JITA

JITA Journal of Information Technology and Applications

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

Goran Đukanović, Milan Šunjevarić, Nataša Gospić

Measuring the Characteristics of DG CAC Algorithm

Original scientific paper

DOI: https://doi.org/10.7251/JIT1101060DJ

Abstract

Users today expect email and instant messaging access, surf, video games and other services through mobile broadband access networks. In order to support this increasing data traffic, advanced resource management has to be implemented. As CAC (Call Admission Control) algorithm plays an important role in this resource management, comparing of two proposed call admission control algorithms has been done in this paper. Algorithms are tested in simulation environment, for two different periods of time. They showed expected characteristics in both 1000 and 10000 seconds periods, and newly proposed DG CAC algorithm showed better results than other algorithm, in number of handover requests, and in the way of returning resources to degraded connections.

Keywords: CAC, QoS, UMTS, Wireless.

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.