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
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
Actuality
Other
causes
20%
Human
error
80%
TRAINING
3
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).
4
Block-scheme of pre-simulating and simulator
training of student’s
Simulator
Training
Basic Training
Air Traffic
Management
Control zone
(CTR)
Navigation
Meteorology
Pre – simulating
Training
Ready
Not ready
Terminal
control area
(TMA)
Aircrafts
Control area
(CTA)
Human Factor
...
5
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 – Ri, , where m number of expert; Ri – system of preference of i-expert.
2. Matrix of group preferences obtained, for CTR:
m
RgrCTR 
 RiCTR
i 1
m
 2,64
3. Coordination of expert’s opinion.
3.1. Calculation of dispersion D.


2
m
m
2

 Rgr  Ri
 RgrCTR  RiCTR 

  0,401099
D  i 1
 i 1
m 1
m 1
6
3.2. Calculation of square average deviation σ:
σ D
DCTR  0,633324
3.3 Obtained coefficient of the variation ν:
ν
σ
100 
Rgr
σ CTR
100  23,9636 %
RgrCTR
If νCTR,TMA,CTA≤33 %, opinion coordinated, and obtained system of
expert group. Calculations shows that opinion was coordinated.
4. Weight coefficients:
wj 
Cj
n
C j
j 1
Calculations for TMA and CTA would be the same variant.
7
Result of estimations of difficulty of airspace part
(CTR, TMA and CTA):
Matrix of group preference:
Coordination of expert’s
opinion
Rgr
Di
i
vi
%
CTR
x1
2,64286
0,40110
0,63332
23,9636
TMA
x2
1,14286
0,13186
0,36313
31,77445
CTA
x3
2,21429
0.33516
0,57893
26,14542
The results of obtaining weight coefficient:
№
Zone
Ri rg
Cj
wi
1
CTR
2,64286
1,12222
0,22619
2
TMA
1,14286
1,21229
0,47619
3
CTA
2,21428
1,14035
0,29762
8
Graphical presentation of estimations difficulty
airspace part: control zone (CTR), terminal
control area (TMA) and control area (CTA) :
9
Interpretation of Multi-Layer Feedforward Neural
network of student admission in Air Traffic
Controller simulator training
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. tcompl – time of task\test completed;
3. wi – test mark.
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Multi-Layer Feedforward Neural network of
student admission in Air Traffic Controller
simulator training
T
Input
NAV
T1
ATM
T2
MET
T3
HF
T4
ACFTs
T5
tcompl
wi
tcompl1
wi1
tcompl2
wi2
tcompl3
wi3
tcompl4
wi4
tcompl5
wi5
Output
T(1,2,3,4,5)
tcompl(1,2,3,4,5)
w i(1,2,3,4,5)
W (T, t, wi)
11
Program realization of Neural network model
1 situation – student ready to simulator training:
1 T >Tmin
№ c\n
Input (dendrites)
2 tcompl >tmin
3 wi >wmin
Ranks Estimates
Weight
Specified Estimated Time of Maximum Admission Test mark
Ri
Ci
coefficients number of number of task\test
time for of time for
(wi)
(synapse)
hours in
hours for completed tests (min) testing
wj
study of preparation (tcompl)
disciplines,
wj Xi
which
(axons)
regulates as
educational
Plan
(T)
Minimum Admission
score
according
to mark
2
1 Navigation
0,8 0,266666667
90
24
55
55
Ready
90
60
Ready
1 0,333333333
0,6
0,2
89 29,66666667
233
46,6
55
55
55
55
Ready
Ready
80
70
60
60
Ready
Ready
0,4 0,133333333
162
21,6
55
55
Ready
80
60
Ready
0,2 0,066666667
611 40,73333333
40
55
Ready
60
60
Ready
1
2 Air Traffic Management
3 Meteorology
3
4
4 Aircrafts
5
5 Human Factor
