坦克火控系统的自适应滤波与自适应解命中

在非制导火控系统中,为实现目标信息的自适应滤波与自适应解命中问题,机动目标模型应能动态识别,又要适应工作周期的各种变化.本文提出的“参数辨识模型”能同时满足上述要求,并可将自适应滤波纳入到现代控制理论的正常思路内予以解决.     

UIMS中的虚拟窗口及实现

为了解决屏幕上安排功能的面积不能满足应用要求的问题,本文提出了虚拟窗口的概念,并简单地介绍了在UIMS中实现虚拟窗口的原理,最后以一个自行开发的用户界面管理系统┅Fastool为例,较为详细地说明了如何在UIMS中增加虚拟窗口的功能。将Fastool应用到实际开发中的结果表明,虚拟窗口能提高系统用户的工作效率,同时也使系统的界面产生了较好的视觉效果。     

运用速度增益制导实现对目标卫星的拦截

本文对运用速度增益制导实现对目标卫星的拦截方法进行了研究。其特点是在拦截器运动参数初值及目标轨道参数给定的条件下,首先确定考虑地球扁率J_2项影响下的拦截目标的预测命中点,然后采用Lambert方法确定给定碰撞时间下拦截器的需要速度,最后实现对目标的拦截。用该方法可保证向未制导转换时有较高精度的转换条件。     

金属铝弹性常数从头计算的一种新方法

本文报导用原子簇模型计算金属铝的弹性常数。具有面心立方结构的Al_(13)原子簇在不同晶格常数下总能由多重散射兄方法(MS—X_a)算出。采用Morse势函数对原子簇总能进行最小二乘法拟合,然后用拟合出的势参数算出金属铝的弹性常数。我们的计算结果与其他理论计算值及实验值符合甚好。     

带有大质量刚体圆锥壳的自由振动分析

本文针对壳体带有大质量刚体的特点,用有限元离散方法,导出了在不同周向波数下壳元节圆位移与刚体质心位移及转角之间的关系式,并将结果与实际运动进行对比分析,论证了结论的正确性。为带有大质量刚体的壳体结构的动力分析提供了理论依据。     

后勤指挥管理自动化系统建设中突破口的选择方法

本文论述了后勤指挥管理自动化系统建设中突破口的3种选择方法,即ABC法、“瓶颈”法和效益扩展法,并且分析了这些方法的应用条件和注意事项。     

康特洛维奇的数值方法在矩形薄板横向弯曲振动中的应用

本文提出的解决矩形薄板弯曲振动问题的康特洛维奇的数值方法,大大减少了所需要的计算机内存。通过算例表明它具有比较高的计算精度和计算速度,是一种有前途的计算方法。     

Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001     

A stochastic machine maintenance and sale problem: Results with different production functions

In this article we solve and analyze a stochastic version of the Boiteux problem by employing the stochastic optimal control method. Our setup is close to that of Thompson [22]. Our focus is to analyze, under the same structure, how different production functions and how the variance in machine deterioration/maintenance affect our optimal machine maintenance and sale date decisions. © 1993 John Wiley & Sons, Inc.     

Parallel machine scheduling with batch deliveries to minimize total flow time and delivery cost

Motivated by the flow of products in the iron and steel industry, we study an identical and parallel machine scheduling problem with batch deliveries, where jobs finished on the parallel machines are delivered to customers in batches. Each delivery batch has a capacity and incurs a cost. The objective is to find a coordinated production and delivery schedule that minimizes the total flow time of jobs plus the total delivery cost. This problem is an extension of the problem considered by Hall and Potts, Ann Oper Res 135 (2005) 41–64, who studied a two‐machine problem with an unbounded number of transporters and unbounded delivery capacity. We first provide a dynamic programming algorithm to solve a special case with a given job assignment to the machines. A heuristic algorithm is then presented for the general problem, and its worst‐case performance ratio is analyzed. The computational results show that the heuristic algorithm can generate near‐optimal solutions. Finally, we offer a fully polynomial‐time approximation scheme for a fixed number of machines. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 492–502, 2016