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Vol. 1 No. 1 (2011): JITA - APEIRON

Mlađan Jovanović, Dušan Starčević, Mirko Petrović

A Multimodal Approach to Design of Aircraft Cockpit Displays

Original scientific paper

DOI: https://doi.org/10.7251/JIT1101016J

Abstract

In this paper, we present an approach to design of command tables in aircraft cockpits. To date, there is no common standard for designing this kind of command tables. Command tables impose high load on human visual senses for displaying flight information such as altitude, attitude, vertical speed, airspeed, heading and engine power. Heavy visual workload and physical conditions significantly influence cognitive processes of an operator in an aircraft cockpit. Proposed solution formalizes the design process describing instruments in terms of estimated effects they produce on flight operators. In this way, we can predict effects and constraints of particular type of flight instrument and avoid unexpected effects early in the design process.

Keywords: Aircraft cockpit, multimodal user interfaces, aircraft instrument, formal description of cockpit display.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.