针对当前反无人机作战研究热点,以要地反无人机装备体系需求分析为输入,基于美国国防部体系结构框架(department of defense architecture framework,DoDAF)标准,采用基于模型的体系架构(model-based systems engineering,MBSE)设计方法...针对当前反无人机作战研究热点,以要地反无人机装备体系需求分析为输入,基于美国国防部体系结构框架(department of defense architecture framework,DoDAF)标准,采用基于模型的体系架构(model-based systems engineering,MBSE)设计方法,构建反无人机装备体系,完成体系结构建模和方法设计,选取典型作战视角和系统视角描述模型,给出较为全面、直观的反无人机装备体系顶层概念框架,可为未来反无人机作战及装备发展提供思路与借鉴。展开更多
针对低慢小飞行器在综合处置中威胁环境复杂、多学科关键技术交互密切等现状,引入美国国防部体系架构框架(Department of Defense Architecture Framework,DoDAF)对低慢小飞行器综合处置体系进行顶层设计。对其视角视图进行“补充、裁...针对低慢小飞行器在综合处置中威胁环境复杂、多学科关键技术交互密切等现状,引入美国国防部体系架构框架(Department of Defense Architecture Framework,DoDAF)对低慢小飞行器综合处置体系进行顶层设计。对其视角视图进行“补充、裁剪、融合”,定义了各视角的建模顺序及各视角下视图的建模方法,形成一套面向各类复杂体系分析与设计问题完备的构建方法及一套体系架构快速设计方法。并以一种新型多元载荷协同作战的低慢小飞行器综合处置体系进行建模与仿真,验证了该方法能为低慢小飞行器的综合处置作战提供系统全面的描述和可靠的概念模型支撑、该模型可为作战体系架构设计与装备技术发展提供牵引。展开更多
Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewpriv...Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.展开更多
保障系统结构建模是发展和构建新一代航空装备智能保障系统的重要基础。航空装备保障系统涉及保障要素多、交联关系复杂,需从系统工程的角度开展顶层设计,并采用统一的结构框架对其体系结构进行建模表征。引入美国国防部架构框架(Depart...保障系统结构建模是发展和构建新一代航空装备智能保障系统的重要基础。航空装备保障系统涉及保障要素多、交联关系复杂,需从系统工程的角度开展顶层设计,并采用统一的结构框架对其体系结构进行建模表征。引入美国国防部架构框架(Departmeant of Defense Architecture Framework,DoDAF)体系结构框架,提出基于“概念-任务-能力”的体系结构开发序列,构建航空装备智能保障系统的能力、保障活动、各保障要素的信息交互及组织关系等视图模型,得到“能力层-需求层-技术层”之间的对应关系。该方法能够全面地描述航空装备智能保障系统体系结构,提高不同保障要素之间的互操作性,并将其转化为具体的设计要求,可为航空装备智能保障系统开发提供支持。展开更多
Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The signif...Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.展开更多
天临空一体协同遥感体系综合利用现代信息技术,聚合天、临、空各域多源异构数据,实现精准应急服务与指挥决策,系统庞大、结构复杂,缺乏体系结构建模与仿真方面的研究。基于天临空一体协同遥感体系结构特点,构建了体系最小原型系统;借鉴D...天临空一体协同遥感体系综合利用现代信息技术,聚合天、临、空各域多源异构数据,实现精准应急服务与指挥决策,系统庞大、结构复杂,缺乏体系结构建模与仿真方面的研究。基于天临空一体协同遥感体系结构特点,构建了体系最小原型系统;借鉴DoDAF(department of defense architecture framework)以及ABM(activity based methodology)方法完成了最小原型系统结构建模,分析了天临空一体协同遥感体系作战资源流程与信息交互方式、功能结构划分与系统接口表述以及在应急信息支援模式以及常规遥感观测模式下的具体应用。验证试验结果表明:模型构建合理,可为体系集成仿真与效能评估奠定基础,为天临空一体协同遥感体系研究与优化设计提供参考。展开更多
