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The Effect Analysis of the High Emission Vehicles by Remote Sensor Detecting
High Emitter Profile Model
Artificial Neural Work Model
Screening highly emitted vehicles and reducing the testing cost and time are the main functions of remote sensing devices. The ROCEPA started the remote sensing program in Taiwan since 1996, and more than 7 millions data have been collected up to 2001. The objective of this study was to analyze these data to find out the characteristics of vehicle emissions in Taiwan. It was found from the remote sensing data that LPG vehicles emitted more pollutants than conventional gasoline vehicles. The frequency of refueling does not affect the emission levels of these LPG cars. Analysis of the reproduced data showed that the emission distributions were the same for vehicles tested more than once. It was found in this paper that the reproduced data of the same vehicle are related. Increasing the reproduction times is one of the ways to reduce the uncertainty of remote sensing data. If a car had been sensed twice and failed each time, then the probability that it would be fail again is over 50% in the third time. A high emitter profile model (HEP) has been built in this paper with the artificial neural work model. It was found that the RSD CO ,the log of RSD HC and the model year are the most important parameters for the CO model. As for the HC model, the automobile manufacturer, the engine displacement, and the model year are the most important parameters. The false ratio of CO is 9-11.2% and 4.5-5.6% in HC for the training data and testing data. For validating data, the false ratio were between 11-13%.
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