Advantages of multiscale detection of defective pills during manufacturing

Craig C. Douglas, Li Deng, Yalchin Efendiev, Gundolf Haase, Andreas Kucher, Robert Lodder, Guan Qin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw conclusions that may not be 100% accurate. Removing all of the defective products, or halting production in extreme cases, will reduce costs and eliminate embarrassing and expensive recalls. We use the knowledge that experts have accumulated over many years, dynamic data derived from networks of smart sensors using both audio and chemical spectral signatures, multiple scales to look at individual products and larger quantities of products, and finally adaptive models and algorithms.

Original languageEnglish (US)
Title of host publicationHigh Performance Computing and Applications - Second International Conference, HPCA 2009, Revised Selected Papers
Pages8-16
Number of pages9
DOIs
StatePublished - 2010
Externally publishedYes
Event2nd International Conference on High-Performance Computing and Applications, HPCA 2009 - Shanghai, China
Duration: Aug 10 2009Aug 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5938 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on High-Performance Computing and Applications, HPCA 2009
Country/TerritoryChina
CityShanghai
Period08/10/0908/12/09

Bibliographical note

KAUST Repository Item: Exported on 2020-04-23
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research was supported in part by NSF grants OISE-0405349, ACI-0305466, CNS-0719626, and ACI-0324876, DOE grant DE-FC26-08NT4, and Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

Keywords

  • DDDAS
  • Dynamic data-driven application systems
  • High performance computing
  • Integrated sensing and processing
  • Manufacturing defect detection
  • Parallel algorithms

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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