Minimum Estimated
coefficient
161,2
3
1
162,6
Function of
activation
ИСТИНА
ИСТИНА
Ready
12
2 situation – student don’t ready to simulator training according to
ATM:
T < Tmin
1
№
c\n
Input (dendrites)
1 Navigation
Ranks Estimates Weight
Specified Estimated
Ri
Ci
coefficients number of number of
(synapse)
hours in
hours for
wj
study of preparation
disciplines,
wj Xi
which
(axons)
regulates as
educational
Plan
(T)
2
0,8
0,266666667
Time of Maximum Admission Test mark
task\test time for of time for
(wi)
completed tests (min) testing
(tcompl)
Minimum Admission
score
according
to mark
90
24
55
55
Ready
90
60
Ready
1
2 Air Traffic Management
3 Meteorology
4 Aircrafts
5 Human Factor
Minimum Estimated
coefficient
3
4
5
161,2
1
0,333333333
70
23,33333333
55
55
Ready
80
60
Ready
0,6
0,2
233
46,6
55
55
Ready
70
60
Ready
0,4
0,133333333
162
21,6
55
55
Ready
80
60
Ready
0,2
0,066666667
611
40,73333333
40
55
Ready
60
60
Ready
3
1
156,2666667
Function of
activation
ИСТИНА
ИСТИНА
Not ready
13
3 situation – student don’t ready to simulator training accordin
to time for tests:
2 tcompl < tmin
№
c\n
Input (dendrites)
1 Navigation
Ranks Estimates Weight
Specified Estimated
Ri
Ci
coefficients number of number of
(synapse)
hours in
hours for
wj
study of preparation
disciplines,
wj Xi
which
(axons)
regulates as
educational
Plan
(T)
2
Time of Maximum Admission Test mark
task\test time for of time for
(wi)
completed tests (min) testing
(tcompl)
Minimum Admission
score
according
to mark
0,8
0,266666667
90
24
60
55
Not ready
90
60
Ready
1
0,333333333
89
29,66666667
55
55
Ready
80
60
Ready
0,6
0,2
233
46,6
55
55
Ready
70
60
Ready
0,4
0,133333333
162
21,6
60
55
Not ready
80
60
Ready
0,2
0,066666667
611
40,73333333
40
55
Ready
60
60
Ready
3
1
1
2 Air Traffic Management
3 Meteorology
4 Aircrafts
5 Human Factor
Minimum Estimated
coefficient
3
4
5
161,2
162,6
Function of
activation
ЛОЖЬ
ИСТИНА
Ready
14
4 situation – student don’t ready to simulator training
according to mark:
3 wi < wmin
№
c\n
Input (dendrites)
1 Navigation
Ranks Estimates Weight
Specified Estimated
Ri
Ci
coefficients number of number of
(synapse)
hours in
hours for
wj
study of preparation
disciplines,
wj Xi
which
(axons)
regulates as
educational
Plan
(T)
2
Time of Maximum Admission Test mark
task\test time for of time for
(wi)
completed tests (min) testing
(tcompl)
Minimum Admission
score
according
to mark
0,8
0,266666667
90
24
55
55
Ready
90
60
Ready
1
0,333333333
89
29,66666667
55
55
Ready
80
60
Ready
0,6
0,2
233
46,6
55
55
Ready
70
60
Ready
0,4
0,133333333
162
21,6
55
55
Ready
80
60
Ready
0,2
0,066666667
611
40,73333333
40
55
Ready
59
60
Not ready
3
1
1
2 Air Traffic Management
3 Meteorology
4 Aircrafts
5 Human Factor
Minimum Estimated
coefficient
3
4
5
161,2
162,6
Function of
activation
ИСТИНА
ЛОЖЬ
Ready
15
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.
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PUBLICATIONs:
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 науковапрактична конференція молодих учених і студентів “ Політ. Сучасні проблеми науки”
НАУ, 2-3 квітня, 2014– С. 107
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Thanks for your attention!
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