基于ＢＩＲＣＨ聚类算法的高维传感器数据异常检测

海量高维传感器数据受网络环境扰动较大,导致其异常值检测难度较大,提出基于BIRCH聚类算法的高维传感器数据异常检测方案。推算节点对应的一阶差分信号序列,信号矢量通过多跳路由传输至网关节点,将空间相关性强的传感器节点划分在同一簇内,采集完整的高维传感器数据;利用分割点预设得到传感器数据特征属性候选分割点,挑选信息增益最大的点为最佳分割点;将传感器数据序列的中位数异常看作异常检测判定条件,利用BIRCH聚类算法中的聚类特征和聚类特征树计算节点特征属性,将数据聚类结果拟作球形簇状架构,输出高维传感器数据序列异常数据。仿真结果证明,该方法的异常节点检测的检出率在95%以上,误报率为0.35%,异常检测耗时在1.5min以内。     

无线传感网中一种基于支持向量机的异常事件检测方案

异常事件检测问题是无线传感器网络中的研究热点之一。针对现有检测方案的不足,设计一种新的时间-空间-属性单类超球面支持向量机来建模异常事件检测问题,然后提出无线传感器网络在线和部分在线离群点检测算法。该算法根据节点间的时间-空间和属性关联度确定超球面的半径,最后以在线方式鉴别到达节点的每一个新的测量值是正常数据还是异常数据。仿真实验结果表明,与基于时空关联度的超球面支持向量机相比,新算法的检测率大大上升,虚警率明显下降。同时,部分在线算法与在线算法的效率相当,大大降低了计算和通信复杂度。     

一种基于分层聚合的分布式异常数据检测方案

在无线传感器网络中,由于传感器节点的带宽、功率、计算能力有限,传统的集中式方案难以区分海量数据中的异常数据.为解决此问题,提出一种基于多层分布式无线传感器网络的异常数据检测方案.在节点层级采用K-Means++算法对数据进行聚类,执行簇合并算法以减少数据传输量,在网关节点执行基于KNN的异常簇检测算法,将正常簇信息返回至底层节点进行局部检测,从而区分异常数据.在高斯数据集与IBRL数据集上的实验结果表明,该方案检测率高于98%,且能显著降低通信消耗.     

基于滑动窗口和置信度的无线传感器网络异常检测算法

针对无线传感器网络中异常检测误报率高及节点间通信开销大的问题,提出了基于滑动窗口和置信度的无线传感器网络异常检测算法(ADABSWC)。该算法使用环境干扰因子量化监测环境中的不确定性,建立异常数据干扰区间识别滑动窗口中的异常数据。提出了数据异常度的计算方法,用来预判异常来源;然后引入多通信半径划分最佳邻域,利用相对熵计算节点信息置信度;根据节点信息置信度协同判定出节点异常数据的来源。通过仿真实验,ADABSWC算法在不同传感器节点规模下均体现了较好的性能。该算法与KNN-PSOELM、OFN算法相比,事件节点、错误节点的检测率均高于98%,且误报率均低于1.5%。实验结果表明,所提出的算法可保证高检测精度的同时控制误报率在较低水平,算法拥有较好的容错性能。     

一种WSN中的三层多维事件协作检测算法

提出了一种适用于无线传感器网络的三层多维事件协作检测算法。传感器节点通过计算均值向量序列的相似度发现异常,并通过投票机制确认事件发生。簇头节点根据边界向量序列的相似度,利用改进的K均值聚类算法对多维事件数据进行分类和合并。汇聚节点利用事件属性数据的概率分布,匹配检测出事件的类型。理论分析和仿真试验的结果表明:与传统集中式的事件检测算法相比,该算法能在噪声干扰下提高对多维事件的检测精度,降低算法的通信量和计算复杂度,延长网络的生存时间。     

去噪处理下的复杂网络聚类优化模型研究

由于当前已有模型未能对加权复杂网络数据进行去噪处理,导致网络聚类结果和聚类质量不理想,聚类实际运行时间上升.提出一种基于空间关联性的加权复杂网络聚类模型,通过法向张量投票技术组建投影时的局部坐标系,获取网络编码局部几何结构,利用邻居节点间感知数据的空间关联性过滤噪声数据,同时使用坐标更新算法调整噪声点位置,实现复杂网络...     

基于图模块度聚类的异常检测算法

社会网络的数据规模在不断扩大,现存的异常检测算法对复杂社会网络进行检测的效果不理想,提出了一种基于图模块度聚类的异常检测算法(anomaly detection algorithm based on graph modularity clustering, GMC＿AD),该算法适用于解决受网络规模以及复杂度的限制导致检测效率不高的问题。GMC＿AD算法在分析网络拓扑结构的基础上,通过引入异常节点加权机制和模块度聚类算法进行异常检测。GMC＿AD算法主要在三个方面进行改进：a)设计网络中节点演化的量化策略,以此识别具有异常演化行为的节点来得到异常节点集合;b)通过模块度聚类的方法降低网络规模;c)在计算网络波动值的过程中使用加权机制合理考虑异常节点的影响,再通过网络波动值变化来检测异常。基于真实社会网络VAST、EU＿E-mail和ENRON进行对比实验,GMC＿AD算法准确地检测出异常发生的时段,实验结果显示在事件检测敏感性上提高了50%～82%,在异常检测运行效率上提高了30%～70%。实验结果表明,GMC＿AD算法不仅提高了异常检测算法的准确率和敏感性,还提高了异常检测算法的效率...     

无线传感器网络中异常读数检测算法研究*

针对无线传感器网络的离群点检测算法由于没有充分考虑数据的时空关联性和网络的分布特性,导致检测精度低、通信量大和计算复杂度高等局限,提出了基于时空关联的分布计算与过滤的在线离群点检测算法。该算法在各传感器节点上利用传感器读数的时间关联性生成候选离群点,并利用空间关联性对候选离群点进行过滤得到局部离群点,最终将所有传感器节点上的局部离群点集中到sink节点上获得全局离群点。利用时空关联性提高了检测精度,利用分布计算与过滤减少了通信量和计算量,理论分析和实验结果均表明该算法优于现有算法。     

河网中具有时空关系的异常事件在线检测

当网络异常事件发生时,传感器节点间的时空相关性往往非常明显.而现有方法通常将时间和空间数据性质分开考虑,提出一种分散的基于概率图模型的时空异常事件检测算法.该算法首先利用连通支配集算法(CDS)选择部分传感器节点监测,避免监测所有的传感器节点;然后通过马尔可夫链(MC)预测时间异常事件;最后用贝叶斯网络(BN)推测空间异常事件是否出现,结合时空事件来预测异常事件是否会发生.与简单阈值算法和基于贝叶斯网络算法对比,实验结果表明该算法有高检测精度、低延迟率, 能大幅降低通信开销,提高响应速度.     

基于聚类中值比较的WSNs故障检测算法

无线传感器网络(WSNs)中的传感器件容易失效而导致测量数据不准确,因而,高效、实用的故障检测算法对于保证WSNs的感知质量非常重要。提出一种基于聚类中值比较(CBMC)的故障检测算法。不同于传统的中值比较的思想,该算法引入聚类方法对待检测节点的邻居节点测量数据进行分组,根据分组信息计算该节点状态。仿真实验表明:CBMC算法具有较高的故障检测率(DR)和较低的故障误检率(FPR)。     

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     

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。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.