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dc.contributor.authorOnggo, BS
dc.contributor.authorJuan, AA
dc.contributor.authorMustafee, N
dc.contributor.authorSmart, A
dc.contributor.authorMolloy, O
dc.date.accessioned2019-05-15T08:17:00Z
dc.date.issued2019-02-04
dc.description.abstractSymbiotic simulation is one of Industry 4.0 technologies that enables interaction between a physical system and the simulation model that represents it as its digital twin. Symbiotic simulation is designed to support decision making at the operational levels by making use of real- or near real- time data that is generated by the physical system, which is used as an input to the simulation model. From the modeling perspective, a symbiotic simulation system comprises a hybrid systems model that combines simulation, optimization and machine learning models as well as a data acquisition module and an actuator. The actuator is needed when the symbiotic simulation system is designed to directly control the physical system without human intervention. This paper reviews the components of a symbiotic simulation system from the perspective of hybrid systems modeling and highlights research questions needed to advance symbiotic simulation study.en_GB
dc.description.sponsorshipTrinity Benefaction Funden_GB
dc.description.sponsorshipUniversity of Exeteren_GB
dc.identifier.citation2018 Winter Simulation Conference (WSC), 9-12 December 2018, Gothenburg, Sweden, pp. 1358 - 1369en_GB
dc.identifier.doi10.1109/WSC.2018.8632407
dc.identifier.urihttp://hdl.handle.net/10871/37096
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rights© 2019 IEEEen_GB
dc.subjectSymbiosisen_GB
dc.subjectData modelsen_GB
dc.subjectAnalytical modelsen_GB
dc.subjectPredictive modelsen_GB
dc.subjectAdaptation modelsen_GB
dc.subjectMathematical modelen_GB
dc.subjectData acquisitionen_GB
dc.titleSymbiotic simulation system: Hybrid systems model meets big data analyticsen_GB
dc.typeConference paperen_GB
dc.date.available2019-05-15T08:17:00Z
dc.identifier.isbn9781538665725
dc.identifier.issn0891-7736
dc.descriptionThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this recorden_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2018-07-02
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2019-02-04
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2019-05-14T14:30:05Z
refterms.versionFCDAM
refterms.dateFOA2019-05-15T08:17:04Z
refterms.panelCen_GB


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