混合电推进能源管理系统模糊逻辑控制

针对混合电推进能源管理方法开展研究,设计了一种基于模糊逻辑控制的混合电推进能源管理系统。通过设置垂直起降飞行器的功率要求、内燃机-发电机输出功率和储能电池荷电状态的模糊隶属度函数,动态最优分配发电机与储能电池的功率输出,从而提升系统的燃油经济性与飞行器的航程。结合模型设置,对100 kg级的垂直起降转平飞的飞行器能源系统变化进行仿真计算,验证了能源控制算法的可行性。结果表明：基于模糊逻辑控制的能源管理方法,在总时长约为1.2 h的飞行过程中,内燃机约有1 h的时间工作在比油耗最低的区域,从而提升了混合电推进系统的能量利用效率。研究为后续混合电推进系统的能源高效控制与管理提供了设计思路和分析方法。     

Dynamic policy for idling time preservation

This study aims to determine and evaluate dynamic idling policies where an agent can idle while some customers remain waiting. This type of policies can be employed in situations where the flow of urgent customers does not allow the agent to spend sufficient time on back-office tasks. We model the system as a single-agent exponential queue with abandonment. The objective is to minimize the system's congestion while ensuring a certain proportion of idling time for the agent. Using a Markov decision process approach, we prove that the optimal policy is a threshold policy according to which the agent should idle above (below) a certain threshold on the queue length if the congestion-related performance measure is concave (convex) with respect to the number of customers present. We subsequently obtain the stationary probabilities, performance measures, and idling time duration, expressed using complex integrals. We show how these integrals can be numerically computed and provide simpler expressions for fast-agent and heavy-traffic asymptotic cases. In practice, the most common way to regulate congestion is to control access to the service by rejecting some customers upon arrival. Our analysis reveals that idling policies allow high levels of idling probability that such rejection policies cannot reach. Furthermore, the greatest benefit of implementing an optimal idling policy occurs when the objective occupation rate is close to 50% in highly congested situations.     

高性能炸药综合性能的多维评估

基于欧几里得几何距离的概念,给出了一种炸药综合性能的多维空间评估方法。这种方法在确定炸药理想方案和最差方案的基础上,综合考虑了各种炸药方案与理想方案和最差方案的欧几里得几何距离,以此评估炸药综合性能。     

The danger of “hybrid warfare” from a sophisticated adversary: the Russian “hybridity” in the Ukrainian conflict

The term “hybrid warfare” is a new one that the West began to use to explain its failure to cope with asymmetric threats. Focusing on the war on global terrorism, the West temporarily withdrew its attention from traditional adversaries, such as Russia, which has used this gap and has audaciously returned to the stage as a global actor. Until the Russian annexation of the Crimean Peninsula in 2014 and inflaming the Ukrainian crisis, most Western authors attributed “hybrid threats” mostly to non-state actors. But the Ukrainian scenario showed the true face of “hybridity” in the modern battlefield when practised by a powerful state actor. Russian “hybrid warfare” in Ukraine has already been seen as a combination of conventional and unconventional methods, that have been complemented with other instruments of national power – diplomatic, economic and information. The purpose of this article is, through an analysis of the Ukrainian scenario, to demonstrate that although the term “hybrid” is new, the concept itself is old and is a continuation of already seen doctrine from the Cold War era. Although “hybrid threats” can come both from state and non-state actors, the Russian interference in Ukraine is proof that they are especially dangerous for the West if, or when, they are initiated from a traditional, sophisticated adversary that has the capacity to use all forms of warfare.     

影响指挥员军事决策的因素分析

现代高技术条件下的局部战争,军事活动的节奏明显加快,作战空间跨域扩展,战场态势复杂多变,尤其是当重大危机发生时,对军事决策的时效性提出了更高要求,指挥员必须能够及时、果断、有效、准确地定下作战决心。指挥员军事决策的有效性不仅受主观因素的影响,还受客观因素的影响。通过对影响因素的分析,提出提高指挥员军事决策能力的有效途径,为军队指挥人才培养提供合理建议。     

基于累积前景理论的时间矩阵多属性综合评判模型

针对动态多属性方案排序优选决策问题,提出了一种基于累积前景理论的时间矩阵序列动态多属性综合评判系统模型。依据累积前景理论,将时间矩阵序列动态多属性决策转化为关于属性期望的益损时间矩阵序列动态决策,综合考虑各属性权重、各时间点的权重,计算各方案的最终综合前景值,并依据其大小对方案进行综合评判排序。最后给出了一个决策实例。     

一种基于分层智能混合决策的多Agent框架

智能对于指挥决策模型非常重要,也是提高模拟训练和作战实验质量的关键。作战中指挥决策内容多,问题性质不一,对智能要求高。在分析指挥控制模型发展现状基础上提出模型对智能的需求,分析设定了智能的5个层次,提出一种混合Agent框架,并在框架中对每一层智能给出解决方案,框架兼具认知Agent、BDI Agent和刺激反应Agent的优长,对构建智能化的训练和实验系统有较大帮助。框架在指挥装备作战运用实验中得到应用,具有较好的理论意义和应用价值。     

基于决策树的空中目标威胁分类研究

将决策树ID3算法应用到空中目标威胁程度分类问题。分析了现有威胁评估的常用方法及其局限性,论述了ID3算法在此问题上的适用性。以28批典型目标的威胁程度为例,对数据进行概化处理后,利用weka平台进行仿真,构造决策树,并从中挖掘出隐含的24条规则,这对决策支持系统知识库的构建是非常必要的,也为指挥员定下作战决心提供重要参考。     

基于Pareto排序法的战时装备维修任务多目标调度方法

针对战时武器装备维修保障问题,建立了包括最大完工时间、延迟时间和维修单元负荷在内的多目标优化调度模型。为提高解的多样性和收敛性,构建了一种基于Pareto排序法和小生境技术相结合的遗传算法用于模型求解,引入Pareto排序和拥挤距离进行适应度计算,通过混沌系统随机生成权重系数,并使用小生境技术改进选择方式。通过实例验证表明,该方法能够有效地解决装备维修多目标调度问题。     

广义逆指数分布元件的可靠性分析

在II型混合截尾样本下,得到了广义逆指数分布未知参数的最大似然估计。利用最大似然估计的渐近正态性构造了参数的渐近置信区间,运用Lindley's逼近方法和TierneyKadane's逼近方法计算出了参数的Bayes估计。最后,运用Monte-Carlo方法对上述估计方法结果作了模拟比较。