Mission planning is the guidance for a UAV team to perform missions, which plays the most critical role in military and civil applications. For complex tasks, it requires heterogeneous cooperative multi-UAVs to satisfy several mission requirements. Meanwhile, airborne sensor allocation and path planning are the critical components of heterogeneous multi-UAVs system mission planning problems, which affect the mission profit to a large extent. This paper establishes the mathematical model for the integrated sensor allocation and path planning problem to maximize the total task profit and minimize travel costs, simultaneously. We present an integrated mission planning framework based on a two-level adaptive variable neighborhood search algorithm to address the coupled problem. The first-level is devoted to planning a reasonable airborne sensor allocation plan, and the second-level aims to optimize the path of the heterogeneous multi-UAVs system. To improve the mission planning framework’s efficiency, an adaptive mechanism is presented to guide the search direction intelligently during the iterative process. Simulation results show that the effectiveness of the proposed framework. Compared to the conventional methods, the better performance of planning results is achieved.
The HTML5 based videos play an important role in promoting the communication on national culture with the rapid development of the mobile internet. However, considering that the HTML5 based videos support Theora, H.264 and MPEG4 video coding formats only and there are various existing video formats on national culture, it is needed to conduct fast conversion on video files so as to adapt to HTML5 video labels. Therefore, a Spark platform based transcoding system is proposed in this article. The HDFS is adopted for storage, and the RDD (Resilient Distributed Dataset) and FFMPEG of Spark are utilized for distributed transcoding. It conducts detailed discussion on segmentation strategy for the distributed storage of videos, and makes comparisons on the thought of the MapReduce and that of the RDD. In addition, it proposes the RDD programming framework based distributed transcoding scheme. According to the comparisons on time consumed for transcoding between the MapReduce framework and the Spark framework with the same size of file block and cluster, compared with the MapReduce transcoding, the time used for transcoding of the Spark framework can be reduced by 25%.
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