Category: Issue 4 (214)

Issue 4 (214), article 5

DOI:https://doi.org/10.15407/kvt214.04.074

Cybernetics and Computer Engineering, 2023, 4(214)

Kutsiak О.А.¹, PhD (Engineering),
Acting Head of the Department of Bioelectrical Control
& Medical Cybernetics
https://orcid.org/0000-0003-2277-7411,
e-mail: spirotech85@ukr.net

Vovk М.І.¹, PhD (Biology), Senior Researcher,
Leading Researcher of the Department of Bioelectrical Control
& Medical Cybernetics
https://orcid.org/0000-0003-4584-9553,
e-mail: imvovk3940@gmail.com

Matsaienko A.M.², PhD (Engineering),
Senior Lecturer
https://orcid.org/0000-0003-1149-7318,
e-mail: matsaenko2007@ukr.net

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

²Kruty Heroes Military Institute of Telecommunications
and Information Technology

INFORMATION TECHNOLOGY FOR EFFICIENT RECOVERY/CORRECTION OF MUSCLE ACTIVITIES FOR MOTOR TASK PERFORMANCE

Introduction. The conditions of wartime and post-war state call for priority requirements for development and utilisation of new information technologies for recovery/correction of motor functions. The major ones are personalisation, mobility, efficiency, ease of implementations both for in- and out-patients.

The purpose of the paper is to consider the theoretical and practical foundations of synthesis of the muscle activity recovery/correction technology for the performance of a motor task with the limbs using a digital-analog device of programmed myoelectric stimulation “MioAktyvSyntez-4”.

Results. The theoretical and practical foundations of synthesis of the information technology, which satisfies the main requirements – personalization, efficiency, mobility, ease of use both in clinical and non-clinical conditions, for recovery/correction of muscle activity to perform a motor task by limbs are developed. The technology is implemented by a new class of digital-analog multi-channel programmed stimulators – four-channel programmed electromyostimulator “MioAktyvSyntez-4”. The device is designed to perform a certain task by movements of limbs’, as well as fine motor skills of the hand to recover the oral speech.

The structural and functional model of the electromyostimulator “MioAktyvSyntez-4” is considered. The main functional units of the device are given and their implementation is determined: unit for selecting the stimulation channels and unit for synthesis of stimulation programs are digital, and the stimulation unit and user interface unit are analog. The use of programmable logic is chosen for processing the information in digital form. The basis of certain algorithm for selecting the stimulation channels for forming the stimulation programs – the truth tables are considered. The structural and functional scheme of the technical implementation of formation of limbs’ movements with the digital-analog device “MioAktyvSyntez-4” is considered.

Conclusion. Further research is aimed at retrofitting the “MioAktyvSyntez-4” type devices with modern interfaces, means of control and diagnostics in order to improve ease of utilisation and efficiency of personalised recovery/correction of the movement of the limbs. This is of paramount importance after military and civilian injuries, in adults and children, during the wartime and in post-war state.

Keywords: information technology, algorithm, programmed module, personalised control, muscle activity, programmable myostimulator, information processing, digital medical data, digital-analog implementation, motor task, motor model, operative adjustment

Download full text!

REFERENCES

1. Vovk M.I., Horbanov V.M., Ivanov V.V., Kutsiak O.A., Matsaienko A.M., Shevchenko A.B. Information technology for personalized control of the coordination of cyclical movements of the limbs. Control Systems and Computers. 2022, No 4, pp. 54-63.
https://doi.org/10.15407/csc.2022.04.054

2. 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, № 1(91),
https://doi.org/10.15407/scin16.01.057

3. Patent. A method of treating speech disorders / М.І. Vovk, Ye.B. Halian, О.М. Pidopryhora (Ukraine); № 111388; publshed 25.04.2016, Bulletin no 18 (in Ukrainian).

4. Enraf Nonius Endomed 482. URL: https://partner.enraf-nonius.org/files/Catalogues_ Brochures_Leaflets/Enraf-Nonius_electro/Enraf-Nonius_Endomed_482B_EN.pdf (Last accessed: 01.09.2023).

5. Enraf Nonius Myomed 134. URL: https://partner.enraf-nonius.org/files/Catalogues_ Brochures_Leaflets/Enraf-Nonius_electro/Enraf-Nonius_Myomed_134_EN.pdf/ (Last accessed: 01.09.2023).

6. Schauer T., Negaard N.-O., Behling C. RehaStimTM Stimulation Device. Description and Protocol. 2009. URL: https://hasomed.de/wp-content/uploads/hasomed-fileadmin/ RehaMove/ ScienceMode/science_mode_200909.pdf (access date: 01.09.2023).

7. Myostimulator Compex SP 4.0 (Switzerland). URL: https://www.manualslib.com/products/Compex-Sp-4-0-4158619.html (access date: 01.09.2023).

8. Cuesta-Gómez A. et al. The Use of Functional Electrical Stimulation on the Upper Limb and Interscapular Muscles of Patients with Stroke for the Improvement of Reaching Movements: A Feasibility Study. Front. Neurol. 2017. Vol. 8.
https://doi.org/10.3389/fneur.2017.00186

9. Trout M.A. et al. A portable, programmable, multichannel stimulator with high compliance voltage for noninvasive neural stimulation of motor and sensory nerves in humans. Sci Rep. 2023. Vol. 13.
https://doi.org/10.1038/s41598-023-30545-8

10. Li X., Zhong S., Morizio J. 16‑Channel biphasic current‑mode programmable charge balanced neural stimulation. BioMed Eng OnLine. 2017. Vol. 16.
https://doi.org/10.1186/s12938-017-0385-0

11. Ferrante S. et al. A Personalized Multi-Channel FES Controller Based on Muscle Synergies to Support Gait Rehabilitation after Stroke. Front. Neurosci. 2016. Vol. 10.
https://doi.org/10.3389/fnins.2016.00425

12. Schick T. et al. Efficacy of Four-Channel Functional Electrical Stimulation on Moderate Arm Paresis in Subacute Stroke Patients-Results from a Randomized Controlled Trial. Healthcare. 2022. Vol 10(4).
https://doi.org/10.3390/healthcare10040704

13. Subir Kumar Sarkar et al. Foundation of Digital Electronics and Logic Design. CRC Press, 2014. 372 p. URL: https://72arkarcy.files.wordpress.com/2016/09/foundation-of-digital-electronics-and-logic-design-2014.pdf (access date: 01.09.2023).

14. Patent. Electric stimulator / L.S. Aleev et al. (Ukraine); № 32376; publshed 12.05.2008, Bulletin no 9 (in Ukrainian).

15. Harris D., Harris S.L. Digital Design and Computer Architecture. ‎ARM Edition, 2016. 720 p.
https://doi.org/10.1016/B978-0-12-800056-4.00006-6

16. Hackworth J.R., Hackworth F.D. Programmable Logic Controllers: Programming Methods and Applications. URL: https://www.etf.ues.rs.ba/~slubura/Procesni%20 racunari/Programmable%20Logic%20Controllers%20Programming%20Methods.pdf (access date: 01.09.2023).

