|
|
Assessing the Aircraft Crew Activity Basing on Video Oculography Data 75
Kuravsky L.S.
Doctor of Engineering, Dean of the Computer Science Faculty , Moscow State University of Psychology and Education , Moscow, Russia
ORCID: https://orcid.org/0000-0002-3375-8446
e-mail: l.s.kuravsky@gmail.com Yuriev G.A.
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-2960-6562
e-mail: g.a.yuryev@gmail.com Zlatomrezhev V.I.
Head of Laboratory, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
ORCID: https://orcid.org/0000-0003-1776-6881
e-mail: vizlatomr@2100.gosniias.ru Greshnikov I.I.
Lead Engineer, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
ORCID: https://orcid.org/0000-0001-5474-3094 Polyakov B.Yu.
Graduate Student, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-6457-9520
e-mail: deslion@yandex.ru
Mathematical models and methods for crew training level assessing based on video oculography data are presented. The results obtained are based on comparing the studied fragments of oculomotor activity of pilots with comparable patterns of video oculography data of various types and performance quality contained in a pre-formed specialized database. To obtain estimates, a complex combination of random process analysis and multivariate statistical analysis is used. The “intelligence” of diagnostic tools is contained in empirical data and can flexibly change as they accumulate. The considered example of determining the flight mode and pilot qualification based on video oculography data allows us to talk about the possibility of significant discrimination of the gaze movement trajectories of pilots at different flight phases and significant discrimination of the gaze movement trajectories of experienced and inexperienced pilots at certain phases of flight. An important new component of the presented results is a discriminant analysis for solving the problem of flight exercises classification, based on the principles of quantum computing. The scope of the considered approach is not limited to aviation applications and can be extended to tasks that are similar in content.
This work was performed as part of the “SAFEMODE” project (grant # 814961) with the financial support of the Ministry of Science and Higher Education of the Russian Federation (UID RFMEFI62819X0014 project)
- Barabanschikov V.A., Zhegallo A.V. Registraciya i analyz napravlennosty
vzora cheloveka [Registration and analysis of the orientation of a person’s
gaze]. Moscow Pub-l: Institute of psychology of the Russian Academy of
Sciences, 2013, P. 316. (In Russ.).
- Zheltov S.Yu., Fedosov E.A., Chuyanov G.A., Zlatomregev V.I., Greshnikov
I.I. etc. Patent No. 101331 Kompleks oborudovaniya (stend) prototipirovaniya
interfeisa cabiny vozdushnogo sudna / Pravoobladateli FGUP GosNIIAS (Russia).
Zayavka 2016500077; Zayav. 15.01.2016; Zaregistr. 15.12.2016.(ROSPATENT). (In
Russ.).
- Krasil’shchikov M.N., Evdokimenkov V.N., Bazlev D.A.
Individualno-adaptirovannye bortovye sistemy kontrolya tehnicheskogo
sostoyaniya samlyota i podderzhki ypravlyayuschikh deistviy letchika
[Individually adapted on-Board systems for monitoring the technical condition
of the aircraft and supporting the pilot’s control actions]. Moscow: MAI Pub-l,
2011. P. 438. (In Russ.).
- Kuravsky L.S., Yuriev G.A. Svidetel’stvo o gosudarstvennoi registratsii
programmy dlyaEHVM №2018660358 Intelligent System for Flight Analysis v1.0
(ISFA#1.0) / Pravoobladateli Kuravskii L.S., Yur’ev G.A. (Russia). Zayavka
№2018617617; Zayav. 18.07.2018; Zaregistr.22.08.2018. (ROSPATENT). (In
Russ.).
- Report on applied research on the topic “Development of human factor risk
models and recommendations for creating a human-machine interface for the
aircraft crew cabin” (intermediate), stage 1, State program of the Russian
Federation “Development of the aviation industry for 2013-2025”, grant
agreement dated 21.10.2019. No. 075-11-2019-018, state registration no.
RFMEFI62819X0014. (In Russ.).
- Report on applied research on the topic “Development of human factor risk
models and recommendations for creating a human-machine interface for the
aircraft crew cabin” (final), stage 2, State program of the Russian Federation
“Development of the aviation industry for 2013—2025”, grant agreement dated
21.10.2019. No. 075-11-2019-018, state registration no. RFMEFI62819X0014. (In
Russ.).
