By introducing polarized light detection and polarization indices analyzing, our research has indicated that some certain polarization indices can identify the morphology and absorption of suspended particulates specifically. In this article, we will present the experimental results of polarization indices from three kinds of pollutants discharged from three different sources with respective shape and absorption. A series of simulation results on different shape and absorption will also be given to show the correlation of polarization indices and particulates’ parameters. At last, the particulates from a certain source will be modeled, and the agreement between the preliminary simulations of pollutant sources with the experiment results in a field test will be shown.
This paper focuses the data processing and then the multi-class classification of suspended particles using a new polarized light measurement scheme. Detection of multidimensional polarization parameters keeps the advantages of fast detection speed and non-invasive local analysis of light scattering method, and increases the information dimension of the analyzed particles. However, the polarization indices are numerous and interrelated. It is difficult to complete classification prediction by a few specific indices. More advanced algorithms are needed. In our research, we selected six kinds of representative particles and three typical machine learning algorithms. k-NN, Neural network and SVM methods were used to construct the classification models and solve different classification tasks. By comparison, we evaluated these models in terms of their performance for classification tasks in different aspects. Furthermore, we discuss how to improve the models by feature selection, and a rough prediction of the capability of each polarization index to reflect the particulate features was made.
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