基于粒子群优化的稀疏分解最优匹配原子搜索算法

信号的稀疏分解能得到信号的稀疏表示形式,便于进一步处理,但其计算非常复杂,是一个NP问题.粒子群优化是群体智能优化算法,算法简单,易于实现,且搜索效果好.把粒子群优化算法用于稀疏分解的最优匹配原子的搜索,能降低稀疏分解复杂度,同时减少稀疏分解的超完备字典对存储空间的占用,以提高用稀疏分解理论进行信号处理的计算效率,满足或接近实时性的要求.实验证明,此方法切实可行.     

Capacity investment decisions under risk aversion

This article studies the optimal capacity investment problem for a risk‐averse decision maker. The capacity can be either purchased or salvaged, whereas both involve a fixed cost and a proportional cost/revenue. We incorporate risk preference and use a consumption model to capture the decision maker's risk sensitivity in a multiperiod capacity investment model. We show that, in each period, capacity and consumption decisions can be separately determined. In addition, we characterize the structure of the optimal capacity strategy. When the parameters are stationary, we present certain conditions under which the optimal capacity strategy could be easily characterized by a static two‐sided (s, S) policy, whereby, the capacity is determined only at the beginning of period one, and held constant during the entire planning horizon. It is purchased up to B when the initial capacity is below b, salvaged down to Σ when it is above σ, and remains constant otherwise. Numerical tests are presented to investigate the impact of demand volatility on the optimal capacity strategy. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 218–235, 2016     

CON/SLK due date assignment and scheduling on a single machine with two agents

We consider scheduling problems involving two agents (agents A and B), each having a set of jobs that compete for the use of a common machine to process their respective jobs. The due dates of the A‐jobs are decision variables, which are determined by using the common (CON) or slack (SLK) due date assignment methods. Each agent wants to minimize a certain performance criterion depending on the completion times of its jobs only. Under each due date assignment method, the criterion of agent A is always the same, namely an integrated criterion consisting of the due date assignment cost and the weighted number of tardy jobs. Several different criteria are considered for agent B, including the maxima of regular functions (associated with each job), the total (weighted) completion time, and the weighted number of tardy jobs. The overall objective is to minimize the performance criterion of agent A, while keeping the objective value of agent B no greater than a given limit. We analyze the computational complexity, and devise polynomial or pseudo‐polynomial dynamic programming algorithms for the considered problems. We also convert, if viable, any of the devised pseudopolynomial dynamic programming algorithms into a fully polynomial‐time approximation scheme. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 416–429, 2016     

两种捆绑火箭弹性振动建模方法对比分析及其对姿控系统的影响

针对长助推和短助推两种类型捆绑火箭的不同模态特点,分析了两者弹性振动建模方法的区别和联系,在此基础上建立了某型固体捆绑火箭姿态动力学新模型,模型中基于有限元法导出了弹性振动方程,基于该模型对箭体复杂弹性振动引起的通道间耦合进行了研究,采用逆Nyquist阵列法进行设计。结果表明,该模型能更准确地反映捆绑火箭纵、横、扭耦合运动特性;新模型三通道之间存在弹性耦合,但耦合矩阵具有对角优势性质,采用逆Nyquist阵列法进行姿控系统设计是有效的,仿真结果表明设计的控制器可行,能够取得比较好的性能。     

四元数的实矩阵表示

四元数代数在计算机图形学、现代物理学、卫星的姿态表示等领域中都扮演着重要角色,从数学角度对四元数进行彻底研究是有价值的,但是,由于不可交换性,四元数并不像人们期望的那样易于掌握。处理四元数的方法之一是把它们等同为实数矩阵,其中各位置上的元素当然是可交换的。这样的"等同"实际上是从四元数代数到Rn×n的代数嵌入。研究了从四元数代数到Rn×n的代数嵌入问题,给出了把虚单位映成带符号置换矩阵条件下的所有可能的代数嵌入。我们用的方法是去考虑四元数代数生成元(即虚单位)的象。我们考察这些象的性质并确定有哪些实数矩阵满足它们。然后,我们运用群作用的语言化简了问题。我们得到的结论是有趣的:决定着嵌入的那些关键性的矩阵对是由的实数矩阵对构成的。而这样的矩阵对本质上只有两对。     

