对基于高层体系结构(High Level Architecture,简称HLA)的仿真系统的校核、验证与确认(Verification, Validation and Accreditation,简称VV&A)问题进行了详细的介绍及分析。在回顾研究历史及现状的基础上,首先介绍了HLA中VV&A...对基于高层体系结构(High Level Architecture,简称HLA)的仿真系统的校核、验证与确认(Verification, Validation and Accreditation,简称VV&A)问题进行了详细的介绍及分析。在回顾研究历史及现状的基础上,首先介绍了HLA中VV&A的主要问题,包括概念、方法、基于联邦开发和运行过程(Federation Development and Execution Process,简称FEDEP)的VV&A过程、VV&A与测试和评估(Test and Evaluation,简称T&E)的关系以及自动化等。接着详细分析了HLA的VV&A研究中的难点:互操作性与可重用性的VV&A问题。最后进行了总结。展开更多
为了规范和统一多分辨率模型的描述方法、建模步骤和活动,提出一种基于BOM(Base Object Model)和FEDEP(Federation Development and Execution Process)的多分辨率建模框架.该框架包括3种基于BOM的多分辨率建模方法和基于FEDEP的多分辨...为了规范和统一多分辨率模型的描述方法、建模步骤和活动,提出一种基于BOM(Base Object Model)和FEDEP(Federation Development and Execution Process)的多分辨率建模框架.该框架包括3种基于BOM的多分辨率建模方法和基于FEDEP的多分辨率建模过程.简要介绍这3种方法,重点分析FEDEP中与BOM和多分辨率建模相关的基本活动,将基于BOM的多分辨率建模过程与FEDEP集成在一起.该框架可以实现多分辨率模型描述的形式化和通用性、建模步骤和过程的规范化,促进多分辨率模型的重用、互操作和组合,保证模型的一致性和多分辨率建模的有效性.展开更多
潜艇艇载武器装备的作战性能是决定潜艇作战效能高低的物质基础,是潜艇战斗生命力的关键所在,潜艇装备应用研究系统(EARS)为潜艇武器装备论证、定型和发展提供了依据。该文基于高层体系结构(HLA)机制,重点研究了潜艇EARS的联邦开发与执...潜艇艇载武器装备的作战性能是决定潜艇作战效能高低的物质基础,是潜艇战斗生命力的关键所在,潜艇装备应用研究系统(EARS)为潜艇武器装备论证、定型和发展提供了依据。该文基于高层体系结构(HLA)机制,重点研究了潜艇EARS的联邦开发与执行过程(Federation Development and Execution Process,FEDEP)。展开更多
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and ...Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.展开更多
文摘对基于高层体系结构(High Level Architecture,简称HLA)的仿真系统的校核、验证与确认(Verification, Validation and Accreditation,简称VV&A)问题进行了详细的介绍及分析。在回顾研究历史及现状的基础上,首先介绍了HLA中VV&A的主要问题,包括概念、方法、基于联邦开发和运行过程(Federation Development and Execution Process,简称FEDEP)的VV&A过程、VV&A与测试和评估(Test and Evaluation,简称T&E)的关系以及自动化等。接着详细分析了HLA的VV&A研究中的难点:互操作性与可重用性的VV&A问题。最后进行了总结。
文摘为了规范和统一多分辨率模型的描述方法、建模步骤和活动,提出一种基于BOM(Base Object Model)和FEDEP(Federation Development and Execution Process)的多分辨率建模框架.该框架包括3种基于BOM的多分辨率建模方法和基于FEDEP的多分辨率建模过程.简要介绍这3种方法,重点分析FEDEP中与BOM和多分辨率建模相关的基本活动,将基于BOM的多分辨率建模过程与FEDEP集成在一起.该框架可以实现多分辨率模型描述的形式化和通用性、建模步骤和过程的规范化,促进多分辨率模型的重用、互操作和组合,保证模型的一致性和多分辨率建模的有效性.
文摘潜艇艇载武器装备的作战性能是决定潜艇作战效能高低的物质基础,是潜艇战斗生命力的关键所在,潜艇装备应用研究系统(EARS)为潜艇武器装备论证、定型和发展提供了依据。该文基于高层体系结构(HLA)机制,重点研究了潜艇EARS的联邦开发与执行过程(Federation Development and Execution Process,FEDEP)。
文摘Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