17. Wassell I.J. Digital Electronics. Part I – Combinational and Sequential Logic. URL: https://www.cl.cam.ac.uk/teaching/0708/DigElec/Digital_Electronics_pdf.pdf (access date: 01.09.2023).

Received 04.09.2023

Issue 4 (214), article 4

DOI:https://doi.org/10.15407/kvt214.04.054

Cybernetics and Computer Engineering, 2023, 4(214)

Aralova N.I.¹, DSc (Engineering), Senior Researcher,
Senior Researcher of the Department of Optimization of Controlled Processes,
https://orcid.org/0000-0002-7246-2736,
e-mail: aralova@ukr.net

Radziejowski P.A.², DSc (Biology), Professor,
Professor of the Educational Studies Department
https://orcid.org/0000-0001-8232-2705,
e-mail: p.radziejowski@wseit.edu.pl

Radziejowska M.P.³, DSc (Biology), Professor,
Professor of the Management Faculty, Department of Innovations
and Safety Management Systems
https://orcid.org/0000-0002-9845-390X,
e-mail: maria.radziejowska@pcz.pl

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

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

²Kazimiera Milanowska College of Education and Therapy,
22,Grabowa str., 61-473, Poznań, Poland

³Czestochowa University of Technology
19b, Armii Krajowej str., 42-200, Częstochowa, Poland

INTELLIGENT DECISION-MAKING SUPPORT TECHNOLOGIES REGARDING THE OPTIMIZATION OF THE PHYSICAL TRAINING
OF MILITARY SERVICEMEN

Introduction. The NATO Medical Doctrine and the Military Medical Doctrine of Ukraine emphasize the need to apply scientific approaches to health care, physical training, and supporting special operations. Of course, the extreme conditions of professional activity require the personnel to have appropriate training and the ability to adapt. Professional selection and training should be, on the one hand, scientifically based and objective, and on the other hand, using an individual approach, should be as effective as possible. Currently, this is impossible without the use of information technologies.

The purpose of the paper is to develop intelligent technology on the basis of mathematical models of the body’s functional systems, to support decision-making regarding the optimization of physical training of military personnel

Methods. Mathematical modeling methods, numerical optimization methods

Results. An intelligent technology has been developed to support decision-making regarding the optimization of the physical training of military personnel, which includes a complex of mathematical, algorithmic and software for assessing the current state and forecasting the functional state of military personnel. Mathematical support includes mathematical models of regulation of oxygen regimes of the human body, transport and mass exchange of respiratory gases in the human body, functional self-organization of the respiratory system and blood circulation, heat exchange in the human body, immune response, and the interaction and mutual influence of these systems. If there is a suitable array of personal data, it can be used for individual planning of physical training of personnel.

Keywords: optimizing the physical training of servicemen, functional respiratory system, extreme conditions of professional military activity, adaptation of the body of a serviceman.

Download full text!

REFERENCES

1. Platonov V.N. The system of training athletes in Olympic sports. The general theory and its practical applications: учеб. for students of physics universities. education and sports. K.: Olympic literature, 2004, 808 p.

2. Military medical doctrine of Ukraine. Government courier. 07.11.2018. No. 209

3. NATO standard AJP-4.10 allied joint doctrine for medical support Edition C Version 1. September 2019. North Atlantic treaty organization. Allied Joint Publication. Published by the NATO standardization office (NSO), 124 р.

4. Shekera O.H. Views on reforming the system of medical support of the armed forces of Ukraine. Medical support of an anti-terrorist operation: scientific-organizational and medical-social aspects: a collection of scientific works / by General. ed. academicians of the National Academy of Sciences of Ukraine V. I. Tsimbalyuk and A. M. Serdyuk – K.: State Enterprise “Prioritety” Scientific Center, 2016, 316 p. ISBN 978-617-7288-43-4 P. 270-271.

5. Galushka A.M., Zahovskyi V.O., Livinskyi V.G. Medical support of the armed forces of Ukraine: experience, achievements, prospects. Ukrainian Journal of Military Medicine. 2021, 1, T.2/
https://doi.org/10.46847/ujmm.2021.1(2)

6. Decree of the President of Ukraine dated September 24, 2015 No. 555/2015 “On the decision of the National Security and Defense Council of Ukraine dated September 2, 2015 “On the new edition of the Military Doctrine of Ukraine”.

7. Resolution of the Cabinet of Ministers of Ukraine dated October 1, 2018 o 910 “On approval of the Military Medical Doctrine of Ukraine”.

8. Order of the General Staff of the Armed Forces of Ukraine dated March 12, 2020 No. 100 “On Approval of the Strategy for the Development of the Medical Forces of the Armed Forces of Ukraine until 2035”

9. The doctrine “Medical Forces of the Armed Forces of Ukraine” was approved by the Commander-in-Chief of the Armed Forces of Ukraine on November 13, 2020.

10. Verba A.V., Zhakovskyi V.O., Livinskyi V.G. Medical support of the Armed Forces of Ukraine: state and views on development prospects. Monograph. Kyiv: Lyudmila Publishing House. 2017, 420 p.

11. Khomenko I.P., Lurin I.A., Tsymbalyuk V.I., Zahovskyi V.O., Livinskyi V.G., Galushka A.M., Humenyuk K.V., Shvets A.V., Ivanko O. .M.Medical support of the Armed Forces of Ukraine during the anti-terrorist operation and the operation of the United Forces on the territory of Luhansk and Donetsk regions: a monograph in 3 parts. K.: Lyudmila Publishing House, 2020. Part I, 386 p.

12. Khomenko I.P., Lurin I.A., Tsymbalyuk V.I., Zahovskyi V.O., Livinskyi V.G., Galushka A.M., Humenyuk K.V., Shvets A.V., Ivanko O. M. Medical support of the Armed Forces of Ukraine during the anti-terrorist operation and the operation of the United Forces on the territory of Luhansk and Donetsk regions: a monograph in 3 parts. K.: Lyudmila Publishing House, 2020, Part II – 437 p.

13. Khomenko I.P., Lurin I.A., Tsymbalyuk V.I., Zahovskyi V.O., Livinskyi V.G., Galushka A.M., Humenyuk K.V., Shvets A.V., Ivanko O. M. Medical support of the Armed Forces of Ukraine during the anti-terrorist operation and the operation of the United Forces on the territory of Luhansk and Donetsk regions: a monograph in 3 parts. K.: Lyudmila Publishing House, 2020. Part III , 487 p.

14. Zhakhovskyi, V.O., Livinskyi, V.H. (2018) Yedynyi medychnyi prostir ta viiskova medytsyna : monohrafiia. [Unified medical space and military medicine]. Kyiv: Vydavnytstvo «Liudmyla». 336. (in Ukrainian).

