Optimal control of noncollaborative servers in two‐stage tandem queueing systems

We consider two‐stage tandem queueing systems with dedicated servers in each station and a flexible server that is trained to serve both stations. We assume no arrivals, exponential service times, and linear holding costs for jobs present in the system. We study the optimal dynamic assignment of servers to jobs assuming a noncollaborative work discipline with idling and preemptions allowed. For larger holding costs in the first station, we show that (i) nonidling policies are optimal and (ii) if the flexible server is not faster than the dedicated servers, the optimal server allocation strategy has a threshold‐type structure. For all other cases, we provide numerical results that support the optimality of threshold‐type policies. Our numerical experiments also indicate that when the flexible server is faster than the dedicated server of the second station, the optimal policy may have counterintuitive properties, which is not the case when a collaborative service discipline is assumed. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 435–446, 2014     

Optimal patrol to uncover threats in time when detection is imperfect

Consider a patrol problem, where a patroller traverses a graph through edges to detect potential attacks at nodes. An attack takes a random amount of time to complete. The patroller takes one time unit to move to and inspect an adjacent node, and will detect an ongoing attack with some probability. If an attack completes before it is detected, a cost is incurred. The attack time distribution, the cost due to a successful attack, and the detection probability all depend on the attack node. The patroller seeks a patrol policy that minimizes the expected cost incurred when, and if, an attack eventually happens. We consider two cases. A random attacker chooses where to attack according to predetermined probabilities, while a strategic attacker chooses where to attack to incur the maximal expected cost. In each case, computing the optimal solution, although possible, quickly becomes intractable for problems of practical sizes. Our main contribution is to develop efficient index policies—based on Lagrangian relaxation methodology, and also on approximate dynamic programming—which typically achieve within 1% of optimality with computation time orders of magnitude less than what is required to compute the optimal policy for problems of practical sizes. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 61: 557–576, 2014     

某方向机传动系统动力学建模与仿真

基于机械系统动力学仿真软件ADAMS建立某方向机传动系统的动力学模型,模型中考虑基于接触理论的齿轮啮合力、轴承刚度变化和预紧机构的作用。针对3种实际工况,进行动态特性仿真,给出关重件的动态载荷,为该系统的故障分析和寿命预测奠定了基础。     

基于混沌特征的高分辨雷达目标识别

根据高分辨导弹制导雷达的基本工作原则和舰船目标的散射特性,利用现有雷达回波的仿真技术,在合理的战术想定下,计算了三类典型舰船的雷达回波.在此基础上,研究了利用雷达回波功率谱的方法来识别目标类型的有效性,并基于混沌理论提出了一种比功率谱方法更加有效的新的识别方法,从而为进一步利用混沌理论来开发雷达目标识别技术奠定了初步的理论基础.     

一种新的MIMO非线性系统动态解耦控制方法

提出了一种基于回归小波网络(recurrent wavelet networks)的多输入多输出系统(multi-input multi-output)动态解耦控制的新方法.该方法为分散式控制结构,采用回归小波网络作为解耦辨识器(decoupling identifier),在线动态辨识、回馈对应输入输出的灵敏度信息(sensitivity information),PID神经网络控制器根据回馈信息实现自适应分散独立控制.小波函数的紧支性、波动性以及回归网络较强的动态非线性映射能力使得回归小波网络具有较好的综合性能.仿真结果表明,用该方法构成的控制系统解耦效果好,收敛速度快,且具有较好的鲁棒性.     

BP神经网络和D-S证据理论的目标识别

目标识别是指挥自动化系统的一个重要组成部分,针对现代战争对抗手段不断增强的特点,运用BP神经网络和D-S证据理论探索作战飞机机型的识别方法.前端采用3层BP神经网络结构,以传感器接收数据为输入,以神经网络输出作为证据,后端对不同传感器的证据按D-S理论进行融合,得到待识别目标的识别概率.经由MATLAB编程对国内外几种主要机型的识别进行仿真研究,与现行目标识别方法相比较,能够更快速、准确、可靠地识别飞机目标,较好地满足了空战中作战指挥系统对飞机机型识别的需求.     

基于超动态测试系统的压力传感器标定技术研究

针对油气爆炸发展过程中压力测量的需要,提出了一种压力传感器标定的新方法。该方法基于超动态测试系统,操作简单,精度可靠,并采用最小二乘法对各测试点进行了拟合,得到了压力传感器的标定系数和其它基本性能指标,保证了测试过程中压力的准确性和精度。可在其它类似的测试系统中应用。     

高动态环境的GPS信号互模糊函数估计器及其性能分析

为了更可靠地跟踪高动态环境GPS信号参数,提出了一种新的适应高动态环境的GPS参数估计方法———互模糊函数方法。在假定高信噪比的条件下,推导了互模糊函数估计器的估计方差以及Cram er-Rao界,得出了采用互模糊函数法对高动态环境GPS信号的时延和多普勒频移进行联合估计是渐近无偏和渐近有效的结论。     

贮存环境对火箭弹动平衡冲量的影响

实验测定了已经在南方和北方2个地区贮存5年的火箭弹动平衡速度,计算了动平衡冲量,并利用秩和检验方法对实验结果进行了处理与分析。通过实验与分析,得到了32发火箭弹的动平衡冲量,综合试验、计算和检验结果表明,贮存环境对于该火箭弹的动平衡冲量有显著影响。     

柴油机动态特性的研究方法

分析了柴油机动态特性与稳态特性的不同,系统总结了柴油机动态特性的研究方法,主要有试验研究和模拟计算两种。按照研究目的的不同,对各种动态特性模型的优缺点和适用范围进行了总结,认为:基于稳态试验数据的动态特性模型主要用于整车性能仿真,基于模拟计算的非线性模型主要用于控制分析、参数优化和匹配。