优化初始聚类中心的K-means聚类算法

针对传统K-means算法对初始中心十分敏感,聚类结果不稳定问题,提出了一种改进K-means聚类算法。该算法首先计算样本间的距离,根据样本距离找出距离最近的两点形成集合,根据点与集合的计算公式找出其他所有离集合最近的点,直到集合内数据数目大于或等于[α]（[α]为样本集数据点数目与聚类的簇类数目的比值）,再把该集合从样本集中删除,重复以上步骤得到K（K为簇类数目）个集合,计算每个集合的均值作为初始中心,并根据K-means算法得到最终的聚类结果。在Wine、Hayes-Roth、Iris、Tae、Heart-stalog、Ionosphere、Haberman数据集中,改进算法比传统K-means、K-means++算法的聚类结果更稳定;在Wine、Iris、Tae数据集中,比最小方差优化初始聚类中心的K-means算法聚类准确率更高,且在7组数据集中改进算法得到的轮廓系数和F1值最大。对于密度差异较大数据集,聚类结果比传统K-means、K-means++算法更稳定,更准确,且比最小方差优化初始聚类中心的K-means算法更高效。     

一种K-means聚类算法的改进与应用

K-means算法是基于距离作为相似性度量的聚类算法,传统的K-means算法存在难以确定中心值个数、受噪声及孤立点影响较大的缺点。对此,利用类间相异度与类内相异度改进初始值K,以尽量减少人工干预;同时计算数据库中每一点与剩余点的距离和距离均和,将两者的大小比较作为识别孤立点和噪声点的依据,从而删除孤立点,减少对数据聚类划分的影响。最后将改进后的Kmeans算法应用于入侵检测系统并进行仿真实验,结果表明,基于改进的K-means算法的入侵检测系统一定程度上降低了误报率及误检率,提高了检测的准确率。     

K-means算法应用现状与研究发展趋势

K-means是一种无监督学习算法,基于数据对象之间的距离度量划分数据簇、欧氏距离等度量方法存在一些问题,比如离群点数据较多,算法准确度较低.互信息可以度量任意两个数据对象之间的互相包含程度,基于互信息改进K-means算法,可以更好地度量数据对象之间的距离,确保簇内高度相同和簇间高度相异,旨在解决离群点数据较多的情况下K-means算法准确度不高的问题.实验结果显示,与K-means算法、模糊K-means算法相比,改进K-means算法实验结果精确度达到了97.8％,该方法明显提高K-means算法的准确度.     

煤矿瓦斯传感器人工调校噪声数据处理方法

传统噪声数据处理方法对输入数据有一定的要求,且运行时间较长。而煤矿瓦斯传感器人工调校噪声数据存在数量少、质量差、时间不一致、易受环境影响等问题,采用传统噪声处理方法难以滤除该噪声数据。针对上述问题,提出了一种煤矿瓦斯传感器人工调校噪声数据处理方法。采用数据平均值填充煤矿瓦斯传感器浓度数据缺失值;采用多时间粒度构建煤矿瓦斯传感器浓度数据的特征集和样本集;采用高斯函数、混合高斯函数、二项式函数、三项式函数、分段二项式函数5种曲线拟合函数拟合人工调校噪声数据,并基于最小二乘法确定拟合函数参数,根据拟合效果得到最优的拟合函数;通过分析人工调校噪声数据得出该噪声数据与瓦斯浓度上升的斜率、峰值、调校前后浓度差等基本特征有关,根据这些基本特征识别出人工调校噪声数据并删除。实验结果验证了该方法的有效性。     

基于分化距离的离群点检测算法*

为了满足大规模数据集快速离群点检测的需要,提出了一种基于分化距离的离群点检测算法,该算法综合考虑了数据对象周围的密度及数据对象间的距离等因素对离群点的影响,通过比较每一对象与其他对象的分化距离来计算其周围的友邻点密度,挖掘出数据集中隐含的离群点。实验表明,该算法能有效地识别离群点,同时能反映出数据对象在数据集中的孤立程度。算法的复杂度较低,适用于大规模数据集快速离群点检测。     

