Thermal power image segmentation is the key step of power equipment infrared diagnosis. Power equipment infrared diagnosis can timely and accurately diagnose the potential accidents and fault precursors of operating power equipment, which is an important part of power inspection. With the increasing popularity of mobile intelligent terminals, modern power inspection requires deploying semantic segmentation models on mobile devices. Considering the small memory capacity of mobile devices, we propose a new lightweight network architecture, called Edge-Assisted Context Guided Network (ECGNet), for semantic segmentation of thermal infrared electrical equipment images. ECGNet has been carefully designed to learn the context information of thermal infrared images and improve the problem of edge blur on the premise of small parameters and small memory consumption. Under the same number of parameters, a large number of experiments on LS-ETS dataset show that ECGNet can obtain better results than the most advanced method.
In recent years, edge data caching has been concerned by more and more research scholars. Caching data, especially popular data, on edge servers can significantly reduce users’ latency, while also greatly reducing direct data transmission between users and remote clouds and slowing down the congestion of the backbone network. Most of the existing research on edge data caching is mainly aim at minimizing users’ latency, saving devices’ energy, and improving the hit rate of data caching, etc. From the perspective of an app vendor, how to formulate an edge data caching solution that can bring him the most profit while complying with a given budget constraint and considering the experience requirements of different users is a very realistic and challenging problem. In this paper, we formulate the Profit-Maximizing Edge Data Caching (PMEDC) as a constrained optimization problem. For PMEDC in small scale scenarios, we propose an optimization model named PMEDC-IP that can derive the optimal caching strategy. For PMEDC in large scale scenarios, we propose an approximation algorithm named MPF (Maximum Profit First) that can find an approximate optimal caching scheme in a reasonable time. We conduct extensive experiments on a real-world dataset, and based on the final experimental results, PMEDC-IP and MPF proposed by us significantly outperform the other three representative methods.
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