Development of industrial process characterisation through data analysis

Aleksandar Josifovic, Jonathan Corney

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

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.

LanguageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Place of PublicationPiscataway, N.J.
PublisherIEEE
Number of pages7
ISBN (Print)978-1-5090-4241-8
DOIs
Publication statusPublished - 13 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Royal Olympic Hotel, Athens, Greece
Duration: 6 Dec 20169 Dec 2016
http://ssci2016.cs.surrey.ac.uk/

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Abbreviated titleSSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16
Internet address

Fingerprint

Data analysis
Data-driven
Large Data Sets
Explosion
Exploitation
Explosions
Parameter Space
Simulation Model
Optimal Solution
Engineering
Sensor
Optimization
Sensors
Experiment
Simulation
Experiments
Optimal solution
Simulation model
Model

Keywords

  • oil technology
  • accelerometers
  • data aquisition

Cite this

Josifovic, A., & Corney, J. (2017). Development of industrial process characterisation through data analysis. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 Piscataway, N.J.: IEEE. https://doi.org/10.1109/SSCI.2016.7849990
Josifovic, Aleksandar ; Corney, Jonathan. / Development of industrial process characterisation through data analysis. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Piscataway, N.J. : IEEE, 2017.
@inproceedings{46c5f28c7de14a9081b7831ba13a09f7,
title = "Development of industrial process characterisation through data analysis",
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.",
keywords = "oil technology, accelerometers, data aquisition",
author = "Aleksandar Josifovic and Jonathan Corney",
note = "(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.",
year = "2017",
month = "2",
day = "13",
doi = "10.1109/SSCI.2016.7849990",
language = "English",
isbn = "978-1-5090-4241-8",
booktitle = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",
publisher = "IEEE",

}

Josifovic, A & Corney, J 2017, Development of industrial process characterisation through data analysis. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. IEEE, Piscataway, N.J., 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7849990

Development of industrial process characterisation through data analysis. / Josifovic, Aleksandar; Corney, Jonathan.

2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Piscataway, N.J. : IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Development of industrial process characterisation through data analysis

AU - Josifovic, Aleksandar

AU - Corney, Jonathan

N1 - (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.

PY - 2017/2/13

Y1 - 2017/2/13

N2 - 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.

AB - 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.

KW - oil technology

KW - accelerometers

KW - data aquisition

UR - http://www.scopus.com/inward/record.url?scp=85016082628&partnerID=8YFLogxK

U2 - 10.1109/SSCI.2016.7849990

DO - 10.1109/SSCI.2016.7849990

M3 - Conference contribution book

SN - 978-1-5090-4241-8

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - IEEE

CY - Piscataway, N.J.

ER -

Josifovic A, Corney J. Development of industrial process characterisation through data analysis. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Piscataway, N.J.: IEEE. 2017 https://doi.org/10.1109/SSCI.2016.7849990