JITA

JITA Journal of Information Technology and Applications

Vol. 5 No. 1 (2015): JITA - APEIRON

Dovgobrod Georgy Moiseevich, Klyachko Lev Mikhaylovich

Control Systems for Automated Vessel Piloting Through Local Stationary Obstacles

Original scientific paper

DOI: https://doi.org/10.7251/JIT1501061M

Abstract

To reduce the “human factor” component in the causes of accidents during pilot age of vessels along areas of fairways with local stationary obstacles we propose a device which provides: a) real-time presentation on a graphical display of information on current and predicted positions of a vessel with regard to a stationary obstacle; b) automated or semi-automated piloting of a vessel in to a straight path for safe passage of an obstacle. Specified device will allow reducing the risk of accidents while piloting a vessel along difficult parts of fairways.

Keywords: control systems, navigation, microcontroller.

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.