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Abstract
ABSTRACT Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).
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Authors (5)
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Caterine Silva de Oliveira
- University of Newcastle, Australia School of Engineering
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Franco Giustozzi
- INSA, France Computer Science
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Cecilia Zanni-Merk
- INSA, France Computer Science
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Cesar Sanin
- University of Newcastle, Australia School of Engineering
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Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (2020 Taylor & Francis Group, LLC)
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
CYBERNETICS AND SYSTEMS
no. 51,
pages 214 - 231,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- de Oliveira C., Giustozzi F., Zanni-Merk C., Sanin C., Szczerbicki E.: Stream Reasoning to Improve Decision-Making in Cognitive Systems// CYBERNETICS AND SYSTEMS -Vol. 51,iss. 2 (2020), s.214-231
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2019.1705553
- Verified by:
- Gdańsk University of Technology
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