Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61845
Title: Visual content representation and retrieval for Cognitive Cyber Physical Systems
Contributor(s): de Oliveira, Caterine Silva (author); Sanin, Cesar  (author)orcid ; Szczerbicki, Edward (author)
Publication Date: 2019
Open Access: Yes
DOI: 10.1016/j.procs.2019.09.400
Handle Link: https://hdl.handle.net/1959.11/61845
Abstract: 

Cognitive Cyber Physical Systems (C-CPS) have gained significant attention from academia and industry during the past few years. One of the main reasons behind this interest is the potential of such technologies to revolutionize human life since they intend to work robustly under complex visual scenes, which environmental conditions may vary, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior like predicting possible events based on cognitive capabilities that are able to sense, analyze, and act based on their analysis results. However, perceiving the environment and translating it into knowledge to be useful for the decision making process, still remains a challenge for real time applications due to the complexity of such process. In this paper, we present a multi-domain knowledge structure based on experience, which can be used as a comprehensive embedded knowledge representation for C-CPS, addressing the representation of visual content issue and facilitating its reuse. The implementation of such representation has been tested in a Cognitive Vision Platform for Hazard Control (CVP-HC) which aims to manage of workers’ exposure to risks in industrial environments, facilitating knowledge engineering processes through a flexible and adaptable implementation.

Publication Type: Conference Publication
Conference Details: KES 2019: 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Budapest, Hungary, 4th to 6th of September, 2019
Source of Publication: Procedia Computer Science, v.159, p. 2249-2257
Publisher: Elsevier BV
Place of Publication: The Netherlands
ISSN: 1877-0509
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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