- Kuravsky L.S., Yuryev G.A. A novel approach for recognizing abnormal
activities of operators of complex technical systems: three non-standard
metrics for comparing performance patterns, International Journal of Advanced
Research in Engineering and Technology (IJARET), 11 (4), 2020, pp. 119—136.
http://www. iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=4.
(Accessed 20.11.2020)
- Kuravsky L.S. Discriminant Analysis Based on the Approaches of Quantum
Computing. Lobachevskii Journal of Mathematics, 2020, Vol. 41, no. 12,
pp. 2338—2344.
- Kuravsky L.S., Yuryev G.A. The intelligent system to support condition
monitoring for activities of operators of complex technical systems. — In:
Proc. 16th International Conference on Condition Monitoring and Asset
Management, Glasgow, UK, June 2019. DOI: 10.1784/cm.2019.108 17 pp.
- Kuravsky L.S., Yuryev G.A., Detecting Abnormal Activities of Operators of
Complex Technical Systems and their Causes Basing on Wavelet Representations.
International Journal of Civil Engineering and Technology (IJCIET),
10(2), 2019, pp. 724—742. http://www.iaeme.com/IJCIET/ issues.asp?JType=IJCIE
T&VType=10&IType=2.
- Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I. New approaches for assessing
the activities of operators of complex technical systems. Eksperimental’naya
psikhologiya = Experimental psychology (Russia), 2019, vol. 12, no. 4, pp.
27—49. doi:10.17759/exppsy.2019120403.
- Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Yuryeva N.E. Assessing the
Aircraft Crew Actions with the Aid of a Human Factor Risk Model.
Eksperimental’naya psikhologiya = Experimental Psychology (Russia),
2020. Vol. 13, no. 2, pp. 153—181. DOI: https://doi.org/10.17759/
exppsy.2020130211.
- Aircraft trajectory clustering techniques using circular statistics.
Yellowstone Conference Center, Big Sky, Montana, 2016. IEEE.
- Bastani V., Marcenaro L., Regazzoni C. Unsupervised trajectory pattern
classification using hierarchical Dirichlet Process Mixture hidden Markov model
// 2014 IEEE International Workshop on Machine Learning for Signal
Processing (MLSP) / IEEE. 2014. Pp. 1—6.
- Enriquez M. Identifying temporally persistent flows in the terminal
airspace via spectral clustering // Tenth USA/Europe Air Traffic Management
Research and Development Seminar (ATM2013) / Federal Aviation Administration
(FAA) and EUROCONTROL. Chicago, IL, USA: 2013. June 10-13.
- Enriquez M., Kurcz C. A Simple and Robust Flow Detection Algorithm Based on
Spectral Clustering // International Conference on Research in Air
Transportation (ICRAT) / Federal Aviation Administration (FAA) and EUROCONTROL.
Berkeley, CA, USA: 2012. May 22—25.
- Faure C., Bardet J.M., Olteanu M., Lacaille J. Using Self-Organizing Maps
for Clustering and Labelling Aircraft Engine Data Phases. In: WSOM (2017):
96—103.
- Gaffney S., Smyth P. Joint probabilistic curve clustering and alignment //
In Advances in Neural Information Processing Systems. Vol. 17.
Cambridge, MA: MIT Press, 2005. Pp. 473—480.
- Gaffney S., Smyth P. Trajectory clustering with mixtures of regression
models // Proceedings of the fifth ACM SIGKDD international conference on
Knowledge discovery and data mining. 1999. Pp. 63—72.
- Laxhammar R., Falkman G. Online learning and sequential anomaly detection
in trajectories // IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2014. Vol. 36, no. 6. Pp. 1158—1173.
- Rintoul M., Wilson A. Trajectory analysis via a geometric feature space
approach // Statistical Analysis and Data Mining: The ASA Data Science
Journal. 2015.
- Wilson A., Rintoul M., Valicka C. Exploratory Trajectory Clustering with
Distance Geometry // International Conference on Augmented Cognition /Springer.
2016. Pp. 263—274.
|
|
