JITA

JITA Journal of Information Technology and Applications

Vol. 5 No. 2 (2018): JITA - APEIRON

Evgenii Skakov, Vladimir Malysh

Simulated Annealing and Evolutionary Algorithm for Base Station Location Problem: a Comparison of Methods

Original scientific paper

DOI: https://doi.org/10.7251/JIT1502088S

Abstract

A modifications of the evolutionary algorithm and simulated annealing method for solving the base station location problem for creating a wireless data network is introduced in the article. By the way of computer simulation a comparison of speed and accuracy of solutions obtained by the proposed methods and the method of exhaustive search is produced. The study revealed that new simulated annealing method show better results than the modified evolutionary algorithm.

Keywords: base station location, evolutionary algorithm, simulated annealing, wireless networks, optimization, SIR.

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.