hh.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Advances in computational intelligence-based print quality assessment and control in offset colour printing
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).ORCID-id: 0000-0003-2185-8973
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).ORCID-id: 0000-0001-8804-5884
Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
2011 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, nr 10, s. 13441-13447Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Nowadays most of information processing steps in printing industry are highly automated, except the last one – print quality assessment and control. Usually quality assessment is a manual, tedious, and subjective procedure. This article presents a survey of non numerous developments in the field of computational intelligence-based print quality assessment and control in offset colour printing. Recent achievements in this area and advances in applied computational intelligence, expert and decision support systems lay good foundations for creating practical tools to automate the last step of the printing process.

sted, utgiver, år, opplag, sider
Amsterdam: Elsevier, 2011. Vol. 38, nr 10, s. 13441-13447
Emneord [en]
Print quality, Decision support, Expert system, Image analysis, Quality inspection
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-15314DOI: 10.1016/j.eswa.2011.04.035ISI: 000292169500156Scopus ID: 2-s2.0-79957992693OAI: oai:DiVA.org:hh-15314DiVA, id: diva2:421853
Tilgjengelig fra: 2011-06-09 Laget: 2011-06-09 Sist oppdatert: 2018-01-12bibliografisk kontrollert
Inngår i avhandling
1. Situation Awareness in Colour Printing and Beyond
Åpne denne publikasjonen i ny fane eller vindu >>Situation Awareness in Colour Printing and Beyond
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods.

Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena.

In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios.

The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2014. s. 51
Serie
Halmstad University Dissertations ; 6
Emneord
Machine learning, Data mining, Colour printing, Smart homes
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-25318 (URN)978-91-87045-12-7 (ISBN)978-91-87045-11-0 (ISBN)
Disputas
2014-06-13, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 13:15 (engelsk)
Opponent
Veileder
Prosjekter
PPQSA3L
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2014-05-09 Laget: 2014-05-09 Sist oppdatert: 2015-09-11bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Verikas, AntanasLundström, Jens

Søk i DiVA

Av forfatter/redaktør
Verikas, AntanasLundström, Jens
Av organisasjonen
I samme tidsskrift
Expert systems with applications

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 293 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf