Equivalent accelerated life testing plans for log‐location‐scale distributions

Accelerated life testing (ALT) is widely used to determine the failure time distribution of a product and the associated life‐stress relationship in order to predict the product's reliability under normal operating conditions. Many types of stress loadings such as constant‐stress, step‐stress and cyclic‐stress can be utilized when conducting ALT. Extensive research has been conducted on the analysis of ALT data obtained under a specified stress loading. However, the equivalency of ALT experiments involving different stress loadings has not been investigated. In this article, a log‐location‐scale distribution under Type I censoring is considered in planning ALT. An idea is provided for the equivalency of various ALT plans involving different stress loadings. Based on this idea, general equivalent ALT plans and some special types of equivalent ALT plans are explored. For demonstration, a constant‐stress ALT and a ramp‐stress ALT for miniature lamps are presented and their equivalency is investigated. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

离散作战模型的连续化兰切斯特模型研究

通过兰切斯特方程的分析,定义了作战力量变换系数概念,推导出作战力量变换系数的兰切斯特方程,得到了衡量交战双方作战力量等效值,而作战力量变换系数的兰切斯特方程正是适用于离散型作战的兰切斯特作战模型,使具有深刻影响力的兰切斯特方程适用范围扩大到了交战双方离散数量型的作战领域,从而赋予兰切斯特方程新的生命,为复杂的格斗作战分析提供了新的工具。     

差分离散方法分布式并行计算的重叠边界优化

针对分布式存储环境下显式差分方法的并行计算问题 ,依据分布式存储多处理机的体系结构特点 ,提出了一个重叠边界优化模型 ;该模型目前已成功应用于中国科学院第三代海洋环流模式的分布式并行计算优化。     

铁路区间信号系统测试评估平台——信号设备与列车运行仿真研究

铁路区间信号系统测试评估平台是对铁路区间列车运行安全控制和防护系统进行可靠性、安全性测试及评估的平台.区间信号设备与列车运行仿真子系统是平台的一个子系统,它为平台提供了一个虚拟的现场环境.采用面向对象技术和离散事件仿真技术对仿真子系统进行了研究.详细地阐述了信号设备及列车的仿真模型、设计和仿真算法,并给出仿真实例,满足平台对仿真子系统地要求.     

跨模态行人重识别的对称网络算法

针对模态间差异,提出基于对称网络的跨模态行人重识别算法,该网络将基于概率分布的模态混淆与对抗学习结合,通过对称网络产生模态不变特征,从而达到模态混淆的目的;针对外观差异和模态内差异,使用不同隐藏层的网络卷积特征构造混合三元损失,提高网络的特征表征能力.RegDB和SYSU-MM01数据集上的大量实验结果表明了该方法的有...     

Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO: A novel hybrid intelligent method

An effective hybrid optimization method is proposed by integrating an adaptive Kriging (A-Kriging) into an improved partial swarm optimization algorithm (IPSO) to give a so-called A-Kriging-IPSO for maxi-mizing the buckling load of laminated composite plates (LCPs) under uniaxial and biaxial compressions. In this method, a novel iterative adaptive Kriging model, which is structured using two training sample sets as active and adaptive points, is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process. The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples. The cell-based smoothed discrete shear gap method (CS-DSG3) is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets. The buckling load of the LCPs is maximized by utilizing the IPSO algorithm. To demonstrate the efficiency and accuracy of the proposed methodology, the LCPs with different layers (2, 3, 4, and 10 layers), boundary conditions, aspect ratios and load patterns (biaxial and uniaxial loads) are investigated. The results obtained by proposed method are in good agreement with the literature results, but with less computational burden. By applying adaptive radial Kriging model, the accurate optimal results-based predictions of the buckling load are obtained for the studied LCPs.     

Discrete stochastic optimization using variants of the stochastic ruler method

We present two random search methods for solving discrete stochastic optimization problems. Both of these methods are variants of the stochastic ruler algorithm. They differ from our earlier modification of the stochastic ruler algorithm in that they use different approaches for estimating the optimal solution. Our new methods are guaranteed to converge almost surely to the set of global optimal solutions under mild conditions. We discuss under what conditions these new methods are expected to converge faster than the modified stochastic ruler algorithm. We also discuss how these methods can be used for solving discrete optimization problems when the values of the objective function are estimated using either transient or steady‐state simulation. Finally, we present numerical results that compare the performance of our new methods with that of the modified stochastic ruler algorithm when applied to solve buffer allocation problems. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

模糊离散变结构控制系统研究

系统研究了模糊离散变结构控制的设计方法,提出了几种具体设计方案.对于变结构控制而言,模糊逻辑的引入可以使滑模(变结构)控制具备推理和学习能力,控制信号得以柔化,能够减轻或避免一般滑模控制的抖振现象;对于模糊控制而言,滑模控制思想的引入可以大大简化控制结构,保证控制系统的鲁棒稳定性,对模糊控制系统的设计和性能分析提供了一种工具.     

Averaging frameworks for simulation optimization with applications to simulated annealing

We present two frameworks for designing random search methods for discrete simulation optimization. One of our frameworks is very broad (in that it includes many random search methods), whereas the other one considers a special class of random search methods called point‐based methods, that move iteratively between points within the feasible region. Our frameworks involve averaging, in that all decisions that require estimates of the objective function values at various feasible solutions are based on the averages of all observations collected at these solutions so far. Also, the methods are adaptive in that they can use information gathered in previous iterations to decide how simulation effort is expended in the current iteration. We show that the methods within our frameworks are almost surely globally convergent under mild conditions. Thus, the generality of our frameworks and associated convergence guarantees makes the frameworks useful to algorithm developers wishing to design efficient and rigorous procedures for simulation optimization. We also present two variants of the simulated annealing (SA) algorithm and provide their convergence analysis as example application of our point‐based framework. Finally, we provide numerical results that demonstrate the empirical effectiveness of averaging and adaptivity in the context of SA. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

空时频域中欠定混合条件下的波达方向估计

波达方向估计是阵列信号处理领域的热点问题,但经典的波达方向估计方法通常要求阵元数大于源信号个数,即满足超定条件,而在实际中往往面临的是源信号个数大于阵元数的欠定条件。基于此,提出了一种基于空间时频分布的多重信号分类扩展算法,通过将空间时频分布矩阵进行扩展,实现了欠定条件下的波达方向估计。相比时频多重信号分类算法,所提算法能同时适应超定和欠定条件;相比已有的欠定波达方向估计方法,其不但保证了波达方向估计的精度,而且放宽了对源信号稀疏性的要求,同时还降低了对快拍数的要求。仿真实验结果证明了该方法的有效性。