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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

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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

Active Kriging-Based Adaptive importance sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator

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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

Face tracking algorithm based on particle filter with mean shift importance sampling

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Journal of Southeast University(English Edition) 2007年第2期23卷 196-201页

作者：高建坡杨浩安国成吴镇扬东南大学信息科学与工程学院南京210096

The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed. First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation. Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly. The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.

关键词： face tracking particle filter importance sampling condensation mean shift

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

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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 Strategy for Oscillatory Stochastic Processes

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Journal of Mechanics Engineering and Automation 2012年第11期2卷 663-670页

作者： Jan Podrouzek Centre for Water Resource Systems (CWRS) Vienna University of Technology Austria

This paper contributes to the structural reliability problem by presenting a novel approach that enables for identification of stochastic oscillatory processes as a critical input for given mechanical models. Identification development follows a transparent image processing paradigm completely independent of state-of-the-art structural dynamics, aiming at delivering a simple and wide purpose method. Validation of the proposed importance sampling strategy is based on multi-scale clusters of realizations of digitally generated non-stationary stochastic processes. Good agreement with the reference pure Monte Carlo results indicates a significant potential in reducing the computational task of first passage probabilities estimation, an important feature in the field of e.g., probabilistic seismic design or risk assessment generally.

关键词： Stochastic process critical excitation reliability analysis importance sampling image processing pattern recognition identification problem.

importance sampling Based on the Kernel Density Estimator

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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

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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

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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

Implicit sampling,with Application to Data Assimilation

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Chinese Annals of Mathematics,Series B 2013年第1期34卷 89-98页

作者： Alexandre J.CHORIN Matthias MORZFELD Xuemin TU Department of Mathematics University of California Lawrence Berkeley National Laboratory Department of Mathematics University of Kansas

There are many computational tasks, in which it is necessary to sample a given probability density function （or pdf for short）, i.e., to use a computer to construct a sequence of independent random vectors x~ （i ---- 1, 2, ~ ~ ~ ）, whose histogram converges to the given pdf. This can be difficult because the sample space can be huge, and more importantly, because the portion of the space, where the density is significant, can be very small, so that one may miss it by an ill-designed sampling scheme. Indeed, Markov- chain Monte Carlo, the most widely used sampling scheme, can be thought of as a search algorithm, where one starts at an arbitrary point and one advances step-by-step towards the high probability region of the space. This can be expensive, in particular because one is typically interested in independent samples, while the chain has a memory. The authors present an alternative, in which samples are found by solving an algebraic equation with a random right-hand side rather than by following a chain; each sample is independent of the previous samples. The construction in the context of numerical integration is explained, and then it is applied to data assimilation.

关键词： importance sampling Bayesian estimation Particle filter Implicit filter Data assimilation