基于多次致命性故障定量判定的复杂可修系统可信度建模的解 …

在定量识别多次致命性故障状态的基础上,通过对发生致命性故障事件和修复事件的描述,提出了复杂可修系统可信度计算的一般方法,同时详细地推导致命性故障和任务修复率为常数时复杂可修系统可信度的计算公式,并且展开讨论了发生多次致命性故障时复杂可修系统可信度的建模问题：最后以实例进行了可信度建模的工程化验证。     

基于任务空间概念模型的导弹作战想定研究

作战想定是军事仿真领域不可缺少的重要组成部分.在目前的军事建模与仿真中,作战想定与仿真模型之间通常都是紧耦合的,由此带来了仿真成员、仿真系统可重用性差等诸多缺点.针对此,基于任务空间概念模型(CMMS)的思想,探讨了CMMS与想定的关系,分析了常规导弹作战想定中需要描述的主要内容,对导弹作战想定中的基本要素进行了分析抽取,并对其进行标准化、规范化的抽象描述,以实现想定与仿真系统相分离.最后,给出了想定数据库中基本表的结构和关系.     

基于B/S结构出差五定单系统的设计与实现

网络办公已成为现代企业发展的趋势,更是企业实现无纸化办公、提高办公效益、节约办公成本的现代化工具。利用现有的网络平台,开发了一套适合企业财务部门使用的"出差五定单"系统,以替换手工填写纸质的办公模式来提高工作效益。本文主要阐述基于B/S三层体系结构模式的出差五定单系统功能模块的开发和研究以及如何使用ASP和Access解决一些相关的技术,其中包括系统的安全性、稳定性、实用性等问题。     

Optimal completed work dependent loading of components in cold standby systems

This paper considers the modelling and optimization of 1-out-of-N: G cold standby (CS) systems with non-repairable components functioning at different levels of productivity or load. The productivity heterogeneity leads to difference in component failure behaviour as well as in operational and replacement costs. Thus, the choice of load or productivity of components can greatly affect the system reliability and mission cost. To make the optimal choice of component loading, we first suggest a method for analysing the reliability and expected mission cost of 1-out-of-N: G non-repairable CS systems with heterogeneous components. The optimal dynamic load distribution problem is then formulated and solved, in which the component loading is chosen depending on the amount of work completed prior to the component activation. The optimal loading is aimed at minimizing the expected mission cost, while meeting a certain system reliability constraint. Examples are given to demonstrate the proposed methodology and the improvement in the optimal design solution through introducing the component productivity’s dependence on the completed work.     

Achieving dynamic, multi-commander, multi-mission planning and execution

The Multi-Agent Distributed Goal Satisfaction (MADGS) system facilitates distributed mission planning and execution in complex dynamic environments with a focus on distributed goal planning and satisfaction and mixed-initiative interactions with the human user. By understanding the fundamental technical challenges faced by our commanders on and off the battlefield, we can help ease the burden of decision-making. MADGS lays the foundations for retrieving, analyzing, synthesizing, and disseminating information to commanders. In this paper, we present an overview of the MADGS architecture and discuss the key components that formed our initial prototype and testbed. Eugene Santos, Jr. received the B.S. degree in mathematics and Computer science and the M.S. degree in mathematics (specializing in numerical analysis) from Youngstown State University, Youngstown, OH, in 1985 and 1986, respectively, and the Sc.M. and Ph.D. degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently a Professor of Engineering at the Thayer School of Engineering, Dartmouth College, Hanover, NH, and Director of the Distributed Information and Intelligence Analysis Group (DI²AG). Previously, he was faculty at the Air Force Institute of Technology, Wright-Patterson AFB and the University of Connecticut, Storrs, CT. He has over 130 refereed technical publications and specializes in modern statistical and probabilistic methods with applications to intelligent systems, multi-agent systems, uncertain reasoning, planning and optimization, and decision science. Most recently, he has pioneered new research on user and adversarial behavioral modeling. He is an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Part B and the International Journal of Image and Graphics. Scott DeLoach is currently an Associate Professor in the Department of Computing and Information Sciences at Kansas State University. His current research interests include autonomous cooperative robotics, adaptive multiagent systems, and agent-oriented software engineering. Prior to coming to Kansas State, Dr. DeLoach spent 20 years in the US Air Force, with his last assignment being as an Assistant Professor of Computer Science and Engineering at the Air Force Institute of Technology. Dr. DeLoach received his BS in Computer Engineering from Iowa State University in 1982 and his MS and PhD in Computer Engineering from the Air Force Institute of Technology in 1987 and 1996. Michael T. Cox is a senior scientist in the Intelligent Distributing Computing Department of BBN Technologies, Cambridge, MA. Previous to this position, Dr. Cox was an assistant professor in the Department of Computer Science & Engineering at Wright State University, Dayton, Ohio, where he was the director of Wright State’s Collaboration and Cognition Laboratory. He received his Ph.D. in Computer Science from the Georgia Institute of Technology, Atlanta, in 1996 and his undergraduate from the same in 1986. From 1996 to 1998, he was a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University in Pittsburgh working on the PRODIGY project. His research interests include case-based reasoning, collaborative mixed-initiative planning, intelligent agents, understanding (situation assessment), introspection, and learning. More specifically, he is interested in how goals interact with and influence these broader cognitive processes. His approach to research follows both artificial intelligence and cognitive science directions.     

