ARTIFICIAL NEURAL NETWORK FOR AIR TRAFFIC CONTROLLER’S PRE-SIMULATOR TRAINING

Authors

  • Tatyana Shmelova National Aviation University, Kyiv, Ukraine
  • Yuliya Sikirda Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine
  • Andriy Zemlyanskiy Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine
  • Olena Danilenko Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine
  • Vitalii Lazorenko National Aviation University, Kyiv, Ukraine

DOI:

https://doi.org/10.18372/2306-1472.68.10905

Keywords:

correctness, fuzzy sets, multimodal system, neural network model, potentially conflict situation, timeliness

Abstract

Purpose: to develop the neural network model for evaluating correctness and timeliness of decision-making by specialist of air traffic services during the pre-simulator training. Methods: researchers are based on the basic concepts of threat and error management in air traffic control, for characteristic of situation complexity (threat- error-undesirable condition) the methods of expert estimation and fuzzy sets theory have been used. Results: stages of the conflict situation developing have been classified and quantitative indicators of complexity level at each stage have been defined. Four layers neural network model for evaluating correctness and timeliness of decision-making by air traffic controller during the pre-simulator training has been built and its parameters have been obtained. The first layer (input) is exercises that perform cadets/listeners to solve potentially conflict situation, the second layer (hidden) is physiological characteristics of learner, the third layer (hidden) is the complexity of the exercise depending on the number of potentially conflict situations, the fourth layer (output) is assessment of cadets/listeners during performance of exercise. Neural network model also has additional inputs (Bias) that including restrictions on calculating parameters. With the help of modelling complex Fusion visualisation of results of educational task implementation by air traffic controller according to specified parameters have been defined. Discussion: taking into account timeliness and correctness of instructor’s tasks performance during the pre-simulator education with the help of using artificial neural networks will allow determining the possibility of access of specialist of air traffic services to simulator training. Multimodal system Fusion will give the possibility to improve the process of training of cadet's/listener's – air traffic controllers through automated assessment of their actions.

Author Biographies

Tatyana Shmelova, National Aviation University, Kyiv, Ukraine

Doctor of Engineering. Associate Professor.

Air Navigation Systems Department, National Aviation University, Kiev, Ukraine.

Education: Kirovohrad Institute of Agricultural Mechanical Engineering, Kirovohrad, Ukraine (1983).

Research area: development the evaluation system of decision-making efficiency by a human-operator of socio-technical air navigation system in the expected and unexpected operating conditions of an aircraft with the influence of the professional and non-professional factors.

Yuliya Sikirda, Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine

Candidate of Engineering. Associate Professor.

Vice-Dean of the Management Faculty, Kirovohrad Flight Academy of the National Aviation University, Associate Professor of the Management, Economy and Law Department, Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine.

Education: State Flight Academy of Ukraine, Kirovohrad, Ukraine (2001).

Research area: evaluation and improving the efficiency of decision-making by a human-operator of air navigation system.

Andriy Zemlyanskiy, Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine

Senior Lecturer

Faculty of Air Traffic Services, Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine.

Education: Faculty of Air Traffic Services, State Flight Academy of Ukraine, Kirovohrad, Ukraine (1995).

Research area: development the intelligent modelling complexes for automation of air traffic controller simulator training.

Olena Danilenko, Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine

Cadet of Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine.

Education: National Aviation University, Kiev, Ukraine (2015), Kirovohrad Flight Academy of the National Aviation University, Kirovohrad, Ukraine (2016).

Research area: identifying and minimizing the impact of threats and errors on the activity of air traffic controller.

Vitalii Lazorenko, National Aviation University, Kyiv, Ukraine

Lecturer.

Air Navigation Systems Department, Institute of Information-Diagnostic Systems, National Aviation University.

Education: State Flight Academy of Ukraine, Kirovohrad, Ukraine (2004).

Research area: research on improving air traffic controller training simulator Traffic Service of Civil Aviation.

References

Leichenko S. Human factor in aviation: monograph in 2 books. S. Leichenko, A. Malyshevsky, N. Mikhailik. Book 1. Kirovohrad, IMEX, 2006. 512 p. (in Russian).

Global Aviation Safety Study: A review of 60 years of improvement in aviation safety. USA: Allianz Global Corporate & Specialty; EMBRY-RIDDLE Aeronautical University, 2014. 63 p.

Statistics of the world's largest aircraft accidents for the years 1974-2014 [Electronic resource]. Access mode: http:.forinsurer. compublic 1407073824. Last mode: 2016 (in Russian).

ATM Services' Personnel: ESARR 5. 2nd ed. European Organisation for the Safety of Air Navigation, 2004. 24 p.

