大型仿真推演系统数据质量评估方法

数据是大型仿真推演系统建立和运行的基础,一定程度上其质量直接关系着整个系统应用的水平、广度及深度。首先建立了大型仿真推演数据质量的评价指标体系,包括3个一级指标和相对应的二级指标,然后提出了定性指标的定量化、定量指标的无量纲化的指标指数确定方法,以及在分析权重确定的基础上提出了主观赋权与客观赋权相结合的方法,最后基于AHP法以某一装甲兵作战仿真推演系统的数据为例进行了质量评估,从宏观上得到了一个整体的数据质量评价。     

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     

火场中的金属变形及其在火灾勘查中的证明作用

火场当中,钢质材料的存在形式很多,常见的有五种特殊存在形式竖直承重、竖直不承重、竖直悬挂、水平悬空和水平放置.研究钢质材料这五种存在形式在火场当中的变形,有助于对起火点的认定和火灾发展蔓延方向的分析.     

论高师教育实习工作的改进

教育实习是高等师范教育工作的重要组成部分,是高等师范教育全过程的一个重要阶段,教育实习质量的高低,直接关系到师范院校毕业生的质量。加强和改进教育实习工作,是提高教育实习质量的重要因素。     

利用AHP确定决策方案排序的一种改进方法

针对在用AHP法求解权重排序向量时 ,用不同的计算方法导致排序方案不一致的矛盾现象 ,提出了一种在单准则下综合各个排序方案信息的方法 ,使AHP法更趋于完善     

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     

模糊综合评价方法在教学质量评估中的应用

针对评价对象的复杂性及其评价指标的模糊性,采用模糊数学的理论与技术,对教学质量进行综合评判,其结果能汇总各类评价人员的评价意见,较全面地反映出评价对象的优劣程爱,同时结合装甲兵工程学院教学质量评价表进行了阐述.     

图像质量主客观评价的相关性分析

图像质量的客观评价与人类主观感知之间一定存在必然的联系,并且这些客观评价方法应该取代主观评价。也就是说,这些客观评价方法允许在不征求任何用户意见的基础上,去评价一个给定系统的性能。本文研究了在变化的压缩体制下实施在若干图像上的一套客观测量和主观评价之间的相互关系,并给出了初步的分析结果。     

重视开展科技创新活动,打造培养学员科研素质平台

创新素质的培养是全面推进素质教育的突破口,要有良好的运作机制与管理模式。军事院校是军事创新的源头,科技创新活动是培养创新人才的有效途径和方法。阐述了创新的概念和科技创新活动的意义,结合军事院校的特点,分析了科技创新活动中存在的问题,提出了实施科技创新活动的基本思路和措施。     

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