15. Bilyi, V.Ya., Zhakhovskyi, V.O., & Aslanian S.A. (2021). Evoliutsiia systemy medychnoho zabezpechennia viisk pid chas boiovykh dii. Monohrafiia K. : «Vydavnytstvo Liudmyla». 348 p. (In Ukrainian)

16. Vaara J.P., Groeller H., Drain J., Kyröläinen H., Pihlainen K., Ojanen T., Connaboy C., Santtila M., Agostinelli P., Nindl B.C. Physical training considerations for optimizing performance in essential military tasks. Eur J Sport Sci. 2022 Jan;22(1):43-57. Epub 2021 Jun 3. PMID: 34006204.
https://doi.org/10.1080/17461391.2021.1930193

17. Smith C., Doma K., Heilbronn B., Leicht A. Effect of Exercise Training Programs on Physical Fitness Domains in Military Personnel: A Systematic Review and Meta-Analysis. Mil Med. 2022, Aug 25;187(9-10):1065-1073. PMID: 35247052
https://doi.org/10.1093/milmed/usac040

18. Vaara J.P., Kalliomaa R., Hynninen P., Kyröläinen H. Physical Fitness and Hormonal Profile During an 11-Week Paratroop Training Period. J Strength Cond Res. 2015, Nov;29 Suppl 11:S163-7. PMID: 26506182.
https://doi.org/10.1519/JSC.0000000000001033

19. Santtila M., Pihlainen K., Viskari J., Kyröläinen H. Optimal Physical Training During Military Basic Training Period. J Strength Cond Res. 2015, Nov29, Suppl 11:S154-7. PMID: 26506180.
https://doi.org/10.1519/JSC.0000000000001035

20. Haddock C.K., Poston W.S., Heinrich K.M., Jahnke S.Aю, Jitnarin N. The Benefits of High-Intensity Functional Training Fitness Programs for Military Personnel. Mil Med. 2016, Nov, 181(11):e1508-e1514. PMID: 27849484; PMCID: PMC5119748
https://doi.org/10.7205/MILMED-D-15-00503

21. Poston W.S., Haddock C.K., Heinrich K.M., Jahnke S.A., Jitnarin N., Batchelor D.B. Is High-Intensity Functional Training (HIFT)/CrossFit Safe for Military Fitness Training? Mil Med. 2016, Jul, 181(7):627-37. PMID: 27391615; PMCID: PMC4940118.
https://doi.org/10.7205/MILMED-D-15-00273

22. Gibala M.J., Gagnon P.J., Nindl B.C. Military Applicability of Interval Training for Health and Performance. J Strength Cond Res. 2015, Nov, 29 Suppl 11:S40-5. PMID: 26506197.
https://doi.org/10.1519/JSC.0000000000001119

23. Gilchrist J., Jones B.H., Sleet D.A., Kimsey C.D; CDC. Exercise-related injuries among women: strategies for prevention from civilian and military studies. MMWR Recomm Rep. 2000, Mar, 31;49(RR-2):15-33. PMID: 15580730.

24. Givens A.C., Bernards J.R., Kelly K.R. Characterization of Female US Marine Recruits: Workload, Caloric Expenditure, Fitness, Injury Rates, and Menstrual Cycle Disruption during Bootcamp. Nutrients. 2023, Mar, 28;15(7):1639. PMID: 37049480; PMCID: PMC10096956.
https://doi.org/10.3390/nu15071639

25. Moran D.S., Israeli E., Evans R.K., Yanovich R., Constantini N., Shabshin N., Merkel D., Luria O., Erlich T., Laor A., Finestone A. Prediction model for stress fracture in young female recruits during basic training. Med Sci Sports Exerc. 2008, Nov, 40(11 Suppl):S636-44. PMID: 18849871.
https://doi.org/10.1249/MSS.0b013e3181893164

26. Yanovich R., Merkel D., Israeli E., Evans R.K., Erlich T., Moran D.S. Anemia, iron deficiency, and stress fractures in female combatants during 16 months. J Strength Cond Res. 2011, Dec, 25(12):3412-21.
https://doi.org/10.1519/JSC.0b013e318215f779

27. Greeves J. P. Physiological Implications, Performance Assessment and Risk Mitigation Strategies of Women in Combat-centric Occupations. Journal of Strength and Conditioning Research 29.11, Suppl. (2015): S. S94-S100.
https://doi.org/10.1519/JSC.0000000000001116

28. Szivak T.K., Mala J., Kraemer W.J. Physical performance and integration strategies for women in combat arms. Strength and conditioning journal. 2015, 37(4), pp. 20-29.
https://doi.org/10.1519/SSC.0000000000000137

29. O’Leary T.J., Saunders S.C., McGuire S.J., Venables M.C., Izard R.M. Sex Differences in Training Loads during British Army Basic Training. Med Sci Sports Exerc. 2018, Dec, 50(12):2565-2574. PMID: 30048410
https://doi.org/10.1249/MSS.0000000000001716

30. Jurvelin H., Tanskanen-Tervo M., Kinnunen H., Santtila M., Kyröläinen H. Training Load and Energy Expenditure during Military Basic Training Period. Med Sci Sports Exerc. 2020, Jan, 52(1):86-93. PMID: 31343524.
https://doi.org/10.1249/MSS.0000000000002092

31. Richmond V.L, Carter J.M., Wilkinson D.M., Homer F.E., Rayson M.P., Wright A., Bilzon J.L. Comparison of the physical demands of single-sex training for male and female recruits in the British Army. Mil Med. 2012, Jun, 177(6):709-15. PMID: 22730848.
https://doi.org/10.7205/MILMED-D-11-00416

32. Blacker S.D., Wilkinson D.M., Rayson M.P. Gender differences in the physical demands of British Army recruit training. Mil Med. 2009, Aug, 174(8):811-6. PMID: 19743735
https://doi.org/10.7205/MILMED-D-01-3708

33. Orme G.J., Kehoe E.J. Women and Men Together in Recruit Training. Mil Med. 2018, May, 1;183(5-6):e147-e152. PMID: 29425352
https://doi.org/10.1093/milmed/usx098

34. Varley-Campbell J., Cooper C., Wilkerson D., Wardle S., Greeves J., Lorenc T. Sex-Specific Changes in Physical Performance Following Military Training: A Systematic Review. Sports Med. 2018, Nov, 48(11):2623-2640. PMID: 30232790; PMCID: PMC6182553.309. 30.
https://doi.org/10.1007/s40279-018-0983-4

35. Gibala M.J., Gillen J.B., Percival M.E. Physiological and health-related adaptations to low-volume interval training: influences of nutrition and sex. Sports Med. 2014, Nov, 44 Suppl 2(Suppl 2):S127-37. PMID: 25355187; PMCID: PMC4213388.
https://doi.org/10.1007/s40279-014-0259-6

36. Dada E.O., Anderson M.K., Grier T., Alemany J.A., Jones B.H. Sex and age differences in physical performance: A comparison of Army basic training and operational populations. J Sci Med Sport. 2017, Nov, 20 Suppl 4:S68-S73. PMID: 29100826.
https://doi.org/10.1016/j.jsams.2017.10.002

