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Intelligent modeling method for OV models in DoDAF2.0 based on knowledge graph
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作者 ZHANG Yue JIANG Jiang +3 位作者 YANG Kewei WANG Xingliang XU Chi LI Minghao 《Journal of Systems Engineering and Electronics》 2025年第1期139-154,共16页
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi... Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method. 展开更多
关键词 system of systems(SoS)architecture operational viewpoint(OV)model meta model bidirectional long short-term memory and conditional random field(BiLSTM-CRF) model generation systems modeling language
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高级地图匹配算法:研究现状和趋势 被引量:14
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作者 于娟 杨琼 +2 位作者 鲁剑锋 韩建民 彭浩 《电子学报》 EI CAS CSCD 北大核心 2021年第9期1818-1829,共12页
地图匹配是许多位置服务与轨迹挖掘应用的基础.随着定位技术和位置服务应用的发展,地图匹配研究不断演进,从早期基于高采样率GPS(Global Position System)的实时匹配,到近期基于低采样率GPS轨迹的离线匹配、再到当前非GPS定位数据或高... 地图匹配是许多位置服务与轨迹挖掘应用的基础.随着定位技术和位置服务应用的发展,地图匹配研究不断演进,从早期基于高采样率GPS(Global Position System)的实时匹配,到近期基于低采样率GPS轨迹的离线匹配、再到当前非GPS定位数据或高精度地图匹配。迄今已有许多地图匹配算法相继提出,但鲜有研究对这些算法进行全面总结.为此,对近十年提出的地图匹配算法进行调研,归纳出地图匹配算法的统一框架及常用时空特征.从模型或实现技术角度分类发现:现有算法大都采用HMM(Hidden Markov Model)模型,其次是最大权重模型;深度学习技术近期开始用于地图匹配,将是未来高精度地图匹配研究的趋势. 展开更多
关键词 地图匹配 路网数据 轨迹数据 HMM CRF(conditional random fields) 路径推断
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