线性需求且耐烦期有限的生产——库存模型

在假设线性需求以及有限生产率的条件下,研究了耐烦期限的一类生产－库存模型的确定问题,提出可借助于计算机用一维搜索方式确定最优生产策略,并给出数字例子以进行说明,其结果为库存系统的管理决策提供了理论依据。     

基于优先权提取计算信息及目标排序

本文讨论目标排序问题, 给出了一个提取可靠优先权并用于排序的决策过程, 介绍了不相容索引的计算方法。     

Solvability and control design for synchronization of Boolean networks

This paper addresses the synchronization problem of Boolean networks. Based on the matrix expression of logic, solvability conditions and design procedures of the synchronization of Boolean networks with outputs are given for both open-loop and feedback control. Necessary and sufficient conditions on open-loop control are proposed first with a constructive design procedure. Then sufficient condition for the feedback control case is obtained, and corresponding design procedure is proposed with the help of algorithms to solve logic matrix equations. Numerical examples are also provided to illustrate the proposed control design.     

Robust dissipative filtering for continuous-time polytopic uncertain neutral systems

This article is concerned with the problem of robust dissipative filtering for continuous-time polytopic uncertain neutral systems. The main purpose is to obtain a stable and proper linear filter such that the filtering error system is strictly dissipative. A new criterion for the dissipativity of neutral systems is first provided in terms of linear matrix inequalities (LMI). Then, an LMI sufficient condition for the existence of a robust filter is established and a design procedure is proposed for this type of systems. Two numerical examples are given. One illustrates the less conservativeness of the proposed criterion; the other demonstrates the validity of the filtering design procedure.     

OVERLOAD ANALYSIS OF MARKOVIAN MODELS

1.IntroductionandMotivationsInavarietyofapplications,oneneedstofindthesteady-statedistributionsoffiniteMarkovchainswithalargenumberofstates,forexample,aqueuingsystemwithalargebiberorabigwaitingroom.Forsuchasystem,thesteady-statedistributionalwaysedestsnomatterhowlargethebibersizeisandnomatterwhetherornotthesystemisoverloaded.Whenthesystemisnotoverloaded,onemayusethecorrespolldingmodelwithaninfinitebufferforanapproximation.Thismethodoftentakestheadvantageofasimplersollltionstructureoftheindnit…     

Estimation on semivarying coefficient models with different degrees of smoothness

Semivarying coefficient models are frequently used in statistical models. In this paper, under the condition that the coefficient functions possess different degrees of smoothness, a two-step method is proposed. In the case, one-step method for the smoother coefficient functions cannot be optimal. This drawback can be repaired by using the two-step estimation procedure. The asymptotic mean-squared error for the two-step procedure is obtained and is shown to achieve the optimal rate of convergence. A few simulation studies are conducted to evaluate the proposed estimation methods. This research is supported in part by the National Natural Science Foundation of China under Grant No. 10871072 and Shanxi's Natural Science Foundation of China under Grant No. 2007011014.     

Sequential Estimate for Generalized Linear Models with Uncertain Number of Effective Variables

For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.     

Efficient estimation of seemingly unrelated additive nonparametric regression models

This paper is concerned with the estimating problem of seemingly unrelated (SU) nonparametric additive regression models. A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components, which takes both of the additive structure and correlation between equations into account. The asymptotic normality of the derived estimators are establishedi. The authors also show they own some advantages, including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property, which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. Applying the proposed procedure to a real data set is also made.     

两坐标雷达融合定位算法

传统的两坐标雷达目标定位算法需通过迭代求取目标位置估计,不仅计算量大,而且有可能得到局部最优解,甚至无效解。提出了一种组网融合定位算法,利用数据融合技术进行目标定位,回避了费时的迭代搜索,同时保证了估值的有效性。仿真实验验证了该方法的有效性。     

A New Continuous Production Lot Size Model with Increasing Time Varying Demand and Variable Rate of Production Considering Shortages

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一种余度捷联惯导系统标定结果的验证方法

以余度捷联惯导系统为研究对象,提出了一种使用余度系统传感器冗余信息对标定结果进行检验的方法.该方法利用已有标定结果对惯性器件进行补偿,然后选取与标定过程不同的敏感轴向组合构建系统并重复标定过程,可以获得标定验证结果.通过对标定验证结果的量级大小进行判断来检测标定结果是否达到预期要求,或者标定过程是否合理.给出了应用该方法的具体过程,通过仿真试验证明该方法的有效性.     

