1. MINISTRY OF EDUCATION AND SCIENCE OF UKRAINE NATIONAL AVIATION UNIVERSITY Institute of Air Navigation Air Navigation Systems Department MASTER’S DEGREE THESIS НЕЙРОМЕРЕЖЕВА МОДЕЛЬ ДОПУСКУ СТУДЕНТА В ТРЕНАЖЕРНІЙ ПІДГОТОВЦІ АВІАДИСПЕТЧЕРА NEURAL NETWORK MODEL OF STUDENT ADMISSION IN AIR TRAFFIC CONTROLLER SIMULATOR TRAINING Performed by Bilko A.P. Supervisor Shmelova T.F. Kyiv 2015

2. PURPOSE: implementation of Multi-Layer Feedforward Neural network of student admission in Air Traffic Controller simulator training with the help of programme language Visual Basic for Application (Microsoft Excel). TASKS: 1.Analysis of Air Traffic Controllers training. 2.Designation of requirements for simulator training. 3.To evaluate pre-simulating training according to airspace part, which provided ATC service. 4.To create Neural network model and its realize for admission student according pre-simulating in automatic form. METHODS using in the master’s degree thesis: 1.Expert Judgment Method. 2.Neural network. 2

3. Actuality 3 TRAINING

4. 4 Air Traffic Controller Training review There are four types of Pre-simulations training: 1.Skill Acquisition (SA). 2.Part-Task Practice (PTP). 3.Guided Skill Acquisition (GSA). 4.Guided Part-Task Practice (GPTP).

5. 5 Block-scheme of pre-simulating and simulator training of student’s

6. 6 Estimation difficulty of airspace part, which provided ATC service Questionnaires for experts – ATC with working experience. 1.Matrix of individual preferences. Evaluation of complexity of airspace parts (CTR, TMA and CTA). Obtained – R i,, where m - number of expert; R i – system of preference of i-expert. 2. Matrix of group preferences obtained, for CTR: 3. Coordination of expert’s opinion. 3.1. Calculation of dispersion D.

7. 7 3.2. Calculation of square average deviation σ: 3.3 Obtained coefficient of the variation ν: If ν CTR,TMA,CTA ≤33 %, opinion coordinated, and obtained system of expert group. Calculations shows that opinion was coordinated. Calculations for TMA and CTA would be the same variant. 4. Weight coefficients:

8. Matrix of group preference: The results of obtaining weight coefficient: 8 Result of estimations of difficulty of airspace part (CTR, TMA and CTA):

9. Graphical presentation of estimations difficulty airspace part: control zone (CTR), terminal control area (TMA) and control area (CTA) : 9

10. 10 1 – Input layer - 5 disciplines: 1.Navigation (NAV). 2.Air Traffic Management (ATM). 3.Meteorology (MET). 4.Human Factor (HF). 5.Aircrafts (ACTFTs). 2,3 and 4 – Hidden layers: 1.T – specified number of hours in study of disciplines, which regulates as educational Plan; 2.t compl – time of task\test completed; 3.w i – test mark. Interpretation of Multi-Layer Feedforward Neural network of student admission in Air Traffic Controller simulator training

11. 11 Multi-Layer Feedforward Neural network of student admission in Air Traffic Controller simulator training

12. Program realization of Neural network model 12 123 № c\nInput (dendrites)Ranks Ri Estimates Ci Weight coefficients (synapse) wj Specified number of hours in study of disciplines, which regulates as educational Plan (T) Estimated number of hours for preparation wj Xi (axons) Time of task\test completed (tcompl) Maximum time for tests (min) Admission of time for testing Test mark (wi) Minimum score Admission according to mark 1 Navigation 2 0,80,266666667902455 Ready9060Ready 2 Air Traffic Management 1 10,3333333338929,6666666755 Ready8060Ready 3 Meteorology 3 0,60,223346,655 Ready7060Ready 4 Aircrafts 4 0,40,13333333316221,655 Ready8060Ready 5 Human Factor 5 0,20,06666666761140,733333334055Ready60 Ready Minimum Estimated coefficient161,231 162,6 ИСТИНА Function of activation Ready

