Network models for monitoring high-dimensional image profiles

Chen Kan, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Modern industries are investing in advanced imaging technology to increase information visibility, address system complexity, and improve quality and integrity of complex systems. The proliferation of high-dimensional images pose significant challenges on traditional and next-generation innovation practices for process monitoring and control in manufacturing and healthcare. Traditional statistical process control (SPC) is not concerned with imaging data but key product or process characteristics, and is limited in its ability to readily address complex structures of high-dimensional imaging profiles. Realizing the full potential of advanced imaging technology for process monitoring and control hinges on the development of new SPC methodologies. This paper presents a novel dynamic network methodology for monitoring and control of high-dimensional imaging streams. Experimental results on biomanufacturing and machining imaging profiles show that the proposed approach not only captures complex image structures but also provides an effective online control charts for monitoring image profiles. New dynamic network monitoring schemes are shown to have strong potentials to be generally applicable to research problems in diverse fields with image profiles.

Original languageEnglish (US)
Title of host publication2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publicationAutomation for a Sustainable Future, CASE 2015
PublisherIEEE Computer Society
Pages1078-1083
Number of pages6
ISBN (Electronic)9781467381833
DOIs
StatePublished - Oct 7 2015
Event11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden
Duration: Aug 24 2015Aug 28 2015

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2015-October
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other11th IEEE International Conference on Automation Science and Engineering, CASE 2015
CountrySweden
CityGothenburg
Period8/24/158/28/15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Kan, C., & Yang, H. (2015). Network models for monitoring high-dimensional image profiles. In 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015 (pp. 1078-1083). [7294242] (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October). IEEE Computer Society. https://doi.org/10.1109/CoASE.2015.7294242