Activated Sludge Image Analysis Data Classification: an Ls-svm Approach
نویسندگان
چکیده
In this paper, a classifier is proposed and trained to distinguish between bulking and non-bulking situations in an activated sludge wastewater treatment plant, based on available image analysis information and with the goal of predicting and monitoring filamentous bulking. After selecting appropriate activated sludge parameters (filament length, floc fractal dimension and floc roundness), an LSSVM approach is used to train a classification function. This classification function is shown to have a satisfactory performance after validation. Copyright c ©2005 IFAC.
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