Insurgency by the numbers I: A reconsideration of the ecology of communist success in jiangxi province,China

Theorizing about Taliban operations in Afghanistan has its limits and it is possible that Kabul-centric strategies do not adequately address the unique circumstances of each region in the country. How exactly has the Taliban gone about attaining its objectives in Kandahar province and how have those approaches evolved since 2002? And how have the Taliban adapted to coalition forces' attempts to compete with the insurgency and stamp it out? The answers to these questions are critical in the formulation of any counterinsurgency approach to Afghanistan.     

航天测控系统任务可靠性分析的EOOPN模型

提出了一种可用于航天测控系统任务可靠性分析的扩展面向对象Petri网（extend object-oriented Petri nets, EOOPN）模型,旨在对给定的航天测控方案进行可靠性评估分析。针对问题特点明确了OOPN扩展思路,给出了EOOPN模型的形式化定义、运行规则和建模步骤,模型通过引入公共库所、激发弧、消息变迁和消息处理函数等概念,体现了面向对象的思想,具有很好的层次性和模块性。所建立的EOOPN模型能够完整的描述航天测控系统的组成和任务特点。通过对算例模型仿真运行,表明实验结果具有良好收敛性,与Markov解析值对比误差在1%以内,从而验证了模型的有效性。     

水下无载波窄脉冲相关特征检测

广泛应用于超宽带雷达的无载波窄脉冲信号具有较高的距离和角度分辨率、超近程探测能力、便于目标识别、抑制混响起伏等独特优势.现将其应用于水下声纳系统,水下窄脉冲信号具有宽低频特性,从相关方法出发,讨论无载频窄脉冲对体目标信号的检测.根据窄脉冲信号脉宽内相关特征不变特性,体目标信号经过相关器和特征判决器,可明显减小虚峰出现的概率.数值模拟和实验均验证并证明单次检测即可检测到目标信号,精确检测峰值出现时间.     

Chirp信号参数估计算法性能比较

通过数值仿真定量地比较了三种Chirp信号参数估计算法——解线调法、迭代估计法和局部搜索最大似然法的性能,并定性地比较了算法的运算量。仿真结果表明,在三种算法中,局部搜索最大似然法的估计性能最好,而运算量居中;解线调法运算量最大,但估计性能居中;迭代估计法的估计性能最差,但运算量最小。对于实际系统,应根据不同的估计精度和运算量要求,灵活选择不同的算法。综合考虑估计性能和运算量之间的折衷可以得到结论,在三种算法中局部搜索最大似然法是一种相对较好的选择。     

弹药库防静电方法研究

根据弹药库静电危险场所的分类等级,以形成静电危害的3个基本条件为依据,讨论了弹药库防静电危害常用措施:设施设备静电接地、防静电工作台、人体静电防护、铺设导电(或防静电)地面以及各自防静电要求.这些措施为防止和消除弹药库的静电危害提供了根本保障.     

基于弹药效能分解的装备战损建模方法研究

针对传统装备战损建模方法通用性较差、难以满足装备实际维修需求的缺点,提出了基于弹药效能分解的装备战损建模方法,具体包括弹药毁伤效应建模、装备结构描述建模、装备抗毁性数据库建模3部分.该方法对装备战损的精细程度进行分析,并且可以求得针对某个可更换单元的维修工序,从而为装备维修保障提供决策依据和参考.     

美国2014年《四年防务评估报告》特点

2014年3月4日,美国国防部向国会提交了2014年版《四年防务评估报告》和《2015财年国防预算报告》。《四年防务评估报告》更新了再平衡战略,为美军建设提出了优先发展方向,要求美军联合部队向“更小型化”转型,以满足未来军事行动需求。     

一类具有饱和发生率的离散型SIS传染病模型

研究一类具有饱和发生率的离散型SIS传染病模型,得到了模型的基本再生数.通过线性化的方法,运用LaSalle-Lyapunov定理,证明当基本再生数R01时,无病平衡点是全局渐近稳定的;通过迭代的方法,证明当基本再生数R01时,地方病平衡点是全局渐近稳定的.     

Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade‐offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014