一种新型的自适应入侵检测系统的研制

针对当前入侵监测系统存在的自适应能力较差、扩展性差的问题，基于面向混合类型数据的快速启发式聚类算法FHCAM和属性约束的模糊规则挖掘算法ACFMAR，提出了一种采用数据挖掘技术的自适应入侵检测系统DMAIDS．该系统通过划分聚类的方法划分出异常入侵记录；模糊关联规则的方法提取入侵模式．通过对1999年举行的数据挖掘大赛所使用的10%子集进行实验，结果表明该系统平均检测率和平均误检率比大赛获得冠军检测方法准确率提高了近2倍，检测率从数据子集1的65．25％自适应提高到数据子集9的85．7％能自适应的检测各种攻击，表明该系统具有很好的应用前景。     

应用数据挖掘的束流状态描述建模

聚类分析是描述数据群体特征的有效方法.然而,随着挖掘数据库规模的增大,评分函数极值搜索是计算复杂度很高的问题.文中提出了一种新的算法,并将其运用到束流状态描述建模中.试验结果表明,该算法快速有效.同时,重复性是电子储存环的重要指标,聚类模型可作为机器研究和决策的依据,具有指导意义.     

Numerical treatment of retarded boundary integral equations by sparse panel clustering

Stefan Sauter Institute for Mathematics, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland ^{We consider the wave equation in a boundary integral formulation.The discretization in time is done by using convolution quadraturetechniques and a Galerkin boundary element method for the spatialdiscretization. In a previous paper, we have introduced a sparseapproximation of the system matrix by cut-off, in order to reducethe storage costs. In this paper, we extend this approach byintroducing a panel clustering method to further reduce thesecosts.}     

激光诱导荧光光谱快速检测食源性致病菌

近年来,由微生物污染引起的食品安全问题对人类健康构成威胁。微生物的快速检测对食品安全具有重要意义。目前,微生物快速检测技术存在操作困难,成本高的不足。激光诱导荧光光谱(LIFS)具有灵敏度高、操作方便、设备相对便宜等优点,为微生物的快速检测提供了一种潜在技术。利用便携式405 nm激光激发三种常见食源性致病菌(粪肠球菌、鼠伤寒沙门氏菌和铜绿假单胞菌)的荧光,并利用微光纤光谱仪检测光谱。通过调节激光器功率(10～100 mW)得到粪肠球菌的荧光强度,验证了激光器功率(Power,P)与细菌荧光强度的关系,结果表明最佳激光器功率范围为50～80 mW。测量了在激光器功率P=50 mW时细菌样品的荧光光谱,并讨论了细菌种类和荧光光谱之间关系。结合文献分析粪肠球菌在528 nm处出现黄酮的荧光峰,铜绿假单胞菌中的原卟啉发射634 nm荧光峰。实验结果表明：(1)铜绿假单胞菌在634和703 nm处的荧光峰,可作为直接识别特征;(2)基于多元统计,将粪肠球菌和鼠伤寒沙门氏菌的光谱划分为9个特征区,采用动态聚类法得到粪肠球菌和鼠伤寒沙门氏菌的识别率均达到100%。结果表明,激光诱导荧光光谱法可有效检测铜绿假单胞菌、粪肠球菌和鼠伤寒沙门氏菌。相较于其他微生物快速检测技术,LIFS方法操作方便,检测速度快,识别率高,对食源性致病菌的快速检测具有重要的应用价值。     

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures.     

Damage resistance of carbon fibre reinforced epoxy laminates subjected to low velocity impact: Effects of laminate thickness and ply-stacking sequence

A major concern affecting the efficient use of composite laminates is the effect of low velocity impact damage on the structural integrity [1–3]. The aim of this study is to characterize and assess the effect of laminate thickness, ply-stacking sequence and scaling technique on the damage resistance of CFRP laminates subjected to low velocity impact. Drop-weight impact tests are carried out to determine impact response. Ultrasonic C-scanning and cross-sectional micrographs are examined to assess failure mechanisms of the different configurations.It is observed that damage resistance decreases as impact energy increases. In addition, thicker laminates show lower absorbed energy but, conversely, a more extensive delamination due to higher bending stiffness. Thinner laminates show higher failure depth. Furthermore, quasi-isotropic laminates show better performance in terms of damage resistance. Finally, the results obtained demonstrate that introducing ply clustering had a negative effect on the damage resistance and on the delamination area.     

二维区间密度加权算子及其应用

针对不确定多属性决策中的属性信息分布不均匀,且评价信息多数为二维信息的情况,本文提出了二维区间密度加权算子(TDIDW算子)的属性信息集结方法.依据密度算子的集结过程特点,文章首先定义了二维区间密度加权算子及其合成算子,然后介绍了基于灰色区间聚类法的评价信息分组方法以及基于非线性模型的密度加权向量确定方法,最后进行了算例验证.验证结果表明,该方法可以有效地解决由于属性信息分布不均匀而导致评价结果不准确的问题.     

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.     

A modified rough c-means clustering algorithm based on hybrid imbalanced measure of distance and density

Traditional c-means clustering partitions a group of objects into a number of non-overlapping sets. Rough sets provide more flexible and objective representation than classical sets with hard partition and fuzzy sets with subjective membership function for a given dataset. Rough c-means clustering and its extensions were introduced and successfully applied in many real life applications in recent years. Each cluster is represented by a reasonable pair of lower and upper approximations. However, the most available algorithms pay no attention to the influence of the imbalanced spatial distribution within a cluster. The limitation of the mean iterative calculation function, with the same weight for all the data objects in a lower or upper approximation, is analyzed. A hybrid imbalanced measure of distance and density for the rough c-means clustering is defined, and a modified rough c-means clustering algorithm is presented in this paper. To evaluate the proposed algorithm, it has been applied to several real world data sets from UCI. The validity of this algorithm is demonstrated by the results of comparative experiments.     

一种基于无监督学习的社交网络流量快速识别方法

在分析了社交网络的发展和研究现状后,结合现有的网络流量识别方法和社交网络流量特征属性,提出了一种基于KMeans聚类算法的无监督学习社交网络流量识别方法.为了提高处理的高效性、实时性,利用开源云计算平台如doop上提供的M印Reduce架构进行分布式并行处理.对比实验结果表明,提出的方法能快速、高效的识别社交网络流量,并且识别准确率有显著提高.