JITA

JITA Journal of Information Technology and Applications

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

Zoran Ž. Avramović, Radomir Z. Radojičić, Saša D. Mirković

A new Approach to Computer Analysis of Queuing Systems Without Programming

Original scientific paper

DOI: https://doi.org/10.7251/JIT1501025A

Abstract

The paper presents original object oriented programming system ARS for modelling and simulation queuing systems. Programming system was developed in programming language C++. It establishes connection with intrinsic, but also with other Windows programming packages, in a simple way, through object oriented environment. Basic characteristics and possibilities of programming system, as well as comparative analysis of simulators: mathematical model (analytical solution) – GPSS/H – ARS, on the example of closed queuing network in the paper is given. The significant application for computer performance evaluation is reported.

Keywords: simulation, queuing system, programming system, computer performance evaluation.

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.