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Distinguishing Chemicals Using CMUT Chemical Sensor Array and Artificial Neural

Title
Distinguishing Chemicals Using CMUT Chemical Sensor Array and Artificial Neural
Author
박관규
Keywords
Machine Learning,; CMUT,; Chemical Sensor,; Neural Network
Issue Date
2014-09
Publisher
IEEE
Citation
IEEE International Ultrasonics Symposium(IUS): 2014 IEEE International, [Sep. 2014], pp. 162-165
Abstract
Capacitive micromachined ultrasonic transducers (CMUTs) can function as extremely sensitive mass-loading chemical sensors. The resonant frequency of the CMUT changes as mass is added due to chemicals absorbing into a chemical-sensitive layer on the top of the plate. However, these sensors suffer from the problem that they are not selective to a single chemical. As a solution, we present a system of four CMUT chemical sensors with different functionalization layers. Neural networks are used to do pattern recognition on the sensor outputs in order to distinguish different chemicals. The system is capable of distinguishing water, ethanol, acetone, ethyl acetate, methane and carbon dioxide in air at concentrations less than 1% with 98% accuracy. Once the chemical is identified, the concentration can be determined using polynomial regression with an RMS percentage error ranging from 1.1% to 13%, depending on the analyte.
URI
http://ieeexplore.ieee.org/document/6931854/keywordshttp://hdl.handle.net/20.500.11754/52389
ISBN
978-1-4799-7049-0; 978-1-4799-7048-3
ISSN
1051-0117
DOI
10.1109/ULTSYM.2014.0041
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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