利用区间着色 Petri 网的 C~3I 系统时延分析

Ｃ３Ｉ系统是一个复杂的分布式离散事件动态系统,适合于用Ｐｅｔｒｉ网来描述其异步,并发行的,而且Ｃ３Ｉ系统是一个时间准则系统,文中提出的区间着色Ｐｅｔｒｉ网非常适合于分析其严格时限,并给出计算时延上（下）界的算法。     

截尾序贯验后加权检验截尾点的优化设计

选取截尾点是截尾序贯验后加权检验很关键的一步,目前理论上尚无有效的选取方法。本文用优化技术确定截尾点Ｃ,克服了实践中确定停时Ｎ和截尾点Ｃ遇到的计算困难,避免了一般取Ｃ＝１的盲目性,使截尾点Ｃ的选取有了理论根据。优选的截尾点不仅使双方承担的风险之和最小,而且使双方承担的风险值相对合理,易于接受。     

伺服系统速度环控制器参数的自整定及优化

针对交流伺服系统速度环控制器参数自整定及优化的需求,为使工程技术人员避免繁琐的调参过程,提出了一种基于转动惯量辨识的速度环控制器参数自整定及优化方法。首先,采用频率法分析伺服系统速度环控制器参数的设计规则;随后,采用遗忘因子递推最小二乘法,对控制器参数设计所需的系统转动惯量进行辨识;最后,在此基础上利用设计的变步长迭代算法,完成控制器参数寻优过程。仿真结果表明,采用遗忘因子递推最小二乘法,能够有效辨识出电机轴端的转动惯量。提出算法进行整定及优化后,速度环控制器参数能使系统具有良好的动态响应和鲁棒性。     

磁性目标跟踪的后验克拉美罗下限分析与计算

为了求解磁性目标跟踪问题的后验克拉美罗下限(PCRB),提出了PCRB-GMSPPF算法。该算法利用高斯混合采样粒子滤波算法对目标状态的真实后验概率密度分布进行抽样,再通过蒙特卡洛积分法迭代求解每个观测时刻的Fisher信息矩阵,进而得出目标状态估计的PCRB;克服了基于PF算法求解PCRB过程中由于粒子退化和贫化问题造成不能从后验概率分布中正确抽样的缺点;在建立磁性目标跟踪的状态模型和观测模型的基础上进行仿真分析,将求解出的PCRB与采用GMSPPF及PF算法进行跟踪的均方根误差做对比,验证所提的PCRB-GMSPPF算法的有效性,结果表明:针对磁性目标跟踪问题,PCRB-GMSPPF算法较PCRB-PF算法具有更好的准确性,并可用于一般的非线性模型跟踪误差下限分析。     

二维TOA定位中的最小GDOP问题

基于二维TOA定位的Cramer?Rao下界,对传感器测量精度不尽相同的一般情形,推导获得了最优GDOP值及其充要条件,并利用GDOP等值线图进行了验证。结果表明,最佳的GDOP 值是传感器测量精度的简单表达式,或一个带约束条件的组合优化问题的最优值。     

基于拉盖尔模型的超声信号渡越时间测量性能分析

传统窄带模型仅能通过数值计算统计出超声信号渡越时间的测量性能。针对这一问题,提出基于拉盖尔模型计算理论克拉下限的方法。研究了拉盖尔函数及其时间导数的性质,得到时间导数内积矩阵,并将其应用到费舍尔矩阵中,能够快速精确计算克拉下限。对混合指数模型仿真表明,在高斯白噪声背景下,渡越时间方差与理论克拉下限具有良好的一致性;受窄带噪声和反射等因素影响,实测数据的渡越时间方差与理论克拉下限存在较大偏差。     

When are observed failures more informative than observed survivals?

A framework involving independent competing risks permits observing failures due to a specific cause and failures due to a competing cause, which constitute survival times from the cause of primary interest. Is observing more failures more informative than observing survivals? Intuitively, due to the definitiveness of failures, the answer seems to be the former. However, it has been shown before that this intuition holds when estimating the mean but not the failure rate of the exponential model with a gamma prior distribution for the failure rate. In this article, we address this question at a more general level. We show that for a certain class of distributions failures can be more informative than survivals for prediction of life length and vice versa for some others. We also show that for a large class of lifetime models, failure is less informative than survival for estimating the proportional hazards parameter with gamma, Jeffreys, and uniform priors. We further show that, for this class of lifetime models, on average, failure is more informative than survival for parameter estimation and for prediction. These results imply that the inferential purpose and properties of the lifetime distribution are germane for conducting life tests. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Optimizing the quality control station configuration

We study unreliable serial production lines with known failure probabilities for each operation. Such a production line consists of a series of stations, existing machines, and optional quality control stations (QCSs). Our aim is to decide on the allocation of the QCSs within the assembly line, so as to maximize the expected profit of the system. In such a problem, the designer has to determine the QCS configuration and the production rate simultaneously. The profit maximization problem is approximated assuming exponentially distributed processing times, Poisson arrival process of jobs into the system, and the existing of holding costs. The novel feature of our model is the incorporation of holding costs that significantly complicated the problem. Our approximation approach uses a branch and bound strategy that employs our fast dynamic programming algorithm for minimizing the expected operational costs for a given production rate as a subroutine. Extensive numerical experiments are conducted to demonstrate the efficiency of the branch and bound procedure for solving large scale instances of the problem and for obtaining some qualitative insights.

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On‐line parameter estimation for a partially observable system subject to random failure

In this paper, we study the on‐line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous time homogeneous Markov process with a finite state space. The system state is not observable, except for the failure state. The information related to the system state is available at discrete times through inspections. A recursive maximum likelihood (RML) algorithm is proposed for the on‐line parameter estimation of the model. The RML algorithm proposed in the paper is considerably faster and easier to apply than other RML algorithms in the literature, because it does not require projection into the constraint domain and calculation of the gradient on the surface of the constraint manifolds. The algorithm is illustrated by an example using real vibration data. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

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。