The remote sensing technology of unmanned aerial vehicle (UAV) is a low altitude remote sensing technology. The technology has been widely used in military, agricultural, medical, geographical mapping, and other fields by virtue of the advantages of fast acquisition, high resolution, low cost, and good security. But limited by the flying height of UAV and the focal length of the digital camera, the single image obtained by the UAV is difficult to form the overall cognition of the ground farmland area. In order to further expand the field of view, it is necessary to mosaic multiple single images acquired by UAV into a complete panoramic image of the farmland. In this paper, aiming at the problem of UAV low-altitude remote sensing image splicing, an image mosaic technique based on Speed Up Robust Feature (SURF) is introduced to achieve rapid image splicing. One hundred fifty ground farmland remote sensing images collected by UAV are used as experimental splicing objects, and the image splicing is completed by the global stitching strategy optimized by Levenberg-Marquardt (L-M). Experiments show that the strategy can effectively reduce the influence of cumulative errors and achieve automatic panoramic mosaic of the survey area.
Many long-running applications (LRAs) are increasingly using containerization in shared production clusters. To achieve high resource efficiency and LRA performance, one of the key decisions made by existing cluster schedulers is the placement of LRA containers within a cluster. However, they fail to account for estimating the size and affinity of LRA containers before executing placement. We present LraSched, a cluster scheduler that places LRA containers onto machines based on their sizes and affinities while providing consistently high performance. LraSched introduces an automated method that leverages historical data and collects new information to estimate container size and affinity for an LRA. Specifically, it uses an online machine learning method to map a new incoming LRA to previous workloads from which we can transfer experience and recommends the amount of resources (size) and the degree of collocation (affinity) for the containers of the new incoming LRA. By means of recommendations, LraSched adapts the heuristic for vector bin packing to LRA scheduling and places LRA containers in a manner that both maximizes the number of LRAs deployed and minimizes the resource fragmentation, but without affecting LRA performance. Testbed and simulation experiments show that LraSched can improve the resource utilization by up to 6.2% while meeting performance constraints for LRAs.
Multi-agent collaboration is the core task of multi-agent system (MAS) research. In order to explore a kind of multi-agent collaboration way with dynamic environment, this paper proposes a method based on fuzzy learning and applies it to RoboCup2D. In the RoboCup2D simulation competition, eleven players need to cooperate to win, so score becomes extremely critical. Therefore, this paper focus on shooting technique based on fuzzy control. In order to increase the scoring rate, We speculate through observation and testing that finding the correct shooting position and angle is the key. For solving the problem, this paper firstly design fuzzy controller for selecting the shooting path; and then, this paper purpose fuzzy neural network for optimizing shooting time; At last, this paper do lots of experiments to verify its effectiveness and find it does work on increasing the scoring rate. After optimization on the fuzzy control, we are fortunate to have won the national second prize in 2021 RoboCup China Open, and the goal accuracy has increased by 21.36%.
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