多传感器参数融合研究

防空导弹武器系统为了适应“网络中心战”,对多传感器参数融合进行了研究。给出了同时间点参数融合的加权最小二乘法与不同时间点参数融合的Kalman综合滤波法。对于各方法的性能,不仅进行了简要的理论分析,还给出了仿真实验的数据结果。理论分析与仿真实验都证明了这2种方法具有优异性能。     

用VB6.0实现分布式监控系统中的数据通讯

本文详细介绍了用VB实现分布式监控系统中的数据通讯,用数据通信将微机连在一起实现数据共享,从而对发射机状态进行集中监视管理和分散控制。     

基于随机Petri网的综合航电系统建模及分析

提出了一种利用随机Petri网对综合航电系统进行建模和性能分析的方法.首先,建立了综合航电系统数据总线传输系统的随机Petri网系统模型,通过系统模型进行综合航电系统的性能分析;其次,给出了航空电子综合系统数据总线传输基于固定主控端周期指令的总线性能分析结果;最后,通过两种分析结果的比较,得出了基于随机Petri网对航空电子综合系统数据总线传输建模分析是可行的结论.     

A DYNAMIC PANEL DATA MODEL FOR ANALYZING THE RELATIONSHIP BETWEEN MILITARY EXPENDITURE AND GOVERNMENT DEBT IN THE EU

This paper examines the impact of military spending on general government debt in the enlarged European Union (EU) countries. For this purpose, we use panel data analysis and provide estimates from a dynamic Generalized Method of Moments (GMM) panel model. The dynamics are found to be important and the results suggest that military expenditures do have a large positive impact on the share of general government debt in the EU.     

A theory of war finance

This paper analyses the financial and war‐spending policies of a state that faces a conflict in which defeat would result in the loss of sovereign power and in which the material consequences, conditional on avoiding defeat, are stochastic. The analysis takes explicit account of the historical experiences of lenders, who face debt repudiation if the state to whom they have lent is defeated and who also face partial default if the material consequences of the war are unfavorable for the debtor state, even if it avoids defeat. In this analysis, the state uses war debt to smooth expected consumption intertemporally in response to temporary war spending, and the state also uses contingent debt servicing to insure realized consumption against the risk associated with the material consequences of the war. An important innovation in the analysis is to treat the equilibrium amount of war spending, the state's resulting probability of avoiding defeat, as well as the equilibrium amount of borrowing as a set of endogenous variables to be determined simultaneously.     

位置随动系统动态性能自动检测研究

通过分析某型位置随动系统的组成和动态技术指标,提出了位置随动系统动态性能自动检测的设计方案和测试方法,利用信号仿真模块和基于DSP的高速数据采集与处理模块,建立了自动检测的硬件电路和软件结构,确定了测试位置随动系统的动态性能指标、测试方法和软件功能,实现了对某型位置随动系统动态性能的自动检测．     

基于小样本数据的作战计划生成方法

针对部队实兵对抗实验数据生成和采集的投入高、难度大的实际,提出基于小样本数据生成作战计划的方法：首先,采用偏最小二乘回归分析法建立作战因素与作战结果间的多元非线性模型;其次,采用改进的遗传算法,在MATLAB平台编程实现了对模型的整数规划求解,并通过实例验证算法的有效性;最后,利用作战因素的重要性排序分析作战计划的调整流程。     

航空导航技术的发展方向

针对航空导航的特点,对现有航空导航装备的特点进行了分析,并结合未来作战的需求,给出了航空导航技术的发展方向,主要是组合式导航体制,将卫星导航与惯导的组合进一步与地形辅助导航、地球物理导航、地磁导航等方式结合起来.     

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     

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