Deep learning-based intelligent management for sewage treatment plants

It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.

Investigation of Geiger-mode detector in multi-hit model for laser ranging

The performance of detector limits the overall performance of laser ranging system. And the design of multi-hit detector is one of the feasible ways to promote the performance of detector. Currently, the segmentation method or the recursive method is commonly used to analyze the multi-hit detector model. To the best of our knowledge, this paper is the first to propose a combinatorial method to solve the multi-hit detector model from the perspective of discrete time. Then, universal formulas of total signal detection probability and the average count are deduced based on the Poisson distribution signal. Furthermore, analysis is made to figure out how the average count changes with different parameters, such as the dead time, gating time, rate intensity. As a result, for GM-APD, the multi-hit detector model is verified advantageously compared to the single-hit detector model in improving the average count theoretically. Meanwhile, a discrete step feature is presented when average count changes with dead time or the gating time, which is of great significance in gating time optimization.