飞船再入制导最佳反馈增益系数规律

为了获取基于标准轨道返回舱再入纵向制导的最佳反馈增益系数 ,建立了纵平面运动方程 ,推导了对应于纵向制导规律的摄动微分方程。根据Pontryagin极小值原理 ,给出了最优反馈控制律 ,求解Riccati方程 ,即可得到最佳反馈增益系数。考虑到实现的可行性 ,将时变的增益系数逼近成常数或分段常数 ,得到次优反馈增益系数 ,取得了满意的制导效果。     

三组元液体火箭发动机实现单级入轨的优化分析

建立了三组元液体火箭发动机性能计算模型、单级入轨可重复使用运载器质量模型和弹道模型,以起飞质量为目标函数,优化得到了实现单级入轨的发动机基本参数。结果显示:三组元发动机性能比氢氧发动机高10%。本研究是三组元发动机系统设计和分析的基础。     

边界扫描测试的数学描述模型

IEEE1149.1边界扫描机制是一种新型的VLSI电路测试及可测试性设计的有效方法,为了高效地应用边界扫描机制对电路系统进行测试,必须对其所涉及的理论方法进行深入探讨。本文应用布尔矩阵理论建立起边界扫描测试的数学描述模型,并基于所建立的模型导出了边界扫描测试中的故障检测条件和故障隔离条件。为边界扫描测试生成算法的深入研究奠定了理论基础。     

线性调频基带数字产生的关键技术研究

介绍超宽带线性调频信号基带的数字产生方法及其实现技术。首先对线性调频信号的两种主要数字产生方法进行了简要阐述和比较，然后根据波形存储直读法提出了一种超宽带线性调频信号产生方案，分析了基带数字产生的主要关键技术，并进行了系统实现和测试。测试结果表明本文所提出的方法是可行的。     

符号执行火控系统诸元解算程序测试用例生成技术

火控系统作为各种武器装备的中枢和大脑,控制着武器系统的运转,其有效性直接关系着射击的成败甚至武器系统的综合效能。诸元解算程序是将各种输入条件依据弹道模型经过多次迭代转换为射击诸元,由于其复杂性,当前软件测试用例只是在几个关键点上进行校验,很难覆盖程序所有分支和路径,存在着测试不充分。针对这个问题,提出了一种基于符号执行的用例自动生成技术,结合解弹道方程组约束条件和程序分支控制变量使用符号执行框架内能够得到覆盖所有可能的执行路径,从而保证测试的充分性。     

禁忌单亲遗传算法的某指挥控制舱协同任务规划

指挥控制舱是武器系统中指挥控制的功能核心,通过多人协同交互完成指控任务。为提高人员协同效率和操控效率。建立了以完成所有任务的时间最短为目标函数的多人协同任务规划模型。在求解模型时,采用禁忌单亲遗传混合优化算法,通过无性繁殖的方法,减少了在子代中产生无效染色体的情况,提升了算法的效率,通过禁忌算法有效避免陷入局部极小值。通过实例分析,算法使总任务执行时间减少了12豫。本模型以及求解算法在解决指挥控制舱多人协同任务规划时具有可行性,对指挥控制舱任务流程设计具有一定参考价值。     

A risk function for the stochastic modeling of electric capacity expansion

We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate‐size problems are presented along with comparison to a general‐purpose nonlinear optimization package. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:662–683, 2001     

改进分块局部最佳维纳滤波算法的干涉相位滤波

针对合成孔径雷达干涉相位滤波问题,提出了一种改进的分块局部最佳维纳滤波算法。该算法是加性高斯白噪声下的线性最小均方误差估计,利用目前图像滤波最前沿的技术——非局部技术,来联合估计图像的一、二阶矩。针对干涉相位中噪声的空变性,在应用中提出了两点改进:估计噪声的标准差时,用均值代替中值;根据噪声标准差的最大值和均值的比值,自适应地确定类的数量。仿真和实测数据表明,改进后的分块局部最佳维纳滤波算法是有效的,并优于其他三种算法。     

Scheduling jobs and maintenance activities on parallel machines

Most machine scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines need to be maintained and hence may become unavailable during certain periods. In this paper, we study the problem of processing a set of n jobs on m parallel machines where each machine must be maintained once during the planning horizon. Our objective is to schedule jobs and maintenance activities so that the total weighted completion time of jobs is minimized. Two cases are studied in this paper. In the first case, there are sufficient resources so that different machines can be maintained simultaneously if necessary. In the second case, only one machine can be maintained at any given time. In this paper, we first show that, even when all jobs have the same weight, both cases of the problem are NP-hard. We then propose branch and bound algorithms based on the column generation approach for solving both cases of the problem. Our algorithms are capable of optimally solving medium sized problems within a reasonable computational time. We note that the general problem where at most j machines, 1 ≤ j ≤ m, can be maintained simultaneously, can be solved similarly by the column generation approach proposed in this paper. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 145–165, 2000     

An exact branch‐and‐price algorithm for multitasking scheduling on unrelated parallel machines

We consider the multitasking scheduling problem on unrelated parallel machines to minimize the total weighted completion time. In this problem, each machine processes a set of jobs, while the processing of a selected job on a machine may be interrupted by other available jobs scheduled on the same machine but unfinished. To solve this problem, we propose an exact branch‐and‐price algorithm, where the master problem at each search node is solved by a novel column generation scheme, called in‐out column generation, to maintain the stability of the dual variables. We use a greedy heuristic to obtain a set of initial columns to start the in‐out column generation, and a hybrid strategy combining a genetic algorithm and an exact dynamic programming algorithm to solve the pricing subproblems approximately and exactly, respectively. Using randomly generated data, we conduct numerical studies to evaluate the performance of the proposed solution approach. We also examine the effects of multitasking on the scheduling outcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.