 976 952; 03.08.1982. (in Russian).

7. United States Patent. Bioelectrically controlled electric stimulator of human muscles / L. Aleev, S. Bunin, M. Vovk, V. Gorbanev, A. Shevchenko, F. Balchev; No 4, 165, 750; 28.08.1979.

8. Gritsenko V.I. Vovk M.I., Kotova A.B., Kiforenko S.I., Belov V.M. Information technologies in biology and medicine. Course of lectures. Kyiv: Naukova Dumka, 2007. 382p. (In Ukrainian)

9. Vovk M.I., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019. No 4 (198). pp. 62-77
https://doi.org/10.15407/kvt198.04.062

10. Patent. A method of treating speech disorders / M.I. Vovk, Ye.B. Halian, O.M. Pidopryhora (Ukraine); No 111388; publshed 25.04.2016, Bulletin no 18 (in Ukrainian)

11. Vovk, M.I., Halian, Ye.B., Kutsiak, O.A. Computer Software & Hardware Complex for Personal Oral Speech Restoration after a Stroke. Sci. innov. 2020. Vol. 16, No 1(91). pp. 54-68. https://doi.org/10.15407/scine15.05.054

12. Vovk M.I., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019. No 4 (198). pp. 62-77
https://doi.org/10.15407/kvt198.04.062

13. Vovk M.I., Kutsiak O.A., Lauta A.D., Ovcharenko M.A. Information Assistance of Researches on the Dynamics of Movement Restoration After the Stroke. Kibernetika i vycislitel’naa tehnika. 2017. No 3 (189), pp. 61-78. (in Ukrainian)
https://doi.org/10.15407/kvt189.03.061

14. Belova A., Shchepetova O. Scales, tests and questionnaires in medical rehabilitation. Moscow: Antidor, 2002. 440 p. (in Russian)

15. Smychek V., Ponomareva E. Craniocerebral trauma (clinic, treatment, examination, rehabilitation). Minsk: Research Institute of ME and R, 2010. 430 p. (in Russian)

16. Vovk M.I., Kutsyak O.A. Information technology for forming a personal movement rehabilitation plan after a stroke. Cybernetics and Computer Engineering. 2020. No 3 (201). pp. 87-99.
https://doi.org/10.15407/kvt201.03.087

17. Vovk M.I., Kutsyak O.A. Mobile AI-technology for forming the personalized movements rehabilitation plan after a stroke. Cybernetics and Computer Engineering. 2021. No 4 (206). pp. 73-88.
https://doi.org/10.15407/kvt206.04.073

18. Vovk M.I., Kutsyak O.A. AI-technology of motor functions diagnostics after a stroke. Cybernetics and Computer Engineering. 2021. No 2 (204). pp. 84-100.
https://doi.org/10.15407/kvt204.02.084

Received 24.03.2022

Issue 1 (207), article 6

DOI:https://doi.org/10.15407/kvt207.01.074

Cybernetics and Computer Engineering, 2022, 1(207)

Kiforenko S.I., DSc (Biology), Senior Researcher
Leading Researcher of the Department of Application
Mathematical and Technical Methods in Biology and Medicine
ORCID: 0000-0001-2345-6789
e-mail: skifor@ukr.net

Belov V.M., DSc (Medicine), Professor,
Head of the Department of Application
Mathematical and Technical Methods in Biology and Medicine
ORCID: 0000-0001-8012-9717
e-mail: motj@ukr.net

Hontar T.M., PhD (Biology), Senior Researcher
Senior Researcher of the Department of Application
Mathematical and Technical Methods in Biology and Medicine
ORCID: 0000-0002-9239-0709
e-mail: gtm_kiev@ukr.net

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

THE HIERARCHY PRINCIPLE AS THE BASIS OF BIOLOGICAL SYSTEMS RESEARCH

Introduction. The article illustrates the feasibility of using the methodology of a systematic approach for the rational organization of research in solving biomedical problems at the stages of diagnosis, prognosis and correction of the condition. The effectiveness of using the principle of hierarchy as one of the main organizational principles of systems analysis is illustrated by specific examples of quantitative assessment of Health and its components and in the development of hierarchical modeling technology using mathematical models of varying complexity in a single technological cycle simultaneously.

