梁的蠕变脆性各向同性弯曲损伤模型

本文在梁的纯弯曲损伤基本假设条件下,导出了弯曲损伤的基本方程,与Ｋａｃｈａｎｏｖ的材料刚度劣化（受载横截面积减小）定义拉伸损伤变量类似,以梁的弯曲刚度劣化（惯性矩减小）定义弯曲损伤变量,从而建立了与Ｋａｃｈａｎｏｖ拉伸损伤模型相类似的梁的各向同性弯曲损伤模型。最后,以受蠕变纯弯曲梁为实例进行了损伤分析,在一次近似条件下,该弯曲损伤模型的材料常数可由Ｋａｃｈａｎｏｖ拉伸损伤模型的材料常数确定,且所得计算结果与Ｋａｃｈａｎｏｖ拉伸损伤模型所得结果比较吻和,表明该弯曲损伤模型是合理适用的。     

螺线管磁场性能的优化

采用正反“单匝”线圈优化螺线管轴向磁场的均匀性和陡化端口磁场的下降沿。计算和实验研究表明,该方法明显改善磁场的均匀性和端口磁场的下降沿。     

杂波环境下多目标跟踪数据关联的快速算法

数据关联是实现杂波环境下多目标跟踪的关键问题,目前公认较好的方法是联合概率数据关联（ＪＰＤＡ）,但是其计算和存储量很大,故在实际中寻求一种快速或近似的算法是十分必要的。本文提供一组有效快速算法,并在我们有关课题中得到初步验证。     

由序列图像获取点目标的三维运动参数和空间位置

利用图像获取目标的运动参数和空间位置在目标定位、跟踪和计算机视觉中具有重要的意义。本文在目标和成像系统都运动的情况下,给出了由单目序列图像获取点目标绝对运动参数和三维空间坐标的条件,提出了利用多帧单目序列图像和应用非线性递推滤波算法来估计点目标的运动参数和空间位置。实验仿真结果证明,本文提出的算法是有效的。     

机械系统维修性预计方法分析与应用

通过对美国及前苏联维修性预计方法的分析,确定前苏联“以类似产品的维修性多元线性回归方程作为新产品维修性预计模型”的方法为机械系统维修性预计方法.在限定的条件下,采集了装甲车辆机械系统的维修性数据,并应用这些数据进行分析得出相应的机械系统维修性预计模型.     

低分辨雷达的目标特征提取方法

基于现役低分辨警戒雷达 ,研究了进行目标分类和识别的途径 ,提出了基于波形特征和时间谱信息的目标分类和识别方法 ,试验分析了基于目标波形信息的特征 ,得出目标波形信息可用于目标识别的结论。现场试验表明 ,该方法对目标大小分类和架次识别有好的效果。     

Disruption management in production planning

We study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

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     

基于深度优先搜索与增量式求解的极小一阶不可满足子式提取算法

随着寄存器传输级甚至行为级的硬件描述语言应用越来越广泛,基于一阶逻辑的可满足性模理论(Satisfiability Modulo Theories,SMT)逐渐替代布尔可满足性(Boolean Satisfiability,SAT),在VLSI形式化验证领域具有更加重要的应用价值。而极小不可满足子式能够帮助EDA工具迅速定位硬件中的逻辑错误。针对极小SMT不可满足子式的求解问题,采用深度优先搜索与增量式求解策略,提出了深度优先搜索的极小SMT不可满足子式求解算法。与目前最优的宽度优先搜索算法对比实验表明:该算法能够有效地求解极小不可满足子式,随着公式的规模逐渐增大时,深度优先搜索算法优于宽度优先搜索算法。