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Vol. 4 No. 2 (2014): JITA - APEIRON

Yu-Min Yang, Chao-Tsong Fang-Tsou

Aquaculture Cloud Management System

Original scientific paper

DOI: https://doi.org/10.7251/JIT1402075Y

Abstract

This study proposes an aquaculture system combining wireless sensors with the Internet of Things and expert system concepts. Built on the accumulated expertise and experience of professionals and researchers, the knowledge base advises aqua-farms on relevant farming practices. We hope that this system will conserve resources and secure product quality. The system also provides production data to consumers, thus facilitating information transparency and allowing consumers to purchase products with full knowledge and guarantee of food safety.

Keywords: Expert system, Internet of Things, Cloud services.

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.