.png){kind=icon}
返回
Gas classification in motion: An experimental analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.snb.2016.09.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work deals with the problem of volatile chemical classification with an electronic nose (e-nose), and particularly focuses on the case where the e-nose is not collecting samples in a stationary fashion but is carried by a moving platform (mobile robot, car, bike, etc.). We bring to light that, under these specific circumstances, substantial changes in the transient response of the gas sensors arise (something that has not been considered until now). We experimentally demonstrate that these changes in the sensor's response have an important impact on the classification accuracy if not properly considered, resulting in a decrease of up to 30% in some configurations. We back our conclusions with an extensive experimental evaluation consisting of a mobile robot inspecting a long indoor corridor with two chemical volatiles sources (ethanol and acetone) more than 240 times, at four different motion speeds. The paper also reveals the relevance of training the classifiers with data collected in motion, and proposes different training schemes suitable to this problem. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Volatile chemical classification
Gas recognition
Robotics olfaction
Motion
Electronic nose
Pollution monitoring
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.7
论文数:
3.4W
被引数:
12.6W
机构
引用论文
分享收藏
Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容