基于马尔科夫链的一种评价方法

应用了数理统计与随机过程理论,从分析动态系统的状态以及状态转移情况给出了一种合理的评价方法,经过实例验证,该方法效果较好.     

Effect of particle gradation of aluminum on the explosion field pressure and temperature of RDX-based explosives in vacuum and air atmosphere

To optimize the energy output and improve the energy utilization efficiency of an aluminized explosive, an explosion device was developed and used to investigate the detonation pressure and temperature of R1 (Al6) aluminum powder and the aluminum powder particle gradation of R2 (Al6+Al13), R3 (Al6+Al24) and R4 (Al6+Al flake) in a confined space. By using gas chromatography, quantitative analysis and calculations were carried out to analyze the gaseous detonation products. Finally, the reaction ratios of the aluminum powder and the explosion reaction equations were calculated. The results show that in a confined space, the quasi-static pressures and equilibrium temperature of the aluminum powder in air are higher than in vacuum. In vacuum, the quasi-static pressures and equilibrium temperatures of the samples in descending order are R1>R3>R4>R2 and R3>R4>R1>R2, respectively. In air, the quasi-static pressures and equilibrium temperatures of the samples in descending order are R1>R2>R4>R3 and R1>R4>R2>R3, respectively. R4 (Al6+Al flake) and R3 (Al6+Al24) have relatively higher temperatures after detonation, which shows that the particle gradation method can enhance the reaction energy output of aluminum during the initial reaction stage of the explosion and increase the reaction ratio by 10.6% and 8.0%, respectively. In air, the reaction ratio of Al6 aluminum powder can reach as high as 78.16%, and the reaction ratio is slightly reduced after particle gradation. Finally, the reaction equations of the explosives in vacuum and in air were calculated by quantitative analysis of the explosion products, which provides a powerful basis for the study of RDX-based explosive reactions.     

经马氏修正的Poisson过程的极大似然估计

近年来 ,隐马氏模型成为研究相依随机变量的一个十分有用的工具。实际应用过程中的一个很重要的问题是如何对隐马氏模型的参数进行估计。将一类连续时间隐马氏模型的问题转化为离散时间隐马氏模型的问题 ,给出了具体的隐马氏模型———经马氏修正的Poisson过程的极大似然估计及其算法。此类过程被广泛用来对复杂电信网络的交通流进行建模     

基于马尔可夫分析的人才拥有量研究

基于马尔可夫分析理论,给出了人才拥有量预测的马尔可夫模型,并且讨论了模型中转移概率的确定及模型的预测质量等问题.     

战斗机突防效能模型研究

对战斗机作为突防机突防防守方远程拦截机、高射导弹、高炮阵地问题进行了研究.突防机突防远程拦截机运用齐次马尔科夫链模型,高射导弹防空区和高炮防空区均属于高射武器防空区,将两者作为一个统一的整体进行研究,根据突防飞机数的多少分两种情况分别进行了研究.仿真结果证明了所选用模型的正确性和所采用方法的实用性和有效性.     

多核异构冗余模型设计与可靠性分析

为进行电磁仿生研究,提高复杂电磁环境下控制系统运行的可靠性,在一种新型计算机系统结构框架的基础上,提出了多核异构冗余模型,设计了互关总线协议,实现了相应的容错策略,进而进行了异构双机冗余系统的实验,并利用马尔可夫过程模型分析了系统的电磁抗扰能力。实验表明,该模型能够有效地提高系统可靠性,满足相关仿生研究的设计要求。     

指标融合和隐马尔可夫模型的舰船装备技术状态评估

科学合理的技术状态评估技术是保障舰船安全性和任务完好性的有效途径之一,针对目前舰船装备技术状态评估缺乏动态性以及评估指标过多的问题,构建舰船装备技术状态多指标融合模型,结合技术状态评估指标融合模型和隐马尔可夫模型所具有双随机性和严谨数学推理能力的特点,建立基于指标融合模型和隐马尔可夫模型的舰船装备技术状态动态评估模型。最后通过案例分析验证了所提出的方法能够有效体现技术状态评估中的动态性,为舰船装备技术状态评估提供新的途径。     

Optimal timing of switches between product sales for sports and entertainment tickets

Like airlines and hotels, sports teams and entertainment venues can benefit from revenue management efforts for their ticket sales. Teams and entertainment venues usually offer bundles of tickets early in their selling horizon and put single‐event tickets on sale at a later date; these organizations must determine the best time to offer individual tickets because both types of ticket sales consume the same fixed inventory. We model the optimal a priori timing decision for a seller with a fixed number of identical tickets to switch from selling the tickets as fixed bundles to individual tickets to maximize the revenue realized before the start of the performance season. We assume that bundle and single‐ticket customers each arrive according to independent, nonhomogeneous Markovian death processes with a linear death rate that can vary over time and that the benefit from selling a ticket in a package is higher than from selling the ticket individually. We characterize the circumstances in which it is optimal for the seller to practice mixed bundling and when the seller should only sell bundles or individual tickets, and we establish comparative statics for the optimal timing decision for the special case of constant customer arrival rates. We extend our analytical results to find the optimal time for offering two groups of tickets with high and low demand. Finally, we apply the timing model to a data set obtained from the sports industry. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于MCMC方法的Lorenz混沌系统的参数估计

基于贝叶斯理论,提出用马尔科夫链蒙特卡罗(MCMC)方法来估计Lorenz混沌系统的未知参数。首先导出了未知参数分布规律的后验概率密度函数;接着采用自适应Metropolis算法构造Markov链;然后截取收敛的链序列,计算混沌系统参数的估计值。数值试验表明:该方法具有很高的估计精度,同时具有较好的抗噪声性能。     

9/11: WHAT DID WE KNOW AND WHEN DID WE KNOW IT?

In February 1998, Osama Bin Laden published a signed statement calling for a fatwa against the United States for its having ‘declared war against God’. As we now know, the fatwa resulted in the unprecedented attack of 9/11. The issue of whether or not 9/11 was in any way predictable culminated in the public debate between Richard Clarke, former CIA Director George Tenet and the White House. This paper examines whether there was any evidence of a structural change in the terrorism data at or after February 1998 but prior to June 2001, controlling for the possibility of other breaks in earlier periods. In doing so, we use the standard Bai–Perron procedure and our sequential importance sampling (SIS) Markov Chain Monte Carlo (MCMC) method for identifying an unknown number of breaks at unknown dates. We conclude that sophisticated statistical time‐series analysis would not have predicted 9/11.