奎屯市大气环境质量分析评价

在城市化的过程中,随着城市人口的增加,城市产业的发展,城市"三废"的增加成为必然,如何合理地解决这一问题,已引起广泛的关注。奎屯地处干旱区,特殊的气候条件和能源结构是影响该地区大气环境的主导因素。文章用该地区的大气监测资料和能源消耗资料为依据,分析了污染物的种类和来源,并对大气质量进行了评价分析。提出这一地区大气环境的保护措施。     

飞越北极航行时间模型的研究

对飞越北极,缩短航线,节省航行时间问题进行了研究。采用了多种算法,对其有效性、适用性和复杂性进行分析讨论。假设"在旋转椭球体中,以通过球心和该椭球体上任意两点所构成的平面截此椭球体所得轨迹弧长作为曲面上两点间最短距离的近似值",在此基础上,通过运用数学软件计算得出结论:从北京出发,飞越北极抵达底特律的飞行时间可节省4.079 6h,从而使问题得到了进一步的验证。     

大机动格斗及近距空战的若干问题

较系统地综述了大机动格斗的一些基本战术和技术概念,引入了超机动性的一些度量,然后提出了使用具有刚体性质的质点飞机模型的可行性.最后提出在大机动格斗实战环境中,火力控制需要解决的若干问题.     

基于舰载直升机的舰艇编队对空预警性能配置研究

针对现代海战中空袭目标的特点,合理配置舰载直升机在舰艇编队对空防御中的预警过程。在舰载直升机前出距离的数学模型基础上,从预警机本身因素、敌方攻击因素和我方编队防御因素这三个方面入手,对舰载直升机的对空预警性能进行优化配置,采用MATLAB数学工具得出计算结果,绘制性能匹配曲线(或曲面)并得出约束条件。从不同角度对舰载直升机对空预警的配置进行了分析,可为舰艇编队预警探测和作战指挥决策提供依据。     

采用时序多属性评价方法的空战威胁排序

鉴于空战中威胁目标的多属性及动态变化特点,提出采用时序多属性评价方法来解决空战目标动态威胁排序问题。在建立空战威胁指标体系基础上,采用熵权法确定目标属性权重,并通过指数分布法确定各时刻权重,最后综合依据威胁目标多时刻的属性信息,实现了空战动态威胁排序。仿真结果说明该方法的合理性。     

未来中远距协同空战多目标攻击决策研究

根据先进战斗机的技术特点和未来中远距协同空战发展方向,提出一种基于先敌发现、先敌发射、先敌摧毁能力的空战态势分析模型。同时,采用一种将并行遗传算法与分布估计算法相结合的并行分布遗传算法并将其用于多目标攻击决策。最后利用具体算例进行仿真。结果表明空战态势分析模型能够较为准确地描述未来中远距多机协同空战,采用的算法具有较好的可行性和实时性,为中远距多机协同空战的多目标攻击决策提供了新的思路和方法。     

小型电动垂直起降飞行器推进系统性能分析

小型电动垂直起降飞行器的悬停续航性能取决于推进系统的性能。基于电池、电机、电调的质量模型和螺旋桨的推力-功耗关系,利用电池的恒流放电模型建立了垂直起降飞行器的续航时间评估模型,分析了推进系统参数和有效载荷对飞行器续航性能的影响规律,研究结论可用于电动垂直起降飞行器的总体设计和推进系统选型。     

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.     

Fitting Lanchester equations to the battles of Kursk and Ardennes

Lanchester equations and their extensions are widely used to calculate attrition in models of warfare. This paper examines how Lanchester models fit detailed daily data on the battles of Kursk and Ardennes. The data on Kursk, often called the greatest tank battle in history, was only recently made available. A new approach is used to find the optimal parameter values and gain an understanding of how well various parameter combinations explain the battles. It turns out that a variety of Lanchester models fit the data about as well. This explains why previous studies on Ardennes, using different minimization techniques and data formulations, have found disparate optimal fits. We also find that none of the basic Lanchester laws (i.e., square, linear, and logarithmic) fit the data particularly well or consistently perform better than the others. This means that it does not matter which of these laws you use, for with the right coefficients you will get about the same result. Furthermore, no constant attrition coefficient Lanchester law fits very well. The failure to find a good‐fitting Lanchester model suggests that it may be beneficial to look for new ways to model highly aggregated attrition. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

基于排列法的目标威胁评估模型

目标威胁评估是一个推理、决策的行为,结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。