An information fusion framework for context-based accidents prevention

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dc.contributor.author Sánchez-Pi, Nayat
dc.contributor.author Martí, Luis
dc.contributor.author Molina López, José Manuel
dc.contributor.author Bicharra García, Ana Cristina
dc.date.accessioned 2018-10-16T09:41:53Z
dc.date.available 2018-10-16T09:41:53Z
dc.date.issued 2014-10-07
dc.identifier.bibliographicCitation Proceedings of the 17th International Conference on Information Fusion (FUSION 2014), Salamanca, 7-10th July 2014. IEEE, 2014, [8 p.]
dc.identifier.isbn 978-8-4901-2355-3
dc.identifier.uri http://hdl.handle.net/10016/27579
dc.description.abstract The oil and gas industry is increasingly concerned with achieving and demonstrating good performance with regard occupational health and safety (OHS) issues, through the control of its OHS risks, which is consistent with its core policy and objectives. There are standards to identify and record workplace accidents and incidents to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in an accident. Therefore, events recognition is central to OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. In this paper we propose a machine learning algorithm to learn from past anomalous events related to accident events in time and space. It also uses additional knowledge, like the contextual knowledge: user profile, event location and time, etc. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accident investigation by means of well-defined spatiotemporal constraints in the offshore oil industry domain.
dc.description.sponsorship This work was partially funded by CNPq BJT Project 407851/2012–7 and CNPq PVE Project 314017/2013–5.
dc.format.extent 8
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subject.other Accidents
dc.subject.other Algorithms
dc.subject.other Data mining
dc.subject.other Ontologies
dc.subject.other Cognition
dc.subject.other Databases
dc.subject.other Spatiotemporal phenomena
dc.title An information fusion framework for context-based accidents prevention
dc.type conferenceObject
dc.type bookPart
dc.relation.publisherversion https://ieeexplore.ieee.org/document/6916105
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.relation.eventdate 2014, 07-10 de julio
dc.relation.eventnumber 17
dc.relation.eventplace Salamanca
dc.relation.eventtitle International Conference on Information Fusion (FUSION 2014)
dc.relation.eventtype proceeding
dc.identifier.publicationtitle Proceedings of the 17th International Conference on Information Fusion (FUSION 2014), Salamanca, 7-10th July 2014
dc.identifier.uxxi CC/0000023373
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