Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations

Summary.  As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey data have been developed. These methods are the first to work with sampling weights and to be able to cope with missing values. Two of these methods are presented here. The epidemic algorithm simulates the propagation of a disease through a population and uses extreme infection times to find outlying observations. Transformed rank correlations are robust estimates of the centre and the scatter of the data. They use a geometric transformation that is based on the rank correlation matrix. The estimates are used to define a Mahalanobis distance that reveals outliers. The two methods are applied to a small data set and to one of the evaluation data sets of the EUREDIT project.     

冗余度机器人的运动规划碰撞算法

在考虑了操作机的关节极限、自碰撞和静态障碍物的情况下,从给定的初始位形出发,发现一条到末端效应器目标位置和姿态的相连可到达路径。方法给出了机器人操作机点到点逆运动学问题求解算法,利用碰撞算法实现了冗余度机器人运动规划,仿真验证了该方法的有效性,并表明了该方法具有较大的实用价值。     

基于移动代理的新型IDS维护更新机制设计

提出了一种基于移动代理的新型分布式入侵检测系统。该系统是针对广域网环境专门设计的,数据的处理通过各节点所设置的代理来进行分布式计算,不仅能实现全网络范围内的入侵检测功能,具有良好的可移植性;而且对网络系统和主机的资源占用较低,减少了出现网络瓶颈的可能。还建立了移动代理的新型分布式入侵检测系统的体系结构和理论分析模型,并讨论了该系统的维护更新机制。     

遗传算法在入侵检测中的应用

介绍了基于模型推理和基于模型两种入侵检测系统,提出了一种新的基于智能体技术的入侵检测系统体系结构,解决了传统集中式入侵检测系统的弊病,将任务处理和数据分布到网络各个结点上,充分利用网络资源协同完成入侵检测任务;介绍了遗传算法在该系统中的应用,因系统安全的先验知识体现在对原始数据中有价值特征属性变量集的选择上,故利用遗传算法对特征属性变量子集的选择进行优化,找到相对最优的由特征向量表示的特征属性变量集,以降低入侵检测系统的负荷。     

CCD图象检测信号的失真校正

本文从CCD图象输入系统误差引起图象检测信号失真的原因分析出发,以降低系统装校误差精度要求和增大元器件离散性的宽容度为目标,讨论朱真校正方法的原理和数学描述。最后,介绍两种失真校正方法。     

横向塞曼稳频He－Ne激光器的研究

稳频横向塞曼Ｈｅ－Ｎｅ激光器作为一种新型光源，具有功率大、频差低、易于相位细分的特点，因而能广泛应用于各种精密测量中．本文介绍了我们研制的稳频横向塞曼激光器，实验结果表明，其激光频率稳定度达４．７×１０（－１０），再现性达１．８×１０（－８）．     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

Teachers’ Knowledge and Beliefs About Child Sexual Abuse

Child sexual abuse is one of the main types of abuse still to be addressed within the field of education, yet the education system itself can serve as a primary tool for its prevention. A better understanding of teachers’ knowledge and beliefs about child sexual abuse will allow us to establish key starting points from which to utilize the system for prevention. Four hundred and fifty teachers participated in this study, completing a questionnaire regarding their knowledge and beliefs about child sexual abuse. The study revealed that over half the teachers, 65.3% (n = 294), had never received any type of training in child sexual abuse education and that the majority were not familiar with methods of identifying child sexual abuse, 90.7% (n = 279). Various mistaken beliefs were identified among the participating teachers, such as pathological profiles of abusers, that the vast majority of child sexual abuse implies violent behavior, and that there cannot be abusers the same age as the victim. These results indicate that knowledge deficiencies do exist about child sexual abuse among teachers and highlight the need for training in this field.     

FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

互联网环境下学术期刊防范学术不端行为技术研究

利用互联网环境，从技术的角度出发，对一稿多投、抄袭行为、剽窃行为以及捏造行为等学术不端行为的检测与防范进行研究，提出了各种操作上可行的方法。实践证明，运用这些方法，可有效地防范有学术不端行为的文章在学术期刊上发表。