基于动态时间规整算法的车辆目标分类研究

将动态时间规整(Dynamic Time Warping)算法应用于地面车辆目标的分类识别中。基于微多普勒效应原理,建立了轮式车辆和履带式车辆雷达回波模型,对两种车辆目标微多普勒信号的差异性进行了分析,并结合实测数据,验证了理论分析的正确性。在杂波抑制及速度归一化处理的基础上,利用动态时间规整算法,将提取出的车辆目标的累积失真距离作为目标分类识别的依据,实现了轮式车辆和履带式车辆的自动分类。基于实测数据的实验结果表明,该方法在不同信噪比条件下都具有较好的分类性能。     

一种基于蚁群算法的准动态防空武器分配算法

武器目标分配问题是一个典型的限制组合优化问题,旨在得到在整个防御阶段中针对目标函数的最优武器分配方案。分配算法主要分为静态和动态两大类。针对传统静态分配模型中存在的几点问题,提出了基于时间窗的准动态武器目标分配算法,该算法综合考虑拦截概率、拦截时间和武器耗费多个优化指标,并将该算法推广至多类防空武器的优化分配中。通过大量实验验证,该算法在性能、时间复杂度等方面均有较大优势,并且能较好地适应战场态势的变化,及时调整分配方案,具有很好的实用性。     

坦克底盘角振动对火炮射击精度影响机理研究

实车试验表明,在行进间射击试验时,当车辆超过一定车速(行进间射击车速)时,射击精度或射击命中率会发生大幅度的下降。针对这个问题,建立了坦克底盘角振动导致的射击偏差的数学模型;并结合实车试验数据,对射击延迟时间内底盘角振动造成的火炮射击偏差进行了全面分析。分析研究表明:在相同的行驶车速条件下,车辆底盘角速度、姿态角变化量随着射击延迟时间的增大而增大;在相同的射击延迟时间内,车辆底盘角速度、姿态角变化量与射角偏差随着行驶车速的增大而增大;其中射角偏差引起的目标距离偏差是弹丸横向速度导致的目标距离偏差的3倍~5倍。因此,随着行驶车速增加而增加的射角偏差增大是行进间射击车速受限的主要原因。     

燃气射流引起的火箭发射系统振动特性分析

以火箭发射系统为研究对象,对其56°射角工况下发射过程中的振动特性进行分析。创新点在于确定发射过程中动态载荷的加载方法,即首先根据发射系统有限元分析结果确定迎气面上试验测量点的具体位置,然后测得燃气射流冲击压强随时间变化的数值,并对试验测得数据进行处理,最后推导冲击压力对于径向距离的直线公式。所采用的冲击压强分析方法为研究其他武器系统受燃气射流冲击的影响提供了重要的指导意义。     

2种结构驱动线圈对电枢受力的影响

介绍了电磁同步线圈驱动器工作原理,并利用有限元分析软件ANSYS建立驱动线圈的三维模型。在不同结构的驱动线圈的驱动下,分别对电枢所受电磁力进行仿真,得到电枢在不同情况下的受力变化规律;在加载情况完全一致的情况下,对比不同结构的驱动线圈对电枢的作用力,得出2层驱动线圈对电枢综合作用效果较好的结论。     

含孔洞铜板复合材料修复疲劳寿命数值分析

用ANSYS有限元软件对复合材料修复含中心圆孔钢板的疲劳寿命进行了数值分析。研究了孔洞大小、补片长度、宽度和厚度对钢板疲劳寿命的影响;分析了复合材料胶接修补的效果,并对修复所用的复合材料补片的大小及厚度进行了优化设计。研究结果表明,利用复合材料胶接修补带中心圆孔的钢板可以使其疲劳性能提高1.6~18倍。修补时,增加补片宽度和厚度都可增加结构疲劳寿命;补片宽度为孔径8倍以上时,增加宽度对疲劳寿命影响不大;补片的长度为孔径的3~4倍时,修复效果最佳。     

Network design for time‐constrained delivery

To meet customer demand, delivery companies are offering an increasing number of time‐definite services. In this article, we examine the strategic design of delivery networks which can efficiently provide these services. Because of the high cost of direct connections, we focus on tree‐structured networks. As it may not be possible to identify a tree‐structured network that satisfies all of the delivery guarantees, we allow these guarantees to be violated but seek to minimize the sum of the violations. We establish the complexity of the problem and exploit an empirically identified solution structure to create new neighborhoods which improve solution values over more general neighborhood structures. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Scheduling parallel machines with inclusive processing set restrictions

We consider the problem of assigning a set of jobs to different parallel machines of the same processing speed, where each job is compatible to only a subset of those machines. The machines can be linearly ordered such that a higher‐indexed machine can process all those jobs that a lower‐indexed machine can process. The objective is to minimize the makespan of the schedule. This problem is motivated by industrial applications such as cargo handling by cranes with nonidentical weight capacities, computer processor scheduling with memory constraints, and grades of service provision by parallel servers. We develop an efficient algorithm for this problem with a worst‐case performance ratio of + ε, where ε is a positive constant which may be set arbitrarily close to zero. We also present a polynomial time approximation scheme for this problem, which answers an open question in the literature. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

On the hierarchy of γ‐valid cuts in global optimization

Concavity Cuts play an important role in concave minimization. In Porembski, J Global Optim 15 ( 17 ), 371–404 we extended the concept underlying concavity cuts which led to the development of decomposition cuts. In numerical experiments with pure cutting plane algorithms for concave minimization, decomposition cuts have been shown to be superior to concavity cuts. However, three points remained open. First, how to derive decomposition cuts in the degenerate case. Second, how to ensure dominance of decomposition cuts over concavity cuts. Third, how to ensure the finite convergence of a pure cutting plane algorithm solely by decomposition cuts. These points will be addressed in this paper. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010