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Vol. 8 No. 2 (2018): JITA - APEIRON

Nedim Smailović

Psychological Connection between Colors and Certain Characteristic Terms

Original scientific paper

DOI:https://doi.org/10.7251/JIT1802075S

Abstract

This paper presents results of a research on psychological connection between 40 offered colors and 91 terms from everyday life. Similar researches have been conducted and published in a number of instances in domestic and foreign bibliography, but this research has certain particularities that are not often present in other articles. For one thing, colors whose associativity with certain terms is being analyzed are shown in a table, their name and code in the RGB system of color marking are provided. Colors were not merely described, e.g. blue, green, yellow, etc., as it may be confusing or lead to misinterpretation of answers since there are many degrees of blue color, for example. In the second part of the poll, the subjects answered the Ishihara test, in order to check the ability to correctly interpret the colors. The third specific is triple visual interpretation of received results using colored graphs.

Keywords: color, Ishihara test, psychology, visualization of data.

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.