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 language||English (US)|
|Title of host publication||High Performance Computing and Applications - Second International Conference, HPCA 2009, Revised Selected Papers|
|Number of pages||9|
|State||Published - 2010|
|Event||2nd International Conference on High-Performance Computing and Applications, HPCA 2009 - Shanghai, China|
Duration: Aug 10 2009 → Aug 12 2009
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||2nd International Conference on High-Performance Computing and Applications, HPCA 2009|
|Period||08/10/09 → 08/12/09|
Bibliographical noteKAUST 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.
- Dynamic data-driven application systems
- High performance computing
- Integrated sensing and processing
- Manufacturing defect detection
- Parallel algorithms
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)