美国陆军面向2010年发展规划概述

概述美国陆军现阶段实施的2010年发展战略规划,分析阐明美军制定和实行该规划理论基础和指导方针,陆军结构调整与改革的思路和实施的内容,在军队建设上如何充分发挥科学技术优势等.     

基于生存能力的作战方案评估仿真模型

导弹武器系统的生存能力是反映作战方案好坏的定量依据之一。给出了基于生存能力的导弹作战方案评估系统的模型体系和构成,通过对该系统评估模型中的指标分析,建立了伪装生存能力、防护生存能力、机动生存能力等多个模型,并将其综合为对作战方案的生存能力评估模型。经过一定的仿真试验证明,模型正确,运行结果符合实际。     

复杂网络波动扩展衍射欠定采样预测模型

复杂网络环境中对网络波动的准确预测可以有效监测网络环境,防范网络入侵和拥堵。由于在复杂网络受到干扰的可能性更大,其网络波动具有扩展衍射特征,不可预测性强。传统方法中采用自回归移动平均模型进行复杂网络波形预测算法设计,在波动信号的时频重叠调制过程中未能纳入杂波先验信息,波动序列的扩展衍射特征形成欠定采样,预测效果不好。提出基于空间扩展自回归移动平均模型的复杂网络波动欠定预测算法,采用LTE线性均衡滤波,进行降噪去除杂波干扰,提取波动序列的扩展衍射特征形成欠定采样样本序列,设计网络波动时空序列扩展衍射点阵,准确预测网络波动的参数信息。以病毒入侵,网络监听和拥塞堵塞等波动产生模型为实例,进行仿真实验,结果表明该算法具有较高预测精度,监测点波动误差较小,实现复杂网络波动状态的动态跟踪和评估。     

基于扩展HTN规划的作战任务分析方法

任务分析是一个认识和了解任务本质的过程,作为作战计划拟制过程中的一个核心环节,作战任务分析的结论是否准确决定着作战计划的好与坏,并影响战争的结局。为了解决目前任务分析没有统一的标准及合适方法的问题,分析了层级任务网络(HTN)在任务分析中的不足之处,给出了OOR框架进行任务细化的方法和步骤,将其作为领域知识引入HTN规划,为任务分析提供了一个有效的方法,并设计了图元素用于表示任务树中任务之间的层级关系以及时序逻辑关系。最后以空间信息支援作战为例,验证了方法的可行性和有效性。     

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     

应急物资保障计划辅助决策模型

提出了一个制定应急物资保障计划的流程,构建了制定应急物资保障计划的数学模型,并应用运筹学的方法对其进行了优化.该模型为制定应急物资保障计划提供了有效的途径.     

罗兰C信号模拟器的设计与实现

运用数控技术产生罗兰C信号 ;通过对罗兰C脉冲的傅立叶分析和采样分析 ,确定仪器所用的采样频率和滤波器 ,给出了设计及实现罗兰C信号模拟器的原理     

Sequential and systematic sampling plans for the Markov‐dependent production process

Acceptance sampling plans are used to assess the quality of an ongoing production process, in addition to the lot acceptance. In this paper, we consider sampling inspection plans for monitoring the Markov‐dependent production process. We construct sequential plans that satisfy the usual probability requirements at acceptable quality level and rejectable quality level and, in addition, possess the minimum average sample number under semicurtailed inspection. As these plans result in large sample sizes, especially when the serial correlation is high, we suggest new plans called “systematic sampling plans.” The minimum average sample number systematic plans that satisfy the probability requirements are constructed. Our algorithm uses some simple recurrence relations to compute the required acceptance probabilities. The optimal systematic plans require much smaller sample sizes and acceptance numbers, compared to the sequential plans. However, they need larger production runs to make a decision. Tables for choosing appropriate sequential and systematic plans are provided. The problem of selecting the best systematic sampling plan is also addressed. The operating characteristic curves of some of the sequential and the systematic plans are compared, and are observed to be almost identical. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 451–467, 2001     

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.     

在线学习的主用户仿冒攻击策略

在认知无线网络中,次用户通过频谱感知来学习频谱环境,从而接入那些没有被主用户占用的频谱空隙。事实上,多种恶意攻击的存在会影响次用户频谱感知的可靠性。只有深入研究恶意攻击策略,才能确保认知无线网络的安全。基于此,研究了一种认知无线网络中的欺骗性干扰策略,即主用户仿冒攻击策略,该攻击策略通过在信道上传输伪造的主用户信号来降低次用户频谱感知的性能。具体来说,将攻击策略问题建模为在线学习问题,并提出基于汤普森采样的攻击策略以实现在探索不确定信道和利用高性能信道间的权衡。仿真结果表明,与现有的攻击策略相比,提出的攻击策略能更好地通过在线学习优化攻击决策以适应非平稳的认知无线网络。