13. 13 № c\n Input (dendrites)Ranks Ri Estimates Ci Weight coefficients (synapse) wj Specified number of hours in study of disciplines, which regulates as educational Plan (T) Estimated number of hours for preparation wj Xi (axons) Time of task\test completed (tcompl) Maximum time for tests (min) Admission of time for testing Test mark (wi) Minimum score Admission according to mark 1Navigation 2 0,80,266666667902455 Ready9060Ready 2Air Traffic Management 1 10,3333333337023,3333333355 Ready8060Ready 3Meteorology 3 0,60,223346,655 Ready7060Ready 4Aircrafts 4 0,40,13333333316221,655 Ready8060Ready 5Human Factor 5 0,20,06666666761140,733333334055Ready60 Ready Minimum Estimated coefficient161,231 156,2666667 ИСТИНА Function of activation Not ready 1

14. 14 № c\n Input (dendrites)Ranks Ri Estimates Ci Weight coefficients (synapse) wj Specified number of hours in study of disciplines, which regulates as educational Plan (T) Estimated number of hours for preparation wj Xi (axons) Time of task\test completed (tcompl) Maximum time for tests (min) Admission of time for testing Test mark (wi) Minimum score Admission according to mark 1 Navigation 2 0,80,26666666790246055 Not ready9060Ready 2 Air Traffic Management 1 10,3333333338929,6666666755 Ready8060Ready 3 Meteorology 3 0,60,223346,655 Ready7060Ready 4 Aircrafts 4 0,40,13333333316221,66055 Not ready8060Ready 5 Human Factor 5 0,20,06666666761140,733333334055Ready60 Ready Minimum Estimated coefficient161,231 162,6 ЛОЖЬ ИСТИНА Function of activation Ready 2

15. 15 № c\n Input (dendrites)Ranks Ri Estimates Ci Weight coefficients (synapse) wj Specified number of hours in study of disciplines, which regulates as educational Plan (T) Estimated number of hours for preparation wj Xi (axons) Time of task\test completed (tcompl) Maximum time for tests (min) Admission of time for testing Test mark (wi) Minimum score Admission according to mark 1Navigation 2 0,80,266666667902455 Ready9060Ready 2Air Traffic Management 1 10,3333333338929,6666666755 Ready8060Ready 3Meteorology 3 0,60,223346,655 Ready7060Ready 4Aircrafts 4 0,40,13333333316221,655 Ready8060Ready 5Human Factor 5 0,20,06666666761140,733333334055Ready5960 Not ready Minimum Estimated coefficient161,231 162,6 ИСТИНА ЛОЖЬ Function of activation Ready 3

16. CONCLUSION 1.Done analysis of training and pre-simulating training of Air Traffic Controller. 2.Estimate control area’s (CTA, TMA) and control zone(CTR). 3.Build Multi-Layer Feedforward Neural network of student admission in Air Traffic Controller simulator training. 4.Program realization of Neural network in automatic form. 16

17. PUBLICATIONs: 17 1.T. F. Shmelova Estimation of pre-simulating training tasks complexity / T. F. Shmelova, V.A.Lazorenko, A.P. Bilko// Proceedings of the National Aviation University. – 2015. –№1. – 17-22 p. 2.A.P. Bilko, Neural network for automated estimation of pre-simulating training / A.P. Bilko, V.A. Lazorenko, O.V. Poluhovich, T.F. Shmelova, // 6th World Congress „Aviation in the XXIst century. Safety in Aviation And Space Technologies” NAU, Sept.24, 2014– С. 3.1.6– 3.1.10. 3.A.P. Bilko, Estimation of air traffic control zone/ A.P. Bilko// XIV Міхнародна наукова- практична конференція молодих учених і студентів “ Політ. Сучасні проблеми науки” НАУ, 2-3 квітня, 2014– С. 107

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