MOSFET
鉴定(生物学)
类型(生物学)
材料科学
计算机科学
光电子学
电气工程
工程类
地质学
晶体管
电压
植物
生物
古生物学
作者
Gyuweon Jung,Hyeong-Su Kim,Yujeong Jeong,Yongtaek Hong,Meile Wu,Seongbin Hong,Wonjun Shin,Dong Kee Jang
出处
期刊:2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)
日期:2019-05-01
被引量:3
标识
DOI:10.1109/isoen.2019.8823181
摘要
The principal component analysis (PCA) and deep neural network (DNN) are used to classify the gas types (reducing and oxidizing) and to identify the concentration of gases. The pMOSFET-type gas sensor is used to provide sensing data for learning. The gas sensor has 15-nm-thick ZnO as a sensing layer processed by atomic layer deposition (ALD). The sensing characteristics of NO 2 and H 2 S gases are investigated in changing temperature and concentration conditions. The same gas sensor data and temperature sensor data are analyzed by PCA and DNN algorithms. PCA provides gas type classification results without information on gas concentration. However, DNN regression model has the ability to precisely identify gas concentration and gas type in changing temperature conditions.
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