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The reason the default produced is analyzed and a new latest available unscheduled channel with smallest void algorithm is presented. The performance of the two algorithms is simulated. The results show that in LAUC-SV algorithm the effective window of the packet loss versus unit delay time of fiber delay lines is expanded significantly and the packet loss ratio is decreased about 100.5. The algorithm complexity of the LAUC-SV is only increased arithmetically with LAUC. Both the algorithms can not support delay reservation protocol effectively.

分析了产生此缺陷的原因并提出了一种LAUC算法的改进算法LAUC-SV算法,采用仿真的方法分析比较了两种算法的性能,结果表明LAUC-SV算法比LAUC算法的丢包率下降100.5,且LAUC-SV算法下的丢包率关于光纤延时线单元延时时间的工作窗口与传统LAUC相比大大展宽,而前者的算法复杂度与后者相比仅呈算术增长,但两算法均不能有效支持延时预约协议。

This paper summarizes the background and advantage of data mining, as well as the significance of data mining in the scientific research management of colleges and universities firstly, and then discusses the theory of data mining, association rules and the ideas of main algorithm, analyzes the classic Apriori algorithm and its existing problems as well as the basic solutions. After that, this paper proposes Multi-Dimensional Apriori algorithm which is designed specially for the mining of this paper; Then describes the structure of scientific research data mining system, defines subject-oriented mining tasks, including: mining the data about research projects, mining the data about papers, mining the data about academic writings. The association mining process is implemented by programing, a number of stimulating association rules are found, interpreted and analyzed.

本文首先综述了数据挖掘的研究背景、意义以及数据挖掘技术在高校科研管理中的应用现状和意义,然后在对数据挖掘相关理论、关联规则思想及主要算法进行讨论,分析经典Apriori算法及其存在的问题、基本解决方案后,提出了适合本文挖掘的多维Apriori算法的设计方案,并应用于本文挖掘中;接着论文介绍了科研数据的关联挖掘系统的结构,确定了面向主题的挖掘任务,包括:科研项目信息的挖掘、论文信息的挖掘、学术专著信息的挖掘等;设计了关联规则的实施过程,并通过程序编码得以实现,获得了多条有启发性的关联规则,并对其进行了解释与分析。

Because of the local astringency of genetic algorithm, an improved recurrence search algorithm is presented based on the essential genetic algorithm.

针对遗传算法存在的局部收敛性,设计了一种循环搜索遗传算法,算法融入了保优策略和自适应调整等方法。

Combine the advantage of immune clone algorithm and imitate anneal algorithm, put forward task of a kind of reseau to attemper optimize model and algorithm.

结合免疫克隆算法和模拟退火算法的优点,提出了一种网格任务调度优化模型和算法。

The traditional Givens rotation algorithm involves root computation that needs more hardware resources, a Givens rotation algorithm without root and division computation is presented. And, combined with special structure of input signal matrix, a interference suppression algorithm with low computation is proposed. The narrowband interferences are modeled as autoregressive process and digital signal.

传统Givens旋转算法涉及平方根运算,要消耗较多硬件资源或需要多次循环来实现,为此,提出一种无平方根与除法运算的Givens旋转算法,并结合信号输入矩阵的特殊结构,给出一种低复杂度的干扰抑制算法,将窄带干扰建模为自回归过程以及数字窄带信号。

The micro vehicle rear axle of car main gear box noise question became the worker matter of concern. In this paper, the micro vehicle rear axle of car main gear box noise performance present situation and the traditional assessment method are discussed, and the general BP neural network and the Levenberg-Marguardt algorithm are introduced in detail. Improves the BP neural network using the LM algorithm, at present is trains the neural network the quickest algorithm.

微车后桥主减速器噪声问题成为工作者所关注的问题,本文介绍了后桥主减速器噪声性能现状及传统评价方法,阐述了BP神经网络的一般算法,并详细介绍了Levenberg-Marguardt算法,利用LM算法来改进BP神经网络,目前是训练神经网络的最快算法。

In this paper, an extended divide and conquer algorithm is intended proposed, which is for solving the real symmetric band generalized eigenvalue problem under distributing environment Eigenvalue partition theorem is presented and proved Based on divide and conquer by extension, this algorithm computes generalized eigenpaires of symmetric band matrix pencil by bisection and generalized Rayleigh quotient iteration Theoretic analysis and numerical results show that this algorithm is better than the classic software package LAPACK when bandwidth is small and the scale is large Combined with multisection, which has good parallelism, it got good effects under distributed environments

提出了分布式环境下计算对称带状广义特征值问题的一种扩展分治算法,给出了特征值分割定理及其证明算法在扩展分治的基础上,利用二分压缩结合广义Rayleigh商迭代计算广义特征对理论分析和数值实验表明,对于窄带宽大规模的广义特征值问题,该分治算法明显优于LAPACK软件包结合并行性好的多分法,在分布式环境下获得了很好的并行效果1 引言本文研究了对称带状广义特征值问题Ax =λBx ( 1)的并行计算,其中,A ,B均为半带宽为r的n阶实对称带状矩阵且其中之一是正定的本文总假设B是正定的求解此问题有两种传统方法,第1种方法是通过计算矩阵B的Cholesky分解,将问题( 1)转化为标准特征值问题[1~3] ,进一步

Under same condition, on the top of 100 ranked documents, the number of documents obtained by the proposed integral algorithm is more than Borda Count algorithm 3-4 items, and ComMIN algorithm 7-8 items, respectively.

在相同的条件下,所提算法在前100篇文档的排序中所获得的相关文档数比Borda Count算法多3~4篇,比ComMIN算法多7~8篇。

Abstract〕similar to regression analysis,cluster analysis,etc.,correlation analysis is also the basic work for information researches.canonical correlation analysis can be used to analyze the relationship between two groups of variables,and thus worth for using in information science.method of canonical correlation analysis was presented in present study.a java-based network algorithm of canonical correlation analysis was developed,which can be used to calculate the canonical correlation coefficients,canonical variable pairs,and statistical significance of canonical correlation coefficients.the algorithm may be run on java-compatible web browser.at the same time,paper publishing information and citation statistics in information science of china in past years were analyzed using the algorithm,and a number of conclusions were drawn.

摘 要〕与因果分析等一样,相关分析也是信息资料分析的基本工作。典范相关分析用于分析两组变量之间的关系,在情报信息的相关性分析方面有潜在的应用价值。本研究介绍了典范相关分析方法,给出了典范相关分析的java网络算法软件,可用于计算典范相关系数,典范变量对,以及典范相关系数的统计显著性。该软件可在兼容java的网络浏览器上运行。同时,用该算法对国内情报信息论文的发文和引文统计数据进行了分析,得到了一些结论。

The theory of bit-plane in spatial-domain method of image information hiding technology was introduced. The traditional least significant bit hiding algorithm and the improved parity identifier hiding algorithm were analysed. Then the index data chain hiding algorithm was put forward.

介绍图像信息隐藏技术中基于空间域方法中位平面的思想,分析了传统的最低有效位隐藏算法以及在此基础上改进的奇偶标识位隐藏算法,提出了一种失真度更低、安全性更高的新隐藏方法-索引数据链隐藏算法。

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You are asking for the moon.

你的要求太过分了。