过程方法在防化科研质量管理系统中的应用

介绍了根据国军标要求建立的科研质量管理系统．运用“过程方法”,对现行科研体制下的科研进程进行了识别,并提出了具体的控制要求。     

武器系统效能评估数学模型描述

在武器系统效能评估中,武器系统的战技指标是评估武器系统效能的重要因素.对武器系统的可靠性和维修性进行了研究,并应用于可用性矩阵计算中.利用随机过程理论,建立了武器系统可信性矩阵和武器系统效能能力指数模型,最后建立了武器系统效能评估模型.     

军警体育教学中认知结构与动作技能的形成

军警动作技能是军警部队官兵所必备的素质之一,也是其具备野外作战能力的基础。但是,有些身体素质良好的军警人员,掌握和发挥动作技能的水平却很低。在教学中要从全新的角度对军警人员获得动作技能的过程进行认知分析,以促进认知结构与动作技能的形成。     

整环上不定方程组的通解公式及其应用

借助Z上矩阵A∈Mm×n(Z)的标准形式D=VAU=diag(d1,d2,…,dq,0,…,0),得到了整环上不定方程组AX=B的通解公式以及矩阵初等变换法.     

DEFENSE SPENDING AND ECONOMIC GROWTH IN TURKEY: A LINEAR AND NON‐LINEAR GRANGER CAUSALITY APPROACH

This paper uses linear and non‐linear Granger causality methods to determine the causal relationship between defense spending and economic growth in Turkey for the period 1949–2004. The innovative feature of this paper is that it provides evidence regarding the nonlinear causal dependence between military spending and economic growth in Turkey. The empirical results contribute to the empirical literature by indicating support for both linear and non‐linear causality between military expenditures and economic development and they may prove useful in theoretical and empirical research by regulators and policy makers.     

基于时滞分割法的具有leakage项时滞的离散型神经网络状态估计

摘要：研究一类具有leakage时滞的离散型神经网络的状态估计问题．通过构造新的Lyapunov泛函得到保证估计误差全局渐近稳定的充分条件,并通过求解一个线性矩阵不等式（LMI）得到状态估计器的增益矩阵．采用一种新的时滞分割方法将变时滞区间分割为多个子区间,使该结果在获得更小的保守性同时也降低了计算的复杂度．     

基于灰色AHP方法的综合航电系统效能

针对在对综合航电系统效能进行评估的过程中经常遇到多指标度量效能的情况,提出了一种将层次分析法和灰色系统理论相结合的效能评估方法,对航空电子系统中多指标度量效能进行评估.最后,通过实例计算表明使用这种方法获得的评估结果更加客观、科学,对综合航空电子系统效能的评估具有一定的参考价值.     

Demand forecasting by temporal aggregation

Demand forecasting performance is subject to the uncertainty underlying the time series an organization is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lower‐frequency “time buckets.” The approach under concern is termed to as temporal aggregation, and in this article, we investigate its impact on forecasting performance. We assume that the nonaggregated demand follows either a moving average process of order one or a first‐order autoregressive process and a single exponential smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical mean‐squared error expressions are derived for the aggregated and nonaggregated demand to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant, and the process parameters. Valuable insights are offered to practitioners and the article closes with an agenda for further research in this area. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 479–498, 2013     

Some aspects in estimating warranty and post‐warranty repair demands

When customers buy a product, they are often eligible for free repairs for a certain warranty period. In this article, we study some important aspects, which are often overlooked in the literature but are of interest to the manufacturer, in estimating both warranty and post‐warranty repair demands. We consider that the installed base of the product (i.e., the number of units of the product actually in use) varies with time due to both new sales and units being taken out of service. When estimating warranty and post‐warranty repair demands, we explicitly address the fact that customers may not always request repairs for failed units. For the case where the product failure time distribution is exponential, we derive the closed‐form expressions for both types of repair demands of a single unit and of the time‐varying installed base. The insights into some risk‐related quantities are also presented. Furthermore, the proposed model is extended by considering delayed warranty claims that are frequently seen in practice. Numerical examples illustrate that understanding both types of repair demands and the related decision variables is important for managing the obligatory and profitable repair services. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 499–511, 2013     

Nonparametric bayesian lifetime data analysis using dirichlet process lognormal mixture model

We propose a nonparametric Bayesian lifetime data analysis method using the Dirichlet process mixture model with a lognormal kernel. A simulation‐based algorithm that implements the Gibbs sampling is developed to fit the Dirichlet process lognormal mixture (DPLNM) model using rightly censored failure time data. Five examples are used to illustrate the proposed method, and the DPLNM model is compared to the Dirichlet process Weibull mixture (DPWM) model. Results indicate that the DPLNM model is capable of estimating different lifetime distributions. The DPLNM model outperforms the DPWM model in all the examples, and the DPLNM model shows promising potential to be applied to analyze failure time data when an appropriate parametric model for the data cannot be specified. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013