检索结果-南通市图书馆

Computer Modeling in Engineering & Sciences 2023年第3期134卷 1871-1897页

作者： Hong Zhang Lukai Song Guangchen Bai School of Energy and Power Engineering Beihang UniversityBeijing100191China Research Institute of Aero-Engine Beihang UniversityBeijing100191China

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current *** this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is ***,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate *** the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower *** proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade *** comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.

关键词： Markov chain Monte Carlo active Kriging adaptive kernel density estimation importance sampling

Neural Networks Combined with importance sampling Techniques for Reliability Evaluation of Explosive Initiating Device

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2012年第2期25卷 208-215页

作者： GONG Qi ZHANG Jianguo TAN Chunlin WANG Cancan Reliability System Engineering Institute Beihang Universitys Beij ing 100191 China Beijing lnstitute of Spacecraft System Engineering Beijing 100081 China

Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function （RBF） neural network is used, and the RBF neural network based on first-order reliability method （FORM） is to approximate the unknown implicit limit state functions and calculate the most probable point （MPP） with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm.

关键词： neural networks importance sampling explosive initiating device reliability nonlinearity

Application of importance sampling Method in Sliding Failure Simulation of Caisson Breakwaters

在线全文

学校读者我要写书评

暂无评论

China Ocean Engineering 2016年第3期30卷 469-482页

作者：王禹迟王元战李青美陈良志 State Key Laboratory for Simulation & Safety of Hydraulic Engineering Tianjin Key Laboratory of Harbor and Ocean Engineering Tianjin University

It is assumed that the storm wave takes place once a year during the design period, and Nhistories of storm waves are generated on the basis of wave spectrum corresponding to the N-year design period. The responses of the breakwater to the N histories of storm waves in the N-year design period are calculated by mass-spring-dashpot mode and taken as a set of samples. The failure probability of caisson breakwaters during the design period of N years is obtained by the statistical analysis of many sets of samples. It is the key issue to improve the efficiency of the common Monte Carlo simulation method in the failure probability estimation of caisson breakwaters in the complete life cycle. In this paper, the kernel method of importance sampling, which can greatly increase the efficiency of failure probability calculation of caisson breakwaters, is proposed to estimate the failure probability of caisson breakwaters in the complete life cycle. The effectiveness of the kernel method is investigated by an example. It is indicated that the calculation efficiency of the kernel method is over 10 times the common Monte Carlo simulation method.

关键词： caisson breakwater complete life cycle failure probability importance sampling

Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling,genetic algorithm and simulation applications

在线全文

学校读者我要写书评

暂无评论

Journal of Intelligent and Connected Vehicles 2018年第1期1卷 28-38页

作者： Yiming Xu Yajie Zou Jian Sun Tongji University ShanghaiChina

Purpose–It would take billions of miles’field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare ***/methodology/approach–This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling(IS)*** the typical cut-in scenario as example,the proposed method extracts the critical variables of the ***,the distributions of critical variables are statistically *** genetic algorithm is used to calculate the optimal IS parameters by solving an optimization *** the error of distribution fitting,the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real ***–Based on the naturalistic driving data in Shanghai,the proposed method is validated by *** result shows that compared with the existing methods,the proposed method improves the test efficiency by 35 per cent,and the accuracy of accelerated test result is increased by 23 per ***/value–This paper has three ***,the genetic algorithm is used to calculate IS parameters,which improves the efficiency of ***,the result of test is modified by the error correction parameter,which improves the accuracy of test ***,typical high-risk cut-in scenarios in China are analyzed,and the proposed method is validated by simulation.

关键词： Genetic algorithm Simulation Automated vehicles importance sampling Lane changing Safety evaluation High-risk scenarios

importance sampling Based on the Kernel Density Estimator

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Importance Sampling Based on the Kernel Density Estimator

Simulation method for reliability of TT&C mission with high redundancy and small time horizon

2013年教育技术与管理科学国际会议

作者： XueGao Zhang Department of Business Administration Yunnan Normal University Business School

importance sampling is an unbiased sampling method used to sample random variables form different densities than originally defined. The importance sampling densities should be constructed to pick up ’important’ random variables to improve the estimation of a interesting statistics. In this article, we present an importance sampling in which its density function is constructed from the kernel density estimators. This method can generate a sufficient number of samples, and then increase the accuracy of the probability estimate.

