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针对定向毫米波网络各节点间无波束方向先验信息导致邻居发现困难的问题,提出了一种基于盲交汇算法的邻居发现协议,推导了邻居发现过程中时隙长度、波束个数与邻居发现时间之间的理论关系。进一步,为了缩短邻居发现的时间,在盲交汇算法的邻居发现协议的基础上,提出了基于位置预测的邻居发现协议。仿真结果表明,基于盲交汇算法的邻居发现协议的最长邻居发现时间小于无协调的定向邻居发现协议,波束个数N接近2n(2n-1n,n>1,n∈Z)时,平均邻居发现时间低于无协调的定向邻居发现算法。此外,基于位置预测的邻居发现协议可以有效缩短邻居发现时间。 相似文献
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《防务技术》2022,18(12):2150-2159
Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsupervised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighborhood mutual information is proposed, and the feature clusters with certain event correlation are obtained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l1 norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding algorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery. 相似文献
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基于聚类的相对性原则:簇内对象具有较高的相似度,而簇间对象则相反,提出一种基于相对密度的增量式聚类算法,它继承了基于绝对密度聚类算法的抗噪声能力强、能发现任意形状簇等优点[1],并有效解决了聚类结果对参数设置过于敏感、参数值难以确定以及高密度簇完全被相连的低密度簇所包含等问题。同时,通过定义新增对象的影响集和种子集能够有效支持增量式聚类。 相似文献
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为了消除不相似基因对基因表达谱中缺失值估计的影响,提出了一种基于KNN SVR的缺失值估计方法.该方法先通过最近邻法选出与目标基因表达最相似的一组完全基因,再用这些基因通过支持向量回归对缺失值进行估计.还提出了用标准化偏差的方差来度量算法的稳定性和估计值的可信度.该方法通过对基因的过滤提高了缺失值估计的有效性.实验结果表明,KNN SVR法具有较高的估计精度和稳定性. 相似文献
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