Development of industrial process characterisation through data analysis

Josifovic, Aleksandar and Corney, Jonathan; (2017) Development of industrial process characterisation through data analysis. In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. IEEE, GRC. ISBN 978-1-5090-4241-8

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    Abstract

    Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.

    ORCID iDs

    Josifovic, Aleksandar ORCID logoORCID: https://orcid.org/0000-0003-4024-6729 and Corney, Jonathan ORCID logoORCID: https://orcid.org/0000-0003-1210-3827;
    • Item type:Book Section
      ID code:61597
      Dates:
      Date
      Event
      13 February 2017
      Published
      27 September 2016
      Accepted
      Notes:(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
      Keywords:oil technology, accelerometers, data aquisition, Electrical engineering. Electronics Nuclear engineering, Computer Networks and Communications, Information Systems and Management, Control and Optimization, Artificial Intelligence
      Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
      Department:Faculty of Engineering > Design, Manufacture and Engineering Management
      Strategic Research Themes > Innovation Entrepreneurship
      Depositing user: Pure Administrator
      Date deposited:17 Aug 2017 13:16
      Last modified:19 Apr 2021 01:08
      Related URLs:
      URI:https://strathprints.strath.ac.uk/id/eprint/61597
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