In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance fu...In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.展开更多
This note introduces a method for sampling Ising models with mixed boundary conditions.As an application of annealed importance sampling and the Swendsen-Wang algorithm,the method adopts a sequence of intermediate dis...This note introduces a method for sampling Ising models with mixed boundary conditions.As an application of annealed importance sampling and the Swendsen-Wang algorithm,the method adopts a sequence of intermediate distributions that keeps the temperature fixed but turns on the boundary condition gradually.The numerical results show that the variance of the sample weights is relatively small.展开更多
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 vehi...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 event.Design/methodology/approach–This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling(IS)techniques.Taking the typical cut-in scenario as example,the proposed method extracts the critical variables of the scenario.Then,the distributions of critical variables are statistically fitted.The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem.Considering the error of distribution fitting,the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.Findings–Based on the naturalistic driving data in Shanghai,the proposed method is validated by simulation.The 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 cent.Originality/value–This paper has three contributions.First,the genetic algorithm is used to calculate IS parameters,which improves the efficiency of test.Second,the result of test is modified by the error correction parameter,which improves the accuracy of test result.Third,typical high-risk cut-in scenarios in China are analyzed,and the proposed method is validated by simulation.展开更多
An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to ...An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to improve the sampling efficiency, the simulated annealing algorithm is adopted to optimize the density center of the importance sampling for each failure mode, and results that the more significant contribution the points make to fuzzy failure probability, the higher occurrence possibility the points are sampled. For the system with multiple fuzzy failure modes, a weighted and mixed importance sampling function is constructed. The contribution of each fuzzy failure mode to the system failure probability is represented by the appropriate factors, and the efficiency of sampling is improved furthermore. The variances and the coefficients of variation are derived for the failure probability estimations. Two examples are introduced to illustrate the rationality of the present method. Comparing with the direct Monte-Carlo method, the improved efficiency and the precision of the method are verified by the examples.展开更多
极坐标格式算法(Polar Format Algorithm,PFA)通常应用于正侧视聚束SAR成像,当PFA应用斜视聚束时,传统沿视线插值(Line Of Sight Interpolation,LOSI)PFA方法会导致方位频谱非等间隔采样。该文针对上述问题提出一种新的方位频谱插值方法...极坐标格式算法(Polar Format Algorithm,PFA)通常应用于正侧视聚束SAR成像,当PFA应用斜视聚束时,传统沿视线插值(Line Of Sight Interpolation,LOSI)PFA方法会导致方位频谱非等间隔采样。该文针对上述问题提出一种新的方位频谱插值方法,根据斜视聚束的几何模型可以得到方位频谱精确的插值形式,从而实现对方位频谱等间隔重采样。在获得了均匀的频谱后进行2维逆傅里叶变换,便可以得到大范围的斜视聚束场景。为了验证该文算法的有效性,进行了实验仿真及实测数据验证,该方法与传统插值的方法进行比较,能够增大斜视聚束场景范围。展开更多
文摘In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.
文摘This note introduces a method for sampling Ising models with mixed boundary conditions.As an application of annealed importance sampling and the Swendsen-Wang algorithm,the method adopts a sequence of intermediate distributions that keeps the temperature fixed but turns on the boundary condition gradually.The numerical results show that the variance of the sample weights is relatively small.
基金The authors would like to thank the Natural Science Foundation of China(U1764261,51422812)the Shanghai Science and technology project of international cooperation(16510711400)for supporting this research.
文摘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 event.Design/methodology/approach–This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling(IS)techniques.Taking the typical cut-in scenario as example,the proposed method extracts the critical variables of the scenario.Then,the distributions of critical variables are statistically fitted.The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem.Considering the error of distribution fitting,the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.Findings–Based on the naturalistic driving data in Shanghai,the proposed method is validated by simulation.The 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 cent.Originality/value–This paper has three contributions.First,the genetic algorithm is used to calculate IS parameters,which improves the efficiency of test.Second,the result of test is modified by the error correction parameter,which improves the accuracy of test result.Third,typical high-risk cut-in scenarios in China are analyzed,and the proposed method is validated by simulation.
基金This project is supported by National Natural Science Foundation of China (No.10572117)Aerospace Science Foundation of China(No.N3CH0502,No.N5CH0001)Provincial Natural Science Foundation of Shanxi, China(No.N3CS0501).
文摘An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to improve the sampling efficiency, the simulated annealing algorithm is adopted to optimize the density center of the importance sampling for each failure mode, and results that the more significant contribution the points make to fuzzy failure probability, the higher occurrence possibility the points are sampled. For the system with multiple fuzzy failure modes, a weighted and mixed importance sampling function is constructed. The contribution of each fuzzy failure mode to the system failure probability is represented by the appropriate factors, and the efficiency of sampling is improved furthermore. The variances and the coefficients of variation are derived for the failure probability estimations. Two examples are introduced to illustrate the rationality of the present method. Comparing with the direct Monte-Carlo method, the improved efficiency and the precision of the method are verified by the examples.
文摘极坐标格式算法(Polar Format Algorithm,PFA)通常应用于正侧视聚束SAR成像,当PFA应用斜视聚束时,传统沿视线插值(Line Of Sight Interpolation,LOSI)PFA方法会导致方位频谱非等间隔采样。该文针对上述问题提出一种新的方位频谱插值方法,根据斜视聚束的几何模型可以得到方位频谱精确的插值形式,从而实现对方位频谱等间隔重采样。在获得了均匀的频谱后进行2维逆傅里叶变换,便可以得到大范围的斜视聚束场景。为了验证该文算法的有效性,进行了实验仿真及实测数据验证,该方法与传统插值的方法进行比较,能够增大斜视聚束场景范围。