The purpose of the paper is to show the expediency of using the principle of hierarchy on the examples of developing information-structural model of health category as an integrative structural concept and synthesis of hierarchical modeling technology as a basis for modern preclinical trials.

Results A hierarchical structure of health assessment technology has been developed, which includes conceptual level, management level: synthesis of assessment models and algorithms for calculating health reserves according to the norm index, level of synthesis of technological scaling procedures and diagnostic conclusions.

The technology of mathematical modeling using the hierarchy of models of different complexity for simulation research of different algorithms for glycemic control (analytical, numerical, simulation) to predict the glycemic profile at the stage of preclinical trials.

Conclusions. The hierarchical organization of the structure of the study of the category of health allowed to receive quantitative and verbal conclusions about the state of health reserves in general and all its components, taking into account the norm index, which increased the resolution of estimation algorithms. The proposed technology of hierarchical modeling of glycemic regulation in patients with diabetes allows to assess at the preclinical stage the peculiarities of the use of regulatory algorithms to prevent errors directly in the practice of treatment.

Keywords: the hierarchy principle, information-structural model of the health, hierarchical modeling, glycemic control system, simulation pre-clinical trials.

Download full text!

REFERENCES

1. Rapoport A. General System Theory: Essential Concepts & Applications. Kent: Abacus Press, 1986. 250 p.

2. Wasson, Charles S. System engineering analysis, design, and development: Concepts, principles, and practices. John Wiley & Sons, 2015.

3. Rebizant. W., Janusz S., Wiszniewski A. “Fundamentals of System Analysis and Synthesis. Digital Signal Processing in Power System Protection and Control. Springer, London, 2011. 29-52.
https://doi.org/10.1007/978-0-85729-802-7_4

4. Von Bertalanffy L. General system theory – A critical review.” Modern systems research for the behavioral scientist. Buckley, Walter (ed.) Aldine Publishing Co, Chicago. 1968.

5. Dantzig, T. Number The Language of Science. Edited by Joseph Mazur. Plume, New York. 2005.

6. Saaty, T.L. The analytic hierarchy process McGraw-Hill. New York, 1980. 324.
https://doi.org/10.21236/ADA214804

7. Anokhin P.K. Fundamental questions of the general theory of functional systems. Moscow: Science , 1971. 64 p. (In Russian)

8. Antomonov Yu.G. Principles of neurodynamics. Kiev: Naukova Dumka, 1974. 200 p. (In Russian)

9. Antomonov Yu.G. Systems. Complexity. Dynamics. Kyiv: Naukova Dumka, 1969. 127p. (In Russian)

10. Antomonov Yu.G., Kiforenko S.I., Mikulskaya I.A. etc. Mathematical theory of the blood sugar system. Kiev: Naukova Dumka, 1971. 83 p. (In Russian)

11. Antomonov Yu.G., Kotova A.B. Introduction to the structural-functional theory of the nerve cell. Kyiv: Naukova Dumka, 1976. 263p. (In Russian)

12. Antomonov Yu.G. Modeling of biological systems. Kyiv: Naukova Dumka, 1977. 260 p. (In Russian)

13. Methods of mathematical biology: textbook. allowance in 8 books. Kyiv: Vishcha school., 1980 -1984. (In Russian)

14. Antomonov Yu.G., Belov V.M., Gritsenko V.I., Kotova A.B. et al. Open concept of health. Preprint, Glushkov Institute of Cybernetics. Kiev, 1993, 26 P. (In Russian)

15. Gritsenko V.I., Kotova A.B., Vovk M.I. etc. Bioecomedicine. United information space. Kiev: Naukova Dumka, 2001. 318 p. (In Russian)

16. Gritsenko V.I. Vovk M.I., Kotova A.B., Kiforenko S.I., Belov V.M. Information technologies in biology and medicine. Course of lectures. Kyiv: Naukova Dumka, 2007. 382p. (In Ukrainian)

17. Belov V.M., Kotova A.B. Human health: challenges, methods, approaches. Kyiv: Naukova Dumka, 2017. 132 p. (In Russian)

18. Kiforenko S.I., Kotova A.B. Multidimensionality as a basis for systematic health assessment. Kibernetika i vycislitel’naa tehnika. 2006. Issue. 150. S. 60-69. (In Russian)

19. Akhutin V.M., Nefedov V.P., Sakharov M.P. and etc. Engineering physiology and modeling of body systems. Novosibirsk: Nauka, 1987.