37. Santtila M., Pihlainen K., Vaara J., Tokola K., Kyröläinen H. Changes in physical fitness and anthropometrics differ between female and male recruits during the Finnish military service. BMJ Mil Health. 2022, Oct, 168(5):337-342. Epub 2020 Sep 30. PMID: 32999088.
https://doi.org/10.1136/bmjmilitary-2020-001513

38. Santtila M., Pihlainen K., Vaara J., Nindl B.C., Heikkinen R., Kyröläinen H. Aerobic fitness predicted by demographics, anthropometrics, health behaviour, physical activity and muscle fitness in male and female recruits entering military service. BMJ Mil Health. 2022, Oct, 21:e002267. Epub ahead of print. PMID: 36270735.
https://doi.org/10.1136/military-2022-002267

39. Wentz L, Liu P.Y., Haymes E., Ilich J.Z. Females have a greater incidence of stress fractures than males in both military and athletic populations: a systemic review. Mil Med. 2011, Apr, 176(4):420-30. PMID: 21539165.
https://doi.org/10.7205/MILMED-D-10-00322

40. Bell N.S., Mangione T.W., Hemenway D., Amoroso P.J., Jones B.H. High injury rates among female army trainees: a function of gender? Am J Prev Med. 2000, Apr, 18(3 Suppl):141-6. PMID: 10736550.
https://doi.org/10.1016/S0749-3797(99)00173-7

41. Anderson M.K., Grier T., Dada E.O., Canham-Chervak M., Jones B.H. The Role of Gender and Physical Performance on Injuries: An Army Study. Am J Prev Med. 2017, May, 52(5):e131-e138. Epub 2016 Dec 21. PMID: 28012810.
https://doi.org/10.1016/j.amepre.2016.11.012

42. Yanovich R., Evans R., Israeli E., Constantini N., Sharvit N., Merkel D., Epstein Y., Moran D.S. Differences in physical fitness of male and female recruits in gender-integrated army basic training. Med Sci Sports Exerc. 2008, Nov, 40(11 Suppl):S654-9. PMID: 18849869.э
https://doi.org/10.1249/MSS.0b013e3181893f30

43. Daniels W.L., Wright J.E., Sharp D.S., Kowal D.M., Mello R.P., Stauffer R.S. The effect of two years’ training on aerobic power and muscle strength in male and female cadets. Aviat Space Environ Med. 1982, Feb, 53(2):117-21. PMID: 7059326.

44. Daniels W.L., Kowal D.M., Vogel J.A., Stauffer R.M. Physiological effects of a military training program on male and female cadets. Aviat Space Environ Med. 1979, Jun, 50(6):562-6. PMID: 475702.

45. Jacobson I.G., Donoho C.J., Crum-Cianflone N.F., Maguen S. Longitudinal assessment of gender differences in the development of PTSD among US military personnel deployed in EMBED support of the operations in Iraq and Afghanistan. J Psychiatr Res. 2015, Sep, 68:30-6. Epub 2015 Jun 1. PMID: 26228397.
https://doi.org/10.1016/j.jpsychires.2015.05.015

46. Blacker S.D., Wilkinson D.M., Rayson M.P. Gender differences in the physical demands of British Army recruit training. Mil Med. 2009, Aug, 174(8):811-6. PMID: 19743735.
https://doi.org/10.7205/MILMED-D-01-3708

47. Wood P.S., Grant C.C., du Toit P.J., Fletcher L. Effect of Mixed Basic Military Training on the Physical Fitness of Male and Female Soldiers. Mil Med. 2017, Jul, 182(7):e1771-e1779. PMID: 28810971.
https://doi.org/10.7205/MILMED-D-16-00218

48. Chikrii A.A. Conflict controlled processes. Boston; London; Dordrecht: Springer Science and Business Media, 2013, 424 p.

49. Bobryakova I.L. Sensitivity of the mathematical model and optimal regulation of the functional respiratory system. diss. Candidate of Physics and Mathematics Nauk, Kyiv, 2000. 179 p.

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

51. Kolchinskaya A.Z., Lauer N.V., Shkabara. Ye.A.. About the regulation of body oxygen regimes. Oxygen organism regime and its regulation. K.: Nauk.Dumka, 1966. P. 157-200.

52. Aralova А.А., Aralova N.I., Klyuchko O.M., Mashkin V.I., Mashkina I.V. Information system for the examination of organism adaptation characteristics of flight crews’ personnel. Electronics and control systems. 2018, 2, pp. 106-113.
https://doi.org/10.18372/1990-5548.52.11882

53. Onopchuk Yu.N. Homeostasis of functional respiratory system as a result of intersystem and system-medium informational interaction. Bioecomedicine. Uniform information space. Ed. by V. I. Gritsenko. Kyiv. Naukova dumka, 2001, pp. 59-84.

54. Onopchuk Yu. N.Homeostasis of the functional circulatory system as a result of intersystem and system-medium informational interaction. Bioecomedicine. Uniform information space. Ed. by V. I. Gritsenko. Kyiv, Naukova dumka, 2001, pp. 85-104.

55. Polynkevich K.B., Onopchuk Yu.N. Conflict situations at regulating of the main function of organism respiratory system and mathematical models of their resolution. Cybernetics. 1986, 3, pp. 100-104. (In Russian)
https://doi.org/10.1007/BF01069979

56. Filippov A.F. Differentional equations with discontinuous right-hand sides. Mathematics and Its Applications. Springer Dordrecht, 1988, 304 p.
https://doi.org/10.1007/978-94-015-7793-9

57. Ermakova I.Y. Mathematical modeling of human thermoregulation processes. M.: BLAME, 1987, 134 p.

58. Marchuk G.I. Mathematical models in immunology. M.: Nauka, ch. Ed. Phys.-Math. Lit, 1991, 304 p.

59. Shakhlina L.J.G. Medizinisch-biologische Grunglagendes sportlichen Trainings von Frauen. Bundesinstitut für Sportwissenschaft. Bonn: 2010, 322 p.

60. Aralova N.I., Shakhlina L.Ya.-G. The mathematical models of functional self-organization of the human respiratory system with a change pf the hormonal states of organism. Journal of Automation and Information Sciences. 2018, 3: 132-141.pages 49-59
https://doi.org/10.1615/JAutomatInfScien.v50.i5.50

61. Aralova N.I. Shakhlina L.Ya.-G. Futornyi S.M. Mathematical Model of High-Skilled Athlete’s Immune System. Journal of Automation and Information Sciences. 2019, pp. 56-67.
https://doi.org/10.1615/JAutomatInfScien.v51.i3.60

Received 28.08.2023

Issue 4 (214), article 3

DOI:https://doi.org/10.15407/kvt214.04.040

Cybernetics and Computer Engineering, 2023, 4(214)

Melnychenko A.S., PhD Student,
the Pattern Recognition Department
https://orcid.org/0009-0009-2445-8271
e-mail: toscha.1232013@gmail.com

Vodolazskyi Ye. V. , PhD Engineering,
Senior Researcher, the Pattern Recognition Department
https://orcid.org/0000-0003-3906-256X
e-mail: waterlaz@gmail.com

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

TEXTURE MISSING PARTS GENERATION BASED ON IMAGE STATISTICAL ANALYSIS

Introduction. Restoration of damaged images is a long lasting problem that currently does not have a generalized solution. Many methods which are being used nowadays are damage type specific, which means that for each case of damaged image an algorithm must be picked by a human. A state of the art generative algorithms, which may handle many of the damage types, still lack the precision and require huge training datasets. Thus an algorithm that is able to handle most common damage types and does not demand lots of time and computational power is still in need.

