The graph association problem: Mathematical models and a lagrangian heuristic

Graph association is the problem of merging many graphs that collectively describe a set of possibly repetitive entities and relationships into a single graph that contains unique entities and relationships. As a form of data association, graph association can be used to identify when two sensors are observing the same object so information from both sensors can be combined and analyzed in a meaningful and consistent way. Graph association between two graphs is related to the problem of graph matching, and between multiple graphs it is related to the common labeling of a graph set (also known as multiple graph matching) problem. This article contribution is to formulate graph association as a binary linear program and introduce a heuristic for solving multiple graph association using a Lagrangian relaxation approach to address issues with between‐graph transitivity requirements. The algorithms are tested on a representative dataset. The developed model formulation was found to accurately solve the graph association problem. Furthermore, the Lagrangian heuristic was found to solve the developed model within 3% of optimal on many problem instances, and found better solutions to large problems than is possible by directly using CPLEX. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

The Israel Defense Forces as an epistemic authority: An intellectual challenge in the reality of the Israeli – Palestinian conflict

The changes in the nature of warfare and its transformation toward Low Intensity Conflict (LIC) intrastate conflict have challenged the patterns of interaction between the political and the military echelons in Israel. It seems that the political echelon's superiority is maintained at the institutional and formal levels, but on the substantive level, which demands relying on knowledge and systematic staff work, the political echelon's position is weakened and loses its validity.

Introducing the military echelon in Israel as an epistemic authority regarding the violent confrontation and the main outlines of the military knowledge development process might clarify why the absence of the required dialogue between the echelons and the weakness of the intellectual effort increased the military's influence over the shaping of Israeli conflict-management strategy. The argument's validity and its explanatory power can be found relevant for other countries whose militaries are deeply involved in the management of LIC.     

基于粗糙集理论的武器系统效能评定

简述了各种武器效能评定方法。建立武器参数效能模型,首先要挑选特征参数,这里采用知识约简方法选择武器的特征参数。利用神经网络理论建立了参数效能模型,武器系统的效能与武器特征参数的关系可通过神经网络的阈值和权值得到体现。通过实例对神经网络法与指数法所得的结果进行了比较,结果表明神经网络法比指数法精确。     

战略联盟中知识转移影响因素概念模型构建：网络角度的思考

根据知识转移的一般过程,从社会网络视角出发,不同于以往研究中二元维度的分析,从网络结点能力、网络关系和认知、网络结构三个维度提出了影响战略联盟网络组织中知识转移绩效的关键影响因素,并深入分析各个关键因素的细分变量对知识转移绩效的影响作用机理,最后提出战略联盟网络知识转移影响因素的相关假设和概念模型,为下一步对假设的验证做铺垫。     

基于专家系统的防化保障预案评估研究

人工智能技术在众多领域获得广泛应用．为满足现代战争对防化指挥手段的现代化要求,针对不易量化的防化指挥决策问题的评估,提出了构建知识库并应用模糊推理机制建立专家系统,据此进行防化保障预案的评估选优方法,并对该方法的求解过程进行了探讨．     

基于FCM与粗糙集的连续数据知识挖掘方法

对连续数据知识挖掘可采用模糊C均值聚类将其离散化,再应用粗糙集进行约简.文中针对该过程中样本数据离散化后出现多个聚类中心隶属度值较接近的情况,提出了隶属度重叠度的方法来扩展对应离散类别的选取.离散类别确定后再应用粗糙集理论对其进行知识挖掘,可得到连续数据的本质特性.此方法应用于汽轮机轴系振动数据的知识挖掘.与采用最大隶属度确定对应类别的方法相比,该方法不影响对连续数据的知识挖掘,并能真实反映连续数据的特性.     

想定驱动的仿真资源发现方法

为解决在云间共享架构的仿真环境下资源的自动发现问题,研究了仿真资源请求的描述方法及相应的资源发现算法,提出了想定驱动的仿真资源发现方法.方法通过扩展的军事想定描述语言表示仿真资源请求,使用双映射三过滤算法实现仿真资源的自动发现.分析表明,仿真想定驱动的资源发现方法可以实现在云间共享架构下仿真运行所需的大量资源的自动发现,并对发现结果具有语用校验功能,对于仿真环境的快速部署具有积极意义.     

应用知识推理的防空兵射击指挥辅助决策模型

针对现代空袭方式的新特点,提出了应用知识推理的防空兵射击指挥辅助决策模型,在已经研究比较成熟的模型算法的基础上进行了改进,并将人工智能的知识推理引入辅助决策.定量计算与定性分析相结合,为解决防空兵射击指挥这种半结构化问题提供了新的思路.     

ABDARDSS中基于候选消除学习算法的知识推理

飞机战伤评估和修理的知识表示与推理机制,是建立飞机战伤评估与修理决策支持系统(ABDARDSS)需要解决的一个关键问题.采用一种候选消除算法来提取战伤评估与修理样本数据的变型空间,以变型空间实现战伤评估与修理决策支持系统中的知识表示和推理.对于ABDARDSS来说,候选消除学习算法是一种新的有效的知识推理方法.     

基于装备水平和知识的空中作战效果分析

分析了按作战效果进行空军武器装备需求分析的三种常用方法的特点,建立了一种全自动化解算模型,比较完整地讨论并描述了根据装备水平分析作战效果时需要考虑的关键因素,总结了需要使用的相关军事知识,并给出了表达方法,研究了推理机制.构建了各种战斗类型的效果评估模块,实现了软件系统并应用于一个重要课题研究,取得了比较理想的效果.