Automated control of microfluidic devices based on machine learning
Abstract
A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.
- Inventors:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1576338
- Patent Number(s):
- 10408852
- Application Number:
- 15/498,282
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
B - PERFORMING OPERATIONS B01 - PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL B01L - CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
- DOE Contract Number:
- AC52-07NA27344
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2017 Apr 26
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING
Citation Formats
Giera, Brian, Duoss, Eric, Nguyen, Du, Smith, William, Talathi, Sachin Subhash, Wilson, Aaron Creighton, and Ye, Congwang. Automated control of microfluidic devices based on machine learning. United States: N. p., 2019.
Web.
Giera, Brian, Duoss, Eric, Nguyen, Du, Smith, William, Talathi, Sachin Subhash, Wilson, Aaron Creighton, & Ye, Congwang. Automated control of microfluidic devices based on machine learning. United States.
Giera, Brian, Duoss, Eric, Nguyen, Du, Smith, William, Talathi, Sachin Subhash, Wilson, Aaron Creighton, and Ye, Congwang. Tue .
"Automated control of microfluidic devices based on machine learning". United States. https://www.osti.gov/servlets/purl/1576338.
@article{osti_1576338,
title = {Automated control of microfluidic devices based on machine learning},
author = {Giera, Brian and Duoss, Eric and Nguyen, Du and Smith, William and Talathi, Sachin Subhash and Wilson, Aaron Creighton and Ye, Congwang},
abstractNote = {A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}