ID 原文 译文
47966 防空火力分配包含武器目标匹配和武器单元的射击时机分配两部分,其目的是明确由哪些火力装置对哪些目标在何时进行射击。 Contains the target matching and weapons of weapon-target assignment unit shooting time distribution in two parts, the goal is to clear by which fire device on which target when shooting.
47967 在对防空导弹和高炮弹道特性及射击特点之间的差异进行分析的基础上,引入实例,分别对导弹有控弹道模型以及高炮弹头的飞行时间曲线进行了计算; The characteristics of air defense missile and anti-aircraft gun ballistic and shooting, on the basis of the analysis of the differences between the introduction of instances, is accused of ballistic model for missile and anti-aircraft gun warheads flying time curve was calculated.
47968 根据它们的飞行时间曲线,分析了二者对同一航路的射击时机,定量论证了防空导弹和高炮的互补性,为弹炮结合防空系统的设计优化提供依据。 According to their flight time curve, analyses the shooting opportunity, both on the same route quantitative demonstrates the complementarity of air defense missile and anti-aircraft gun, provide the basis for the design of the integrated missile air defense system optimization.
47969 面对物联网中的海量空间资源,在空间资源索引的构建中引入语义分类的方法,使得传统的IR树具备语义特征, Facing the vast space resources in the Internet of things, in the process of building space resource index is introduced into the semantic classification method, makes the traditional IR tree have the semantic features and support user semantic query of resources,
47970 支持用户对资源的语义查询,并提出一种top-k语义查询优化算法,通过对实际数据的使用和实验分析, and puts forward a top - k semantic query optimization algorithm, through the use of actual data and experimental analysis,
47971 得出索引结构的有效性和查询结果的正确性,从索引构建时间、查询时间和查询准确率3个指标来评估算法的性能, it is concluded that the effectiveness of the proposed index structure and the correctness of the query results, from the index build time, query and query the accuracy of three indicators to evaluate the performance of the algorithm,
47972 实验结果表明,算法支持语义空间资源索引构建,并且查询性能好,准确率高。 the experimental results show that the algorithm supports semantic index build space resources, and the query performance is good, high accuracy.
47973 在基于稀疏表示的幻觉脸重建过程中,由于冗余的过完备字典会降低稀疏编码的效率和精度,提出用紧的聚类子字典来表示人脸图像的不同结构对象。 In the process of illusion face reconstruction based on sparse representation, due to redundancy over complete dictionary will lower efficiency and precision of the sparse coding, subdictionaries with tight clustering is proposed to represent the different objects of face image.
47974 由高分辨率(high resolution,HR)/低分辨率(low resolution,LR)的人脸图像样本集进行K-均值聚类,为使紧的聚类子字典能够表达图像块的整体特征,对各聚类子集采用主成分分析(principal component analysis,PCA)方法构造字典。 By high resolution, high resolution, HR)/low resolution (low resolution, LR) of k-means clustering are human face image sample set, to make the tight cluster of dictionary can express the overall characteristics of the image block, using principal component analysis of each cluster subset (principal component analysis, PCA) method to construct a dictionary.
47975 得到同构的HR/LR的聚类字典后,对于输入的LR人脸图像块,经自适应选择合适的子字典后,对稀疏编码添加正则化项, Get the isomorphism of HR/LR clustering dictionary, for input LR human face image block, after adaptive to choose the appropriate subdictionaries, add regularization item of sparse coding,