The purpose of the paper is to research the current state of the art algorithms that solve texture missing part generation problem as well as to propose a new method, which might provide both precision and ease of use for solving said problem for most of the damage types using the same approach.

Methods. Research and analytics are used for processing found literature on the topic to substantiate the main approaches and best practices for the solution of the texture missing parts generation problem. As for purposed method, Gibbs sampling is used as a means of generating missing pixels of the image. Some additional algorithms, which might be used to generate probabilistic distribution for sampler and the means of getting the pixel value from the sampling process, are mentioned in the article itself.

Results. State of the art approaches for solving texture missing parts generation are analyzed and compared. Main groups of generative, texture reparation, gradient filling and combined methods are described and compared. New method for generating missing parts of the texture based on statistical analysis of the scene images is proposed. The generation of the pixel values in said method is based on Gibbs sampling. The first results of purposed method with patch based probabilistic distribution generation are shown.

Conclusions. The proposed Gibbs sampling based method is able to provide results, which are comparable with those generated by other modern methods. As a future work, it is planned to develop new more sophisticated and precise patches matching algorithms as well as to research other methods of both generating probability distribution and gathering pixel value from the sampling process.

Keywords: Gibbs sampling, texture restoration, image restoration, patches matching

Download full text!

REFERENCES

1 Tien-Ying Kuo, Yu-Jen Wei, Po-Chyi Su and Tzu-Hao Lin. Learning-Based Image Damage Area Detection for Old Photo Recovery. Sensors. Vol. 22. No. 21. 2022. 8580.
https://doi.org/10.3390/s22218580

2 Peyman Milanfar. A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical. IEEE Signal Processing Magazine. Vol. 30. No. 1. 2013. P. 106-126.
https://doi.org/10.1109/MSP.2011.2179329

3 Pitas I., Venetsanopoulos A.N. Median Filters. Nonlinear Digital Filters. Vol 84. 1990. P. 63-116.
https://doi.org/10.1007/978-1-4757-6017-0_4

4 Kamel Boukerrou, Ludwik Kurz. Suppression of “salt and pepper” noise based on Youden designs. Information Sciences. Vol.110. No. 3-4. 1998. P. 217-235.
https://doi.org/10.1016/S0020-0255(98)00004-8

5 A. Polesel, G. Ramponi, V. J. Mathews. Image enhancement via adaptive unsharp masking. IEEE Transactions on Image Processing. Vol. 9, No. 3. 2000. P. 505-510.
https://doi.org/10.1109/83.826787

6 Rui Wang, Wei Li, Rui Li, Liang Zhang. Automatic blur type classification via ensemble SVM. Signal Processing: Image Communication. Vol. 71. 2019. P. 24-35.
https://doi.org/10.1016/j.image.2018.08.003

7 Yu Huihui, Li Daoliang, Chen Yingyi. A state-of-the-art review of image motion deblurring techniques in precision agriculture. Heliyon. Vol. 9. No. 6. 2023. P. e17332.
https://doi.org/10.1016/j.heliyon.2023.e17332

8 Norbert Wiener. Extrapolation, Interpolation, and Smoothing of Stationary Time Series. The MIT Press. New York. NY. USA. 1964.

9 W. H. Richardson, Bayesian-Based Iterative Method of Image Restoration. Journal of the Optical Society of America. Vol. 62, No. 1. 1972. P. 55-59.
https://doi.org/10.1364/JOSA.62.000055

10 L. B. Lucy. An iterative technique for the rectification of observed distributions. Astronomical Journal. Vol. 79. No. 6. 1974. P. 745-754.
https://doi.org/10.1086/111605

11 A. K. Katsaggelos, K. T. Lay. Maximum likelihood blur identification and image restoration using the EM algorithm. IEEE Transactions on Signal Processing. Vol. 39. No. 3. 1991. P. 729-733.
https://doi.org/10.1109/78.80894

12 Seung-Gu Kim. Explicit Solution of EM Algorithm in Image Deblurring: Image Restoration without EM iterations. Communications for Statistical Applications and Methods. Vol. 16. 2009. P. 409-419.
https://doi.org/10.5351/CKSS.2009.16.3.409

13 J. F. Cai, Hui Ji, Chaoqiang Liu, Z. Shen. Blind motion deblurring from a single image using sparse approximation. IEEE Conference on Computer Vision and Pattern Recognition. Miami. FL. USA. 2009. P. 104-111.

14 Dong Yang, Shiyin Qin. Restoration of Partial Blurred Image Based on Blur Detection and Classification. Journal of Electrical and Computer Engineering. 2016. P. 1-12.
https://doi.org/10.1155/2016/2374926

15 W. Dong, L. Zhang, G. Shi, X. Wu. Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization. IEEE Transactions on Image Processing. Vol. 20. No. 7. 2011. P. 1838-1857.
https://doi.org/10.1109/TIP.2011.2108306

16 Sudha Yadav, Charu Jain, Aarti Chugh. Evaluation of Image Deblurring Techniques. Vol. 139. No. 12. 2016. P. 32-36.
https://doi.org/10.5120/ijca2016909492

17 Shamik Tiwari and V. P. Shukla and Awantika Singh and S. R. Biradar. Review of Motion Blur Estimation Techniques. Journal of Image and Graphics. Vol. 1. No. 4. 2013. P. 176-184.
https://doi.org/10.12720/joig.1.4.176-184

18 D. Li, R. M. Mersereau, S. Simske. Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis. IEEE Geoscience and Remote Sensing Letters. Vol. 4. No. 3. 2007. P. 340-344.
https://doi.org/10.1109/LGRS.2007.895691

19 L. Yan, M. Jin, H. Fang, H. Liu, T. Zhang. Atmospheric-Turbulence-Degraded Astronomical Image Restoration by Minimizing Second-Order Central Moment. IEEE Geoscience and Remote Sensing Letters. Vol. 9. No. 4. 2012. P. 672-676.
https://doi.org/10.1109/LGRS.2011.2178016

20 Tristan Dagobert, Yohann Tendero, Stephane Landeau. Study of the Principal Component Analysis Method for the Correction of Images Degraded by Turbulence. Image Processing On Line. Vol. 8. 2018. P. 388-407.
https://doi.org/10.5201/ipol.2018.47

21 X. Hua, C. Pan, Y. Shi, J. Liu, H. Hong. Removing Atmospheric Turbulence Effects Via Geometric Distortion and Blur Representation. IEEE Transactions on Geoscience and Remote Sensing. Vol. 60. 2022. P. 1-13.
https://doi.org/10.1109/TGRS.2020.3043627

22 A. D. Dongare, R. R. Kharde, A. D. Kachare. Introduction to Artificial Neural Network. International Journal of Engineering and Innovative Technology. Vol. 2. No. 1. 2012.