20. Cobelli, C., & Dalla Man, C. (2021). Minimal and maximal models to quantitate glucose metabolism: tools to measure, to simulate and to run in silico clinical trials. Journal of diabetes science and technology, 19322968211015268.
https://doi.org/10.1177/19322968211015268

21. Aliev T.I. Research of complex systems based on a combined approach

http://simulation.su/uploads/files/default/immod-2003-1-50-55.pdf (In Russian)

22. Kiforenko S.I. Hierarchical modeling as the basis of the technology of preclinical testing of algorithms for the treatment of equal glycaemia. Kibernetika i vycislitel’naa tehnika. Iss.187, 2017. P. 80-96. (In Ukrainian)
https://doi.org/10.15407/kvt187.01.080

23. Dalla Man C., Micheletto F., Lv D., Breton M., Kovatchev B., Cobelli C. The UVA/PADOVA type 1 diabetes simulator: new features. J. Diabetes Sci. Technol. 2014; 8 (1): 26-34.
https://doi.org/10.1177/1932296813514502

Received 21.02.2022

Issue 1 (207), article 5

DOI:https://doi.org/10.15407/kvt207.01.059

Cybernetics and Computer Engineering, 2022, 1(207)

O.S. Kovalenko, DSc (Medicine), Professor,
 Head of the Medical Information Systems Department
ORCID 0000-0001-6635-0124
e-mail: askov49@gmail.com

L.M. Kozak, DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department
ORCID: 0000-0002-7412-3041
e-mail: lmkozak52@gmail.com

M. Najafian Tumajani,
Junior Researcher of the Medical Information Systems Department,
ORCID:
e-mail: najafian@mail.ru

O.O. Romanyuk,
Junior Researcher of the Medical Information Systems Department
ORCID:0000-0002-6865-1403
e-mail: ksnksn7@gmail.com

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, Glushkov ave., Kyiv, 03187, Ukraine

EXPERIENCE AND PROSPECTS OF CREATING MEDICAL INFORMATION SYSTEMS AND INFORMTION TECHNOLOGIES TO SUPPORT MEDICAL CARE

Introduction. One of the four flagship initiatives identified by the WHO as health priorities for the coming years is the Flagship Initiative to enable citizens for receive quality health care through digital health care. The use of digital medical technologies to provide health care will serve for strengthening the health care system, empowering patients and achieving the principle of “health for all”.

The purpose of the paper is to summarize the experience and latest results of the scientists of the Medical Information Systems Department of the International Center for Research and Development of Medical Information Systems and Information Technologies of Digital Medicine against the background of the general process of digital transformation in medicine.

Results. The main characteristics and principles of building modern medical information systems (MIS) as components of the digital medicine ecosystem are determined. Internal and external information flows of MIS are analyzed. To further differentiate the representative attributes of these documents, three similar but different technologies associated with the patient card were identified: electronic medical records, electronic health records and electronic patient health passport, each of which is differentiated based on the level of patient orientation. Based on one of the principles of “5Ps medicine”, the principle of personalization, the structure of personal medical storage is determined, which according to modern challenges is needed by all participants in digital medicine infrastructure (patients, doctors, laboratories and functional diagnostics departments, etc.). To ensure the interconnection of such repositories, models of business processes of accumulation and exchange of digital medical data have been created and based on them mobile applications, modules for accumulation and exchange of digital medical data between different users in the process of diagnostic data analysis have been developed. The interaction of mobile applications with the local information environment of the health care institution is analyzed and its features are taken into account in the created specialized mobile software modules of accumulation and analysis of personal medical data.

Conclusion. The developed model of digital transformation in medicine, which includes digital methods of obtaining and analyzing biomedical signals, digital medical images, methods of forming electronic medical records and documents, allowed to create methods and tools for building the digital medicine ecosystem using global intellectual resources to provide the necessary level for analysis Big Data and decision support for doctors at all stages of medical care. The use of developed mobile applications of accumulation, analysis and exchange of personal medical data allows to review the accumulated data, assess and predict human health according to the developed Data Mining models and implement medical data exchange of different origins between patient and doctor.

