Water surface plastic pollution turns out to be a global issue, having aroused rising attention worldwide. How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research. (1) Background: Over the past few years, unmanned aerial vehicles (UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste. On the whole, the monitored data are stored in the UAVS to be subsequently retrieved and analyzed, thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated. (2) Methods: An investigation was conducted on the relationship, function and relevant mechanism between various types of plastic waste in the water surface system. On that basis, this study built a deep learning-based lightweight water surface plastic waste detection model, which was capable of automatically detecting and locating different water surface plastic waste. Moreover, a UAV platform-based edge computing architecture was built. (3) Results: The delay of return task data and UAV energy consumption were effectively reduced, and computing and network resources were optimally allocated. (4) Conclusions: The UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.
Surface roughness control is one of the most important subjects during producing stainless steel strips. In this paper, under the conditions of introducing to the concepts of transferring ratio and genetic factor and through the further theoretical analysis, a set of theoretical models about strip surface roughness were put forward in stainless steel cold tandem rolling. Meanwhile, the lubrication experiment in cold rolling process of SUS430 stainless steel strip was carried out in order to comprehensively study surface roughness. The effect of main factors on transferring ratio and genetic factor was analyzed quantitatively, such as reduction, initial thickness, deformation resistance, emulsion technological parameters and so on. Attenuation function equations used for describing roll surface roughness were set up, and also strip surface roughness at the entry of last mill was solved approximately. Ultimately, mathematical model on strip surface roughness for cold tandem rolling of stainless steel was built, and then it was used into the practical production. A great number of statistical results show that experimental data is in excellent agreement with the given regression equations, and exactly, the relative deviation on roughness between calculated and measured is less than 6.34%.
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