23 Nantheera Anantrasirichai. Atmospheric turbulence removal with complex-valued convolutional neural network. Pattern Recognition Letters. Vol. 171. 2023. P. 69-75.
https://doi.org/10.1016/j.patrec.2023.05.017

24 Jiuming Cheng et al. Restoration of Atmospheric Turbulence-Degraded Short-Exposure Image Based on Convolution Neural Network. Photonics. Vol. 10. 2023. P. 666.
https://doi.org/10.3390/photonics10060666

25 M. Asim, F. Shamshad, A. Ahmed. Blind Image Deconvolution Using Deep Generative Priors. IEEE Transactions on Computational Imaging. Vol. 6. 2020. P. 1493-1506.
https://doi.org/10.1109/TCI.2020.3032671

26 Qian Zhao, Hui Wang, Zongsheng Yue, Deyu Meng. A deep variational Bayesian framework for blind image deblurring. Knowledge-Based Systems. V. 249. 2022. P. 109008.
https://doi.org/10.1016/j.knosys.2022.109008

27 Amudhavel Jayavel et al. Improved Classification of Blurred Images with Deep-Learning Networks Using Lucy-Richardson-Rosen Algorithm. Photonics. Vol. 10. No. 4. 2023.
https://doi.org/10.3390/photonics10040396

28 C. K. Liang, L. W. Chang, H. H. Chen. Analysis and Compensation of Rolling Shutter Effect. IEEE Transactions on Image Processing. Vol. 17. No. 8. 2008. P. 1323-1330.
https://doi.org/10.1109/TIP.2008.925384

29 D. Bradley, B. Atcheson, I. Ihrke, W. Heidrich. Synchronization and rolling shutter compensation for consumer video camera arrays. Proceedings of the International Workshop on Projector-Camera Systems. 2009.
https://doi.org/10.1109/CVPRW.2009.5204340

30 Simon Baker, Eric Bennett, Sing Bing Kang, Richard Szeliski. Removing rolling shutter wobble. The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition. San Francisco. CA. USA. 2010. P. 2392-2399.
https://doi.org/10.1109/CVPR.2010.5539932

31 M. Grundmann, V. Kwatra, D. Castro, I. Essa. Calibration-free rolling shutter removal. 2012 IEEE International Conference on Computational Photography. Seattle. WA. USA. 2012. P. 1-8.
https://doi.org/10.1109/ICCPhot.2012.6215213

32 Y. Lao, O. Ait-Aider. A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City. UT. USA. 2018. P. 4795-4803.
https://doi.org/10.1109/CVPR.2018.00504

33 P. Liu, Z. Cui, V. Larsson, M. Pollefeys. Deep Shutter Unrolling Network. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle. WA. USA. 2020. P. 5940-5948.
https://doi.org/10.1109/CVPR42600.2020.00598

34 B. Fan, Y. Mao, Y. Dai, Z. Wan, Q. Liu. Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver. BC. Canada. 2023. P. 5671-5681.
https://doi.org/10.1109/CVPR52729.2023.00549

35 Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, Coloma Ballester. Image inpainting. Proceedings of the 27th annual conference on Computer graphics and interactive techniques. 2000. USA. P. 417-424.
https://doi.org/10.1145/344779.344972

36 M. Ashikhmin. Synthesizing natural textures. ACM Symposium on Interactive 3D Graphics. 2001. P. 217-226.
https://doi.org/10.1145/364338.364405

37 A. Criminisi, P. Perez, K. Toyama. Region filling and object removal by exemplar-based image inpainting. IEEE Transactions on Image Processing. Vol. 13. No. 9. 2004. P. 1200-1212.
https://doi.org/10.1109/TIP.2004.833105

38 Zihan Liu. Literature Review on Image Restoration. Journal of Physics: Conference Series. Vol. 2386. P. 012041.
https://doi.org/10.1088/1742-6596/2386/1/012041

39 Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, Gang Hua. CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training. 2017.

40 Diederik P. Kingma, Max Welling. An Introduction to Variational Autoencoders. 2019.

41 S. Geman, D. Geman. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. PAMI-6, No. 6. 1984. P. 721-741.
https://doi.org/10.1109/TPAMI.1984.4767596

42 Georgy L. Gimel’farb. Image Textures and Gibbs Random Fields. Springer Dordrecht. 1999.
https://doi.org/10.1007/978-94-011-4461-2

Received 03.10.2023

Issue 4 (214), article 2

DOI:https://doi.org/10.15407/kvt214.04.024

Cybernetics and Computer Engineering, 2023, 4(214)

Popov I.V., PhD Student,
Junior Researcher of the Intelligent Control Department
https://orcid.org/0009-0009-7961-9431,
e-mail: popigor7@gmail.com

Lakhtyr D.A., PhD Student,
Juniour Researcher of the Intelligent Control Department,
https://orcid.org/0009-0003-8696-466X,
e-mail: danilkovnir@gmail.com

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

ALGORITHMS AND METHODS FOR SURFACE RECONSTRUCTION OF FREEFORM SHAPE INFRASTRUCTURE OBJECTS FOR BUILDING INFORMATION MODELLING

Introduction. The construction industry actively uses new technologies and tools, in particular, the technologies of intellectualization of management of data collection, using various types of unmanned aerial vehicles (UAVs). The development of these technologies is not an exception, on the contrary, it is actively used, both as part of the building information modeling system as well as without full integration into similar information complexes. To improve the effectiveness of quality control and monitoring, methods of using drones of various types to collect data for creating BIM models have been created. 3D models of buildings are created with the help of drones using active LIDAR (Light Identification, Detection and Ranging) sensors, which require the use of surface reconstruction algorithms from point clouds. The article provides an attempt to research algorithms, combinations of algorithms, and approaches to their combination when applied to intelligent systems based on UAVs.

The purpose of the paper is to investigate surface reconstruction algorithms from a cloud of points obtained using methods of laser terrain scanning and analysis of visual data obtained from an unmanned aerial vehicle and to determine the conditions for their effective combined use for building information modeling technology and approaches to their combination when applied by intelligent systems based on UAVs.

Justification of the criteria for choosing combinations of algorithms and assessment of the perspective of their further research and improvement for tasks related to the features of the use of various types of unmanned aerial vehicles as a means of creating multidimensional models of building and infrastructure objects.

The results. Algorithms for the reconstruction of surfaces from a cloud of points obtained using the methods of laser terrain scanning and analysis of visual data obtained from an unmanned aerial vehicle were studied. The conditions for their effective combined use for building information modeling technology and approaches to their combination when applied to intelligent systems based on UAVs were defined.

The criteria for selecting combinations of algorithms were substantiated and the prospects of their further research and improvement were assessed for tasks related to the specifics of using various types of unmanned aerial vehicles as a means of creating multidimensional models of building and infrastructure objects.

