CGCS2000大地问题解算工程运用研究

军事信息系统研制中经常涉及CGCS2000坐标系下的大地问题解算,但直接采用韦森特公式进行软件设计存在一些问题。通过对韦森特公式在工程运用中的问题进行了分析,对正解、反解算法进行了补充完善并给出了相关的计算实例供编程检查。改进后的算法简单实用,适合军事信息系统软件的实现。     

基于Multi-agent协商的多干扰机资源管理

多干扰机协同是组网雷达对抗的一种重要方法。针对多干扰机协同中面临的数据传输率、时效性等因素所引起的干扰资源管理问题,建立了基于中心控制协商和公约协商的干扰资源管理模型,并给出了相应的协同算法与仿真。仿真结果表明：两种方法在组网雷达对抗中均能取得良好的干扰效果及较低的漏威胁目标信号脉冲比例。其中,基于公约的Multi?agent协商在组网干扰资源管理方面具有更好的可行性,其效果优于基于中心控制协商的方法。     

第19届国际信息融合会议及获奖论文评述

总结了第19届国际信息融合会议的总体情况和主要特点,通过对大会报告、分会场报告以及国际信息融合界一些著名专家团队发表论文情况的统计分析,提炼出国际信息融合界普遍关注的焦点和难点问题;通过对获奖论文的分析,了解到受大会评奖委员会一致认可的年度优秀成果和技术进步点;最后,面向高层信息融合中的不确定性处理,对信息融合领域的发展进行展望。     

火炮火控系统命中解的分布和存在性

定义了火控系统中问题联立方程应满足的“基本假设”,从理论上讨论了在“基本假设”条件下诸命中解的分布特性 ,证明临近的命中解 (如果存在的话 )是唯一的 ,给出了临近解存在的充分必要条件。用一个简单的实例表出不同目标速度下诸命中解分布的实验数据     

Balancing and optimizing a portfolio of R&D projects

A mathematical formulation of an optimization model designed to select projects for inclusion in an R&D portfolio, subject to a wide variety of constraints (e.g., capital, headcount, strategic intent, etc.), is presented. The model is similar to others that have previously appeared in the literature and is in the form of a mixed integer programming (MIP) problem known as the multidimensional knapsack problem. Exact solution of such problems is generally difficult, but can be accomplished in reasonable time using specialized algorithms. The main contribution of this paper is an examination of two important issues related to formulation of project selection models such as the one presented here. If partial funding and implementation of projects is allowed, the resulting formulation is a linear programming (LP) problem which can be solved quite easily. Several plausible assumptions about how partial funding impacts project value are presented. In general, our examples suggest that the problem might best be formulated as a nonlinear programming (NLP) problem, but that there is a need for further research to determine an appropriate expression for the value of a partially funded project. In light of that gap in the current body of knowledge and for practical reasons, the LP relaxation of this model is preferred. The LP relaxation can be implemented in a spreadsheet (even for relatively large problems) and gives reasonable results when applied to a test problem based on GM's R&D project selection process. There has been much discussion in the literature on the topic of assigning a quantitative measure of value to each project. Although many alternatives are suggested, no one way is universally accepted as the preferred way. There does seem to be general agreement that all of the proposed methods are subject to considerable uncertainty. A systematic way to examine the sensitivity of project selection decisions to variations in the measure of value is developed. It is shown that the solution for the illustrative problem is reasonably robust to rather large variations in the measure of value. We cannot, however, conclude that this would be the case in general. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 18–40, 2001     

奇型非线性抛物问题的有限元误差估计

引入带权的Sobolev空间 ,讨论了奇型非线性抛物问题的有限元方法 ,在a(u) ,f(u)均满足Lips chitz条件下 ,证明了相应椭圆投影算子Rhu与u之间误差估计式 ,并在一定的假设下 ,给出了奇型非线性抛物问题的广义解u及半离散问题的有限元解uh 误差估计式 .     

基于非线性扩张状态观测器的直线电机PD控制

为了解决直线电机伺服系统跟踪速度与峰化现象之间的矛盾,设计一种基于非线性扩张状态观测器的比例微分(Proportion Differentiation,PD)控制器。将直线电机伺服系统中的未建模动态和外界干扰定义为总和扰动并扩充为系统新的状态变量,利用非线性扩张状态观测器(Non Linear Extended State Observer,NLESO)估计不可测量的直线电机动子速度以及总和扰动。利用NLESO和跟踪微分器TD的输出,基于动态补偿线性化思想设计了引入补偿量的PD控制器,并给出了闭环控制系统稳定性证明。在Googol公司的实验平台上,通过与两种基于LESO的PD控制器对比,验证了所设计的基于NLESO的PD控制器的可行性。实验结果表明,基于NLESO的PD控制器可使直线电机伺服系统具有跟踪速度快、跟踪精度高、峰化现象小、鲁棒性强的优点。     

A self‐adapting genetic algorithm for project scheduling under resource constraints

This papers deals with the classical resource‐constrained project scheduling problem (RCPSP). There, the activities of a project have to be scheduled subject to precedence and resource constraints. The objective is to minimize the makespan of the project. We propose a new heuristic called self‐adapting genetic algorithm to solve the RCPSP. The heuristic employs the well‐known activity list representation and considers two different decoding procedures. An additional gene in the representation determines which of the two decoding procedures is actually used to compute a schedule for an individual. This allows the genetic algorithm to adapt itself to the problem instance actually solved. That is, the genetic algorithm learns which of the alternative decoding procedures is the more successful one for this instance. In other words, not only the solution for the problem, but also the algorithm itself is subject to genetic optimization. Computational experiments show that the mechanism of self‐adaptation is capable to exploit the benefits of both decoding procedures. Moreover, the tests show that the proposed heuristic is among the best ones currently available for the RCPSP. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 433–448, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10029     

能量均衡的围捕任务分配方法

随着水下机器人反围捕策略研究的不断深入,水下机器人围捕变得越来越困难。为此构建一种多层环状伏击围捕模型并设计了基于围捕任务的任务分配方法,使得水下机器人能够充分利用自身的特点更好地完成任务。同时,考虑在围捕过程中随时间推移,系统内能量会出现消耗不均的现象,据此提出一种能量均衡方法平衡系统能量的消耗。实验证明所提出的基于多层环状围捕模型的能量均衡策略任务分配方法能有效提高围捕成功率,延长系统寿命。     

The one‐warehouse multiretailer problem with an order‐up‐to level inventory policy

We consider a two‐level system in which a warehouse manages the inventories of multiple retailers. Each retailer employs an order‐up‐to level inventory policy over T periods and faces an external demand which is dynamic and known. A retailer's inventory should be raised to its maximum limit when replenished. The problem is to jointly decide on replenishment times and quantities of warehouse and retailers so as to minimize the total costs in the system. Unlike the case in the single level lot‐sizing problem, we cannot assume that the initial inventory will be zero without loss of generality. We propose a strong mixed integer program formulation for the problem with zero and nonzero initial inventories at the warehouse. The strong formulation for the zero initial inventory case has only T binary variables and represents the convex hull of the feasible region of the problem when there is only one retailer. Computational results with a state‐of‐the art solver reveal that our formulations are very effective in solving large‐size instances to optimality. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010