用于军事态势估计的协作知识模型

分布式人工智能（ＤＡＩ）是ＡＩ领域的一个新分支。ＤＡＩ研究的主要目的在于分析和设计大型复杂的协作智能系统。本文以军事智能决策过程中的重要环节──态势估计任务为背景,提出了用于实现分布式协作求解的知识表示框架ＤＥＳＷ系统。文章简要分析了军事态势估计领域背景,然后从系统设计的三个方面依次介绍了ＤＥＳＷ的系统组织模型、主体概念模型和分布式协作推理模型。     

记录可寻址随机访问文件的设计与实现

记录可寻址随机访问文件是一种新型ＦＯＲＴＲＡＮ文件,它集中了顺序与直接文件两者的优点,为用户随机访问文件中不等长记录提供了更为有效的手段。本文不仅介绍了其使用方法,而且还讨论了实现中的有关问题。     

精度鉴定与试验决策系统

本文综合运用序贯分析方法和Ｂａｙｅｓ方法,提出用序贯Ｂａｙｅｓ决策进行战略导弹的精度鉴定和试验设计,序贯Ｂａｙｅｓ决策中的损失函数不仅考虑了决策损失,还考虑了试验费用,这样即可将鉴定方法与试验方法结合起来考虑,给出最佳鉴定方案及试验次数。     

光学图样光电探测的动态仿真

以圆孔夫琅和弗衍射图样的光电探测为例,介绍了对光学图样的光电探测进行动态仿真的方法。     

多传感器信息融合技术述评

近十几年来,多传感器信息融合技术获得了普遍的关注和广泛的应用,其理论与方法已成为智能信息处理的一个重要研究领域。本文概述了信息融合的基本原理,从系统的信息层次和功能划分两个方面阐述了信息融合的基本结构,并对信息融合的具体方法及应用进行了层次化概括,较完整地展示了这一研究领域的全貌。最后进一步分析了信息融合研究领域所存在的一些主要问题,以此指明这一研究领域的发展侧重点。     

八毫米低副瓣卡氏天线的优化设计

为了解决副面遮挡导致小口径卡氏天线（Ｄ＝２９３ｍｍ）副瓣电平恶化这一难题,本文提出了一种优化赋形方法。采用优化口径场分布,对主、副面形状进行赋形设计。用该法设计了一种八毫米低副瓣卡氏天线（ｓｌｌ１≤－２５ｄＢ）,实验结果与理论计算十分吻合。     

Disruption management in production planning

We study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Quantifying optimal mesh and ring design costs

During the last decade telecommunication operators have been deploying WDM (Wavelength Division Multiplexing) technology to satisfy the exponential growth in global communication. While facilitating the advanced information society of today, this has also led to a higher dependency on the networks, and furthermore the high capacity utilization of optical fibers means that a single link failure will influence many users and enterprises. For these reasons, protection of network connections has become a major competitive parameter for the operators. Currently, the most popular protection method is ring protection, due to its simplicity, requiring only basic management functionality and operating with local restoration control. While many optical rings have been deployed, little work has been published on exactly what the cost of ring networks are, compared to general mesh networks. In this article we perform a quantitative comparison between ring protection and mesh protection, using real world network data and realistic prices for network components. Extending classic LP flow models to take rings and node costs into account, and using a link‐path based mesh network LP model, we are able to perform a total cost comparison of the two architectures, and of manual ring network design. The results suggest that the price of mesh network components must be reduced significantly to be competitive with ring based networks, and also that manual network design does not necessarily lead to the most cost‐efficient designs. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Averaging frameworks for simulation optimization with applications to simulated annealing

We present two frameworks for designing random search methods for discrete simulation optimization. One of our frameworks is very broad (in that it includes many random search methods), whereas the other one considers a special class of random search methods called point‐based methods, that move iteratively between points within the feasible region. Our frameworks involve averaging, in that all decisions that require estimates of the objective function values at various feasible solutions are based on the averages of all observations collected at these solutions so far. Also, the methods are adaptive in that they can use information gathered in previous iterations to decide how simulation effort is expended in the current iteration. We show that the methods within our frameworks are almost surely globally convergent under mild conditions. Thus, the generality of our frameworks and associated convergence guarantees makes the frameworks useful to algorithm developers wishing to design efficient and rigorous procedures for simulation optimization. We also present two variants of the simulated annealing (SA) algorithm and provide their convergence analysis as example application of our point‐based framework. Finally, we provide numerical results that demonstrate the empirical effectiveness of averaging and adaptivity in the context of SA. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

基于深度优先搜索与增量式求解的极小一阶不可满足子式提取算法

随着寄存器传输级甚至行为级的硬件描述语言应用越来越广泛,基于一阶逻辑的可满足性模理论(Satisfiability Modulo Theories,SMT)逐渐替代布尔可满足性(Boolean Satisfiability,SAT),在VLSI形式化验证领域具有更加重要的应用价值。而极小不可满足子式能够帮助EDA工具迅速定位硬件中的逻辑错误。针对极小SMT不可满足子式的求解问题,采用深度优先搜索与增量式求解策略,提出了深度优先搜索的极小SMT不可满足子式求解算法。与目前最优的宽度优先搜索算法对比实验表明:该算法能够有效地求解极小不可满足子式,随着公式的规模逐渐增大时,深度优先搜索算法优于宽度优先搜索算法。