Conclusions. The use of a single surface reconstruction algorithm to create multidimensional BIM simulation models cannot be considered optimal. The conducted review shows that for the optimal solution of this problem, it is necessary to continue research in this direction. This will avoid excessive demands on the computing power of BIM systems when modeling a geometric shape while preserving properties and details with minimal data loss.

Keywords: unmanned aerial vehicle, building information modeling, LIDAR, surface reconstruction, visual data, digital object models.

Download full text!

REFERENCES

1 Wang, R. 3D building modeling using images and LiDAR: a review. International Journal of Image and Data Fusion, 2013 4(4), pp. 273-292,
https://doi.org/10.1080/19479832.2013.811124

2 O.V.Levchenko, BIM – information modeling of buildings in Autodesk software products. Modern problems of architecture and urban planning 2010 25, https://repositary.knuba.edu.ua/ erver/api/core/bitstreams/ecbce3ab-efd6-4ee5-8ff0-4cab1b380f32/content (in Ukrainian)

3 Volk R., Stengel, J., & Schultmann, F. Building Information Modeling (BIM) for existing buildings – Literature review and future needs. Automation in Construction, 2014 38, pp. 109-127. https://www.researchgate.net/publication/59518042_Building_ nformation_Modeling_BIM_for_existing_buildings-Literature_review_and_future_needs_Autom_Constr_38_March_2014_109-127
https://doi.org/10.1016/j.autcon.2013.10.023

4 An introduction to Building Information Modelling (BIM). The Institution of Structural Engineers, February 2021 https://www.istructe.org/IStructE/media/Public/n_introduction_o-Building_Information_Modelling_BIM.pdf

5 Wang, J., Sun, W., Shou, W., Wang, X., Wu, C., Chong, H.-Y., Sun, C. Integrating BIM and LiDAR for Real-Time Construction Quality Control. Journal of Intelligent & Robotic Systems, 2014 79(3-4), pp. 417-432,
https://doi.org/10.1007/s10846-014-0116-8

6 McCabe, B. Y., Hamledari, H., Shahi, A., Zangeneh, P., & Azar, E. R. Roles, Benefits, and Challenges of Using UAVs for Indoor Smart Construction Applications. Computing in Civil Engineering 2017,
https://doi.org/10.1061/9780784480830.043

7 López, F., Lerones, P., Llamas, J., Gómez-García-Bermejo, J., & Zalama, E. A Review of Heritage Building Information Modeling (H-BIM). Multimodal Technologies and Interaction, 2018 2(2), 21,
https://doi.org/10.3390/mti2020021

8 Wang, R., Peethambaran, J., & Chen, D. LiDAR Point Clouds to 3-D Urban Models: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018 11(2), pp. 606-627,
https://doi.org/10.1109/JSTARS.2017.2781132

9 Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. Reconstruction and representation of 3D objects with radial basis functions. SIGGRAPH ’01: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques 2001, pp. 67-76
https://doi.org/10.1145/383259.383266

10 M. Bolitho, M. Kazhdan, R. Burns, and H. Hoppe, Multilevel streaming for out-of-core surface reconstruction. in Proc. 5th Eurograph. Symp. Geom. Process., Aire-la-Ville, Switzerland, 2007, pp. 69-78.

11 Manson, J., Petrova, G., & Schaefer, S. Streaming Surface Reconstruction Using Wavelets. Computer Graphics Forum, 2008 27(5), pp. 1411-1420,
https://doi.org/10.1111/j.1467-8659.2008.01281.x

12 M. Kazhdan, Reconstruction of solid models from oriented point sets. in Proc. 3rd Eurograph. Symp. Geom. Process., Aire-la-Ville, Switzerland, 2005, p. 73

13 Y. Ohtake, A. Belyaev, M. Alexa, G. Turk, and H.-P. Seidel, Multilevel partition of unity implicits. ACM Trans. Graph., vol. 22, no. 3, 2003 pp. 463-470,
https://doi.org/10.1145/882262.882293

14 Nina Amenta, Sunghee Choi, Ravi Krishna, The Power crust. SMA ’01: Proceedings of the sixth ACM symposium on Solid modeling and applications, 2001 pp. 249-266,
https://doi.org/10.1145/376957.376986

15 Dobrina Boltcheva, Bruno Lévy. Simple and Scalable Surface Reconstruction. (Research Report) LORIA – Université de Lorraine; INRIA Nancy. 2016

Received 14.09.2023

Issue 4 (214), article 1

DOI:https://doi.org/10.15407/kvt214.04.004

Cybernetics and Computer Engineering, 2023, 4(214)

Gladun A.Y.¹, PhD (Engineering), Associate Professor,
Leading Researcher of the Department of Complex Research 
of Information Technologies and Systems,
https://orcid.org/0000-0002-4133-8169,
e-mail: glanat@yahoo.com

Rogushina J.V.², PhD (Phys.-Math.), Associate Professor,
Senior Researcher of the Automated Information Systems Department,
https://orcid.org/0000-0001-7958-2557,
e-mail: ladamandraka2010@gmail.com

Pryima S.M.³, DSc (Pedagogy), Professor,
Professor of the Computer Science Department,
https://orcid.org/0000-0002-2654-5610,
e-mail: pryima.serhii@tsatu.edu.ua

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

²Institute of Software Systems of National Academy of Sciences of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

³Dmytro Motornyi Tavria State Agrotechnological University,
66, Zhukovskogo street, Zaporizhzhia, 72312, Ukraine

COMPLEX INFORMATION OBJECTS REPOSITORY AS A COMPONENT OF THE SEMANTIC ANALYTIC-INFORMATION WEB-ORIENTED SYSTEMS DEVELOPMENT

Introduction.  The paper examines the issue of reusing ontological knowledge in semantic analytical and informational web-oriented systems and analyzes the problems that arise in the process of searching for and exporting such knowledge from external ontologies. It proposes to create a repository of complex information objects, which should expand the functionality of services provided by ontology repositories, and provide opportunities to search for elements of such ontologies at the content level, taking into account the semantics of the relationships between them. The work states the basic requirements for such a repository, analyzes the technologies that can be used to replenish it, and offers some examples of areas of its practical use. The proposed approach consideres on a practical example of the creation of a semantic directory for finding educational materials, which is oriented towards functioning in an open web environment and exporting information from external sources. The prototype of the system is implemented on the basis of the semantic extension of wiki technology, and the elements of the structure of complex information objects processed in the system are obtained from relevant external ontologies.

The purpose of the paper is to develop algorithms and methods of using formalized ontological knowledge of the subject area for the creation of applied semantically oriented information and analytical systems, to export knowledge from external ontologies, to create a repository of complex information objects with extended functionality of services.

