采用多模式控制的电磁发射蓄电池充电谐波抑制方法

针对电磁发射蓄电池组大倍率快速充电过程中的电网谐波陡增问题,提出多模式模糊滞环控制谐波抑制方法。结合电磁发射混合储能系统的时序串联拓扑结构,说明蓄电池的超大倍率充电应用工况和策略,进而介绍蓄电池充电的拓扑结构,明确谐波的来源、特点和产生原因。在分析单台充电机充电时的电网谐波电压和谐波电流的基础上,研究不同充电机并充台数、不同充电电流情况下电网谐波电压和谐波电流的分布规律。设计多模式控制方法的组成方式和切换判据,建立蓄电池充电机、瞬时无功功率检测、谐波抑制、多模式控制的仿真模型。仿真和试验数据表明：谐波抑制方法能够快速抑制电磁发射蓄电池充电机大规模启停、波动时的谐波陡增问题,补偿效果满足国家标准。     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

Assortment optimization with position effects under the nested logit model

We study the assortment optimization problem with position effects under the nested logit model, whose goal is to find the revenue-maximizing subset of products as well as their corresponding display positions. In this joint assortment-position optimization problem, the choices of products are affected by not only their qualities and prices but also the positions where they are displayed. Despite determining the assortment and their corresponding display positions sequentially, we propose to solve this problem in an integrated way to obtain the optimal solution. We formulate this problem as a nonlinear binary integer programming model and develop a dynamic programming based solution approach to obtain the optimal assortment-position assignments. We carry out extensive numerical experiments to evaluate the benefit of our integrated approach. The most important insight we discover is that it is not necessarily better to put the most attractive products in the best position. Moreover, we show that compared to the sequential approaches, our approach can improve revenue by 10.38% on average, which suggests that firms should take into consideration position effects when making assortment decisions. Finally, we discuss results related to two extensions of this problem, that is, the special case when positions are preassigned to nests, and the joint assortment-position-price optimization problem.     

基于深度森林的网络流量分类方法

随着网络应用的迅猛发展,流量分类在网络资源分配、流量调度和网络安全等诸多研究领域受到广泛关注。现有的机器学习流量分类方法对流量数据特征的选取和分布要求苛刻,导致在实际应用中的复杂流量场景下分类精确度和稳定度难以提高。为了解决样本特征属性的复杂性给分类性能带来的不利影响,引入了基于深度森林的流量分类方法。该算法通过级联森林和多粒度扫描机制,能够在样本数量规模和特征属性选取规模有限的情况下,有效地提高流量整体分类性能。通过网络流量公开数据集Moore对支持向量机、随机森林和深度森林机器学习算法进行训练和测试,结果表明基于深度森林的网络流量分类器的分类准确率能够达到96. 36%,性能优于其他机器学习模型。     

军事物流服务商准入标准设置和甄选研究综述

军事物流服务商甄选研究是新军事变革下供应商管理发展的重要方向,是有效完成后勤保障任务、顺利实施作战行动的重要保证。基于此,本文从供应商关系理论和发展、物流服务商指标体系选择以及结合其他方法对物流服务商甄选三个方面对国内外研究现状进行总结概述,对整个军事物流服务商的选择过程加以研究,将其科学客观地运用于军事物流服务商选择的实际操作中,对于加强军事物流服务商需求管理、提高选择质效以及优化工作流程具有重大意义。同时,分析现有研究成果的不足,并对其未来发展前景提出展望,针对性地提出了军事物流服务商初步准入标准以及后续复选的方法,为军事物流服务商选择体系的完善提供参考。     

电磁发射的出口速度及负载质量变化对导轨热量的影响

为分析不同发射出口速度、不同发射负载质量对导轨积累热量的影响,建立了导轨温度场模型,计算了导轨各点温度变化过程,并和光纤光栅温度传感器测量结果对比,验证了模型及测量结果的正确性。在此基础上,设计并进行了三种类型实验,分析了在不同发射速度、不同发射负载质量以及相同发射能级不同发射速度下的导轨热量积累差异。该结论为确定连发间隔、导轨冷却设计提供了有效参考。     

快速航天发射现状与建设

快速航天发射以其响应快、成本低等优势,能较好地满足军民商快速太空进入的需求。概述了快速航天发射军用牵引、商用助推的发展特点,分析了未来军民商领域对快速航天发射的新需求。归纳了快速航天发射的发展历程和发展模式,分析了美军太空军事演习对快速航天发射的促进作用。基于美国在快速进入太空的需求评估、技术发展、管理制度等方面取得的经验教训,探讨我国快速航天发射的主要制约因素。针对美国新太空战略威慑和太空霸权,为我国快速航天发射力量建设提出相应的发展建议。     

舰载雷达低空作战性能评估

根据美军用的ADC模型,结合低空目标的特点,提出对舰载雷达低空作战性能的评估方法,把性能评估的重点放在探测能力、定位能力、抗干扰能力和识别能力这四个能力因子上,并给出确定方法.所作研究可为定量分析舰载雷达对低空目标作战性能提供依据.     

MANET多路径路由中最大可靠性路径选择算法

如何选择路径的数量和质量对多路径路由机制的性能有着重要的影响。已有的多路径算法没有深入研究如何选择多路径的问题。对目前存在的两个典型问题进行了分析,在此基础上研究了路径可靠性模型和虚拟完全非交叉多路径模型,然后提出一个最大可靠性多路径选择算法。算法利用路径权重作为路径可靠性的近似解决方案,以此克服路径可靠性度量问题(NP难题)研究的复杂性,根据路径可靠性模型和完全非交叉多路径模型来选择可靠的路径集,使用这组路径集并行分布流量。应用OPNET模拟平台实现了算法,结果表明,本算法能增加聚合带宽,优化网络带宽的应用,提高网络的吞吐率和多路径路由的性能。     

基于模糊综合评判的地空导弹阵地选择

在现代防空中,由于精确制导技术在空袭兵器当中的运用,使防空作战中兵力机动显得格外重要.为实现机动作战,阵地选择就成为机动作战中的重要环节.运用模糊数学原理和方法,结合地空导弹部队阵地选择的特点,给出了阵地选择评价因素指标体系,建立了阵地选择方案的二级模糊综合评判模型,并采用层次分析法(AHP)对阵地选择评价因素的权重进行了确定,最终得出阵地选择合理的评价结果.