JITA

JITA Journal of Information Technology and Applications

Vol. 12 No. 1 (2022): JITA - APEIRON

Alexander Yu. Chunikhin, Vadym Zhytniuk

Semantic Numeration Systems as Information Tools for Fuzzy Data Processing

Original scientific paper

Abstract

We describe the concept of semantic numeration systems (SNS) as a certain class of context-based numeration methods. The main attention is paid to the key elements of semantic numeration systems – cardinal semantic operators. A classification of semantic numeration systems is given. The concept of fuzzy cardinal semantic transformation as a basis for creating fuzzy semantic numeration systems is advanced. Both fuzziness of the initial data – cardinals of abstract entities – and fuzziness of the parameters of the cardinal semantic operators are considered. The principle of formation of the fuzzy common carry in the cardinal semantic operators with multiple inputs is formulated

Keywords: Cardinal Abstract Entity, Cardinal Semantic Operator, Semantic Numeration System, Fuzzy Cardinal Semantic Transformation.

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.