Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement.Currently,the urban drainage pipeline network is upgrading to improve its classification and collection ability.However,there is a lack of efficient online monitoring and identification technology.UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size,reagent-free and fast detection.Because the performance parameters of probe like optic resolution,dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level,it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes.In this study,drainage network samples were online collected and tested,and four types were labeled according to sample sites and environment situation.Derivative spectrum were adopted to amplify the shape features,while convolutional neural network algorithm was established to conduct nonlinear spectrum classification.Influence of input and network structure on classification accuracy was compared.Original spectrum,first-order derivative spectrum and a combination of both were set to be three different inputs.Artificial neural network with or without convolutional layer were set be two different network structures.The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage,mixed rainwater,rainwater and industrial sewage.The recognition rate of industrial wastewater was 100%,and the recognition rate of domestic sewage and rainwater mixing system were over 90%.

Design and Implementation of Prototype System for Online Handwritten Uyghur Character Recognition

Based on the analysis of the unique shapes and writing styles of Uyghur characters,we design a framework for prototype character recognition system and carry out a systematic theoretical and experimental research on its modules.In the preprocessing procedure,we use the linear and nonlinear normalization based on dot density method.Both structural and statistical features are extracted due to the fact that there are some very similar characters in Uyghur literature.In clustering analysis,we adopt the dynamic clustering algorithm based on the minimum spanning tree（MST）,and use the k-nearest neighbor matching classification as classifier.The testing results of prototype system show that the recognition rates for characters of the four different types（independent,suffix,intermediate,and initial type） are 74.67%,70.42%,63.33%,and 72.02%,respectively;the recognition rates for the case of five candidates for those characters are 94.34%,94.19%,93.15%,and 95.86%,respectively.The ideas and methods used in this paper have some commonality and usefulness for the recognition of other characters that belong to Altaic languages family.