粗集理论在目标识别信息处理中的应用

为提取传感器获得的较粗糙的原始信息,运用粗集理论进行目标识别信息的处理,提出了一种采用关系表存储原始信息,通过简化关系表删去冗余信息,从而达到提取有用信息的处理方法.根据此方法,实例中通过对生成的规则进行优选,得到了简单准确的目标识别规则.此理论在信息处理中的应用,较好地满足了目标识别中原始信息处理的需求.     

一种新的基于图像复杂度的掩密分析技术

基于掩密图像与原始图像在图像复杂度上的差异,提出了一种新的针对图像LSB嵌入的掩密分析算法,通过分析待检图像在嵌入测试消息前后图像复杂度均值和方差的变化情况,利用Fisher判决准则,从而准确判断图像中是否嵌入了秘密消息.实验结果表明,文章所提算法不仅能准确检测顺序LSB嵌入,并且能估计出嵌入消息的长度,而且能够以98％的准确率检测随机LSB嵌入,同时具有实现简单,运行速度快等优点.     

军队数字化油库研究

阐述了数字化油库的基本概念、结构体系、建设内容、建设阶段及数字化油库建设在后勤信息化建设中的地位和作用,重点论述三层结构体系的内容和相互关系,详细阐述了数字化油库建设的5个方面。提出的思想对军队油库信息化建设有一定的指导意义。     

产品网络化协同开发系统信息处理模型研究

产品网络协同开发系统信息处理模型是现代制造企业从事产品开发、提高企业核心竞争力的关键。在分析产品网络化协同开发系统现状的基础上,阐明了产品网络化协同开发系统的基本内涵及实现方法。提出了基于输入、输出、知识、关系、方法和时间约束六个方面组成的产品网络化协同开发系统的信息处理统一模型;建立了一个面向产品开发全过程协同的信息流模型,构建了产品网络化协同开发平台信息流模型的软件结构,为制造企业建立产品网络协同开发平台,实现产品开发过程的信息化、科学化和规范化进行了有益的工作。     

目标自动跟踪与信息处理综合系统的研究与实现

根据将目标自动跟踪与信息处理综合的思想,在目标自动跟踪与信息处理综合的实现方面作了大量工作,并进行了实验.实验结果表明,只有在自动跟踪系统中综合目标信息处理,火控系统性能才能有质的提高.同时,数字信号处理(DSP)技术、自适应滤波算法的应用,使得系统设计能够基本达到实际应用的阶段.     

分布式虚拟环境中环境信息动态更新研究

环境信息动态更新是分布式虚拟环境的一个主要特点,如何使环境信息更新更活跃、更反应实际情况,一直是分布式虚拟环境研究的重点。在典型的分布式交互环境中,环境信息动态更新是基于协议数据单元(PDU),它不提供数据可靠性传输保证。如何保证分布式环境中各结点环境信息一致性,本文提出了一种信息校验和协议,它通过极少的信息传输就可保证各结点环境信息高度一致。文中定义了保证分布式环境信息动态一致性更新的协议数据单元,并在试验环境中证明了协议的可靠性。     

The impact of exponential smoothing forecasts on the bullwhip effect

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two‐stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 269–286, 2000     

Stochastic production capacity: A bane or a boon for quick response supply chains?

The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold.     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

陆军无人装备作战指挥信息系统

本文对陆战场无人化、智能化作战需求及其对指挥信息系统的能力要求进行分析。以美军为典型样例对无人装备的指挥信息系统发展现状进行分析,对无人装备作战指挥信息系统体系架构进行构想,并提出未来需重点突破的关键技术,最后对军队无人装备指挥信息体系的发展提出了几点建议。