High-Level Information Fusion for Risk and Accidents Prevention in Pervasive Oil Industry Environments View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Nayat Sanchez-Pi , Luis Martí , José Manuel Molina , Ana Cristina Bicharra Garcia

ABSTRACT

Information fusion studies theories and methods to effectively combine data from multiple sensors and related information to achieve more specific inferences that could be achieved by using a single, independent sensor. Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications. In this sense, the deployment of an Intelligent Offshore Oil Industry Environment will help to figure out a risky scenario based on the events occurred in the past related to anomalies and the profile of the current employee (role, location, etc.). In this paper we propose an information fusion model for an intelligent oil environment in which employees are alerted about possible risk situations while their are moving around their working place. The layered architecture, implements a reasoning engine capable of intelligently filtering the context profile of the employee (role, location) for the feature selection of an inter-transaction mining process. Depending on the employee contextual information he will receive intelligent alerts based on the prediction model that use his role and his current location. This model provides the big picture about risk analysis for that employee at that place in that moment. More... »

PAGES

202-213

References to SciGraph publications

  • 2014. Text Classification Techniques in Oil Industry Applications in INTERNATIONAL JOINT CONFERENCE SOCO’13-CISIS’13-ICEUTE’13
  • Book

    TITLE

    Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

    ISBN

    978-3-319-07766-6
    978-3-319-07767-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-07767-3_19

    DOI

    http://dx.doi.org/10.1007/978-3-319-07767-3_19

    DIMENSIONS

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