Aiming at the current challenges of the complexity， low accuracy， and inability to capture detailed local features of artificial targets from optimal angles in generating refined inspection trajectories for Unmanned Aerial Vehicles （UAVs） inspecting aerial artificial targets such as power transmission lines， a real-time depth perception and line component segmentation and localization algorithm for refined UAV inspection of power transmission lines was proposed， and an optimal inspection point path for monocular vision perception， positioning， and navigation of power transmission lines was constructed. In the method， by adjusting the UAV position and gimbal camera shooting angle quantitatively during the inspection process in real time， a safe inspection distance was maintained while allowing the gimbal camera to shoot images containing the targets to be inspected clearly and accurately. Experimental simulations were carried out by using real data collected by DJI UAV and the data under Unreal Engine 4 scenario. The results demonstrate that the optimized depth perception algorithm as well as the line component segmentation and localization algorithm meets real-time requirements. Under the guidance of the output information from depth perception as well as segmentation and localization， these algorithms can adjust the UAV position and gimbal camera posture optimally， resulting in high-quality UAV inspection images of power transmission lines， and the finally generated refined inspection trajectories can improve the efficiency of inspections of operation and maintenance personnel significantly.

In a Virtual Machine (VM) computing environment, it is difficult to monitor and allocate the VM's memory in real-time. To overcome these shortcomings, a real-time method of monitoring and adjusting memory for Xen virtual machine called Xen Memory Monitor and Control (XMMC) was proposed and implemented. This method used hypercall of Xen, which could not only real-time monitor the VM's memory usage, but also dynamically real-time allocated the VM's memory by demand. The experimental results show that XMMC only causes a very small performance loss, less than 5%, to VM's applications. It can real-time monitor and adjust on demand VM's memory resource occupations, which provides convenience for the management of multiple virtual machines.