采用状态转移矩阵的拦截中段制导方法

基于相对运动理论提出拦截中段制导方法。方法原理为,将拦截器初始轨迹与施加修正后的轨迹视为一主一从两空间目标,以相对运动理论描述从目标相对于主目标的运动规律,于是初始的修正量可由终点处相对状态求解。给出了一般形式的相对运动模型,运用几何法与变分法推导得到了J2摄动影响下相对运动的状态转移矩阵,在此基础上,提出了采用状态转移矩阵的拦截中段制导方法。仿真算例表明,提出的方法能够为工程实际中的拦截中段制导提供有效支持。     

A historical overview of US counter-insurgency

Say not the struggle naught availeth,

The labour and the wounds are vain,

The enemy faints not, nor faileth,

And as things have been, things remain.

Arthur Hugh Clough     

基于J2EE平台的WebGis实现技术研究

WebGis 是一种基于互联网技术标准和通讯协议的地理信息系统,它是地理信息系统的发展趋势。J2EE 平台是当前非常流行的软件开发平台。本文首先介绍了 WebGis 和 J2EE 开发平台,分析了利用 J2EE 开发 WebGis 的优势,最后提出了基于 J2EE 平台的 WebGis 的框架结构。     

Estimating defence budget saving from disarmament: the United States’ case

Probing the technology in the production of US national defence by using a dynamic cost‐function model with adjustment costs, this paper evaluates the effect of reducing the level of national defence on the defence budget saving. Our inquiry involves estimating the defence production structure without output data for non‐market goods that are normally unavailable. Our findings include: (i) the United States behaves rationally to minimize cost in the production of national defence; (ii) the adjustment costs are larger in disarmament than in military build‐up; (iii) due to the adjustment costs peculiar to disarmament, the defence budget saving from disarmament appears small, but cutbacks allow great savings on the defence budget.     

COIN fights: A response to Etzioni

Etzioni both exaggerates and minimizes the influence of my book Learning to Eat Soup with a Knife on different components of US military doctrine, mischaracterizes my treatment of the Malayan Emergency, and unfairly denigrates the successes of counterinsurgency in Iraq from 2007 to 2011 while misattributing the reasons for its failures in both Iraq and Afghanistan.     

基于改进遗传算法的平流层飞艇定点悬停控制(英文)

平流层飞艇定点悬停控制是飞艇应用领域的关键技术。该控制问题可以转化为非线性多目标优化问题来求解。遗传算法是解决现代非线性多目标优化问题的一种重要方法。但是基本遗传算法在解决定点悬停飞艇实际控制问题时,具有易陷入局部最优解、在遗传进化过程中随机性较强、搜索效率低下及耗时多等缺陷。通过借鉴并行遗传算法、模拟退火算法和向量评价遗传算法的基本思想,在基本遗传操作中添加切断算子和拼接算子,设计一种改进遗传算法来改善上述缺陷。仿真结果表明该方法是有效可行的。     

高精度空间非合作式相对轨道状态预报

为了解决接近空间非合作目标过程中的相对导航问题,提出一种基于相对轨道动力学方程和二阶龙格库塔积分公式的高精度空间非合作目标相对轨道预报模型。考虑地球J2摄动加速度,将目标轨道方程在参考轨道附近展开,保留至引力加速度差的二阶展开项,并进一步推导考虑J2摄动的参考轨道角速度和角加速度,建立相对轨道动力学微分方程;在给定相对轨道初值的情况下,采用二阶龙格库塔积分公式进行相对轨道预报。该模型采用数值积分方法,对相对轨道动力学模型形式没有限制,通用性好,适用范围广;选择低阶龙格库塔公式积分预报,既减少了计算量又保证了计算精度。设置高轨和低轨两种相对运动仿真场景,仿真结果表明了模型的通用性和精确性。     

氘-金碰撞中J/ψ产生截面随x_F变化的研究

在不同碰撞质心能量下对氘-金(d-Au)碰撞中J/ψ核吸收效应进行了研究,结果表明,在不同碰撞质心能量下J/ψ核吸收截面随动量分数xF变化趋势明显不同。利用以上结论,计算了美国相对论重离子对撞机(RHIC)能量下J/ψ在氘-金(d-Au)碰撞中与质子-质子(p-p)碰撞中的产生截面比,并与实验数据比较发现,理论结果能很好的解释实验现象。最后,为欧洲大强子对撞机(LHC)实验作了理论预言,理论结果将为即将运行的LHC实验所检验。     

考虑J2项摄动的卫星编队飞行

针对参考卫星运行于圆轨道的编队飞行,分析了J2项摄动的影响.在此基础上提出了一种方法,通过调整环绕卫星的半长轴,大大减小了J2项摄动对编队飞行的破坏作用.此外,基于冲量假设估计了编队保持的代价.数值结果表明,调整长半轴的卫星编队可以长期保持,节省了大量的控制燃料.     

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C), strains (0.05–0.2) and strain rates (1000–5500/s) are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.