Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessm...Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.展开更多
针对通用测试系统对系统统一模型的迫切需求以及目前ATE(AutomaticTest Equipment)模型化设计的不足,基于信号建立了一套完整、有效的ATE系统模型.此模型将ATE系统的硬件资源以及UUT(Unit Under Test)均进行了模型化处理.提出了基于TFF(...针对通用测试系统对系统统一模型的迫切需求以及目前ATE(AutomaticTest Equipment)模型化设计的不足,基于信号建立了一套完整、有效的ATE系统模型.此模型将ATE系统的硬件资源以及UUT(Unit Under Test)均进行了模型化处理.提出了基于TFF(Test Foundation Framework)的基本信号模型;提出了完全基于信号的仪器驱动模型;提出了基于测试与诊断相结合思想的UUT模型;提出了基于模型间信息共享的思想和路径以逻辑开关描述思想的路径模型.该模型体系为系统资源配置、仪器可互换、测试程序自动生成以及故障诊断提供了强有力的模型基础,从而大大加快了ATE的设计过程,从很大程度上降低了开发成本.展开更多
针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,...针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,分析了多UUT测控资源合并的条件,得出最短并行测试时间基础上的最少资源需求,给出了成本效率的定义,设计了一种满足多UUT并行测试任务调度的基因编码方法和路径选择方案.算法初期利用遗传算法的快速收敛性,为蚁群算法提供初始信息素分布,蚁群算法采用双向收敛的信息素反馈方式,避免了对参数的依赖,减少了局部收敛性,加快了收敛速度.实例表明,该算法能很好地解决多UUT任务资源最优调度与配置问题.展开更多
基金Airport New City Utility Tunnel PhaseⅡProject,China。
文摘Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.
文摘针对通用测试系统对系统统一模型的迫切需求以及目前ATE(AutomaticTest Equipment)模型化设计的不足,基于信号建立了一套完整、有效的ATE系统模型.此模型将ATE系统的硬件资源以及UUT(Unit Under Test)均进行了模型化处理.提出了基于TFF(Test Foundation Framework)的基本信号模型;提出了完全基于信号的仪器驱动模型;提出了基于测试与诊断相结合思想的UUT模型;提出了基于模型间信息共享的思想和路径以逻辑开关描述思想的路径模型.该模型体系为系统资源配置、仪器可互换、测试程序自动生成以及故障诊断提供了强有力的模型基础,从而大大加快了ATE的设计过程,从很大程度上降低了开发成本.
文摘针对多UUT(Unit Under Test)并行测试任务调度与资源配置问题,提出了一种遗传蚁群融合算法.应用遗传蚁群融合算法能快速、准确地寻找到具有最大成本效率的多UUT并行测试资源配置和任务序列.建立了多UUT并行测试任务资源描述的数学模型,分析了多UUT测控资源合并的条件,得出最短并行测试时间基础上的最少资源需求,给出了成本效率的定义,设计了一种满足多UUT并行测试任务调度的基因编码方法和路径选择方案.算法初期利用遗传算法的快速收敛性,为蚁群算法提供初始信息素分布,蚁群算法采用双向收敛的信息素反馈方式,避免了对参数的依赖,减少了局部收敛性,加快了收敛速度.实例表明,该算法能很好地解决多UUT任务资源最优调度与配置问题.