Keywords: medical information systems, digital medicine ecosystems, medical information technologies, mobile applications, classification models Data Mining.

Download full text!

REFERENCES
1. Draft global strategy on digital health 2020-2025. July 2020 by WHO. https://www.who.int/ docs/ default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf (Last accessed: 29.12.2021)

2. The European Programme of Work, 2020-2025: United Action for Better Health. Copenhagen: WHO Regional Office for Europe; 2021 https://apps.who.int/iris/bitstream/handle/10665/ 339209/WHO-EURO-2021-1919-41670-56993-eng.pdf?sequence=1&isAllowed=y (Last acces-sed: 29.05.2021)

3. Medical information system. Kyiv: Nauk. Dumka, 1975. 508 p. (in Russian).

4. EN ISO 12052: 2011. Health informatics. Digital work, including workflow and data management URL: http://iso.org. (Last accessed: 15.10.2021)

5. Oosterwijk H. DICOM Basics (Third Edition). O Tech. 2005.

6. Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A. Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine. Kibernetika i vycislitel’naa tehnika. 2018. 4(194). P. 61-78.
https://doi.org/10.15407/kvt194.04.061

7. The Digital Imperative. The imperative for a consumer-centric, digitally enabled health ecosystem. Delloite. 10 p.: https://www.kff.org/health-costs/poll-finding/data-note-americans-challenges-with-health-care-costs/

8. Haider J. Warraich, Robert M. Califf, Harlan M. Krumholz The digital transformation of medicine can revitalize the patient-clinician relationship. www.nature.com/npjdigitalmed

9. What are the differences between electronic medical records, electronic health records, and personal health records? https://www.healthit.gov/faq/ what-are-differences-between-electronic-medical-records-electronic-health-records-and-personal;

10. Hoerbst A., Ammenwerth E. Electronic Health Records. A Systematic Review on Quality Requirements. Methods Inf Med, 2010; 49(04): 320-336.
https://doi.org/10.3414/ME10-01-0038

11. Nguyen L., Bellucci E., Thuy Nguyen L. Electronic health records implementation: An evaluation of information system impact and contingency factors. International Journal of Medical Informatics. Vol. 83, Iss. 11, November 2014, pp.779-796
https://doi.org/10.1016/j.ijmedinf.2014.06.011

12. Ch!é!n O.Y., Roberts B. R. Personalized Health Care and Public Health in the Digital Age. Front. Digit. Health, 30 March 2021. V. 3. Article 595704.
https://doi.org/10.3389/fdgth.2021.595704

13. Kovalenko A.S., Kozak L.M., Ostashko V.G. Telemedicine – development of a unified medical information space. Upravla!û!!ŝ!ie sistemy i ma!š!iny. 2005. No3. C. 86-92. (In Russian)

14. Kovalenko A.S., Kozak L.M., Romanyuk O.A. Information technology of digital medicine. Kibernetika i vycislitel’naa tehnika. 2017. No1(187). C.67-79. (In Russian)
https://doi.org/10.15407/kvt187.01.067

15. Romaniuk, O. O., Kozak, L. M., and Kovalenko, O. S. Formation of Interoperable Digital Medicine Information Environment: Personal Medical Data. Sci. innov. 2021. V. 17, no. 5. P. 50-62.

16. Kryvova O.A., Kozak L.M. Information Technology for Classification of Donosological and Pathological States Using the Ensemble of Data Mining Methods. Cybernetics and Computer Engineering. 2021, 1(203), pp 77-96.
https://doi.org/10.15407/kvt203.01.077

17. Officials Should Target 20 Key Areas to Transform Health Care System https://www8.nationalacademies.org/onpinews/newsitem.aspx?RecordID=10593

18. Nanotechnology is a key priority for the foreseeable future in medicine http:// www.nanolab.com.ua/publicacii/article4.html

19. Australian Medical Research and Innovation Priorities 2018-2020 Determination 2018. https://www.legislation.gov.au/Details/F2018L01550

20. Reddy, M. Digital Transformation in Healthcare in 2021: 7 Key Trends. https://www.digitalauthority.me/resources/stateofdigitaltransformationhealthcare/ (Last accessed: 14.01.2021).

