高校OA系统正式运行前的工作

很多高校都在进行办公自动化建设,但办公自动化(OA)系统在正式运行前一般要做什么准备工作?本文通过事例阐述了OA系统在正式运行需要经过的步骤和一些具体的实施方法,并说明了OA系统的实施,必须充分发挥各级各类人员的作用才能取得成功。最后指出,OA系统的运行并不能够彻底地废除纸质公文。     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

PT仪数据采集系统中数据显示和快速导入导出的实现

PT仪数据采集系统涉及到大数据量的采集显示以及大数据量的导入导出。PT仪与上位机采用串口通讯方式。通过设置适当的Mscomm控件属性值,利用定时器实现数据的采集与显示。针对VB控制Excel导入导出数据,常用的方法效率低下,采用数据文件实现快速导出;通过一个具有二维数组性质的变量实现数据快速导入。有效地提高了PT仪数据采集系统的人机交互能力。本设计已运用于采集实验中,效果良好。     

Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential. The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other classifiers, and improves the accuracy reported in literature for one of the problems.

一种新的服务器性能监控软件研究

论述了服务器监控软件各个模块、功能的具体实现过程及方法,深入地剖析软件的设计过程,对系统核心部分＂提取数据＂做了详细论述。运用WCF、Ajax、Silverlight等关键技术实现了软件的功能,实验结果表明,所设计的系统能很好地满足系统的需求。     

移动环境中基于规则的数据收集技术

数据收集是指在断连前把用户将来可能访问的数据预先存储到本地缓存的过程,在研究基于失效报告的缓存策略的基础上,提出了一种超图模型的自动数据收集技术。理论分析和实验结果表明,该技术开销较低,可以有效提高Cache命中率和降低平均访问时间,尤其适用于缓存较大和注意力频繁转移的情况。     

基于人脸几何结构的表情识别

介绍一种利用人脸部几何结构进行表情识别的方法。在对眼睛和嘴巴的12个特征点进行准确提取的基础上,把已经得到的特征点连接起来并以向量的形式表示,称之为特征向量,把欲识别的表情图像的特征向量与已知的中性表情的特征向量进行长度和方向的比较,根据人脸表情在生理结构上的先验知识通过局部表情识别和数据融合来识别表情。     

PPDM中面向k-匿名的MI Loss评估模型

隐私保护数据挖掘（PPDM）利用匿名化等方法使数据所有者在不泄露隐私信息的前提下,安全发布在数据挖掘中有效可用的数据集。k-匿名算法作为PPDM研究使用最广泛的算法之一,具有计算开销低、数据形变小、能抵御链接攻击等优点,但是在一些k-匿名算法研究中使用的数据可用性评估模型的权重设置不合理,导致算法选择的最优匿名数据集在后续的分类问题中分类准确率较低。提出一种使用互信息计算权重的互信息损失（MI Loss）评估模型。互信息反映变量间的关联关系,MI Loss评估模型根据准标识符和标签之间的互信息计算权重,并通过Loss公式得到各个准标识符的信息损失,将加权后的准标识符信息损失的和作为数据集的信息损失,以弥补评估模型的缺陷。实验结果证明,运用MI Loss评估模型指导k-匿名算法能够明显降低匿名数据集在后续分类中的可用性丢失,相较于Loss模型和Entropy Loss模型,该模型分类准确率提升了0.73%~3.00%。     

非均衡数据分类经典方法综述与面向医疗领域的实验分析

近年来,人工智能技术被广泛地应用于多个领域.其中,智慧医疗场景得到了普遍关注,并产生了大量临床辅助诊断和医疗方案推荐的实际应用.然而,由于人工智能技术的本质在于通过从大量真实数据中进行模式抽取,从而预测未知情况,因此真实数据的数据特征和数据质量将直接影响人工智能应用的效果.相比其他智能应用领域,由于罕见病患者在人群中总...