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Screening web breaks in a pressroom by soft computing
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
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2011 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 11, no 3, p. 3114-3124Article in journal (Refereed) Published
Abstract [en]

The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of 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 second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%. (C) 2010 Elsevier B. V. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2011. Vol. 11, no 3, p. 3114-3124
Keywords [en]
Data mining, GA, Curvilinear component analysis, SVM, Variable selection, Printing press, Paper web break
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hh:diva-14603DOI: 10.1016/j.asoc.2010.12.014ISI: 000287479200017Scopus ID: 2-s2.0-79951855846OAI: oai:DiVA.org:hh-14603DiVA, id: diva2:404941
Available from: 2011-03-19 Created: 2011-03-19 Last updated: 2018-03-23Bibliographically approved

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Verikas, AntanasHållander, MagnusAlzghoul, Ahmad

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