Deterministic and stochastic scheduling with teamwork tasks

We study a class of new scheduling problems which involve types of teamwork tasks. Each teamwork task consists of several components, and requires a team of processors to complete, with each team member to process a particular component of the task. Once the processor completes its work on the task, it will be available immediately to work on the next task regardless of whether the other components of the last task have been completed or not. Thus, the processors in a team neither have to start, nor have to finish, at the same time as they process a task. A task is completed only when all of its components have been processed. The problem is to find an optimal schedule to process all tasks, under a given objective measure. We consider both deterministic and stochastic models. For the deterministic model, we find that the optimal schedule exhibits the pattern that all processors must adopt the same sequence to process the tasks, even under a general objective function GC = F(f₁(C₁), f₂(C₂), … , f_n(C_n)), where f_i(C_i) is a general, nondecreasing function of the completion time C_i of task i. We show that the optimal sequence to minimize the maximum cost MC = max f_i(C_i) can be derived by a simple rule if there exists an order f₁(t) ≤ … ≤ f_n(t) for all t between the functions {f_i(t)}. We further show that the optimal sequence to minimize the total cost TC = ∑ f_i(C_i) can be constructed by a dynamic programming algorithm. For the stochastic model, we study three optimization criteria: (A) almost sure minimization; (B) stochastic ordering; and (C) expected cost minimization. For criterion (A), we show that the results for the corresponding deterministic model can be easily generalized. However, stochastic problems with criteria (B) and (C) become quite difficult. Conditions under which the optimal solutions can be found for these two criteria are derived. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

基于改进核极限学习机和集成学习理论的目标机动轨迹预测

为了提高目标轨迹预测的精度以及预测模型的泛化能力,提出基于改进蝙蝠算法优化的核极限学习机(Kernel Extreme Learning Machine,KELM)和集成学习理论目标机动轨迹预测模型。构建KELM模型,并采用改进的蝙蝠算法对KELM的参数进行优化;以优化后的KELM神经网络为弱预测器,结合集成学习算法生成强预测器,通过训练不断优化强预测的结构和参数,得到一种基于集成学习理论的目标机动轨迹预测模型;基于不同规模的样本,将所得预测模型与逆传播神经网络、支持向量机和极限学习机等模型进行对比分析。仿真结果表明:所提目标机动轨迹预测模型具有较好的预测精度和泛化能力。     

基于时间反转声学理论的超声相控阵超分辨率成像方法

在超声相控阵检测领域,常规成像方法遵守瑞利准则,其成像分辨率受到波长限制。研究时间反转多信号分类(Time Reversal-MUltiple SIgnal Classification, TR-MUSIC)方法,在保持超声波工作频率不变且不影响系统探测深度的前提下,可提高成像分辨率,实现超分辨率成像。通过全矩阵采集方法获取被测对象的超声阵列数据;利用平面B扫描和TR-MUSIC方法处理数据,得到二维和三维超声图像;依据图像特征评估被测对象内部状况。实验选取加工不锈钢试块作为被测对象,搭建超声相控阵检测系统,在试块内部加工6个直径为1 mm、可视为点散射体的相邻贯通孔作为缺陷。实验表明,TR-MUSIC方法能够区分并定位这6个相邻点散射体,而平面B扫描方法则不能。因此,基于时间反转声学理论的TR-MUSIC方法可以提高成像分辨率,改善超声图像质量。     

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang-bang evasive maneuver with a random switching time. Combined Fast multiple model adaptive estimation (Fast MMAE) algorithm, the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration. Introduced the detection error model related to the line-of-sight (LOS) separation angle of two interceptors, an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance. Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion, Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately. The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.     

ATS:一种基于通告时间戳的BGP收敛性改进机制

研究BGP收敛性改进问题,考虑网络运行状况,提出一种新的机制,称为通告时间戳机制ATS(advertisement time stamp)。在该机制中,BGP路由器在向对等体发送路由通告报文时,将相应的路由前缀打上时间戳。当通告报文准备好时,通过查看相应前缀的时间戳来判断报文是否可以立即发送还是应该等待。该机制充分融合了每对等体每目的网络速率限制定时器和每对等体速率限制定时器各自的优点,克服各自缺点。分析表明,该机制可以较大地减少Tup、Tlong及Tshort事件的收敛延迟。     

舰艇编队对空中目标的威胁程度判断模型

根据舰艇编队对空防御的要求,综合考虑了目标运动状态和历史战术动作,将目标攻击企图和攻击达成概率纳入目标威胁判断之中,建立了空中运动目标实时威胁程度判断模型,为编队防空作战火力组织提供了基本依据。该模型解决了传统静态威胁判断模型存在的一些问题,模型的仿真结果符合一般战术推理和经验判断,能较好地描述空中目标威胁程度的变化,可以为编队防空指挥自动化决策研究提供一定的参考。     

回转对称非球面光学零件磁流变成形抛光的驻留时间算法

介绍了一种基于代数算法的回转对称非球面计算机控制表面成形的驻留时间算法。该算法将驻留时间转化为工件自转的整数圈数,并且将抛光模对工件的材料去除效率体现到材料去除矩阵上进行计算。利用非负最小二乘法求解驻留时间向量。最后,利用该算法在自研的磁流变抛光实验装置上对一回转对称光学零件进行3次迭代加工,使其面形精度从8μm提高到0.5μm以内。     

基于正交最小二乘估计的非线性时间序列的预测

在对非线性时间序列的短期预测中经常采用局部线性化的预测算法 ,原有的算法使用普通最小二乘法 (LS)估计近似线性模型的参数。对于存在噪声的数据 ,该算法的数值稳定性较差。本文在对非线性空间进行局部线性化的基础上 ,采用正交最小二乘方法 (OLS)对线性模型同时进行结构选择与参数辨识 ,改善了数值的病态特性 ,增强了算法的稳定性     

战车火控外弹道实时解算的研究

论证了战车火控系统弹道实时解算技术的现状和发展 ,提出了现代火控弹道问题实时解算的新的数学模型。这一先进的弹道解算设计方法 ,适应于计算机运算速度已大幅提高的技术水平 ,具有高度通用化的特点 ,并且使弹道问题的解算可以完全脱离射表进行     

一种新的对潜应召搜索法及其效能分析

论述了一种新的对潜应召搜索法 ,并对其效能进行了分析 ,同时就平均发现概率、搜索时间、转向次数三个指标与海军现在使用的扩展方阵搜索法进行了仿真比较