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Vol. 12 No. 2 (2022): JITA - APEIRON

Lyzlov Sergey Sergeevich, Uvarov Sergey Sergeevich, Katina Marina Vladimirovna

Methodology for calculating the probabilistic characteristics of the objectivity of the test results of students in the distance learning system Moodle

Original scientific paper

DOI:https://doi.org/10.7251/JIT2202111S

Abstract

The article discusses the methodology for calculating the probabilistic characteristics of the objectivity of test results in distance learning. Calculation expressions are obtained and simulation modeling of the process of forming test tasks for students is carried out. The results of calculations and simulation modeling are given, estimates of a random discrete value are obtained, defined as the number of tests at which information about the content of all questions in test tasks becomes known to all students.

Keywords:Moodle distance learning system, simulation modeling, calculation expressions and simulation results for assessing the probabilistic characteristics of the objectivity of student testing results.

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.