人类多任务处理
鉴定(生物学)
功率消耗
计算机科学
气体消耗
功率(物理)
机器学习
人工智能
汽车工程
工程类
工艺工程
心理学
植物
物理
量子力学
认知心理学
生物
作者
Yi Zhuang,Xiaojiang Liu,Xue Wang,Gaoqiang Niu,Ran Cheng,Fei Wang
出处
期刊:IEEE sensors letters
[Institute of Electrical and Electronics Engineers]
日期:2023-10-01
卷期号:7 (10): 1-4
标识
DOI:10.1109/lsens.2023.3310366
摘要
This letter investigates the utilization of pulse heating and machine learning techniques to overcome the limitations associated with traditional testing methods for metal oxide semiconductor (MOS) gas sensors. These limitations include long-term drift, high power consumption, and challenges in multitasking. Pulsed heating is used to improve long-term stability and significantly reduce power consumption. Three machine learning approaches on top of two models are specially tailored to simultaneously handle gas identification and concentration detection tasks. The experimental results corroborate the robust classification aptitude of all three models and their satisfactory regression accuracy. Moreover, each model offers distinct advantages and can be utilized to meet particular requirements. This letter highlights the potential of pulse heating combined with machine learning to enhance the capabilities of MOS gas sensors.
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