The development of a management decision-making method based on the analysis of information from space observation systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.269027

Keywords:

management decision, space surveillance system, image segmentation, state of the environment, forecasting

Abstract

The object of this study is the process of making a management decision based on the analysis of information from space surveillance systems.

Unlike the well-known ones, the method of making a management decision based on the analysis of information from space surveillance systems involves:

– segmentation of an optoelectronic image;

– determination and prediction of a priori probabilities of possible environmental states;

– an application for making a management decision of a combination of Bayes criteria and a minimum of variance.

Experimental studies have been carried out on making a management decision based on the analysis of information from space surveillance systems. To conduct experimental research on making a management decision based on the analysis of information from space surveillance systems, a model problem has been stated. As images from space surveillance systems, images obtained from the WorldView-2 spacecraft (USA) with a difference of four days were considered. The vegetation index was calculated, and the probabilities of degradation dynamics of plant segments were determined. It was established that the maximum value of the estimated functional is achieved when choosing a solution φ1, which is optimal according to the Bayesian criterion and the criterion of minimum variance.

The quality of management decision-making was assessed by the well-known and developed methods. To assess the quality of management decision-making, the concepts of objectivity of the decision-making method and the selectivity of the decision-making method by known and developed method were introduced. It has been established that both methods are objective, and the improved method is more selective (the gain is 2.6 times). This becomes possible through the use of information from space surveillance systems.

Author Biographies

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Oleksandr Makoveichuk, Academician Yuriy Bugay International Scientific and Technical University

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Ihor Butko, Academician Yuriy Bugay International Scientific and Technical University

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Mykola Butko, Chernihiv Polytechnic National University

Doctor of Economic Sciences, Professor

Department of Management and Civil Service

Veronika Khudolei, Academician Yuriy Bugay International Scientific and Technical University

Doctor of Economic Sciences, Professor

Department of Management, Marketing and Public Administration

Stanislav Kukhtyk, Academician Yuriy Bugay International Scientific and Technical University

