Optimal stopping in software testing

In this paper we address the problem of how to decide when to terminate the testing/modification process and to release the software during the development phase. We present a Bayesian decision theoretic approach by formulating the optimal release problem as a sequential decision problem. By using a non‐Gaussian Kalman filter type model, proposed by Chen and Singpurwalla (1994), to track software reliability, we are able to obtain tractable expressions for inference and determine a one‐stage look ahead stopping rule under reasonable conditions and a class of loss functions. © 2002 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

蚁群算法在光电载荷标校中的应用

针对空中平台光电载荷安装误差的梗准问题,提出了一种利用蚁群算法实现动态标校的方法.通过光电裁荷测量得到的目标方位角、俯仰角和平台导航信息,结合GPs转化得到的方位角、俯仰角真值,建立了基于最优估计理论的目标函数,并利用蚁群算法求得了系统的安装误差.通过蒙特卡洛模拟仿真,验证了该方法能够较为准确地标定出安装误差;通过校准,有效避免了由安装误差引起的结果有偏,使得目标跟踪结果收敛至真值附近.     

扭力矩对曲折槽保险机构动力学响应影响分析

在考虑扭力矩作用的基础上,建立曲折槽保险机构的动力学模型,以某机构为计算实例,与传统的动力学模型进行比较。数值计算结果表明：引信跌落到硬质地面时,扭力矩的影响作用不明显,而跌落到松软地面上时扭力矩使惯性简的位移响应明显增大,不能忽略。其结论为判断长储引信勤务处理意外跌落的安全状态提供了理论参考。     

随机动态规划的混合算法研究

针对随机条件下动态规划模型的主要特点,运用智能算法混合编程理论,设计了一种探索多阶段决策问题的智能混合算法.该算法首先将问题转化成一族同类型的一步决策子问题,然后利用随机模拟和遗传算法,依据训练样本形成的训练神经元网络,在单步决策中寻求最优策略和最优目标值,逐个求解,再据初始状态逆序求出最优策略序列和最优目标值.仿真结果表明,该算法具有一定的通用性,初始设计点可以随机产生,其计算精度不因函数的非线性强弱而受影响,对目标和约束的限制较少,可应用于多种形式的随机多阶段决策优化问题,较好地满足了随机动态规划模型求解和优化的要求.     

一种基于AMDF和ACF的基音周期提取算法研究

文中提出一种基于短时平均幅度差分函数(AMDF)和短时自相关函数(ACF)的基音周期提取算法。首先,将每帧语音信号通过AMDF算法进行计算,然后把得到的AMDF数值再通过ACF算法进行计算,得出每帧信号的基音周期,最后运用动态平滑技术计算出最终语音信号的基音周期。通过仿真实现该算法,并同基于MAMDF和ACF的基音周期提取算法进行比较。     

Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

基于动态模型的机场配电网潮流计算新方法

机场配电网因非常情况成为孤立电力系统后,配电系统的电压、潮流和频率等发生巨大变化,传统的潮流计算方法往往会出现收敛性差、计算结果与实际不相符的情况。提出的机场配电网动态潮流计算模型能有效解决此类问题,阐述了机场配电网动态潮流计算模型的构成原理、算法并给出相应算例。通过算例计算证明了动态潮流计算模型适用于计算机场配电网的潮流。     

锚杆支护岩质边坡地震动力响应分析

目前的锚杆支护边坡地震动力设计主要采用拟静力的方法,无法考虑锚杆和边坡在地震作用下的相互作用,汶川地震边坡破坏现象调研表明,锚杆具有良好的抗震效果,所以有必要对锚杆抗震效果进行研究。利用动力有限差分软件FLAC^3D,通过分析一个岩质边坡算例坡面上监测点动力响应加速度峰值放大系数在锚杆支护前后变化,研究了锚杆在各种不同地震波作用下的抗震效果。结果表明：在不同地震波作用下,锚杆都能显著降低坡面加速度峰值放大系数,提高边坡的动力稳定性,具有良好的抗震效果。最后结合强度折减动力分析法对锚杆在地震作用下的破坏机理进行了研究,为锚杆支护边坡的抗震设计奠定基础。     

基于动态窗口的灰色加权填充算法及应用?

为解决复杂系统中单属性缺失数据填充困难问题,提出了基于动态窗口的灰色加权填充算法。该算法通过建立双向灰色预测模型,并采用模型精度评价系数加权填充缺失数据,有效增强了算法的准确性;提出基于灰色模型评价系数反馈的动态伸缩窗口概念,寻找产生最优模型的训练数据序列,使算法具有良好的鲁棒性和适应性。实验结果表明,该方法在RMSE和MA两项指标上均优于传统的双向灰插值、灰插值、多项式插值等方法。