武器系统概率指标的贝叶斯决策评定方法

剖析了武器系统试验工程领域中常用二项分布经典假设检验方法存在的问题,从贝叶斯决策思想和理论出发提出了概率指标评定的新方法,通过实例对比证明了贝叶斯决策评定方法有效解决了二项分布经典假设检验方法中存在的问题。     

FFT-MTD滤波器组优化设计与仿真

介绍了用FFT算法实现雷达信号动目标检测(MTD)的方法,为提升MTD在强杂波环境下的检测性能,对加权FFT优化MTD时窗函数的选取进行了讨论,比较了6种窗函数加权时FFT等效多普勒滤波器组的幅频特性和改善因子,得出了适合于MTD的最佳窗函数,并针对某型雷达信号体制进行了仿真分析和验证,结果表明优化方法增强了MTD在杂波背景下的检测性能.     

防空导弹武器中末制导交班灵敏度分析

复杂战场环境下,中远程防空导弹武器的中末制导交班问题越来越突出。实战要素影响到武器系统的各个环节,尤其是传感器测量精度,从而影响中末制导交班概率。通过灵敏度分析方法,对实战条件下影响中末制导交班的关键因素进行了分析,筛选出了高敏感影响因素,并得到了表征其影响程度的定量化灵敏度系数。     

黄葛增压站噪声治理工艺措施

为解决黄葛增压站内4台室外整体式天然气压缩机组造成的噪声污染问题,结合生产运行要求、周边环境状况、噪声源分布及噪声特性,综合吸声、隔声、消声、阻尼减振、通风和泄压等技术,采用半密闭式降噪厂房和阻抗复合排气消声器等治理措施。治理后厂界噪声从64.2～72.1 dB(A)降至49.7～55.0 dB(A),距厂房最近厂界25 Hz声压值从97.9 dB降至84.2 dB,排气消声器出口噪声从84.3 dB(A)降至72.6 dB(A)且25Hz声压值从124.7 dB降至97 dB。厂界噪声达到GB 12348—2008《工业企业厂界环境噪声排放标准》3类要求,中、低频噪声强度减弱,解决了厂界噪声超标和噪声扰民问题。同时,为整体式天然气压缩机组噪声治理开辟了一条新途径,为日后降噪厂房和阻抗复合排气消声器广泛应用奠定了实践基础,为压缩机组噪声治理设计积累了宝贵经验。     

相位调制水声通信同步算法

水声信道中存在大量的脉冲噪声和多普勒频移效应,这会给通信同步带来困难,造成水声通信系统最佳采样位置发生较大偏移,严重影响后续通信算法的处理效果。从复杂水声信道中实现较精确通信同步的目的出发,借助相位调制水声通信系统的特点,综合利用线性调频序列和m序列各自的优势,提出了一种由帧同步、位同步双重机制共同组成的水声通信同步算法,经过仿真、湖试及江试,证明了该算法的有效性。     

信号自屏蔽电荷耦合原理及应用

本文利用静电信号高压电极自身作为传感器的屏蔽电极,使屏蔽深度与仪器灵敏度要求相一致,并使输入端接收的电荷量与被测电位成正比关系,而获得被测电位数值,提出“信号自屏蔽电荷耦合”原理,并从理论上和实验上论述了原理的正确性和先进性。利用此原理研制成ZPD-l型静电电位动态测试仪,该仪器与美制263型高速静电表、日本MH型人体静电电位仪等先进仪器相比,具有输入电阻高(>10~(14)Ω)、动态特性好,抗干扰能力强、高电压非线性失真小等特点。     

基于多尺度压缩感知的信号重构

压缩感知是近几年新兴的采样理论,它指出如果信号在某些基上是可压缩的,那么通过很少的观测即可获得信号的准确重构。当信号采用小波基并在压缩感知的基础上,提出了多尺度压缩感知,数值仿真结果表明多尺度压缩感知可以给出更好的重构效果。     

潜艇内置隔板流体特性

为了探究隔板对隔舱特性的影响,以带流水孔潜艇内置隔板为研究对象,对潜艇内置隔舱设置不同的隔板数目,基于大涡模拟(LES)方法进行三维仿真模型进行数值模拟,分析内置隔板对潜艇隔舱阻力以及流噪声特性的影响。结果表明,内置隔板与流体的作用直接影响隔舱的整体特性。隔板数越多,隔舱阻力越大,噪声级也越高,并且阻力以及流噪声与来流速度呈正比例关系,这为潜艇内置隔板的设计提供了参考。     

Optimal control of a capacitated inventory system with multiple demand classes

This article studies the optimal control of a periodic‐review make‐to‐stock system with limited production capacity and multiple demand classes. In this system, a single product is produced to fulfill several classes of demands. The manager has to make the production and inventory allocation decisions. His objective is to minimize the expected total discounted cost. The production decision is made at the beginning of each period and determines the amount of products to be produced. The inventory allocation decision is made after receiving the random demands and determines the amount of demands to be satisfied. A modified base stock policy is shown to be optimal for production, and a multi‐level rationing policy is shown to be optimal for inventory allocation. Then a heuristic algorithm is proposed to approximate the optimal policy. The numerical studies show that the heuristic algorithm is very effective. © 2011 Wiley Periodicals, Inc. Naval Research Logistics 58: 43–58, 2011     

Production‐inventory systems with imperfect advance demand information and updating

We consider a supplier with finite production capacity and stochastic production times. Customers provide advance demand information (ADI) to the supplier by announcing orders ahead of their due dates. However, this information is not perfect, and customers may request an order be fulfilled prior to or later than the expected due date. Customers update the status of their orders, but the time between consecutive updates is random. We formulate the production‐control problem as a continuous‐time Markov decision process and prove there is an optimal state‐dependent base‐stock policy, where the base‐stock levels depend upon the numbers of orders at various stages of update. In addition, we derive results on the sensitivity of the state‐dependent base‐stock levels to the number of orders in each stage of update. In a numerical study, we examine the benefit of ADI, and find that it is most valuable to the supplier when the time between updates is moderate. We also consider the impact of holding and backorder costs, numbers of updates, and the fraction of customers that provide ADI. In addition, we find that while ADI is always beneficial to the supplier, this may not be the case for the customers who provide the ADI. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011