PhD, Associate Professor

Department of Law

References

  1. Khater, E.-S. G., Ali, S. A., Afify, M. T., Bayomy, M. A., Abbas, R. S. (2022). Using of geographic information systems (GIS) to determine the suitable site for collecting agricultural residues. Scientific Reports, 12 (1). doi: https://doi.org/10.1038/s41598-022-18850-0
  2. Horbulin, V. P. (2021). The use of space information in the system of geographic information provision of managerial decision-making on Ukraine’s national security and defense issues. Visnyk of the National Academy of Sciences of Ukraine, 9, 3–11. doi: https://doi.org/10.15407/visn2021.09.003
  3. Harrison, T., Strohmeyer, M. (2022). Commercial Space Remote Sensing and Its Role in National Security. CSIS BRIEFS. Available at: https://csis-website-prod.s3.amazonaws.com/s3fs-public/publication/220202_Harrison_Commercial_Space.pdf?VgV9.43i5ZGs8JDAYDtz0KNbkEnXpH21
  4. Military Imaging and Surveillance Technology (MIST) (Archived). Available at: https://www.darpa.mil/program/military-imaging-and-surveillance-technology
  5. Hosseini, S., Baziyad, H., Norouzi, R., Jabbedari Khiabani, S., Gidófalvi, G., Albadvi, A. et al. (2021). Mapping the intellectual structure of GIS-T field (2008–2019): a dynamic co-word analysis. Scientometrics, 126 (4), 2667–2688. doi: https://doi.org/10.1007/s11192-020-03840-8
  6. Rashidi, K., Noorizadeh, A., Kannan, D., Cullinane, K. (2020). Applying the triple bottom line in sustainable supplier selection: A meta-review of the state-of-the-art. Journal of Cleaner Production, 269, 122001. doi: https://doi.org/10.1016/j.jclepro.2020.122001
  7. Cherfi, A., Nouira, K., Ferchichi, A. (2018). Very Fast C4.5 Decision Tree Algorithm. Applied Artificial Intelligence, 32 (2), 119–137. doi: https://doi.org/10.1080/08839514.2018.1447479
  8. Katranzhy, L., Podskrebko, O., Krasko, V. (2018). Modelling the dynamics of the adequacy of bank’s regulatory capital. Baltic Journal of Economic Studies, 4(1), 188–194. doi: https://doi.org/10.30525/2256-0742/2018-4-1-188-194
  9. Kachayeva, G. I., Mustafayev, A. G. (2018). The use of neural networks for the automatic analysis of electrocardiograms in diagnosis of cardiovascular diseases. Herald of Dagestan State Technical University. Technical Sciences, 45 (2), 114–124. doi: https://doi.org/10.21822/2073-6185-2018-45-2-114-124
  10. Zhdanov, V. V. (2016). Experimental method to predict avalanches based on neural networks. Ice and Snow, 56 (4), 502–510. doi: https://doi.org/10.15356/2076-6734-2016-4-502-510
  11. Kanev, A., Nasteka, A., Bessonova, C., Nevmerzhitsky, D., Silaev, A., Efremov, A., Nikiforova, K. (2017). Anomaly detection in wireless sensor network of the “smart home” system. 2017 20th Conference of Open Innovations Association (FRUCT). doi: https://doi.org/10.23919/fruct.2017.8071301
  12. Sreeshakthy, M., Preethi, J. (2016). Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search. BRAIN: Broad Research in Artificial Intelligence and Neuroscience, 6 (3-4), 60–73. Available at https://doaj.org/article/4c120012c5d44b6abc4e2b389888c7a3
  13. Chica, J., Zaputt, S., Encalada, J., Salamea, C., Montalvo, M. (2019). Objective assessment of skin repigmentation using a multilayer perceptron. Journal of Medical Signals & Sensors, 9 (2), 88. doi: https://doi.org/10.4103/jmss.jmss_52_18
  14. Massel, L. V., Gerget, O. M., Massel, A. G., Mamedov, T. G. (2019). The Use of Machine Learning in Situational Management in Relation to the Tasks of the Power Industry. EPJ Web of Conferences, 217, 01010. doi: https://doi.org/10.1051/epjconf/201921701010
  15. Abaci, K., Yamacli, V. (2019). Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem. Advances in Electrical and Computer Engineering, 19 (4), 57–64. doi: https://doi.org/10.4316/aece.2019.04007
  16. Mishchuk, O. S., Vitynskyi, P. B. (2018). Neural Network with Combined Approximation of the Surface of the Response. Research Bulletin of the National Technical University of Ukraine “Kyiv Politechnic Institute,” 2, 18–24. doi: https://doi.org/10.20535/1810-0546.2018.2.129022
  17. Kazemi, M., Faezirad, M. (2018). Efficiency estimation using nonlinear influences of time lags in DEA Using Artificial Neural Networks. Management Journal, 10 (1), 17–34. doi: https://doi.org/10.22059/IMJ.2018.129192.1006898
  18. Parapuram, G., Mokhtari, M., Ben Hmida, J. (2018). An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation. Energies, 11 (3), 680. doi: https://doi.org/10.3390/en11030680
  19. Prokoptsev, N. G., Alekseenko, A. E., Kholodov, Y. A. (2018). Traffic flow speed prediction on transportation graph with convolutional neural networks. Computer Research and Modeling, 10 (3), 359–367. doi: https://doi.org/10.20537/2076-7633-2018-10-3-359-367
  20. Mennis, J., Liu, J. W. (2005). Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change. Transactions in GIS, 9 (1), 5–17. doi: https://doi.org/10.1111/j.1467-9671.2005.00202.x
  21. Miller, H. J., Han, J. (Eds.) (2009). Geographic Data Mining and Knowledge Discovery. CRC Press, 486. doi: https://doi.org/10.1201/9781420073980
  22. Manea, E., Di Carlo, D., Depellegrin, D., Agardy, T., Gissi, E. (2019). Multidimensional assessment of supporting ecosystem services for marine spatial planning of the Adriatic Sea. Ecological Indicators, 101, 821–837. doi: https://doi.org/10.1016/j.ecolind.2018.12.017
  23. Cressie, N. (1993). Statistics for spatial data. John Wiley & Sons, Inc. doi: https://doi.org/10.1002/9781119115151
  24. History and evolution of big data analytics. Available at https://www.sas.com/en_us/insights/analytics/big-data-analytics.html
  25. Li, S. Zhi, Hu, Q., Deng, X. Hong, Cai, Z. Quan (2019). Reversible image watermarking based on texture analysis of grey level co-occurrence matrix. International Journal of Computational Science and Engineering, 19 (1), 83. doi: https://doi.org/10.1504/ijcse.2019.099642
