Principles of Developing a Software and Hardware Complex for Crew Intelligent Support and Training Level Assessment



Presented is a new approach to aircraft crew intelligent support, which is based on comparing flight fragments (maneuvers) under study with the relevant patterns contained in the database and representing the system “empirical intelligence”. Principal components of this approach are four new metrics for comparing flight fragments, viz.: the Euclidean metric in the space of wavelet coefficients; the likelihood metric of eigenvalue trajectories for transformations of activity parameters; the Kohonen metric in the space of wavelet coefficients; the likelihood metric for comparing gaze trajectories. Features of the presented approach are: the presence of an “intelligent component” that is contained in empirical data and can be flexibly changed as they accumulate; the use of integral comparisons of the flight fragments under study and video oculography data with relevant patterns of various types and performance quality from a specialized database, with transferring characteristics of the nearest pattern from this specialized database to the fragment under study; applying a complex combination of the methods for stochastic processes analysis and multivariate statistical techniques.

General Information

Keywords: operators of complex technical systems, intelligent crew support, crew training level assessment, video oculography, likelihood metric, Kohonen metric.

Journal rubric: Data Analysis

Article type: scientific article


For citation: Greshnikov I.I., Kuravsky L.S., Yuryev G.A. Principles of Developing a Software and Hardware Complex for Crew Intelligent Support and Training Level Assessment. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2021. Vol. 11, no. 2, pp. 5–30. DOI: 10.17759/mda.2021110201. (In Russ., аbstr. in Engl.)


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Information About the Authors

Ivan I. Greshnikov, PhD in Engineering, Lead Engineer, State Research Institute of Aviation Systems (GosNIIAS), Graduate Student, Moscow State University of Psychology and Education (MSUPE), Moscow, Russia, ORCID:, e-mail:

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID:, e-mail:

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID:, e-mail:



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