• 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