  26. De O. Bastos, L., Liatsis, P., Conci, A. (2008). Automatic texture segmentation based on k-means clustering and efficient calculation of co-occurrence features. 2008 15th International Conference on Systems, Signals and Image Processing. doi: https://doi.org/10.1109/iwssip.2008.4604387
  27. Hung, C.-C., Song, E., Lan, Y. (2019). Image Texture, Texture Features, and Image Texture Classification and Segmentation. Image Texture Analysis, 3–14. doi: https://doi.org/10.1007/978-3-030-13773-1_1
  28. Tian, Y., Li, Y., Liu, D., Luo, R. (2016). FCM texture image segmentation method based on the local binary pattern. 2016 12th World Congress on Intelligent Control and Automation (WCICA). doi: https://doi.org/10.1109/wcica.2016.7578571
  29. Khudov, H., Makoveichuk, O., Butko, I., Gyrenko, I., Stryhun, V., Bilous, O., Shamrai, N. et al. (2022). Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm. Eastern-European Journal of Enterprise Technologies, 3 (9 (117)), 6–14. doi: https://doi.org/10.15587/1729-4061.2022.259759
  30. Jing, Z., Wei, D., Youhui, Z. (2012). An Algorithm for Scanned Document Image Segmentation Based on Voronoi Diagram. 2012 International Conference on Computer Science and Electronics Engineering. doi: https://doi.org/10.1109/iccsee.2012.144
  31. Shanmugavadivu, P., Sivakumar, V. (2012). Fractal Dimension Based Texture Analysis of Digital Images. Procedia Engineering, 38, 2981–2986. doi: https://doi.org/10.1016/j.proeng.2012.06.348
  32. Simon, P., V, U. (2020). Deep Learning based Feature Extraction for Texture Classification. Procedia Computer Science, 171, 1680–1687. doi: https://doi.org/10.1016/j.procs.2020.04.180
  33. Khudov, H., Makoveichuk, O., Khizhnyak, I., Oleksenko, O., Khazhanets, Y., Solomonenko, Y. et al. (2022). Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Eastern-European Journal of Enterprise Technologies, 2 (9 (116)), 6–13. doi: https://doi.org/10.15587/1729-4061.2022.255203
  34. Berezina, S., Solonets, O., Lee, K., Bortsova, M. (2021). An information technique for segmentation of military assets in conditions of uncertainty of initial data. Information Processing Systems, 4 (167), 6–18. doi: https://doi.org/10.30748/soi.2021.167.01
  35. Ruban, I., Khudov, H., Makoveichuk, O., Chomik, M., Khudov, V., Khizhnyak, I. et al. (2019). Construction of methods for determining the contours of objects on tonal aerospace images based on the ant algorithms. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 25–34. doi: https://doi.org/10.15587/1729-4061.2019.177817
  36. Mittal, H., Pandey, A. C., Saraswat, M., Kumar, S., Pal, R., Modwel, G. (2021). A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimedia Tools and Applications, 81 (24), 35001–35026. doi: https://doi.org/10.1007/s11042-021-10594-9
  37. Khudov, H., Makoveichuk, O., Khudov, V., Maliuha, V., Andriienko, A., Tertyshnik, Y. et al. (2022). Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm. Eastern-European Journal of Enterprise Technologies, 5 (9 (119)), 17–24. doi: https://doi.org/10.15587/1729-4061.2022.265775
  38. Butko, I. (2021). Model and method of making management decisions based on the analysis of geospatial information. Advanced Information Systems, 5 (2), 42–48. doi: https://doi.org/10.20998/2522-9052.2021.2.07
  39. Mashtalir, V., Ruban, I., Levashenko, V. (Eds.) (2020). Advances in Spatio-Temporal Segmentation of Visual Data. Studies in Computational Intelligence. doi: https://doi.org/10.1007/978-3-030-35480-0
  40. Khudov, H., Makoveichuk, O., Misiuk, D., Pievtsov, H., Khizhnyak, I., Solomonenko, Y. et al. (2022). Devising a method for processing the image of a vehicle’s license plate when shooting with a smartphone camera . Eastern-European Journal of Enterprise Technologies, 1 (2 (115), 6–21. doi: https://doi.org/10.15587/1729-4061.2022.252310
  41. Khudov, H., Makoveychuk, O., Butko, I., Khizhnyak, I. (2021). A Model for Prediction of Geospatial Data in Systems for Processing Geospatial Information. Systems of Arms and Military Equipment, 2 (66), 123–28. doi: https://doi.org/10.30748/soivt.2021.66.16
  42. Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control. Englewood Cliffs, NJ: Prentice Hall. Available at https://www.worldcat.org/title/28888762
  43. Brockwell, P. J., Davis, R. A. (2009). Time Series: Theory and Methods. Springer, 580. doi: https://doi.org/10.1007/978-1-4419-0320-4
  44. Khudov, G. V. (2003). Features of optimization of two-alternative decisions by joint search and detection of objects. Problemy Upravleniya i Informatiki (Avtomatika), 5, 51–59. Available at https://www.researchgate.net/publication/291431400_Features_of_optimization_of_two-alternative_decisions_by_joint_search_and_detection_of_objects
  45. Satellite Imagery. Available at https://www.maxar.com/products/satellite-imagery
  46. Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234–241. doi: https://doi.org/10.1007/978-3-319-24574-4_28
The development of a management decision-making method based on the analysis of information from space observation systems

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Published

2022-12-30

How to Cite

Khudov, H., Makoveichuk, O., Butko, I., Butko, M., Khudolei, V., & Kukhtyk, S. (2022). The development of a management decision-making method based on the analysis of information from space observation systems. Eastern-European Journal of Enterprise Technologies, 6(9 (120), 59–69. https://doi.org/10.15587/1729-4061.2022.269027

Issue

Vol. 6 No. 9 (120) (2022): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2022 Hennadii Khudov, Oleksandr Makoveichuk, Ihor Butko, Mykola Butko, Veronika Khudolei, Stanislav Kukhtyk

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