无人机飞行训练资源配置效率的AHP-Arena综合评价方法

为降低传统层次分析法(analytic hierarchy process,AHP)赋权过程主观片面性影响,提出了一种综合AHP与Arena仿真建模技术的无人机训练资源配置效率评价方法。基于飞行训练流程分析,确定了训练资源配置效率影响因素和效率评价指标,建立了层次型综合评价体系,概括了AHP-Arena综合评价步骤。利用Arena建模软件构建训练流程动态仿真系统并验证有效性,采用单变量数值仿真法、均方差决策法完成了影响因素客观赋权,通过线性加权综合法得出综合评价系数,实现资源配置效率评价与方案优选。实例应用说明了该方法的有效性,同时,该方法可推广至其他类似的训练过程中,亦可为其他多因素、多指标评价决策过程提供借鉴。     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

密集环境中无人机协同机动飞行运动规划方法综述

从单无人机机动飞行向多机协同扩展的通用规划框架出发,介绍了其中各模块相关研究的基本原理、代表性方法和前沿研究,主要包括用于环境障碍感知的实时导航地图构建、离散空间的路径规划、连续空间的轨迹规划、基于离散连续混合空间的规划、多航迹或轨迹的协同规划。综合无人机通用规划框架的关键技术,提出了无人机协同机动规划下一步需要重点研究的方向。     

运载火箭随机振动环境试验条件的变带宽设计方法

针对运载火箭随机振动环境试验条件提出了基于能量等效思想的变带宽设计方法,通过采用分数倍频程数据处理方法,并确保各个频带内的均方根值与原随机振动环境的均方根值相同,有效解决传统的等带宽随机振动环境试验条件总均方根过高的问题。为了验证所提方法的有效性,通过典型管路产品进行了随机振动响应计算。结果表明,该方法既能够有效地对产品进行考核,又不至于对产品造成过考核。所提变带宽设计方法可为运载火箭随机振动环境条件的合理设计与分析提供有效手段。     

城市火灾自动报警监控网络系统的应用与管理

介绍城市火灾自动报警监控网络系统及其组成,阐述系统的技术应用,并展望该系统的发展前景及存在的技术难题。     

自动变速车辆换挡规律的研究

研究了自动变速车辆的换挡规律的算法,制定了车辆的动力性换挡规律和经济性换挡规律,建立了车辆动力学仿真模型.通过仿真,分析了不同换挡规律对车辆性能的影响.     

装甲车辆远程故障诊断技术

基于网络的远程故障诊断是诊断系统的发展趋势,在论述远程故障诊断的基本概念、优势及关键技术的基础上,提出了构造装甲车辆远程故障诊断系统的总体框架结构和工作模式,阐述了系统实施的可行性及在部队应用的重大意义.     

Averaging frameworks for simulation optimization with applications to simulated annealing

We present two frameworks for designing random search methods for discrete simulation optimization. One of our frameworks is very broad (in that it includes many random search methods), whereas the other one considers a special class of random search methods called point‐based methods, that move iteratively between points within the feasible region. Our frameworks involve averaging, in that all decisions that require estimates of the objective function values at various feasible solutions are based on the averages of all observations collected at these solutions so far. Also, the methods are adaptive in that they can use information gathered in previous iterations to decide how simulation effort is expended in the current iteration. We show that the methods within our frameworks are almost surely globally convergent under mild conditions. Thus, the generality of our frameworks and associated convergence guarantees makes the frameworks useful to algorithm developers wishing to design efficient and rigorous procedures for simulation optimization. We also present two variants of the simulated annealing (SA) algorithm and provide their convergence analysis as example application of our point‐based framework. Finally, we provide numerical results that demonstrate the empirical effectiveness of averaging and adaptivity in the context of SA. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

基于深度优先搜索与增量式求解的极小一阶不可满足子式提取算法

随着寄存器传输级甚至行为级的硬件描述语言应用越来越广泛,基于一阶逻辑的可满足性模理论(Satisfiability Modulo Theories,SMT)逐渐替代布尔可满足性(Boolean Satisfiability,SAT),在VLSI形式化验证领域具有更加重要的应用价值。而极小不可满足子式能够帮助EDA工具迅速定位硬件中的逻辑错误。针对极小SMT不可满足子式的求解问题,采用深度优先搜索与增量式求解策略,提出了深度优先搜索的极小SMT不可满足子式求解算法。与目前最优的宽度优先搜索算法对比实验表明:该算法能够有效地求解极小不可满足子式,随着公式的规模逐渐增大时,深度优先搜索算法优于宽度优先搜索算法。     

高空长航时无人机编队协同侦察任务规划

针对高空长航时无人机侦察任务规划特点,分析了高空长航时无人机执行侦察任务过程中的飞行航线约束和通信条件约束,以最小化无人机总飞行航程和最终编队飞行时间为优化目标,建立无人机编队协同侦察任务规划问题模型。同以往的通用侦察任务模型相比,该模型突出考虑了高空长航时无人机执行侦察任务过程的特点。以基本粒子群算法为基础,通过粒子群离散化和结合遗传算法进行改进,使其适用于求解复杂组合优化问题。仿真结果验证了算法求解复杂任务规划问题的有效性。