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Investigation of combined ensemble methods for diagnostics of the quality of interaction of human-machine systems

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Category: Content №4 2023

Last Updated on 28 August 2023

Published on 30 November -0001

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Authors:

Oleksandr Laktionov*, orcid.org/0000-0002-5230-524X, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Leonid Lievi, orcid.org/0000-0003-4619-8764, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Andrii Tretiak, orcid.org/0000-0003-3971-3078, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Mykola Movin, orcid.org/0000-0002-7470-6482, National University “Yuri Kondratyuk Poltava Polytechnic”, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (4): 138 - 143

https://doi.org/10.33271/nvngu/2023-4/138

Abstract:

Purpose. Study on the process of combining several methods for determining the quality indices of human-machine interaction, containing various configurations for determining the weight coefficients in an ensemble.

Methodology. The process of diagnosing the quality of the interaction of a human-machine system with four elements of subsystems is studied using the example of the system “Operator–Machining Center – Control Program – Safe Environment”. The main hypothesis of the study is the combination of several methods for determining the quality indices of human-machine interaction, containing different configurations for determining the weight coefficients in the ensemble. A combined method for diagnosing the quality of interaction between human-machine systems based on ensemble models, which include non-ensemble ones, has been proposed. The ensemble index has been determined by averaging the non-ensemble indices. The defined ensemble indices and element scores of the four subsystems are used as input scores to a multiple regression model to generate prediction.

Findings. Four combinations of ensemble indices have been developed and implemented in software, which are characterized by a minimum value of the standard deviation compared to the existing ones. According to the results of experimental verification, the proposed models demonstrate the value of the standard deviation of 0.1404; 0.1401; 0.1411; 0.1397, and the existing ones are 0.1532; 0.1535; 0.1532; 0.1532.

Originality. The combined ensemble method for diagnosing the quality of interaction between elements of subsystems takes into account linear models with non-linear variables and different ways of determining weight coefficients.

Practical value. The scenario for the practical use of the results obtained is a possible option for optimizing production, where, depending on the final result, specialists can adjust the value of a particular subsystem to achieve the desired result.

Keywords: ensemble index, averaging model, nonlinear relationship, linear equation, nonlinear transformation, IT-technologies

References.

1. Zhang, Y., Liu, W., Shi, Q., Huang, Y., & Huang, S. (2022). Resilience assessment of multi-decision complex energy interconnection system. International Journal of Electrical Power & Energy Systems, 137, 107809. https://doi.org/10.1016/j.ijepes.2021.107809.

2. Komelina, O., Dubishchev, V., Lysenko, M., & Panasenko, N. (2018). Ukraine Unified Transport System Potential and Its Development Management Effectiveness Integral Assessment. International Journal of Engineering & Technology, 7(4.3), 633. https://doi.org/10.14419/ijet.v7i4.3.19972.

3. Belitz, K., & Stackelberg, P. E. (2021). Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models. Environmental Modelling & Software, 139, 105006. https://doi.org/10.1016/j.envsoft.2021.105006.

4. Yu, C., Zhu, Y.-P., Luo, H., Luo, Z., & Li, L. (2023). Design assessments of complex systems based on design oriented modelling and uncertainty analysis. Mechanical Systems and Signal Processing, 188, 109988. https://doi.org/10.1016/j.ymssp.2022.109988.

5. Zhang, K., Shao, X., Chen, Z., Liu, X., & Wang, W. (2021). A multi-level diagnostic method for a human-machine system based on the fusion of ELM and the D-S evidence theory. Information Fusion, 22. https://doi.org/10.3390/s22155902.

6. Jiao, J., Zhao, M., Lin, J., & Liang, K. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, 36-63. https://doi.org/10.1016/j.neucom.2020.07.088.

7. Justus, V., & Kanagachidambaresan, G. R. (2022). Intelligent Single-Board Computer for Industry 4.0: Efficient Real-Time Monitoring System for Anomaly Detection in CNC Machines. Microprocessors and Microsystems, 104629. https://doi.org/10.1016/j.micpro.2022.104629.

8. Ghasemi, A., Ashoori, A., & Heavey, C. (2021). Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems. Applied Soft Computing, 106, 107309. https://doi.org/10.1016/j.asoc.2021.107309.

9. López Martínez, P., Dintén, R., Drake, J. M., & Zorrilla, M. (2021). A big data-centric architecture metamodel for Industry 4.0. Future Generation Computer Systems, 125, 263-284. https://doi.org/10.1016/j.future.2021.06.020.

10. Yang, B., Qiao, L., Zhu, Z., & Wulan, M. (2016). A Metamodel for the Manufacturing Process Information Modeling. Procedia CIRP, 56, 332-337. https://doi.org/10.1016/j.procir.2016.10.032.

11. Dunke, F., & Nickel, S. (2020). Neural networks for the metamodeling of simulation models with online decision making. Simulation Modelling Practice and Theory, 99, 102016. https://doi.org/10.1016/j.simpat.2019.102016.

12. Chattopadhyay, A., Nabizadeh, E., Bach, E., & Hassanzadeh, P. (2023). Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems. Journal of Computational Physics, 445, 110613. https://doi.org/10.1016/j.jcp.2023.111918.

13. Ochella, S., Shafiee, M., & Dinmohammadi, F. (2022). Artificial intelligence in prognostics and health management of engineering systems. Engineering Applications of Artificial Intelligence, 108, 104552. https://doi.org/10.1016/j.engappai.2021.104552.

14. De Souza, I. T., Carolina Rosa, A., Patriarca, R., & Haddad, A. (2022). Soft computing for nonlinear risk assessment of complex socio-technical systems. Expert Systems with Applications, 117828. https://doi.org/10.1016/j.eswa.2022.117828.

15. Singh, M., & Chauhan, S. (2023). A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems. Electric Power Systems Research, 214, 108923. https://doi.org/10.1016/j.epsr.2022.108923.

16. Nguyen, P. T., Di Ruscio, D., Pierantonio, A., Di Rocco, J., & Iovino, L. (2021). Convolutional neural networks for enhanced classification mechanisms of metamodels. Journal of Systems and Software, 172, 110860. https://doi.org/10.1016/j.jss.2020.110860.

17. Krouska, A., Troussas, C., & Sgouropoulou, C. (2019). Fuzzy Logic for Refining the Evaluation of Learners’ Performance in Online Engineering Education. European Journal of Engineering Research and Science, 4(6), 50-56.

18. Laktionov, О. (2021). Improvement of methods for determination of quality indices of interaction elements of system subsystems. Eastern-European Journal of Enterprise Technologies, 6(3(114)), 72-82. https://doi.org/10.15587/1729-4061.2021.244929.

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