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251.
针对不确定环境下无人机区域搜索问题,建立了实时探测更新的搜索方法,提出了机载光电载荷参数优化配置策略。建立了基于二维离散网格的无人机区域搜索模型,采用概率地图描述目标信息的实时获取与更新;引入不确定度指标、目标网格的重访和网格探测次数控制,建立搜索目标函数;建立了基于粒子群算法的搜索路径滚动优化方法;通过对任务区域平均探测时间步数和误判概率的估计分析,建立了机载光电载荷参数优化配置策略。使用蒙特卡洛方法验证了区域搜索方法的有效性和光电载荷参数配置对搜索效率、误判概率的影响。 相似文献
252.
《防务技术》2020,16(1):217-224
Experiments on shaped charge penetration into high and ultrahigh strength steel-fiber reactive powder concrete (RPC) targets were performed in this paper. Results show that the variation of penetration depth and crater diameter with concrete strength is different from that of shaped charge penetration into normal strength concrete (NSC). The crater diameter of RPC is smaller than that of NSC penetrated by the shaped charge. The jet particles are strongly disturbed and hardly reach the crater bottom because they pass through the narrow channel formed by jet penetration into the RPC. The effects of radial drift velocity and gap effects of jet particles for a shaped charge penetration into RPC target are discussed. Moreover, a theoretical model is presented to describe the penetration of shaped charge into RPC target. As the concrete strength increases, the penetration resistance increases and the entrance crater diameter decreases. Given the drift velocity and narrow crater channel, the low-velocity jet particles can hardly reach the crater bottom to increase the penetration depth. Moreover, the narrow channel has a stronger interference to the jet particles with increasing concrete strength; hence, the gap effects must be considered. The drift velocity and gap effects, which are the same as penetration resistance, also have significant effects during the process of shaped charge penetration into ultrahigh-strength concrete. The crater profiles are calculated through a theoretical model, and the results are in good agreement with the experiments. 相似文献
253.
《防务技术》2020,16(5):1062-1072
Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database. 相似文献
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