Computing near-optimal solutions for the dominating subset with minimal weight problem

In this work, we consider a combinatorial “dominating subset with minimal weight” problem, which is an associative problem for solving global optimization problem. This problem can be expressed as a kind of assignment problem. The mathematical model and the economical interpretations of the problem are given and its properties are described. Then, we propose a new algorithm which has a ratio bound in polynomial time, by using above properties for solving the problem and present the results of numerical experiments.     

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.     

矩阵填充与主元分析在受损图像配准中的应用

本文提出了一种有效的针对受损图像(元素丢失)的图像配准方法。利用矩阵填充技术将受损图像的丢失元素恢复,然后将主元分析法(PCA)应用于尺度不变特征变换(SIFT)中进行图像的配准。针对SIFT算法采用128维特征向量表示特征点,存储空间、匹配时间与特征点数量成正比,文本采用主元分析法对多维特征向量进行降维处理,以提高运算效率;并采用高斯加权欧氏距离代替欧氏距离进行特征点的匹配。实验结果表明,该算法具有较好的稳定性、准确率和匹配速度,针对受损图像配准具有较好的鲁棒性,可应用在基于内容的图像与视频检索等机器视觉领域。     

如何提高寻呼接通率

寻呼接通成功率在网络质量考核中、网络运营维护以及网络优化中是一项重要指标。文章主要阐述通信网络中寻呼接通率概要、影响寻呼接通率的分析、提高寻呼接通率的处理、寻呼接通的优化等内容。     

高职学生“向师性”与“独立性”心理特征对辅导员综合素质的要求

高职学生因其年龄和心理特点,"向师性"与"独立性"心理倾向具有异于其他学段学生的独特性和复杂性,这也对辅导员综合素质提出了更高的要求。辅导员要善于利用学生的"向师性"全方位提升个人素质,为人师表,感染学生,又要尊重学生的"独立性",培养学生独立、自信的人格,帮助学生塑造自我,实现自身价值。     

底水油藏一水平井筒耦合模型研究

采用势叠加和镜像原理对底水油藏水平井人流形态进行研究,考虑摩擦压降、加速度压降以及混合压降等因素,并结合油藏渗流与井筒压降协调条件,建立了底水油藏一水平井筒耦合模型,计算了底水油藏水平井压力和流量分布。实例计算结果表明,沿井筒长度方向流量呈“U”型分布,压力从趾端41．449MPa到跟端41．351MPa逐渐变小,趾端变化较小,跟端变化较大,该模型对优化底水油藏水平井设计具有一定的参考价值。     

基于互信息的贝叶斯网络结构学习算法

结构学习是贝叶斯网络的重要分支之一,而由数据学习贝叶斯网络是NP-完全问题,提出了一个由数据学习贝叶斯网络的改进算法。该算法基于互信息知识构造初始无向图,并通过条件独立测试对无向边添加方向;同时提出了一个针对4节点环和5节点环的局部优化方法来构造初始框架,最后利用贪婪搜索算法得到最优网络结构。数值实验结果表明,改进的算法无论是在BIC评分值,还是在结构的误差上都有一定的改善,并且在迭代次数、运行时间上均有明显降低,能较快地确定出与数据匹配程度最高的网络结构。     

水平井完井方式优选

本文详述水平井完井方式选择的一般步骤,包括井壁稳定性及出砂判断的数学模型、考虑了多种因素对完井方式选择的影响。     

提高IOCP多媒体网络服务器性能的方法及其实现

面临IOCP多媒体网络服务器所的大复杂度环境设计问题，本文论述该网络服务器的网络底层开发、结构设计及逻辑模块设计，提出提高其性能的方法：优化数据封装以及投递方式，增加数据的处理效率；整体结构模块化设计．提高代码的可重用性以及可扩展性;通过端口分流的方式．实现对逻辑模块性能改进。方法的实用性已通过实验室测试证实。     

Statistical Independence as Linear Independence

A contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other. Thus, this table is a fundamental tool for pattern discovery with conditional probabilities, such as rule discovery. In this paper, a contingency table is interpreted from the viewpoint of granular computing. The first important observation is that a contingency table compares two attributes with respect to the number of equivalence classes. The second important observation is that matrix algebra is a key point of analysis of this table. Especially, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given contingency table.