作战体系优化方法

体系对抗已成为高技术战争的一个基本形态,它的作战能力不仅取决于体系构成要素的强弱,更取决于要素组合状态的优劣,作战体系的优化是实现最大作战效能的必要手段,也是高技术体系对抗作战迫切需要解决的问题.在给出探索性分析和作战体系的基本概念和体系评价准则后,提出了作战体系的优化方法并给出了优化过程描述.该方法主要采用探索性分析方法进行体系对抗仿真,并对仿真结果、作战任务指标、武器费用进行综合分析,最后得出最优的作战体系.     

基于模糊神经网络的武器控制系统信号分析

某新型飞机武器控制系统故障诊断过程复杂,故障征兆和故障原因之间存在着许多不确定因素,精确定位故障存在许多困难.针对这种情况,提出了基于模糊神经网络,研究了模糊神经网络技术在武器控制系统故障诊断领域的应用,并根据系统本身的特点,提出了诊断和算法模型.在此基础上,研制出武器控制系统检查仪,对该方法作了验证.结果表明:该方法是可行和有效的.     

引入权重的防空战场态势分析模型

在防空作战中,战场态势的分析非常重要,是辅助决策的基础.为了分析和把握防空作战时的战场态势,针对战场态势的主要影响因素,应用集对分析理论和方法,以集对分析的同异反联系度和距离测度为基础,构造了防空战场态势的分析模型,并对该模型进行了深入探讨.最后,通过实例计算表明了应用该分析模型进行战场态势评估的可行性,为防空作战的战场态势评估提供了一种有效的方法,具有一定的应用价值.     

红外图像人脸识别方法

红外成像具有抗干扰性强、独立于可见光源、防伪装、防欺诈等优点,这使得红外图像人脸识别可在很大程度上弥补可见光人脸识别技术的缺陷和不足,是今后人脸识别研究的一个重要方向。在深入分析红外人脸图像特性的基础上,研究了红外图像人脸识别的特点,并提出了一种新的红外图像统计人脸识别方法。实验结果表明,这种识别方法不论从理论上还是从实验上都是可行的,具有良好的识别能力。     

飞行器MDO中灵敏度计算的自动微分方法

自动微分方法(ADM)是灵敏度计算的一种新方法,是处理飞行器多学科设计优化中灵敏度分析问题的有力工具。将ADM与估算灵敏度最常用的有限差分方法(FDM)以及与ADM同时期发展起来的另外一种灵敏度计算方法—复变量方法(CVM),从原理上进行了比较,研究了ADM前向模式在Visual C 6.0环境中的实现方法,结合多学科环境中的飞行器设计优化计算实例分析了该模式的优缺点。ADM在科学计算、工程计算等方面有很大的发展空间。     

Fully sequential indifference‐zone selection procedures with variance‐dependent sampling

Fully sequential indifference‐zone selection procedures have been proposed in the simulation literature to select the system with the best mean performance from a group of simulated systems. However, the existing sequential indifference‐zone procedures allocate an equal number of samples to the two systems in comparison even if their variances are drastically different. In this paper we propose new fully sequential indifference‐zone procedures that allocate samples according to the variances. We show that the procedures work better than several existing sequential indifference‐zone procedures when variances of the systems are different. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Bin‐packing problem with concave costs of bin utilization

We consider a generalized one‐dimensional bin‐packing model where the cost of a bin is a nondecreasing concave function of the utilization of the bin. Four popular heuristics from the literature of the classical bin‐packing problem are studied: First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), and Best Fit Decreasing (BFD). We analyze their worst‐case performances when they are applied to our model. The absolute worst‐case performance ratio of FF and BF is shown to be exactly 2, and that of FFD and BFD is shown to be exactly 1.5. Computational experiments are also conducted to test the performance of these heuristics. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Comparison of rank aggregation methods based on inherent ability

Ranking is a common task for selecting and evaluating alternatives. In the past few decades, combining rankings results from various sources into a consensus ranking has become an increasingly active research topic. In this study, we focus on the evaluation of rank aggregation methods. We first develop an experimental data generation method, which can provide ground truth ranking for alternatives based on their “inherent ability.” This experimental data generation method can generate the required individual synthetic rankings with adjustable accuracy and length. We propose characterizing the effectiveness of rank aggregation methods by calculating the Kendall tau distance between the aggregated ranking and the ground truth ranking. We then compare four classical rank aggregation methods and present some useful findings on the relative performances of the four methods. The results reveal that both the accuracy and length of individual rankings have a remarkable effect on the comparison results between rank aggregation methods. Our methods and results may be helpful to both researchers and decision‐makers.     

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.     

Terrorism,Radicalisation and Moral Panics: Media and Academic Analysis and Reporting of 2016 and 2017 ‘Terrorism’

Radicalism or radicalisation has become a serious political and academic theme in recent years and any incident involving Muslims now almost automatically acquires the cachet, as events in 2016 and 2017 have shown. However, despite vast sums and resources expended on the subject no one can define what they mean by ‘it’. This should make us pause and question what precisely it is that causes so much alarm and is it worth the resources, time and effort employed to respond to it?