The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an...The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.展开更多
With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and powe...With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and power distribution systems.Supporting these applications,an important family of methods are based on graphs.For DT applications in modeling and managing smart cities,large-scale knowledge graphs(KGs)are necessary to represent the complex interdependencies and model the urban infrastructure as a system of systems.To this end,this paper develops a conceptual framework:Automated knowledge Graphs for Complex Systems(AutoGraCS).In contrast to existing KGs developed for DTs,AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex systems.The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling,Bayesian analysis,and adaptive decision supports.Besides,AutoGraCS provides flexibility in support of users’need to implement the ontology and rules when constructing the KG.With the user-defined ontology and rules,AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems.The bridge network in Miami-Dade County,FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network,traffic monitoring facilities,and flood water watch stations.展开更多
数字工程被认为是系统工程在数字化时代的延伸,具有能有效缩短装备研制周期、提升产品质量、优化工程体系等优点,实现装备采办过程由“以文本为中心”向“以数据为中心”的范式转变。为全面系统地分析数字工程的发展概况和研究动态,通过...数字工程被认为是系统工程在数字化时代的延伸,具有能有效缩短装备研制周期、提升产品质量、优化工程体系等优点,实现装备采办过程由“以文本为中心”向“以数据为中心”的范式转变。为全面系统地分析数字工程的发展概况和研究动态,通过对Web of Science数据库中“Digital Engineering”相关文献进行CiteSpace计量分析,分别从发文量、研究领域与文献分布、所属国家与机构分布、作者与机构合作知识图谱、共被引知识图谱、关键词共现等不同角度审视研究主题结构,结合可视化知识图谱对数字工程研究的现状、趋势和热点进行了分析。分析结果表明,数字工程研究具有多学科、跨领域的交叉特性,已跨入快速上升期;已初步形成多个数字工程研究团队,但核心作者较分散;美国、英国、德国等发达国家投入数字工程研究力度大且取得成果丰硕,而我国在数字工程研究的资助和战略部署方面亟待加强。此外,从研究趋势的演进来看,未来数字工程研究的重点将聚焦于基于模型的系统工程、Digital Twin等技术体系。最后,进行全文结论性总结,指出数字工程面临的挑战以及未来重点发展方向。展开更多
针对多分类支持向量机算法中的低效问题和样本不平衡问题,提出一种有向无环图-双支持向量机DAG-TWSVM(directed acyclic graph and twin support vector machine)的多分类方法。该算法综合了双支持向量机和有向无环图支持向量机的优势,...针对多分类支持向量机算法中的低效问题和样本不平衡问题,提出一种有向无环图-双支持向量机DAG-TWSVM(directed acyclic graph and twin support vector machine)的多分类方法。该算法综合了双支持向量机和有向无环图支持向量机的优势,使其不仅能够得到较好的分类精度,同时还能够大大缩减训练时间。在处理较大规模数据集多分类问题时,其时间优势更为突出。采用UCI(University of California Irvine)机器学习数据库和Statlog数据库对该算法进行验证,实验结果表明,有向无环图-双支持向量机多分类方法在训练时间上较其他多分类支持向量机大大缩短,且在样本不平衡时的分类性能要优于其他多分类支持向量机,同时解决了经典支持向量机一对一多分类算法可能存在的不可分区域问题。展开更多
基金supported by the China Scholarship Council and the CERNET Innovation Project under grant No.20170111.
文摘The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.
基金support received from US Department of Transportation Tier 1 University Transportation Center CREATE Award No.69A3552348330.
文摘With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and power distribution systems.Supporting these applications,an important family of methods are based on graphs.For DT applications in modeling and managing smart cities,large-scale knowledge graphs(KGs)are necessary to represent the complex interdependencies and model the urban infrastructure as a system of systems.To this end,this paper develops a conceptual framework:Automated knowledge Graphs for Complex Systems(AutoGraCS).In contrast to existing KGs developed for DTs,AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex systems.The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling,Bayesian analysis,and adaptive decision supports.Besides,AutoGraCS provides flexibility in support of users’need to implement the ontology and rules when constructing the KG.With the user-defined ontology and rules,AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems.The bridge network in Miami-Dade County,FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network,traffic monitoring facilities,and flood water watch stations.
文摘数字工程被认为是系统工程在数字化时代的延伸,具有能有效缩短装备研制周期、提升产品质量、优化工程体系等优点,实现装备采办过程由“以文本为中心”向“以数据为中心”的范式转变。为全面系统地分析数字工程的发展概况和研究动态,通过对Web of Science数据库中“Digital Engineering”相关文献进行CiteSpace计量分析,分别从发文量、研究领域与文献分布、所属国家与机构分布、作者与机构合作知识图谱、共被引知识图谱、关键词共现等不同角度审视研究主题结构,结合可视化知识图谱对数字工程研究的现状、趋势和热点进行了分析。分析结果表明,数字工程研究具有多学科、跨领域的交叉特性,已跨入快速上升期;已初步形成多个数字工程研究团队,但核心作者较分散;美国、英国、德国等发达国家投入数字工程研究力度大且取得成果丰硕,而我国在数字工程研究的资助和战略部署方面亟待加强。此外,从研究趋势的演进来看,未来数字工程研究的重点将聚焦于基于模型的系统工程、Digital Twin等技术体系。最后,进行全文结论性总结,指出数字工程面临的挑战以及未来重点发展方向。
文摘针对多分类支持向量机算法中的低效问题和样本不平衡问题,提出一种有向无环图-双支持向量机DAG-TWSVM(directed acyclic graph and twin support vector machine)的多分类方法。该算法综合了双支持向量机和有向无环图支持向量机的优势,使其不仅能够得到较好的分类精度,同时还能够大大缩减训练时间。在处理较大规模数据集多分类问题时,其时间优势更为突出。采用UCI(University of California Irvine)机器学习数据库和Statlog数据库对该算法进行验证,实验结果表明,有向无环图-双支持向量机多分类方法在训练时间上较其他多分类支持向量机大大缩短,且在样本不平衡时的分类性能要优于其他多分类支持向量机,同时解决了经典支持向量机一对一多分类算法可能存在的不可分区域问题。