高效的因子分类筛选方法及仿真应用

针对序贯分支（SB）方法难以对位置效应和散度效应同时筛选的情形，基于多重序贯分支的基本思想，同时考虑位置和散度效应，将显著因子分类别筛选出来。首先，通过“预分支”步骤，将因子按照其位置和散度效应符号分组；然后，采用序贯分支方法将所有子组同时进行位置和散度效应显著性检验，在结合序贯概率比检验（SPRT）控制筛选过程中的第一类和第二类错误的基础上，实现因子的分类筛选，以便后续阶段的建模及优化；最后，通过仿真试验说明所提方法在解决因子分类筛选问题上的有效性、高效性及稳健性。     

Failure prognostic of systems with hidden degradation process

Systems with a hidden degradation process are pervasive in the real world.Degrading critical components will undermine system performance and pose potential failures in the future.Prognostic aims at predicting potential failures before it evolves into faults.A prognostic procedure based on expectation maximization and unscented Kalman filter is proposed.System state,sensor measurement and hidden degradation process are viewed as data (incomplete or missing) in the expectation maximization method.System state and hidden degradation process are estimated by a unscented Kalman filter upon sensor measurements.Component-specific parameters in a degradation process are identified on the estimation of the degradation process.Residual life is measured by the median of estimated residual life distribution.The proposed procedure is verified by simulations on a first-order capacitor-resistance circuit with degrading resistance.Residual life estimation consists conservatively with the trend and is evaluated in terms of relative errors.Simulation results are reasonable.The proposed prognostic method expects applications in practice.     

具有一阶自回归测量误差的步进加速退化可靠性分析

针对传统方法在步进应力加速退化建模时常常忽略测量误差间自相关性的问题,提出一种具有一阶自回归(AR(1))测量误差的步进应力加速退化可靠性分析方法.利用Wiener过程描述产品的性能退化过程,同时引入一阶自回归AR(1)模型表征具有自相关性的测量误差项.然后,建立漂移系数与加速应力之间的加速关系模型,并对漂移系数进行随机化处理以表征产品个体差异性.在首达时概念下,给出失效时间分布函数和概率密度函数的解析表达式.然后,提出一种加速退化模型参数极大似然估计方法.最后,通过激光器算例分析验证了本文方法的适用性和有效性,结果表明:与传统方法相比,本文方法建模合理性更优,且能有效提高产品退化可靠性分析精度,进而为产品合理维修决策的制定提供有力支撑.     

一种基于自然纹理特征的增强现实跟踪算法

给出了一种面向增强现实(augmented reality,AR)的基于自然纹理特征的实时跟踪算法,算法分为目标检测与跟踪两个过程.以真实场景中的目标物体的表面纹理图像作为模板,用基于朴素贝叶斯分类的宽基线匹配方法进行目标检测与方位参数估计;将分层L-K光流算法与鲁棒的IC算法结合,提出一种基于角点与纹理的混合跟踪算法,并用于其跟踪过程.实验结果表明,所提算法具有较好的实时性、准确性与鲁棒性,并解决了宽基线匹配算法在AR应用中出现的抖动现象.     

基于改进PERT的装备使用保障过程建模分析

计划评审技术(program evaluation and review technique,PERT)自提出以来被广泛用于具有不确定时间参数项目的分析评估中,但经典PERT不能对具有随机后续工序的情况进行建模,而且存在时间参数估计主观性强,估计误差较大等不足。本文提出在经典PERT模型中加入跳转虚工序的方法增强其对随机工序的建模能力,并提出了相应的分析评估方法。另外,提出了基于德尔菲评估法与专家权重相结合并综合相似工序的历史数据的方法克服经典方法在时间参数确定方面的不足。最后,利用某装备的使用保障过程PERT建模分析实例验证了该方法的有效性。     

基于协商视角的食品质量链冲突消解策略

针对食品质量链多主体企业协同过程中的冲突问题, 提出一种基于协商的冲突消解方法.首先,基于Pareto最优解对食品质量链协同冲突的本质从数学上进行界定;然后,在充分考虑食品质量链特点的基础上, 提出食品制造商、食品供应商以及食品质量链的收益模型;接着,确定冲突双方之间的讨价还价过程参数:食品供应商提供的质量水平以及制造商成本和对方质量水平所提供的质量检测水平,并构建双方以自身收益最大化为目标的协商过程.最后,应用到具体乳制品质量链中,验证了出方法的有效性.研究表明:所提出的协商策略不仅使得每个企业成员的收益最大,而且使质量链总体收益最大,从而既消解了食品质量链冲突,又提高了质量链的质量水平.     

Electromagnetic Scattering from Fractal Multilayered Cylinde rat Normal Incidence

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基于System C的星载计算机系统设计

分析了小卫星星载计算机系统的公共需求及其应用的特殊性,给出了典型小卫星星载计算机系统的通用体系结构;提出了基于System C的星载计算机系统的设计方法。通过与传统的星载计算机系统设计方法的对比,提出的基于System C的设计方法可使星载计算机系统的研发周期缩短,设计质量获得较大的提升。同时结合实际工程经验,给出了星载计算机系统软硬件协同设计的基本步骤。     

Support vector classification algorithm based on variable parameter linear programming

To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.