军事战略、组织结构及指挥流程与信息技术融合的关系模型研究

提出了信息技术在军事上应用的阶段模型。指出随着信息技术变革力度的增加,其对军事战略、组织结构和指挥流程的影响也在逐渐增加,并在此基础上提出军队信息化中军事战略、组织结构以及指挥流程重组与信息技术融合的关系模型。     

Bayesian inference for a Lanchester type combat model

We undertake inference for a stochastic form of the Lanchester combat model. In particular, given battle data, we assess the type of battle that occurred and whether or not it makes any difference to the number of casualties if an army is attacking or defending. Our approach is Bayesian and we use modern computational techniques to fit the model. We illustrate our method using data from the Ardennes campaign. We compare our results with previous analyses of these data by Bracken and Fricker. Our conclusions are somewhat different to those of Bracken. Where he suggests that a linear law is appropriate, we show that the logarithmic or linear‐logarithmic laws fit better. We note however that the basic Lanchester modeling assumptions do not hold for the Ardennes data. Using Fricker's modified data, we show that although his “super‐logarithmic” law fits best, the linear, linear‐logarithmic, and logarithmic laws cannot be ruled out. We suggest that Bayesian methods can be used to make inference for battles in progress. We point out a number of advantages: Prior information from experts or previous battles can be incorporated; predictions of future casualties are easily made; more complex models can be analysed using stochastic simulation techniques. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 541–558, 2000     

Multivariate meta analysis with potentially correlated marketing study results

A univariate meta analysis is often used to summarize various study results on the same research hypothesis concerning an effect of interest. When several marketing studies produce sets of more than one effect, multivariate meta analysis can be conducted. Problems one might have with such a multivariate meta analysis are: (1) Several effects estimated in one model could be correlated to each other but their correlation is seldom published and (2) an estimated effect in one model could be correlated to the corresponding effect in the other model due to similar model specification or the data set partly shared, but their correlation is not known. Situations like (2) happen often in military recruiting studies. We employ a Monte‐Carlo simulation to evaluate how neglecting such potential correlation affects the result of a multivariate meta analysis in terms of Type I, Type II errors, and MSE. Simulation results indicate that such effect is not significant. What matters is rather the size of the variance component due to random error in multivariate effects. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 500–510, 2000.     

利用AHP确定决策方案排序的一种改进方法

针对在用AHP法求解权重排序向量时 ,用不同的计算方法导致排序方案不一致的矛盾现象 ,提出了一种在单准则下综合各个排序方案信息的方法 ,使AHP法更趋于完善     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

Programming challenges and impediments to reform: identifying pragmatic solutions

Using two recently published essays by the current writer that assesses the dismal record of performance of Planning, Programming, and Budgeting System in enabling communist legacy defence institutions in Central and Eastern Europe to develop viable defence plans, this essay argues the need for deep reforms in the region’s defence institutions. To guide this reform effort, pragmatic solutions are suggested to improve the ability of these organisations to produce viable defence plans. Recommended reforms are: (1) conduct conceptual and cultural “audits,” (2) make operational and financial data central to decision-making, (3) change current organisational sociology, (4) examine planning methods and practices, and (5) stress the need to adopt policy frameworks to drive the operation of defence institutions.     

社会网络分析在军事C4ISR组织结构中的应用

针对C4ISR组织结构的复杂特点,运用社会网络分析概念分析军事C4ISR(指挥、控制、通信、计算机、情报、监视和侦察)组织结构,旨在对军事组织结构中的C4ISR的效能给出一种定量分析的方法.具体讲,就是描述了FINC(力量、情报、网络和指挥控制)方法,给出了大量的基本运算法则来比较和定量分析C4ISR组织结构的组织网络,便于更全面地分析评估不同组织结构的费效比,并将其应用于一个想定的C4ISR组织结构中.     

基因短序列模式分析及其在5'剪接位点识别中的应用

短序列模式分析是基因序列分析的一个重要组成部分,在进行生物信号识别的时候,一般都会利用到短序列模式的信息。通常短序列模式的数目很多,如果每个都应用到生物信号识别中,会产生大量的参数,而且无法体现信号的主要特征。为了找出在识别信号位点中起关键作用的短序列模式,以信息增益作为评价依据,按照逐步选择的策略,将模式进行排队。根据排队结果,选取信息增益突出的短序列模式作为识别生物信号的关键依据,这样可以用较少的模式得到较好的结果。结合选取的短序列模式,用最大熵模型作为信号序列真实分布的估计,从而对给定序列进行识别。最后将这个方法用于5’剪接位点的识别,得到了满意的结果。     

适应任务的模块化卫星快速构建优化决策方法

如何根据应急航天任务,在满足各种约束的前提下,从型号众多、能力各异的卫星平台及有效载荷中快速决策效费比最佳的卫星构造方案引起了越来越多的关注,这就是适应任务的模块化卫星快速构建优化决策问题.在深入分析该问题特点的基础上,建立优化决策数学模型,提出基于遗传算法的优化决策方法,为处理遗传算法迭代过程中产生的不可行解引入基于...