通信对抗装备软件质量保证若干问题探讨

论述了通信对抗装备软件质量保证的基本内涵 ,在分析软件质量保证实施中存在的突出问题的基础上 ,提出了加强软件质量保证工作应该采取的对策     

基于DSP的OEM板GPS定位数据处理

介绍了利用DSP处理器进行OEM板GPS定位数据处理的方法,重点讨论了基于高速实时数字信号处理器的串行通信接口进行数据通信的处理方法.     

军事应用中的多源信息融合技术

多传感器数据融合近年来受到极大关注和发展。首先叙述数据融合在军事应用中的递阶推理体系结构和各种数据融合方法,然后对防空导弹武器C     

罗兰C信号模拟器的设计与实现

运用数控技术产生罗兰C信号 ;通过对罗兰C脉冲的傅立叶分析和采样分析 ,确定仪器所用的采样频率和滤波器 ,给出了设计及实现罗兰C信号模拟器的原理     

Sequential and systematic sampling plans for the Markov‐dependent production process

Acceptance sampling plans are used to assess the quality of an ongoing production process, in addition to the lot acceptance. In this paper, we consider sampling inspection plans for monitoring the Markov‐dependent production process. We construct sequential plans that satisfy the usual probability requirements at acceptable quality level and rejectable quality level and, in addition, possess the minimum average sample number under semicurtailed inspection. As these plans result in large sample sizes, especially when the serial correlation is high, we suggest new plans called “systematic sampling plans.” The minimum average sample number systematic plans that satisfy the probability requirements are constructed. Our algorithm uses some simple recurrence relations to compute the required acceptance probabilities. The optimal systematic plans require much smaller sample sizes and acceptance numbers, compared to the sequential plans. However, they need larger production runs to make a decision. Tables for choosing appropriate sequential and systematic plans are provided. The problem of selecting the best systematic sampling plan is also addressed. The operating characteristic curves of some of the sequential and the systematic plans are compared, and are observed to be almost identical. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 451–467, 2001     

仿真网格原型系统的设计与实现

进行仿真网格原型系统的研究是解决仿真网格关键技术的基础。SGE网格引擎是一个构建本地和集群级网格的工具,通过SGE构建基本的网格平台,根据HLA仿真任务的特点,对分布式仿真网格的原型系统进行了研究和设计。提出了仿真网格的双通道通信机制:一个通道负责仿真网格组件的通信,另一个通道负责HLA仿真任务之间的通信。提出了仿真任务在仿真网格中的三种调度模式:以进程为单位的调度、以联邦成员为单位的调度、以联邦为单位的调度等。通过研究初步实现了仿真网格的原型系统,并对HLA仿真任务的三种调度模式进行了简单的测试。     

The selection allocation problem

The Selection Allocation Problem (SAP) is a single period decision problem which involves selecting profit‐maximizing (or cost‐minimizing) activities from various distinct groups, and determining the volume of those activities. The activities in each group are selected subject to the availability of that group's resource, which is provided by either pooling or blending raw inputs from several potential sources. Imbedded in the decision process is the additional task of determining how much raw input is to be allocated to each group to form the resource for that group. Instances of this problem can be found in many different areas, such as in tool selection for flexible manufacturing systems, facility location, and funding for social services. Our goal in this paper is to identify and exploit special structures in the (SAP) and use those structures to develop an efficient solution procedure. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 707–725, 1999     

栅格地理数据并行格式转换引擎

如今大规模地理数据正在社会各个部门和组织中迅速积累,但是由于部门利益和历史沿袭等原因,大规模地理数据共享仍然极具挑战,相应共享技术需求仍然极其旺盛。作为地理数据共享的基础方式之一,传统单机地理数据格式转换技术,一方面受限于磁盘读写及带宽瓶颈,另一方面面对日趋庞大的数据规模,已很难满足实际应用需求。因此提出一种针对栅格地理数据的并行格式转换引擎,采用高性能计算集群环境支持大规模栅格地理数据转换共享,大幅降低了大规模栅格地理数据转换过程的时间成本。栅格地理数据并行格式转换引擎采用基于公共接口的设计理念,框架灵活、具有良好的扩展性,支持地理数据格式的读写自定义以及新数据格式添加,能够实现接入数据格式间的任意两两转换。为验证引擎框架及其处理效率,在Lustre并行集群环境下以格网数据交换格式(国家地理空间数据交换格式)向常见栅格地理格式的转换为示例进行了测试实验。结果表明,栅格地理数据并行格式转换引擎能够在8个节点Lustre集群中达到7.54的良好并行加速比。     

美军战略传播中的“叙事”策略

随着互联网技术的飞速发展,信息传播对国家政治、经济、外交和国防带来巨大冲击和影响。美军意识到传播叙事是政府和军队塑造形象、谋划战略、输出文化价值观的重要手段。美国从"重讲故事"的角度出发,将"叙事"包装成对外战略传播的新型方式,从思维认知、情感操控、舆论引导等方面强化对对手的心理威慑和影响,同时以新媒体为媒介扩大叙事传播的影响受众,进而达到物理打击所难以发挥的影响。     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.