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Vol. 2 No. 2 (2012): JITA - APEIRON

Uroš Romić, Igor Manić, Ivan Pantelić

Contemporary Java Web Technologies as a Service for the University Employees

Original scientific paper

DOI: https://doi.org/10.7251/JIT1202088R

Abstract

This paper describes the web application used by employees of the School of Electrical Engineering in Belgrade. The application is based on contemporary open source Java web technologies (including frameworks such as Spring, JSF, Hibernate, etc.). They were combined together into an advanced system that provides an environment for the rapid application development, high modularity and configurability. Paper describes main groups of application functionalities related to teaching process and financial operations, as well as additional functionalities. Application three-tier architecture is described in detail, with the description of technologies used in each tier. Application development environment is presented including build process management. Also, the security solution is described, as well as distributed computing model chosen for communication with other information systems within the School of Electrical Engineering.

Keywords: Java, Spring, JSF, Distributed Computing.

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.