Received 14.01.2022

Issue 1 (207), article 4

DOI:https://doi.org/10.15407/kvt207.01.046

Cybernetics and Computer Engineering, 2022, 1(207)

L.S. Zhiteckii, PhD (Engineering),
Acting Head of the Department of
Intelligent Automatic Systems
e-mail: leonid_zhiteckii@i.ua

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

PROBLEMS AND PROSPECTS FOR THE INTELLECTUALIZATION OF AUTOMATIC CONTROL SYSTEMS

Introduction. The improvement of automatic control systems via their intellectualization is the important problem from both theoretical and practical points of view. The presence of adaptation and learning processes intrinsic to the natural intelligence makes it possible to consider the modern adaptive and learning systems as some intelligent control systems of the simplest type.

The purpose of this paper is to outline briefly the world-class results related to the efficient adaptive control and achieved in Intelligent Automatic Systems Department during the last 25 years and also to point out on problems of future research in this scientific area.

Results. A new adaptive control theory which has recently been completed represent the significant achievement to deal with the control systems in the presence of both parameter and nonparameter uncertainties. The main distinguishing feature of this theory is that it requires no information about the constrained membership set of unknown plant parameters and the bounds on arbitrary unmeasurable disturbances. Utilizing its methods, we can ensure the desired performance indices of the control systems with uncertain plants whereas the existing methods become quite unacceptable in the same situation.

Conclusion. Based on recent results concerning the adaptation and learning problems, we propose to take a next step toward to novel intelligent automatic control systems containing complex nonlinear plants. However, new perspective methods guaranteeing a perfect behavior of the closed-loop control systems, in particular, the stability of these control systems should be devised before implementing them in practical applications. This as yet unsolved scientific problem remains the subject of future theoretical research.

Keywords: adaptive and learning control system, automatic intelligent control system, parameter and nonparameter uncertainties, unmeasured disturbance, complex nonlinear plant. 

Download full text!

REFERENCES
1. Kuntsevich V.M. Control under uncertainty: guaranteed results in management and identification problems. Kyiv: Nauk. dumka, 2006, 264 p. (in Russian).

2. Goodwin G.C., Sin1. Kuntsevich V.M. Control under uncertainty: guaranteed results in management and identification problems. Kyiv: Nauk. dumka, 2006, 264 p. (in Russian).

2. Goodwin G.C., Sin K.S. Adaptive filtering, prediction and control. Engewood Cliffs, NJ: Pren-tice-Hall, 1984, 540 p.

3. Fomin V.N., Fradkov A.L., Yakubovich V.A. Adaptive control of dynamic plants. Moscow: Nauka, 1981, 448 p (in Russian).

4. Zhiteckij L.S., Skurikhin V.I. Adaptive control systems with parametric and nonparametric uncertainties. Kyiv: Nauk. dumka, 2010, 301 p. (in Russian).

5. Zhiteckij L.S. An open problem in adaptive nonlinear control theory. Unsolved Problems in Mathematical Systems and Control Theory: V.D. Blondel and A. Megretskl (Eds). Princeton, USA: Princeton University Press. 2004. P. 229-237.

6. Zhiteckii L.S., Solovchuk K.Yu. Robust adaptive controls for a class of nonsquare memoryless systems. Advanced Control Systems: Theory and Applications: Kondratenko Y.P., Kuntsevich V.M., Chikrii A.A., Gubarev V.F. (Eds). Gistrup: River Publishers. 2021, pp. 203-226.

7. Gritsenko V.I., Zhiteckii L.S., Solovchuk K.Yu. Limitations of pseudo inverse method for control of linear interconnected memoryless plants: guaranteed results. Dopovidi NAN Ukrainy. 2019, No 8, pp. 16-24 (in Russian).
https://doi.org/10.15407/dopovidi2019.08.016

8. Skurikhin V.I., Gritsenko V.I., Zhiteckii L.S., Solovchuk K. Yu. Generalized inverse operator method in the problem of optimal controlling linear interconnected static plants. Dopovidi NAN Ukrainy. 2014, No 8, pp. 57-66 (in Russian).
https://doi.org/10.15407/dopovidi2014.08.057

