军事指挥控制中的贝叶斯决策支持

军事指挥控制是一个复杂的决策过程,该过程中信息是结合定性的专家知识和定量的融合数据而得到的.贝叶斯网为指挥控制系统提供了一个框架来处理这种专家知识和具体数据相结合的问题,并且影响图作为贝叶斯网的延伸可被用于分析指挥控制系统中某个决策的可行性.进一步地,为了减少所需指定的条件概率的数量并引入时变的概念,提出动态影响网作为一种新的决策支持方法.     

一种变质心高速旋转炮弹鲁棒姿态控制律

针对采用变质心技术的高速旋转炮弹的姿态控制问题,提出一种基于扩张状态观测器的动态面控制方法。根据由弹体和单滑块组成的多体系统的特点,建立了系统的姿态动力学模型,并对其进行了合理的简化。将系统的滚转通道引起的强耦合、建模误差及外部扰动等视为未知不确定干扰,并且考虑由于炮弹尺寸限制而引起的多体系统控制输入(滑块位移)的有限性,设计了扩张状态观测器和辅助系统,分别对系统的干扰进行观测以及处理控制输入的有限性,综合动态面控制技术设计了姿态控制律,最后基于李雅普诺夫稳定性原理证明了控制器的稳定性。仿真结果表明,该控制器能够在克服干扰的前提下快速稳定地跟踪指令信号,具有良好的控制精度和鲁棒性。     

燃气射流引起的火箭发射系统振动特性分析

以火箭发射系统为研究对象,对其56°射角工况下发射过程中的振动特性进行分析。创新点在于确定发射过程中动态载荷的加载方法,即首先根据发射系统有限元分析结果确定迎气面上试验测量点的具体位置,然后测得燃气射流冲击压强随时间变化的数值,并对试验测得数据进行处理,最后推导冲击压力对于径向距离的直线公式。所采用的冲击压强分析方法为研究其他武器系统受燃气射流冲击的影响提供了重要的指导意义。     

融合战术与修正物理特征的空中目标战术类型识别

针对敌方飞行器隐身、伪装技术的日趋成熟,致使我方由侦察设备获取的电磁信号严重变形,特别是物理特征在目标类型识别中的作用日趋减弱的现实问题,提出了一种融合目标战术特征修正物理特征证据的目标战术类型识别方法。该方法从目标战术类型识别的军事需求出发,提出对冲突物理特征证据的阶梯式修正规则,以及对冲突证据折扣因子的优化方法,并将修正后的证据重新组合得到识别结果。仿真实例证明该方法在处理目标冲突物理特征证据时具有高准确率的特点。该方法为复杂环境下的目标战术类型识别问题提供一种新的解决思路。     

基于动态窗口的灰色加权填充算法及应用?

为解决复杂系统中单属性缺失数据填充困难问题,提出了基于动态窗口的灰色加权填充算法。该算法通过建立双向灰色预测模型,并采用模型精度评价系数加权填充缺失数据,有效增强了算法的准确性;提出基于灰色模型评价系数反馈的动态伸缩窗口概念,寻找产生最优模型的训练数据序列,使算法具有良好的鲁棒性和适应性。实验结果表明,该方法在RMSE和MA两项指标上均优于传统的双向灰插值、灰插值、多项式插值等方法。     

基于Multi-Agent技术的动态电磁环境建模与仿真

针对传统的电磁环境建模方法难以准确地描述电磁环境动态变化的问题,提出了一种基于Multi-Agent技术的动态电磁环境建模方法。首先分析了电磁环境建模的层次结构;然后建立了通信电台、雷达、侦察站、干扰站的Agent实体模型,建立了Agent行动模型和Multi-Agent系统交互模型,解决了个体Agent模型设计中的感知、动作、决策及实现方法上的问题。最后设置某仿真场景,利用Netlogo仿真平台建立仿真验证系统,仿真结果验证了模型的有效性,表明了应用Multi-Agent建模方法解决电磁环境的动态模拟的可行性。     

基于预设模板的系统动态构建与集成技术

针对动态变化的军事任务保障需求,以及信息系统构建过程中所面临的资源动态聚合、功能动态组合、环境动态适应等问题,基于网络化计算与存储环境,利用资源封装、软件仓储等技术,提出一种基于预设模板的信息系统动态构建与集成方法和过程,并通过构建基础环境和方向联指等应用实例开展试验验证,结果表明该方法可有效支撑军事信息系统的柔性组合,大幅提升系统开设效率。     

Nonlinear pricing with consumer satiation

In various scenarios, consumers may become satiated with products, and the degree of satiation is directly associated with their prior experiences. Confronted with consumer satiation, the seller is unable to either identify consumers who have a higher likelihood of being satiated ex ante or distinguish satiated from non‐satiated consumers ex post. Therefore, the seller should address dynamic selling, valuation uncertainty, and quantity decisions, all of which are important operational issues. We consider a two‐period problem in which consumer types are influenced by their prior consumption experiences. Faced with these consumers, the seller intends to optimize quantities and adjust the prices of the products in each period to maximize revenue. We find that the seller may reduce ex ante production quantity as some consumers become satiated. Moreover, the ex ante quantity is first decreasing and then increasing with regard to the satiation rate. Furthermore, two‐period information asymmetries may provide a rationale for upward distortion in quantity when consumer preferences are highly sensitive to first‐period consumption. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 386–400, 2016     

A stochastic assignment problem

There are n boxes with box i having a quota value Balls arrive sequentially, with each ball having a binary vector attached to it, with the interpretation being that if X_i = 1 then that ball is eligible to be put in box i. A ball's vector is revealed when it arrives and the ball can be put in any alive box for which it is eligible, where a box is said to be alive if it has not yet met its quota. Assuming that the components of a vector are independent, we are interested in the policy that minimizes, either stochastically or in expectation, the number of balls that need arrive until all boxes have met their quotas. © 2014 Wiley Periodicals, Inc. 62:23–31, 2015     

Generation and storage dispatch in electricity networks with generator disruptions

We present methods for optimizing generation and storage decisions in an electricity network with multiple unreliable generators, each colocated with one energy storage unit (e.g., battery), and multiple loads under power flow constraints. Our model chooses the amount of energy produced by each generator and the amount of energy stored in each battery in every time period in order to minimize power generation and storage costs when each generator faces stochastic Markovian supply disruptions. This problem cannot be optimized easily using stochastic programming and/or dynamic programming approaches. Therefore, in this study, we present several heuristic methods to find an approximate optimal solution for this system. Each heuristic involves decomposing the network into several single‐generator, single‐battery, multiload systems and solving them optimally using dynamic programming, then obtaining a solution for the original problem by recombining. We discuss the computational performance of the proposed heuristics as well as insights gained from the models. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 493–511, 2015