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Screening paper runnability in a web-offset pressroom by data mining
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
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2009 (English)In: Proceedings of the 9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, Berlin: Springer Berlin/Heidelberg, 2009, p. 161-175Conference paper, Published paper (Refereed)
Abstract [en]

This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2009. p. 161-175
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5633
Keywords [en]
Classifier, GA, Mapping, Variable selection, Web break
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-105DOI: 10.1007/978-3-642-03067-3Scopus ID: 2-s2.0-76249098187ISBN: 978-3-642-03066-6 OAI: oai:DiVA.org:hh-105DiVA, id: diva2:236064
Conference
9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009, Leipzig, 20 - 22 July 2009
Available from: 2009-09-21 Created: 2009-09-21 Last updated: 2018-03-23Bibliographically approved

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Alzghoul, AhmadVerikas, AntanasHållander, Magnus

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CiteExportLink to record
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Citation style
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