DocumentCode :
2655930
Title :
A PKGV-ANN model for vehicle high emitters identification based on remote sensing data
Author :
Jun, Zeng ; Huafang, Guo ; Yuem, Hu
Author_Institution :
Coll. of Electr. Power, South China Univ. of Technol., Guangzhou
fYear :
2008
fDate :
16-18 July 2008
Firstpage :
171
Lastpage :
175
Abstract :
Vehicle emission remote sensing system is an effective real-time method of monitoring vehicle emissions on road. This paper presents an artificial neural network model for identifying high emitters by combing the remote sensing data and the idle test data. On the base, an improved model called PKGV-ANN is proposed. The model combines several advanced methods and useful achievements, including principle components analysis, k-nearest neighbor algorithm, genetic algorithm and the results obtained from the studies about vehicle specific power. Experiments results show that the model is very valid. The percentage of hits reaches 89.40%.
Keywords :
air pollution control; air pollution measurement; neural nets; principal component analysis; road vehicles; PKGV-ANN model; artificial neural network model; genetic algorithm; k-nearest neighbor algorithm; principle components analysis; real-time method; remote sensing data; road vehicle emission monitoring; vehicle emission remote sensing system; vehicle high emitters identification; Algorithm design and analysis; Artificial neural networks; Automation; Automotive engineering; Educational institutions; Electronic mail; Power engineering and energy; Remote monitoring; Remote sensing; Vehicles; High emitters; PKGV-ANN; Remote sensing; Vehicle emission;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
Type :
conf
DOI :
10.1109/CHICC.2008.4604922
Filename :
4604922
Link To Document :
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