9. Zhitetskii L. S., Skurikhin V. I., Solovchuk K.Yu. Stabilization of a nonlinear multivariable discrete-time time-invariant plant with uncertainty on a linear pseudoinverse model. Journal of Computer and Systems Sciences International. 2017, N 5, pp. 12-26.
https://doi.org/10.1134/S1064230717040189

10. Zhiteckii L. S., Solovchuk K. Yu. Pseudoinversion in the problems of robust stabilizing multivariable discrete-time control systems of linear and nonlinear static objects under bounded disturbances. Journal of Automation and Information Sciences. 2017, N 3, pp. 57-70.
https://doi.org/10.1615/JAutomatInfScien.v49.i5.30

11. Fainzilberg L.S. Intelligent information technology for processing of signals with localized information. Schtuchnyi intelekt. 2008, No 2, pp. 100-110 (in Russian).

12. Timofeev A.V., Yusupov R.M. Intelligent automatic control systems. Technicheskaya kibernetika. 1994, No 5, pp. 211-223 (in Russian).

13. Zhiteckii L. S., Nikolaienko S. A., Solovchuk K. Yu. Adaptation and learning in some classes of identification and control systems. Upravla!û!!ŝ!ie sistemy i ma!š!iny. 2015, No 181, pp. 47-65.
https://doi.org/10.15407/kvt181.01.043

14. Intelligent automatic control systems: I.M. Makarov and R.M. Yusupov (Eds). Moscow: State press for physics and mathematical literature, 1991, 576 p. (in Russian).

15. Gritsenko V.I. Intellectualization of information technologies. Nauka i tehnologiyi. Kyiv: V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, 1992, pp. 4-9 (in Ukrainian).

16. Skurikhin V.I., Nikulin V.N., Drymalyk Ya.P. Computing devices in contact welding schemes. Voprosy vychislitel’noy tehniki. Kyiv: State press for technical literature of the Ukrainian SSR, 1961, pp. 105-113 (in Russian).

17. Fel’dbaum A.A. Computing devices in automatic systems. Moscow: State press for physics and mathematical literature, 1959, 798 p. (in Russian).

18. Zhuk K.D., Pyatenko T.G., Skurikhin V.I. Problems of synthesis of control models in multiconnected automatic systems. Methods for mathematical modeling and the theory of electrical chain: Proc. of the workshop. Kyiv: Press of the AS of Ukrainian SSR, 1964, pp. 3!−!17 (in Russian).

19. Pukhov G.Ye., Zhuk K.D. Synthesis of multiconnected control systems by the method of inverse operators. Kyiv: Nauk. dumka, 1966, 218 p. (in Russian).

20. Lee T., Adams G., Gaines W. Computer process control: modeling and optimization. NY: Wiley, 1968, 312 p.

21. Lyubchik L.M. Inverse model control and subinvariance in linear discrete multivariable systems. 3rd European Control Conf. Roma, 1995, Vol. 4, part 2, pp. 3651!−!3659.

22. Krut’ko P.D. Inverse problems of control system dynamics: linear models. Moscow: Nauka, 1987, 304 p. (in Russian).

23. Yakubovixh Ye.D. Solving one problem of optimal control for a discrete linear system. Automatika i telemechanika. 1975, No 9, P. 73-79 (in Russian).

24. Vidyasagar M. Optimal rejection of persistent bounded disturbances. IEEE Trans. Autom. Control. 1986, V 31, pp. 527-535.
https://doi.org/10.1109/TAC.1986.1104315

25. Zhiteckij L.S. On a problem of synthesis of a program control system containing a digital computer. Avtomatyka. 1964, No 5, pp. 36-42 (in Ukrainian).

26. Zhiteckij L.S. Problems of dynamic errors compensation in digital program control systems: PhD Thesis. Kyiv, 1968, 186 p. (in Russian).

27. Astr!ö!m K.J., Hagander P., Sternby J. Zeros of sampled systems. Automatica. 1984, Vol. 20, N 1, pp. 31-38.
https://doi.org/10.1016/0005-1098(84)90062-1

28. Zhiteckij L.S. On the invariance of sampled combined program systems. Avtomatyka. 1967, No 6, pp. 83-85 (in Ukrainian).

