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