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Proceedings Paper

Image classification and control of microfluidic systems
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Paper Abstract

Current microfluidic-based microencapsulation systems rely on human experts to monitor and oversee the entire process spanning hours in order to detect and rectify when defects are found. This results in high labor costs, degradation and loss of quality in the desired collected material, and damage to the physical device. We propose an automated monitoring and classification system based on deep learning techniques to train a model for image classification into four discrete states. Then we develop an actuation control system to regulate the flow of material based on the predicted states. Experimental results of the image classification model show class average recognition rate of 95.5%. In addition, simulated test runs of our valve control system verify its robustness and accuracy.

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11139, Applications of Machine Learning, 1113906 (6 September 2019); doi: 10.1117/12.2530416
Show Author Affiliations
Albert B. Chu, Lawrence Livermore National Lab. (United States)
Du Nguyen, Lawrence Livermore National Lab. (United States)
Alan D. Kaplan, Lawrence Livermore National Lab. (United States)
Brian Giera, Lawrence Livermore National Lab. (United States)

Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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