EATM Training Progression and Concepts. European Organisation for the Safety of Air Navigation, 2004. 56 p.

The rules for issuing certificates of aviation personnel in Ukraine: the order of the Ministry of Transport 07.12.1998 p. 486 number, as amended by the Ministry of Transport and Communications of Ukraine of 24.09.2007. N 842. Kyiv, Ministry of Transport and Communications of Ukraine, 2007. 72 p. (in Ukrainian).

Komashynskiy V. Neural networks and its use in systems of management and communication. V. Komashynskyy, D. Smirnov. Moscow, Hotline - Telecom, 2002. 94 p. (in Russian).

Arkhangelskiy V. Neural networks in systems of automation. V. Arkhangelskiy, I. Bohaenko, G. Grabowskiy, N. Ryumshyn. Kiev, Techniques, 1999. 364 p. (in Russian).

Suzuki K. Artificial Neural Networks – Architectures and Applications. K. Suzuki. USA, InTech, 2013. 264 p.

Gardner M.W. Survey and critique of techniques for extracting rules from trained artificial neural networks. M.W Gardner , S.R Dorling. Atmospheric Environment, Vol. 32, Issues 14–15, 1 August 1998. P. 2627–2636. http://www.sciencedirect.com/science/article/pii/S1352231097004470 - AFF1

Goedeking P. Networks in Aviation: Strategies and Structures. P. Goedeking. USA, Springer, 2010. 145 p.

Neural networks. STATISTICA Neural Networks. translate from English. Moscow, Hotline - Telecom, 2001. 182 p. (in Russian).

A. r. Computer Program «Choosing the Pre-Flight Information and Decision Making about Departure for Automated System of Pre-Flight Information Preparation (АS PIP)»: certificate of registration of copyright in the product N 40062 at 11.04.2011. V. Kharchenko, T. Shmelova, O. Artemenko, V. Otryazhiy. (in Ukrainian).

A. r. Computer Program «Optimizing the Choice of Alternative Variant of Aircraft Flight Completion in Flight Emergencies «Prompt»: certificate of registration of copyright in the product N 37872 at 11.04.2011. V. Kharchenko, T. Shmelova, Yu. Sikirda, О. Gerasimenko. (in Ukrainian).

A. r. Computer Program «Test Extraordinary Incident»: certificate of registration of copyright in the product N 50826 at 21.08.2013 р. T. Shmelova, І. Yakunina, R. Yakunin. (in Ukrainian).

A. r. Computer Program «Network analysis of flight emergency»: certificate of registration of copyright in the product N55587 at 11.07.2014 р. T. Shmelova, І. Yakunina, V. Moyseenko, М. Grinchuk. (in Ukrainian).

Threat and Error Management (TEM) in Air Traffic Control. Cir. ICAO 314 – AN. 178. Canada, Montreal, ICAO, 2008. 36 p.

Kharchenko V. Threat and Error Management in Air Traffic Control. V. Kharchenko, Yu. Chynchenko, S. Raychev. Proceedings of the National Aviation University, 2007, N 3–4. P. 24-29.

Shmelоva T. Fuzzy assessment of the situation’s complexity during simulator training of specialists in air traffic services. T. Shmelоva, Yu. Sikirda, A. Zemlyanskiy, О. Danilenko. Problems of Energy Efficiency and automation in industry and agriculture: Intern. scient.-pract. conf., Kirovohrad, 21-22 October 2015: abstracts. Kirovohrad, Kirovohrad National Technical University, 2015. P. 118-120. (in Ukrainian).

Zade L. Fuzzy sets. L. Zade. Information and Control, 1965, Vol. 8. P. 338-353.

Borisov A. Decision making based on fuzzy models: Examples of using. A. Borisov, O. Krumberh, I. Fedorov. Riga, Zynatne, 1990. 184 p. (in Russian).

Procedures for Air Navigation Services. Air Traffic Management. Doc. 4444 – ATM. 501. 15th ed.

Downloads

Published

11-11-2016

How to Cite

Shmelova, T., Sikirda, Y., Zemlyanskiy, A., Danilenko, O., & Lazorenko, V. (2016). ARTIFICIAL NEURAL NETWORK FOR AIR TRAFFIC CONTROLLER’S PRE-SIMULATOR TRAINING. Proceedings of National Aviation University, 68(3), 13–23. https://doi.org/10.18372/2306-1472.68.10905

Issue

Vol. 68 No. 3 (2016)

Section

AEROSPACE SYSTEMS FOR MONITORING AND CONTROL

License

Most read articles by the same author(s)