基于距离和权重改进的K-means算法

K-means聚类算法简单高效,应用广泛。针对传统K-means算法初始聚类中心点的选择随机性导致算法易陷入局部最优以及K值需要人工确定的问题,为了得到最合适的初始聚类中心,提出一种基于距离和样本权重改进的K-means算法。该聚类算法采用维度加权的欧氏距离来度量样本点之间的远近,计算出所有样本的密度和权重后,令密度最大的点作为第一个初始聚类中心,并剔除该簇内所有样本,然后依次根据上一个聚类中心和数据集中剩下样本点的权重并通过引入的参数[τi]找出下一个初始聚类中心,不断重复此过程直至数据集为空,最后自动得到[k]个初始聚类中心。在UCI数据集上进行测试,对比经典K-means算法、WK-means算法、ZK-means算法和DCK-means算法,基于距离和权重改进的K-means算法的聚类效果更好。     

基于K-means的数据流离群点检测算法

针对数据流中离群点挖掘问题,在K-means聚类算法基础上,提出了基于距离的准则进行数据间离群点判断的离群点检测DOKM算法。根据数据流概念漂移检测结果来自适应地调整滑动窗口大小,从而实现对数据流的离群点检测,与其他离群点算法的一系列实验验证和对比结果表明,DOKM算法在人工数据集和真实数据集中均可以实现对离群点的有效检测。     

基于全向声呐的引水隧洞点云模型去噪方法研究

三维重建技术逐渐成为引水隧洞运营期安全检测的关键手段。而受隧洞特殊水文环境噪声、数据采集设备噪声以及载体运动噪声等影响,采集的点云数据不可避免的会遭受到噪声干扰,导致有用信息缺乏,不利于三维重建的进行。因此,该文提出了基于声呐数据特征点的点云去噪算法,实现隧洞点云数据的去噪。首先,该文依据引水隧洞声呐点云数据的特点,定义视觉距离和视角向量特征参数;其次,通过耦合视角向量与点云法向量估计点云漂移向量,并使用核函数方法估计视角距离参数的概率密度分布从而计算漂移距离;最后,采用漂移算法在保持点云模型特征的同时实现噪声的滤波。实验结果表明,该文提出的算法在去除隧洞点云模型数据噪声的同时能很好的保持引水隧洞模型的细节特征,为后续隧洞病害的检测提供高精度点云数据模型。     

基于目标特征选择和去除的改进K-means聚类算法

针对高维数据聚类中K-means算法无法有效抑制噪声特征、实现不规则形状聚类的缺点,提出一种基于目标点特征选择和去除的改进K-均值聚类算法.该算法使用闵可夫斯基规度作为评价距离进行目标点的分类,增设权重调节参数a、重置权重系数α进行特征选择和去除,可有效减小非聚类指标特征带来的噪声影响.算法验证实验选取UCI真实数据集和人工数据集进行聚类分析,验证改进算法对抑制噪声特征的有效性,与WK-means、iMWK-means算法进行实验对比,分析聚类学习时特征选择的适用性,同时寻找最优的距离系数beta和权重系数α.     

基于IKMNB分类算法在入侵检测中的应用

针对当前入侵检测中存在检测率低,误检率和漏报率高的问题,提出了一种基于K-means聚类的贝叶斯分类算法(IKMNB).用改进的K-means聚类算法对原始数据集中的完整数据进行聚类,然后计算缺失数据集中的每条记录与k个簇中心之间的近似度距离,把记录归属为距离最近的一个簇,使得该记录的缺失值被相应簇中的属性值填充,最后运用贝叶斯分类算法对处理后的完整数据集进行分类.通过仿真实验验证了该算法与朴素贝叶斯算法相比提高了检测率,降低了误检率和漏报率.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。