针对传统文本模式存在的需求域与设计域之间信息离散、关联性差、不易追溯等问题,采用基于模型的系统工程(model-based systems engineering,MBSE)方法开展载人登月系统设计。引入美国国防部架构框架(Department of Defense Architectur...针对传统文本模式存在的需求域与设计域之间信息离散、关联性差、不易追溯等问题,采用基于模型的系统工程(model-based systems engineering,MBSE)方法开展载人登月系统设计。引入美国国防部架构框架(Department of Defense Architecture Framework,DoDAF),提出载人登月总体设计体系结构可执行模型的流程和方法;采用系统建模语言(system modeling language,SysML)建立了视图模型,描述了系统架构、需求模型和逻辑接口。并开展了初步逻辑仿真验证,可为载人登月系统设计和MBSE方法应用提供参考。展开更多
文摘针对当前反无人机作战研究热点,以要地反无人机装备体系需求分析为输入,基于美国国防部体系结构框架(department of defense architecture framework,DoDAF)标准,采用基于模型的体系架构(model-based systems engineering,MBSE)设计方法,构建反无人机装备体系,完成体系结构建模和方法设计,选取典型作战视角和系统视角描述模型,给出较为全面、直观的反无人机装备体系顶层概念框架,可为未来反无人机作战及装备发展提供思路与借鉴。
文摘针对低慢小飞行器在综合处置中威胁环境复杂、多学科关键技术交互密切等现状,引入美国国防部体系架构框架(Department of Defense Architecture Framework,DoDAF)对低慢小飞行器综合处置体系进行顶层设计。对其视角视图进行“补充、裁剪、融合”,定义了各视角的建模顺序及各视角下视图的建模方法,形成一套面向各类复杂体系分析与设计问题完备的构建方法及一套体系架构快速设计方法。并以一种新型多元载荷协同作战的低慢小飞行器综合处置体系进行建模与仿真,验证了该方法能为低慢小飞行器的综合处置作战提供系统全面的描述和可靠的概念模型支撑、该模型可为作战体系架构设计与装备技术发展提供牵引。
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
文摘Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.
文摘保障系统结构建模是发展和构建新一代航空装备智能保障系统的重要基础。航空装备保障系统涉及保障要素多、交联关系复杂,需从系统工程的角度开展顶层设计,并采用统一的结构框架对其体系结构进行建模表征。引入美国国防部架构框架(Departmeant of Defense Architecture Framework,DoDAF)体系结构框架,提出基于“概念-任务-能力”的体系结构开发序列,构建航空装备智能保障系统的能力、保障活动、各保障要素的信息交互及组织关系等视图模型,得到“能力层-需求层-技术层”之间的对应关系。该方法能够全面地描述航空装备智能保障系统体系结构,提高不同保障要素之间的互操作性,并将其转化为具体的设计要求,可为航空装备智能保障系统开发提供支持。
文摘Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.
文摘天临空一体协同遥感体系综合利用现代信息技术,聚合天、临、空各域多源异构数据,实现精准应急服务与指挥决策,系统庞大、结构复杂,缺乏体系结构建模与仿真方面的研究。基于天临空一体协同遥感体系结构特点,构建了体系最小原型系统;借鉴DoDAF(department of defense architecture framework)以及ABM(activity based methodology)方法完成了最小原型系统结构建模,分析了天临空一体协同遥感体系作战资源流程与信息交互方式、功能结构划分与系统接口表述以及在应急信息支援模式以及常规遥感观测模式下的具体应用。验证试验结果表明:模型构建合理,可为体系集成仿真与效能评估奠定基础,为天临空一体协同遥感体系研究与优化设计提供参考。
文摘针对传统文本模式存在的需求域与设计域之间信息离散、关联性差、不易追溯等问题,采用基于模型的系统工程(model-based systems engineering,MBSE)方法开展载人登月系统设计。引入美国国防部架构框架(Department of Defense Architecture Framework,DoDAF),提出载人登月总体设计体系结构可执行模型的流程和方法;采用系统建模语言(system modeling language,SysML)建立了视图模型,描述了系统架构、需求模型和逻辑接口。并开展了初步逻辑仿真验证,可为载人登月系统设计和MBSE方法应用提供参考。