关键词： Kernel Density Estimator importance sampling

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2012年第6期23卷 943-948页

作者： Shuangwei Xu Xiaoyue Wu School of Information Systems and Management National University of Defense Technology Changsha 410073 R R. China

The tracking, telemetry and command （TT＆C） mission is extremely reliable for its characters of small time horizon and high redundancy. The combined forcing and failure biasing （CFFB） method that is usually used for simulating the unreliability of the highly dependable mission system seems not so efficient for the TT＆C mission. The concept about the importance of failure transition is proposed based on the logical relationship between TT＆C mission and its involved resources. Then, the importance is used for readjusting the transition rate of the failure transition when using the forcing and failure biasing during the simulation. Examples show that the improved CFFB method can evidently increase the occurrence of the TT＆C mission failure event and decrease the sample variance. More redundancy of the TT＆C mission leads to the improved CFFB method more efficient.

关键词： tracking telemetry and command (TT&C) mission,high redundancy importance sampling forcing failure biasing.

Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2021年第1期126卷 331-357页

作者： Xiukai Yuan Jian Gu Shaolong Liu School of Aerospace and Engineering Xiamen UniversityXiamen361005China Institute for Risk and Reliability Leibniz Universitat HannoverHannoverGermany

In structural reliability analysis,simulation methods are widely *** statistical characteristics of failure probability estimate of these methods have been well *** this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of *** contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and *** proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of *** is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the ***,numerical examples are used to validate these findings.

关键词： Reliability analysis Monte Carlo simulation importance sampling line sampling subset simulation

Efficient SRAM yield optimization with mixture surrogate modeling

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Semiconductors 2016年第12期37卷 64-69页

作者：蒋中建叶佐昌王燕 Tsinghua National Laboratory for Information Science and Technology Institute of MicroelectronicsTsinghua University

Largely repeated cells such as SRAM cells usually require extremely low failure-rate to ensure a mod- erate chi yield. Though fast Monte Carlo methods such as importance sampling and its variants can be used for yield estimation, they are still very expensive if one needs to perform optimization based on such estimations. Typ- ically the process of yield calculation requires a lot of SPICE simulation. The circuit SPICE simulation analysis accounted for the largest proportion of time in the process yield calculation. In the paper, a new method is proposed to address this issue. The key idea is to establish an efficient mixture surrogate model. The surrogate model is based on the design variables and process variables. This model construction method is based on the SPICE simulation to get a certain amount of sample points, these points are trained for mixture surrogate model by the lasso algorithm. Experimental results show that the proposed model is able to calculate accurate yield successfully and it brings significant speed ups to the calculation of failure rate. Based on the model, we made a further accelerated algo- rithm to further enhance the speed of the yield calculation. It is suitable for high-dimensional process variables and multi-performance applications.

关键词： yield optimization process variations design variations mixture surrogate model statistical analysis importance sampling

B-splines smoothed rejection sampling method and its applications in quasi-Monte Carlo integration

在线全文

学校读者我要写书评

暂无评论

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2002年第3期3卷 90-94页

作者：雷桂媛 Department of MathematicsZhejiang UniversityHangzhouChina

The rejection sampling method is one of the most popular methods used in Monte Carlo methods. It turns out that the standard rejection method is closely related to the problem of quasi Monte Carlo integration of characteristic functions, whose accuracy may be lost due to the discontinuity of the characteristic functions. We proposed a B splines smoothed rejection sampling method, which smoothed the characteristic function by B splines smoothing technique without changing the integral quantity. Numerical experiments showed that the convergence rate of nearly O(N -1 ) is regained by using the B splines smoothed rejection method in importance sampling.

关键词： Quasi Monte Carlo Monte Carlo B splines importance sampling Numerical integration

On Solving a System of Volterra Integral Equations with Relaxed Monte Carlo Method

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Applied Mathematics and Physics 2016年第7期4卷 1315-1320页

作者： Zhimin Hong Xiangzhong Fang Zaizai Yan Hui Hao Department of Mathematics Science College of Inner Mongolia University of Technology Hohhot China Department of Statistics School of Mathematical Sciences Peking University Beijing China

A random simulation method was used for treatment of systems of Volterra integral equations of the second kind. Firstly, a linear algebra system was obtained by discretization using quadrature formula. Secondly, this algebra system was solved by using relaxed Monte Carlo method with importance sampling and numerical approximation solutions of the integral equations system were achieved. It is theoretically proved that the validity of relaxed Monte Carlo method is based on importance sampling to solve the integral equations system. Finally, some numerical examples from literatures are given to show the efficiency of the method.

关键词： Systems of Volterra Integral Equations Quadrature Formula Relaxed Monte Carlo Method importance sampling