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Vol. 10 No. 1 (2020): JITA - APEIRON

Dragan Čubrilović, Miloš Ljubojević

IMPROVING THE SPATIAL DATA QUALITY IN THE GEOGRAPHICAL INFORMATION SYSTEM OF THE TELECOM OPERATOR

Original scientific paper

DOI:https://doi.org/10.7251/JIT2001017C

Abstract

Spatial data about telecommunication infrastructure facilities represent the inevitable resources of each telecom operator. The precision of collected spatial data, used in the geographic information system (GIS) of telecom operators, is very important, especially when it is about urban environments. In this paper, we have presented the possibility of correcting the positions of telecommunication facilities obtained using the Global Positioning System (GPS). Factors affecting the accuracy and quality of spatial data have been analyzed and solutions for quality improvement proposed. We have shown that using permanent stations can achieve the required level of spatial position correction. A fast and efficient position correction allows updating data in GIS of telecom operators, providing correct, accurate, and timely information about telecom infrastructure.

Keywords: GPS, GIS, spatial data quality, differential correction, permanent station.

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.