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Gauging the Black Smoke of a Diesel Car with the Image Process and Neural Network
The emission of black smoke from diesel cars have always been complained by people for it's visible. Currently, there are two ways to detect the emission, one is to employ trained professionals for their judgement and the other is to utilize monitoring equipments for sampling analysis.
For speed a better results, we propose a new approach which is recording the discharge by CCD camera and determining consistently the opacity by image process. With this new approach, we can not only response the emission situation on real time but also ascertain the pollution problem more objectively. The major contributions of the research are:
1. Identification of black smoke's image: We use both continual and interval approaches to carry the work out, and the results seem to be satisfied.
2. Characterization of black smoke's image: Among many features we totally find five characteristic to characterize an the image of black smoke, these are fractal dimension, average value of gray level, standard deviation, area percentage and Euler number.
3. The neural network of black smoke's image: Two neural networks have been trained to find if the images belong to black smoke or not and gauge the components of white smoke respectively. Case studies show that the incorrect judgement of both neural networks is 0.667% and 22% in sequcent.
|Appears in Collections:||機械工程學系所|
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