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Vol. 11 No. 1 (2021): JITA - APEIRON

Stefan Panić, Negovan Stamenković

Impact of Pointing Errors on the Performances of Double Rician FSO Channels

Original scientific paper

DOI:https://doi.org/10.7251/JIT2101017P

Abstract

In this paper we will propose new analytically traceable probability density function (PDF) model for free space optics (FSO) turbulence, obtained as a generalization of double Ricean turbulence model, that encompasses both large-scale and smallscale turbulence eddy effects along by taking into account performance decreasing influence of misalignment introduced through boresight pointing error model. Consequently, after delivering the closed-form expressions for the newly introduced double FSO model, we obtain the analytical expressions for the bit error rate (BER) performance for the Double Rician distribution affected by misalignment. Numerical results will show the impact of system parameters on FSO link performance and we will provide full performance analysis. © 2021.

Keywords: free space optics (FSO), atmospheric turbulence, pointing error, bit error rate (BER).

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.