导弹飞行时安全系统保险失效时间的分布模型

根据安全系统在导弹飞行过程中受外界环境信息的冲击而解除保险这一物理过程,推断了安全系统的保险失效时间服从截尾Ｇａｍ ｍ ａ 分布模型,并讨论了在保险失效建模和评估等统计过程中非常有用的截尾Ｇａｍ ｍ ａ 分布的一些特性。     

亥姆霍兹线圈磁场均匀性的研究

推导了亥姆霍兹线圈所产生的磁感应强度的计算公式,利用该公式进行了编程计算,研究了亥姆霍兹线圈在空间所产生的磁场分布规律．以１％均匀度为例给出了磁场均匀区与环半径（距离）之间的关系．     

Banyan网的一类非阻塞特性及其在ATM交换机设计中的应用

Ｂａｎｙａｎ网具有结构简单、自寻址、硬件复杂度低等优点，因而被许多交换系统和多机系统作为基本互连网络，但其内阻塞特性限制了它的性能。对某些特定输入模式，Ｂａｎｙａｎ网是非阻塞的。本文讨论Ｂａｎｙａｎ网的一类非阻塞特性，说明它们在ＡＴＭ交换机设计中的应用——通过数据分布提高ＡＴＭ交换机的负载均衡能力，用更小的硬件代价实现多目广播功能。     

考虑质心流动与偏移的通用弹道仿真模型

引入了质心坐标系的概念,对弹体坐标系的定义作了适当修正,给出了考虑质心流动与偏移的通用弹道仿真模型及相应的转动惯量与惯性积的计算公式     

杀伤战斗部破片定向飞散特性研究

研究了钢预制破片壳体在四种装药爆轰驱动下的飞散特性。求得了使破片飞行路径呈平行型的预制破片壳体的临界半径及相应的破片初始速度。     

坦克分队火力指挥理论研究

基于数理战术学理论,研究坦克分队最优火力运用策略问题,所得到的结论符合坦克分队作战的特点,为坦克分队辅助指挥决策系统的研究打下基础。     

舰载火箭子母弹最优火力分配

舰载火箭子母弹对岸上目标实施火力压制射击时,火力分配方式直接影响毁伤效果和火力支援任务的完成.针对舰载火箭子母弹的射击特点,首先分析了海上射击误差,然后探讨了集群目标的简化处理方法,最后给出了最优火力分配时表尺差计算公式,并进行了举例分析.利用最优火力分配方法来计算舰载火箭子母弹效力射表尺差,避免了指挥员战场射击指挥的盲目性,可以在相同弹药消耗量情况下取得最佳射击效果,对未来舰载火箭子母弹的火力运用具有较高的现实意义.     

A nonparametric Bayes approach to decide system burn-in time

Burn-in is the preconditioning of assemblies and the accelerated power-on tests performed on equipment subject to temperature, vibration, voltage, radiation, load, corrosion, and humidity. Burn-in techniques are widely applied to integrated circuits (IC) to enhance the component and system reliability. However, reliability prediction by burn-in at the component level, such as the one using the military (e.g., MIL-STD-280A, 756B, 217E [23–25]) and the industrial standards (e.g., the JEDEC standards), is usually not consistent with the field observations. Here, we propose system burn-in, which can remove many of the residual defects left from component and subsystem burn-in (Chien and Kuo [6]). A nonparametric model is considered because 1) the system configuration is usually very complicated, 2) the components in the system have different failure mechanisms, and 3) there is no good model for modeling incompatibility among components and subsystems (Chien and Kuo [5]; Kuo [16]). Since the cost of testing a system is high and, thus, only small samples are available, a Bayesian nonparametric approach is proposed to determine the system burn-in time. A case study using the proposed approach on MCM ASIC's shows that our model can be applied in the cases where 1) the tests and the samples are expensive, and 2) the records of previous generation of the products can provide information on the failure rate of the system under investigation. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 655–671, 1997     

Algorithms for computing waiting time distributions under different queue disciplines for the D-BMAP/PH/1

A queueing system characterized by the discrete batch Markovian arrival process (D-BMAP) and a probability of phase type distribution for the service time is one that arises frequently in the area of telecommunications. Under this arrival process and service time distribution we derive the waiting time distribution for three queue disciplines: first in first out (FIFO), last in first out (LIFO), and service in random order (SIRO). We also outline efficient algorithmic procedures for computing the waiting time distributions under each discipline. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 559–576, 1997     

Stage life testing

In progressive censoring, items are removed at certain times during the life test. Commonly, it is assumed that the removed items are used for further testing. In order to take into account information about these additional testing in inferential procedures, we propose a two‐step model of stage life testing with one fixed stage‐change time which incorporates information about both the removed items (further tested under different conditions) and those remaining in the current life test. We show that some marginal distributions in our model correspond either to progressive censoring with a fixed censoring time or to a simple‐step stress model. Furthermore, assuming a cumulative exposure model, we establish exact inferential results for the distribution parameters when the lifetimes are exponentially distributed. An extension to Weibull distributed lifetimes is also discussed.