Efficient reliability analysis of dynamic k-out-of-n phase-AND mission systems

Motivated by real-world applications of satellites and wireless sensor networks, this paper models and evaluates a dynamic k-out-of-n phase-AND mission system (k/n-PAMS). The mission task conducted by a k/n-PAMS involves multiple consecutive phases; the mission is successful as long as the task is successful in any of the phases. Due to factors, such as scheduled maintenance, location changes in task execution during different phases, and resource sharing with other tasks, the total number of available components n for the considered mission task and the required number of working components k may change from phase to phase. In addition, due to varying load and working environments, component failure time distributions are also phase dependent. This paper proposes an analytical modeling approach based on multivalued decision diagrams (MDDs) for assessing reliability of the considered k/n-PAMS. The approach encompasses a new and fast MDD model generation algorithm that considers behaviors of all the mission phases simultaneously based on node labeling. As demonstrated through empirical studies on k/n-PAMSs with different sizes (different numbers of phases and different numbers of system components), the proposed algorithm is more efficient than the traditional phase-by-phase model generation method.     

直升机尾减速器机匣疲劳试验方法研究

根据减速器机匣疲劳损伤应力循环的动、静应力包罗线方法确定了疲劳试验加载方案;根据设计载荷谱或飞行实测,确定各载荷作用点的状态载荷并编制了疲劳试验谱;根据减速器机匣的受力形式,完成减速器机匣疲劳试验装置的设计。结果表明,该试验装置既可以考虑高频动载荷的影响,又能简化疲劳试验的加载程序,明显缩短了疲劳试验的周期。     

什么是一所好的大学——解读《对同济大学的祝愿》

针对温家宝的讲话《对同济大学的祝愿》进行解读,由此探讨了大学的使命、大学的学科、大学的灵魂、大学的操守、大学的未来,并论及什么是一所好的大学。     

面向作战任务的装甲装备群携行备件配置优化

为提高携行备件配置方案的针对性,以遂行多阶段作战任务的装甲装备群为研究对象,综合考虑作战任务、装备群组成及备件仓库预置地点等因素对抢修策略的影响,合理统筹伴随维修保障能力与备件需求间关系,构建面向作战任务的装甲装备群携行备件配置优化模型,实现携行备件资源的针对性配置。通过算例,验证了该优化模型的可行性及有效性。     

任务规划系统验证方法研究

基于对国内外任务规划系统的研究,从研究任务规划系统的划分入手,分析各种任务规划系统的工作内容和工作环境,从运行环境、工作方式、规划内容和部署方式等四种方式对任务规划系统进行了划分。最后从工程研制角度和使用角度,提出了任务规划系统的验证方法和验证流程。指出任务规划系统的验证可划分为硬件验证、软件测试和系统级试验,可通过设计各种想定对各验证项目进行验证。