The results. Development of the concept of a repository of complex information objects for applied systems of artificial intelligence, which provides a search for instances of various ontological classes connected by certain types of semantic relations. Improvement of existing functionalities of ontology repositories due to export of knowledge about the structure of CIO from external sources of knowledge and semantically marked documents. The developed algorithms and methods of creating repositories of complex information objects make it possible to analyze complex collections of different classes of information objects, interconnected by relationships, restrictions and rules for semantic analytical and informational web-oriented systems. The basic requirements for the repository are formed and the method of its replenishment is presented. The obtained results make it possible to create original intelligent information systems for artificial intelligence in the field of big data processing, cyber security, competence analysis when creating professional groups for the implementation of an innovative project, human resources management, finance and business, for companies that work with dynamically changing content of documents (jurisprudence , standardization, state authorities), national security, defense and military spheres.

Conclusions.  The proposed original approach, algorithms and method for improving the repository of complex information objects, expanding its functionality and ensuring its replenishment due to the export of knowledge from external sources (Wikipedia, encyclopedias, dictionaries, repositories of scientific publications, directories) and semantically marked documents and tracking dynamic changes occurring in these sources and documents. A prototype of the semantic web-oriented system “e-Textbook” is created, which ensures the selection of relevant textbooks for teachers and students of educational institutions for work programs of educational disciplines. The application of ontologies and data in the “e-Textbook” system based on the semantic analysis of metadata and the determination of the semantic similarity of structural data models (ontologies, data) and the formation of a ranked set of related ontologies to solve the tasks.

Keywords: wiki, knowledge-oriented information resource, ontology, formal ontology model, intelligent information system, ontology repository, complex information object.

Download full text!

REFERENCES

1 Rogushina J.V. , Gladun A.Ya. The use of ontologycal knowledge for multi-criteria comparison of complex information objects. Problems of programming. 2022, N2-3. P. 249-259. URL: pp.isofts.kiev.ua/ojs1/article/view/526/523. pp 2022.03-04.249 (In Ukrainian)
https://doi.org/10.15407/pp2022.03-04.249

2 Guarino N. Formal Ontology and Information Systems. Formal Ontology in Information Systems. Proceedings of FOIS’98, by N. Guarino (ed.). Trento. Italy, Amsterdam, IOS-Press. 1998. P. 3-15 https://klevas.mif.vu.lt/~donatas/Vadovavimas/Temos/OntologiskaiTeisingas KoncepcinisModeliavi-mas/papildoma/Guarino98-Formal%20Ontology%20and% 20Informa-tion%20Systems.pdf

3 Rogushina J.V. Classification of means and methods of the Web semantic retrieval. Problems of programming. 2017. № 1. P. 30-50. (In Ukrainian)
https://doi.org/10.15407/pp2017.01.030

4 Rogushina J., Priyma S. Use of competence ontological model for matching of qualifications // Chemistry: Bulgarian Journal of Science Education, Volume 26, Number 2, 2017. P.216-228. http://elar.tsatu.edu.ua/bitstream/123456789/ 3181/1/2.pdf.

5 Gladun A.Ya., Rogushina J.V. Ontologies repository as a method to knowledge reusage for information objects recognition. Ontology of design, № 1 (7), 2013. P. 35-50.

6 FAIR_data. https://en.wikipedia.org/wiki/FAIR_data.

7 Rogushina, Y. V. (2023). Use of ontologies and semantic mediawiki for representation and retrieval of scientific data in the FAIR paradigm. CEUR Workshoop Proceedings. Vol. 2866. P. 61-73.
https://doi.org/10.30525/978-9934-26-277-7-200

8 Bassiliades N. EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities. Accepted for presentation at, 15th International Conference on Knowledge Engineering and Ontology Development (KEOD 2023), 13-15 Nov 2023, Rome, Italy. https://intelligence.csd.auth.gr/wp-content/uploads/2023/08/EvdoGraph-CR.pdf
https://doi.org/10.5220/0012153600003598

9 Bizer, C., Heath, T., & Berners-Lee, T. (2023). Linked data-the story so far. Linking the World’s Information: Essays on Tim Berners-Lee’s Invention of the World Wide Web (pp. 115-143).
https://doi.org/10.1145/3591366.3591378

10 Wylot M., Hauswirth M., Cudré-Mauroux P., Sakr, S. RDF data storage and query processing schemes: A survey. ACM Computing Surveys (CSUR). 2018, 51(4), 1-36.
https://doi.org/10.1145/3177850

11 Antoniou G., Van Harmelen F. Web ontology language: Owl. Handbook on ontologies. Springer Berlin Heidelberg, 2004, pp. 67-92.
https://doi.org/10.1007/978-3-540-24750-0_4

12 Hogan A., Blomqvist E., Cochez M., D’amato C., Melo G., Gutierrez C., Zimmermann A. Knowledge graphs. ACM Computing Surveys. 2022, 54(4), pp. 1-37.
https://doi.org/10.1145/3447772

13 Yu, L., & Yu, L. (2011). Linked open data. A Developer’s Guide to the Semantic Web. 2011, pp. 409-466.
https://doi.org/10.1007/978-3-642-15970-1_11

14 Bizer C., Heath T., Berners-Lee T. Linked datathe story so far. International journal on semantic web and information systems. 2009, 5(3), pp. 1-22.
https://doi.org/10.4018/jswis.2009081901

15 Stancin K., Poscic P., Jaksic D. Ontologies in education – state of the art. Education and Information Technologies. 2020, 25(6), pp. 5301-5320.
https://doi.org/10.1007/s10639-020-10226-z

16 Färber M. The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data. ISWC 2019, LNCS. 11779, pp. 113-129. Springer.
https://doi.org/10.1007/978-3-030-30796-7_8

17 Jaradeh M. Y., Oelen A., Farfar K. E., Prinz M., D’Souza J., Kismihók G., Auer S. Open research knowledge graph: Next generation infrastructure for semantic scholarly knowledge. KCAP 2019 (pp. 243-246). ACM.
https://doi.org/10.1145/3360901.3364435

18 Abu-Salih, B. Domain-specific knowledge graphs: A survey. Journal of Network and Computer Applications. 2021, 185.
https://doi.org/10.1016/j.jnca.2021.103076

19 Vandenbussche P.-Y., Atemezing G. A., Poveda-Villalón M., Vatant B. Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web. Semantic Web. 2017, 8(3), pp. 437-452.
https://doi.org/10.3233/SW-160213

20 Corson-Rikert J., Mitchell S., Lowe B., Rejack N., Ding Y., Guo C. The VIVO Ontology. VIVO, Synthesis Lectures on Data, Semantics, and Knowledge. 2012, pp. 15-33. Springer, Cham.
https://doi.org/10.1007/978-3-031-79435-3_2

21 Demartini G., Enchev I., Gapany J., Cudré-Mauroux P. The Bowlogna ontology: Fostering open curricula and agile knowledge bases for Europe’s higher education landscape. Semantic Web. 2013. 4(1), pp. 53-63.
https://doi.org/10.3233/SW-2012-0064

22 ESCO (the European Multilingual Classifier of Skills, Competences, Qualifications and Occupations. https://ec.europa.eu/esco/portal/home.

23 Vrandečić D., Krötzsch K. Wikidata: a free collaborative knowledgebase. Communications ACM. 2014, 10, pp. 78-85.
https://doi.org/10.1145/2629489

Received 30.08.2023