Odor-source Localization In Clean Room By Autonomous Mobile Sensing System

2005 ◽  
Author(s):  
H. Ishida ◽  
Y. Kagawa ◽  
T. Nakamoto ◽  
T. Moriizumi
Keyword(s):  
Source Localization ◽  
Mobile Sensing ◽  
Odor Source ◽  
Clean Room ◽  
Sensing System

Odor-source localization in the clean room by an autonomous mobile sensing system

1996 ◽  
Vol 33 (1-3) ◽  
pp. 115-121 ◽  
Author(s):  
Hiroshi Ishida ◽  
Yukihiko Kagawa ◽  
Takamichi Nakamoto ◽  
Toyosaka Moriizumi
Keyword(s):  
Source Localization ◽  
Mobile Sensing ◽  
Odor Source ◽  
Clean Room ◽  
Sensing System

scholarly journals A Mobile Sensing System for UrbanPM2.5Monitoring with Adaptive Resolution

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Hongjie Guo ◽  
Guojun Dai ◽  
Jin Fan ◽  
Yifan Wu ◽  
Fangyao Shen ◽  
...  
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Computational Cost ◽  
Estimation Method ◽  
Mobile Sensing ◽  
Prototype System ◽  
Adaptive Grids ◽  
Sensing System ◽  
Adaptive Resolution ◽  
Wide Range

This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect originalPM2.5data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer thePM2.5concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources.

Application of a Nature-Inspired Algorithm in Odor Source Localization

2020 ◽  
pp. 51-66
Author(s):  
Kumar Gaurav ◽  
Ramanpreet Singh ◽  
Ajay Kumar ◽  
Ram Dayal
Keyword(s):  
Source Localization ◽  
Odor Source ◽  
Nature Inspired Algorithm

scholarly journals Spontaneous Rapid Odor Source Localization Behavior Requires Interhemispheric Communication

2017 ◽  
Vol 27 (10) ◽  
pp. 1542-1548.e4 ◽  
Author(s):  
José Esquivelzeta Rabell ◽  
Kadir Mutlu ◽  
João Noutel ◽  
Pamela Martin del Olmo ◽  
Sebastian Haesler
Keyword(s):  
Source Localization ◽  
Odor Source ◽  
Interhemispheric Communication ◽  
Localization Behavior
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