@Article{IJNAM-9-2,
author = {Douglas, C., C. and L., Deng and Y., Efendiev and Haase, G. and Kucher, A. and R., Lodder, A.},
title = {Real-Time Multiscale Detection of Defective Pills During Manufacturing},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2012},
volume = {9},
number = {2},
pages = {169--180},
abstract = {<p style="text-align: justify;">We explore methods to automatically detect the quality in individual or batches of
pharmaceutical products as they are manufactures. 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.</p>},
issn = {2617-8710},
doi = {https://doi.org/2012-IJNAM-618},
url = {https://global-sci.com/article/83459/real-time-multiscale-detection-of-defective-pills-during-manufacturing}
}