Detection of transients in steel casting through standard and AI-based techniques

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dc.contributor.authorColla, Valentina
dc.contributor.authorVannucci, Marco
dc.contributor.authorMatarese, Nicola
dc.contributor.authorStephens, Gerard
dc.contributor.authorPianezzola, Marco
dc.contributor.authorAlonso, Izaskun
dc.contributor.authorLamp, Torsten
dc.contributor.authorPalacios, Juan
dc.contributor.authorSchiewe, Siegfried
dc.contributor.institutionCentros PRE-FUSION TECNALIA - (FORMER)
dc.date.accessioned2024-07-24T11:52:12Z
dc.date.available2024-07-24T11:52:12Z
dc.date.issued2011
dc.description.abstractThe detection of transients in the practice of continuous casting within a steel-making industry is a key task for the prediction of final product properties but currently a direct observation of this phenomenon is not available. For this reason in this paper several standard and soft-computing based methods for the detection of transients from plant data will be tested and compared. From the obtained results it emerges that the use of a fuzzy inference system based on experts knowledge achieves very satisfactory results correctly identifying most of the transient events present in the databases provided by different companies.en
dc.description.statusPeer reviewed
dc.format.extent9
dc.identifier.citationColla , V , Vannucci , M , Matarese , N , Stephens , G , Pianezzola , M , Alonso , I , Lamp , T , Palacios , J & Schiewe , S 2011 , Detection of transients in steel casting through standard and AI-based techniques . in Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings . PART 1 edn , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , no. PART 1 , vol. 6691 LNCS , pp. 256-264 , 11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 , Torremolinos-Malaga , Spain , 8/06/11 . https://doi.org/10.1007/978-3-642-21501-8_32
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-642-21501-8_32
dc.identifier.isbn9783642215001
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11556/2164
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=79957944633&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofAdvances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsindustrial problem
dc.subject.keywordsneuro-fuzzy systems
dc.subject.keywordstransient detection
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.titleDetection of transients in steel casting through standard and AI-based techniquesen
dc.typeconference output
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