Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.     

Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

Composite Estimation for Single‐Index Models with Responses Subject to Detection Limits

We propose a semiparametric estimator for single‐index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the non‐parametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors but much more efficient for heavy‐tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.     

基于模糊理论的城市快速路交通状态检测算法

通过分析流量、速度、占有率与城市快速路交通状态的关系,提出了一种基于模糊理论的城市快速路交通状态检测算法。通过在深圳市某快速路上的实际验证表明,该算法在能保证一定的检测率情况下,具有较低的误报率和较少的检测时间,表明此方法在实际应用中是切实可行的。     

基于共振解调模拟电路的滚动轴承早期故障检测性能研究

共振解调模拟电路是滚动轴承故障诊断中最常用的技术之一,但是其故障检测性能的定量评价研究尚未见报导。因此,分别以正弦调制和脉冲调制所构造的故障轴承振动加速度信号为电路输入,定量分析了共振解调电路在不同信噪比条件下的故障检测性能,并研究了电路中的关键设计参数——[WTBX]带通滤波器品质因数Q[WT]的调制规律,为共振解调模拟电路在轴承早期故障检测的实际工程应用提供了可靠的科学依据。     

基于环境传感器和穿戴式传感器的行为异常检测

针对独居老人频繁出现行为异常的情况,提出了基于热释电红外传感器和穿戴式传感器的老人行为异常检测系 统。设计了行为异常检测的硬件环境,介绍了红外传感器和穿戴式传感器的工作原理,提出了依据重力加速度所在轴向 检测老人躺下位置是否发生异常的方法,并融合了红外传感器和穿戴式传感器的信息对躺下位置异常进行检测。在1 居室的家庭环境中对位置异常检测的方法进行验证,通过实验的方法验证了算法的可行性,并确定了检测躺下状态的阈 值。系统能有效实现对独居老人是否发生躺下位置异常进行检测。     

Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method

This paper studies the outlier detection and robust variable selection problem in the linear regression model. The penalized weighted least absolute deviation (PWLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to simultaneously achieve outlier detection, and robust variable selection. An iterative algorithm is proposed to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed methods. The results indicate that the finite sample performance of the proposed methods performs better than that of the existing methods when there are leverage points or outliers in the response variable or explanatory variables. Finally, we apply the proposed methodology to analyze two real datasets.     

高维大数据基因网络中的社区发现——以NC方法为例

从大量基因中识别出致病基因是大数据下的一个十分重要的高维统计问题。基因间网络结构的存在使得对于致病基因的识别已从单个基因识别扩展到基因模块识别。从基因网络中挖掘出基因模块就是所谓的社区发现（或节点聚类）问题。绝大多数社区发现方法仅利用网络结构信息,而忽略节点本身的信息。Newman和Clauset于2016年提出了一个将二者有机结合的基于统计推断的社区发现方法（简称为NC方法）。本文以NC方法为案例,介绍统计方法在实际基因网络中的应用和取得的成果,并从统计学角度提出了改进措施。通过对NC方法的分析可以看出对于以基因网络为代表的非结构化数据,统计思想和原理在数据分析中仍然处于核心地位。而相应的统计方法则需要针对数据的特点及关心的问题进行相应的调整和优化。     

冯契的逻辑范畴学说及其方法论意义

作为当代著名的哲学家、逻辑学家,冯契对逻辑范畴作了开拓性的探索：阐明了逻辑范畴的辩证本性和特点;考察了逻辑范畴体系与认识的辩证运动;建构了以“类”、“故”、“理”为骨架的逻辑范畴体系;探讨了以“类”、“故”、“理”为骨架的逻辑范畴体系的方法论意义。     

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。