Monte Carlo模拟方法中采用了结点再取向“择优转换原则”,并用线性Turnbull动力学晶界移动速率公式概率形式确定结点取向转换概率,对晶粒长大过程进行长时间的模拟.模拟的组织变化图像与实际材料的正常晶粒长大组织完全一致;长大过程...Monte Carlo模拟方法中采用了结点再取向“择优转换原则”,并用线性Turnbull动力学晶界移动速率公式概率形式确定结点取向转换概率,对晶粒长大过程进行长时间的模拟.模拟的组织变化图像与实际材料的正常晶粒长大组织完全一致;长大过程分为不稳定和稳定两个阶段,稳定阶段晶粒长大指数n达到了理论值0.5;在完整的晶粒长大过程中,晶粒尺寸分布并不具有严格的自相似性,系由不稳定长大阶段的对数正态分布(Lognormal)向稳定阶段的伽马(Gamma)分布变化.展开更多
In this paper, we present a novel and efficient track-before-detect （TBD） algorithm based on multiple-model probability hypothesis density （MM-PHD） for tracking infrared maneuvering dim multi-target. Firstly, the ...In this paper, we present a novel and efficient track-before-detect （TBD） algorithm based on multiple-model probability hypothesis density （MM-PHD） for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density （SMC-PHD） TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter （MM-PF）, histogram probability multi-hypothesis tracking （H-PMHT） and Viterbi-like.展开更多
Increasingly natural disasters and man-made malicious attacks threaten the power systems.Improving the resilience has become an inevitable requirement for the development of power systems.The importance assessment of ...Increasingly natural disasters and man-made malicious attacks threaten the power systems.Improving the resilience has become an inevitable requirement for the development of power systems.The importance assessment of components is of significance for resilience improvement,since it plays a crucial role in strengthening grid structure,designing restoration strategy,and improving resource allocation efficiency for disaster prevention and mitigation.This paper proposes a component importance assessment approach of power systems for improving resilience under wind storms.Firstly,the component failure rate model under wind storms is established.According to the model,system states under wind storms can be sampled by the non-sequential Monte Carlo simulation method.For each system state,an optimal restoration model is then figured out by solving a component repair sequence optimization model considering crew dispatching.The distribution functions of component repair moment can be obtained after a sufficient system state sampling.And Copeland ranking method is adopted to rank the component importance.Finally,the feasibility of the proposed approach is validated by extensive case studies.展开更多
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona...In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...展开更多
搜索临界滑面是边坡稳定分析的一项重要工作。本文对Venanzlo R Greco提出的Monte Carlo搜索算法进行了改进,增加一条几何合理性条件(除了滑面两端点,滑面与坡面不能相交),增加了方向控制与变自由度机制,这些措施有效地减少了不满足几...搜索临界滑面是边坡稳定分析的一项重要工作。本文对Venanzlo R Greco提出的Monte Carlo搜索算法进行了改进,增加一条几何合理性条件(除了滑面两端点,滑面与坡面不能相交),增加了方向控制与变自由度机制,这些措施有效地减少了不满足几何合理性条件的试算滑面,保证了搜索过程的准确、高效、稳定。笔者还将此算法加入到自主研发的边坡稳定分析软件ZSlope中,它还可以实时显示整个搜索过程。展开更多
文摘In this paper, we present a novel and efficient track-before-detect （TBD） algorithm based on multiple-model probability hypothesis density （MM-PHD） for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density （SMC-PHD） TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter （MM-PF）, histogram probability multi-hypothesis tracking （H-PMHT） and Viterbi-like.
基金supported by Science and Technology Project of State Grid Corporation of China(No.5202011600UG).
文摘Increasingly natural disasters and man-made malicious attacks threaten the power systems.Improving the resilience has become an inevitable requirement for the development of power systems.The importance assessment of components is of significance for resilience improvement,since it plays a crucial role in strengthening grid structure,designing restoration strategy,and improving resource allocation efficiency for disaster prevention and mitigation.This paper proposes a component importance assessment approach of power systems for improving resilience under wind storms.Firstly,the component failure rate model under wind storms is established.According to the model,system states under wind storms can be sampled by the non-sequential Monte Carlo simulation method.For each system state,an optimal restoration model is then figured out by solving a component repair sequence optimization model considering crew dispatching.The distribution functions of component repair moment can be obtained after a sufficient system state sampling.And Copeland ranking method is adopted to rank the component importance.Finally,the feasibility of the proposed approach is validated by extensive case studies.
基金National High-tech Research and Development Pro-gram (2006AA04Z405)
文摘In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...
文摘搜索临界滑面是边坡稳定分析的一项重要工作。本文对Venanzlo R Greco提出的Monte Carlo搜索算法进行了改进,增加一条几何合理性条件(除了滑面两端点,滑面与坡面不能相交),增加了方向控制与变自由度机制,这些措施有效地减少了不满足几何合理性条件的试算滑面,保证了搜索过程的准确、高效、稳定。笔者还将此算法加入到自主研发的边坡稳定分析软件ZSlope中,它还可以实时显示整个搜索过程。
