双向中继系统中断概率下界优化及分集增益

主要研究了瑞利衰落信道下基于放大转发策略的双向中继系统的中断性能及分集性能。首先利用信源节点信噪比间的关系和调和不等式对信噪比的范围进行优化,得到新的中断概率下界,仿真结果表明Monte Carlo仿真得到的中断概率接近新得到的中断概率下界;同时证明了多中继存在时,基于最大最小互信息准则的双向中继系统仍然取得全分集增益且能够优化中断概率下界。     

特征选择算法在故障检测中的应用研究

将基于决策树的特征选择方法应用于卫星故障检测中,在卫星故障检测应用研究中,综合分析了问题的特征,指出了基于决策树的特征选择方法,并且给出了在该方法下,特征选择算法的正确率和在卫星故障检测中的应用效果。     

A Dynamic Building Path Based on Method of Mobile Agent Referral Networks

The mobile agent is a computer program that is able to migrate continuously among hosts in a net- work and use host service to meet its task. The host, known as workplace, can be regarded as a proxy of social member. The sequence of workplaces on which the mobile agent completed its tasks is called path. In this paper, we propose a dynamic building method of mobile agent path with minimum payment based on referral. By referral, the next workplace of mobile agent can be recommended by the current workplace provider based on his acquaintance knowledge. The simulation results on a random network model show that the more acquaintance relationships there are on the referral network, the more efficiently the mo- bile agent path can be built, and the fewer costs need to be paid on the path.     

基于P2P技术的文件分发结构的研究

论述了三种基于P2P技术的文件分发结构及文件分发过程,讨论了这三种结构的文件分发时间与接收节点数量和文件分块数量之间的关系,比较了各自的特点和缺点.     

基于分形的三维树木形态模型

树木三维模型在农林业和娱乐、教育、商业等非农林业领域有着广泛的应用,但现有树木建模方法缺乏通用性：针对不同的对象需要建立不同的模型。运用递归调用分形自相似结构的方法构造了树木的三维模型。通过使用分枝结构随机抖动与双缓冲技术,兼顾了模型真实感与实现速度的问题。结果表明,该方法具有良好的通用性,简洁高效,能生成逼真的树木图形。     

Modeling tree root diameter and biomass by ground-penetrating radar

Roots play a key role in ecosystem functioning as they transfer water and nutrients from soil to plants.Traditional methods for measuring roots are labor-intensive and destructive in nature,which limits quantitative and repeatable assessments in longterm research.Ground-penetrating radar(GPR) provides a non-destructive method to measure plant roots.Based on the superiority of GPR with 2 GHz frequency,we developed a new,practical method to estimate root biomass.First,average root matter density was measured ...     

Ectropy of diversity measures for populations in Euclidean space

Measures to evaluate the diversity of a set of points (population) in Euclidean space play an important role in a variety of areas of science and engineering. Well-known measures are often used without a clear insight into their quality and many of them do not appropriately penalize populations with a few distant groups of collocated or closely located points. To the best of our knowledge, there is a lack of rigorous criteria to compare diversity measures and help select an appropriate one. In this work we define a mathematical notion of ectropy for classifying diversity measures in terms of the extent to which they tend to penalize point collocation, we investigate the advantages and disadvantages of several known measures and we propose some novel ones that exhibit a good ectropic behavior. In particular, we introduce a quasi-entropy measure based on a geometric covering problem, three measures based on discrepancy from uniform distribution and one based on Euclidean minimum spanning trees. All considered measures are tested and compared on a large set of random and structured populations. Special attention is also devoted to the complexity of computing the measures. Most of the novel measures compare favorably with the classical ones in terms of ectropy. The measure based on Euclidean minimum spanning trees turns out to be the most promising one in terms of the tradeoff between the ectropic behavior and the computational complexity.     

用于中文缺省识别研究的机器学习方法

实现一个基于机器学习的中文缺省项识别系统,对语料库进行预处理,选取多个特征及其组合,通过支持向量模型(SVM)构建的缺省识别模型进行中文缺省识别。研究系统在不同句法分析树上的性能。实验结果证明,该识别系统在标准的句法分析树上F值能达到84.01%,在自动句法树上能达到68.22%。     

蓄电池故障对110kV变电站安全运行影响分析探讨

以事故预想的形式,重点指出并列运行110 kV内桥接线变电站在蓄电池故障开路情况下,发生低压侧线路近区故障时可能引起线路保护越级跳闸。原因是由于全站直流系统输出电压达不到继电保护及自动装置的最低动作电压要求而引起站内保护拒动,只能靠电源侧变电站110 kV线路保护动作切除故障,这将导致110 kV变电站全停。通过对该安全隐患的分析,提出了三种相应的改进措施,能有效提高供电可靠性,避免全停事故的发生。     

Unsupervised regions based segmentation using object discovery

We present a new unsupervised algorithm to discovery and segment out common objects from multiple images. Compared with previous cosegmentation methods, our algorithm performs well even when the appearance variations in the foregrounds are more substantial than those in some areas of the backgrounds. Our algorithm mainly includes two parts: the foreground object discovery scheme and the iterative region allocation algorithm. Two terms, a region-saliency prior and a region-repeatness measure, are introduced in the foreground object discovery scheme to detect the foregrounds without any supervisory information. The iterative region allocation algorithm searches the optimal solution for the final segmentation with the constraints from a maximal spanning tree, and an effective color-based model is utilized during this process. The comparative experimental results show that the proposed algorithm matches or outperforms several previous methods on several standard datasets.