Prva međunarodna naučna konferencija

BEZBJEDNO

URBANO

MOBILNO

Vol. 13 No. 1 (2023): JITA - APEIRON

Vladimir Milosević

Basic Mathematical ATM Model for Time Based Navigation In U-Space Environment

Original scientific paper

DOI:https://doi.org/10.7251/JIT2301005M

Abstract

Time Based Navigation Mathematical model is a navigational tool which allows instant management of distance, speed and time on more effective way. Time is a kind of specific imaginary dimension which describes real life dynamicity. Most efficient tool for time measuring, which precisely represents nature of time, is a circle with angles inside, well known as “clock”. Time Based Navigation Mathematical model is only different way of use of this measuring tool where every aircraft on its route has precise navigational time clock for accurate destination arrival. Implementation of this model can offer higher level of navigational precision in longitudinal and lateral domain, effective speed correction calculations and management in time domain, constant identification and recalculation of total time error and also can be used as safety net tool to define conflicts in UTM Air Space.

Keywords: Digitalization, propulsion, aircraft, U-Space, Electrification, ATM, environmental friendly, zero emissions, arrival management, artificial intelligence.

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.