29. Gross E., Tomizuka M. Experimental flexible beam tip tracking control with a truncated series approximation in uncancelable inverse dynamics. IEEE Trans. on Control Systems Technology. 1994, No 2(4), pp. 382-391.
https://doi.org/10.1109/87.338659

30. Skurikhin V.I., Zhiteckij L.S., Protsenko N.M. Iterative table automata. Kyiv: Nauk. dumka, 1977, 165 p. (in Russian).

31. Bondarko V.A. Adaptive suboptimal control of solutions of linear difference equations. Doklady AN SSSR. 1983, No 2, pp. 301-303 (in Russian).

32. Zhiteckij L.S. Adaptive control of systems subjected to bounded disturbances. Bounding ap-proaches to system identification: M. Milanese etc. (Eds.). New York, London: Plenum Press, 1996, Chapt. 24, pp. 383-407.
https://doi.org/10.1007/978-1-4757-9545-5_24

33. Feng G.A robust discrete-time direct adaptive control algorithm. Systems and Control Letters. 1994, V. 22, pp. 203-208.
https://doi.org/10.1016/0167-6911(94)90014-0

34. Zhitetskij L. S. Adaptive control under conditions of the presence of disturbances: an identification approach. Problemy Upravleniya i Informatiki. 1996. N 6, P. 66 – 77.

35. Suarez D.A., Lozano R. Adaptive control of nonminimum phase systems subject to unknown bounded disturbances. IEEE Trans. Automat. Control. 1996, No 12, pp. 1830-1836.
https://doi.org/10.1109/9.545752

36. Zhiteckij L.S. Adaptive control of nonminimum phase systems in the presence of bounded disturbance with unknown bound. Proc. 3rd European Control Conf. (Roma, Italy, 5-8 Sept., 1995), 1995, V 3, pp. 891-896.

37. Zhiteckij L.S. Solution of dissipativity problem for adaptive control system of nonminimum phase plant based on set-membership estimation method. Journal of Automation and Information Sciences. 2001, No 33, pp. 59-69.
https://doi.org/10.1615/JAutomatInfScien.v33.i9.40

38. Zhitetskij L.S. Robustness conditions of adaptive control systems with parametric and nonparametric uncertainties. Journal of Automation and Information Sciences. 1997, No 3, pp. 41-51.

39. Kreisselmeier G., Anderson B.D.O. Robust model reference 8adaptive control. IEEE Trans. utomat. Control. 1986, AC-31, No 2, pp. 127-133.
https://doi.org/10.1109/TAC.1986.1104217

40. Zhiteckij L.S., Skurikhin V.I., Tyupa O.V, Sapunova N.A. Adaptive discrete-time PID control algorithm for controlling infinite-dimensional systems. Proc. European Control Conference ECC-2001 (Porto, Portugal, September 4-7, 2001), 2001, pp. 184-189.
https://doi.org/10.23919/ECC.2001.7075903

41. Zhiteckij L.S., Skurikhin V. I., Tyupa O. V Tuning and self-tuning of discrete-time PID controllers based on model reduction approach. Proc. IFAC Workshop on Digital Control: Past, Present and Future of PID Control (Terrassa, Spain, April 5-7, 2000), 2000, pp. 167-172.

42. Zhiteckii L.S. Robust control of some classes of nonlinear discrete-time plants using linear controllers. Journal of Automation and Information Sciences. 2016, No 48, pp. 383-407.
https://doi.org/10.1615/JAutomatInfScien.v48.i2.50

43. Zhiteckij L.S. Singularity-free stable adaptive control of a class of nonlinear discrete-time systems. Proc. 15th IFAC World Congress (Barcelona, Spain, July 21-26, 2002), 2002, pp. 475-480.
https://doi.org/10.3182/20020721-6-ES-1901.01065

44. Skurikhin V.I., Zhitetskij L.S. Control of thermo- and mass exchange processes by using of adjusted models: practical examples. Upravla!û!!ŝ!ie sistemy i ma!š!iny. 2002, No 6, pp. 77-84 (in Russian).

45. Zhiteckii L.S., Azarskov V.N., Nikolaienko S.A., Solovchuk K.Yu. Some features of neural networks as nonlinearly parameterized models of unknown systems using an online learning algorithm. Journal of Applied Mathematics and Physics. Jan. 2018, No 6, pp. 247-263.
https://doi.org/10.4236/jamp.2018.61024

Received 21.03.2022