UNSUPERVISED K-MEANS LEARNING APPLIED TO THE INVESTIGATION OF INDOOR AIR QUALITY EVENTS WITH ELECTRONIC GAS SENSORS NETWORKS
文献类型： 会议论文
第一作者： Alexandre Caron
作者： Alexandre Caron 1 ; Nathalie Redon 2 ; Benjamin Hanoune 1 ; Patrice Coddeville 2 ;
作者机构： 1.Univ. Lille, CNRS, UMR 8522 - PC2A - Physicochimie des Processus de Combustion et de 1'Atmosphère, F-59000 Lille, France
2.IMT Lille Douai, Univ. Lille, SAGE, F-59500 Lille, France
关键词： electronic gas sensors;multi-sensors systems;indoor air quality;real-time monitoring;k-means clustering
会议名称： International Symposium on Olfaction and Electronic Nose
主办单位：
页码： 185-187
摘要： Indoor environments present high specific pollutant concentrations, especially volatile organic compounds, inducing a risk for human health. Tools able to provide realtime information on indoor air quality (IAQ) are needed, in order to feed into the knowledge of these environments, identify the sources and automatically control remediation systems. Electronic gas micro-sensors are low-cost and nonintrusive instruments, which can be permanently deployed in large numbers to map the air quality of a whole building, but that still need to be evaluated. Five multi-sensors systems, combining several selective and non-selective electronic gas micro-sensors, have been installed inside a classroom at a low energy junior high school, in parallel with a set of standard analytical tools. The aim of this work was to determine if multi-sensors systems, associated with unsupervised k-means learning to process the data, were able to monitor effectively the dynamics of indoor air in a room in real conditions, and at what level of data quality.
分类号： tp212.2-532
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