Advantages of Multiscale Detection of Defective Pills during Manufacturing

Handle URI:
http://hdl.handle.net/10754/597475
Title:
Advantages of Multiscale Detection of Defective Pills during Manufacturing
Authors:
Douglas, Craig C.; Deng, Li; Efendiev, Yalchin; Haase, Gundolf; Kucher, Andreas; Lodder, Robert; Qin, Guan
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. © 2010 Springer-Verlag.
Citation:
Douglas CC, Deng L, Efendiev Y, Haase G, Kucher A, et al. (2010) Advantages of Multiscale Detection of Defective Pills during Manufacturing. High Performance Computing and Applications: 8–16. Available: http://dx.doi.org/10.1007/978-3-642-11842-5_2.
Publisher:
Springer Science + Business Media
Journal:
High Performance Computing and Applications
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2010
DOI:
10.1007/978-3-642-11842-5_2
Type:
Book Chapter
ISSN:
0302-9743; 1611-3349
Sponsors:
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).
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorDouglas, Craig C.en
dc.contributor.authorDeng, Lien
dc.contributor.authorEfendiev, Yalchinen
dc.contributor.authorHaase, Gundolfen
dc.contributor.authorKucher, Andreasen
dc.contributor.authorLodder, Roberten
dc.contributor.authorQin, Guanen
dc.date.accessioned2016-02-25T12:40:27Zen
dc.date.available2016-02-25T12:40:27Zen
dc.date.issued2010en
dc.identifier.citationDouglas CC, Deng L, Efendiev Y, Haase G, Kucher A, et al. (2010) Advantages of Multiscale Detection of Defective Pills during Manufacturing. High Performance Computing and Applications: 8–16. Available: http://dx.doi.org/10.1007/978-3-642-11842-5_2.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-642-11842-5_2en
dc.identifier.urihttp://hdl.handle.net/10754/597475en
dc.description.abstractWe 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. © 2010 Springer-Verlag.en
dc.description.sponsorshipThis 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).en
dc.publisherSpringer Science + Business Mediaen
dc.subjectDDDASen
dc.subjectDynamic data-driven application systemsen
dc.subjectHigh performance computingen
dc.subjectIntegrated sensing and processingen
dc.subjectManufacturing defect detectionen
dc.subjectParallel algorithmsen
dc.titleAdvantages of Multiscale Detection of Defective Pills during Manufacturingen
dc.typeBook Chapteren
dc.identifier.journalHigh Performance Computing and Applicationsen
dc.contributor.institutionUniversity of Wyoming, Laramie, United Statesen
dc.contributor.institutionJSOL Corporation, Tokyo, Japanen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionKarl-Franzens-Universitat Graz, Graz, Austriaen
dc.contributor.institutionUniversity of Kentucky, Lexington, United Statesen
kaust.grant.numberKUS-C1-016-04en
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