机载MIMO雷达稳健非均匀样本选择方法

针对杂波训练样本中混入干扰目标,导致空时自适应处理技术的杂波抑制性能下降问题,提出一种基于目标知识进行局部稀疏恢复的稳健训练样本挑选方法。该方法利用先验知识确定待检测单元中的目标区域,对整个角度-多普勒平面进行遍历,获得稀疏超完备基。通过变换矩阵对超完备基中对应的目标区域进行"挖空"处理,局部稀疏恢复出超分辨的杂波空时谱,获得杂波协方差矩阵估计。结合广义内积算法,实现非均匀训练样本挑选的过程。与常规结合广义内积方法相比,该方法对于不同干扰强度的训练样本,均有良好的检测效果。经仿真验证,所提方法的检验统计量之间区分度更加明显,对于干扰样本的挑选更加彻底,从而有效地提高了空时自适应处理技术的目标检测性能。     

复杂非线性隔冲减振系统计算分析

基于结构动力学理论,提出了一种用于分析计算复杂非线性隔冲减振系统的双模型两步计算法。该方法将冲击振动分成2个阶段:冲击作用阶段和自由振动阶段。冲击作用阶段,采用两自由度模型进行响应计算,该两自由度模型可有效地解决基础冲击输入问题;自由振动阶段,采用单自由度模型进行响应计算,设备在该阶段以冲击终了时刻的速度和位移为初始条件进行振动。文中还对一个典型的复杂非线性系统的冲击过程进行了数值计算.该方法亦可用于线性隔冲减振系统的计算分析.     

异步非平稳干扰抑制方法

为解决单通道条件下异步非平稳干扰抑制问题,提出基于数据驱动的稀疏分量分析干扰抑制方法,旨在从接收到的混叠信号中恢复期望信号。该方法利用深度卷积神经网络对输入/输出端数据间的复杂映射关系的强大建模能力,实现了目标信号稀疏域的自适应选择、稀疏域中目标信号稀疏表示的自适应学习以及目标信号的自动恢复。与以往干扰抑制算法不同,所提方法在时域上完成了“端到端”的信号波形恢复,且对混叠观测无先验要求,相比现有方法更具普适性。仿真实验验证了所提干扰抑制方法在不同环境噪声和干扰信号强度及泛化测试条件下的有效性,对干扰的抑制性能显著优于现有算法。     

稀疏卷积计算高效数据加载与输出缓存策略

针对现有神经网络加速器在处理稀疏神经网络时存在的数据加载效率低、乘加资源利用率低、输出缓存寻址逻辑复杂等问题,提出了稀疏卷积计算高效数据加载与输出缓存策略。将属于同一输入通道的非零输入特征图像数据和非零权重进行全对全乘累加运算,降低了非零数据配对难度,提高了乘加资源利用率;通过采用输入驻留计算,以及密集型循环加载特征图像数据,大幅减少了数据片外调取次数;优化了输出缓存设计,解决了现有方案中存在的输出缓存地址访问争用、存储拥塞等问题。实验表明,与采用类似架构的细粒度脉动加速器相比,在处理单元面积上减少了21.45%;在数据加载速度方面平均提高了117.71%在平均乘法器利用率方面提高了11.25%,达到89%。     

Technical note: Worst‐case performance of power‐of‐two policies for serial inventory systems with incremental quantity discounts

This study presents power‐of‐two policies for a serial inventory system with constant demand rate and incremental quantity discounts at the most upstream stage. It is shown that an optimal solution is nested and follows a zero‐inventory ordering policy. To prove the effectiveness of power‐of‐two policies, a lower bound on the optimal cost is obtained. A policy that has a cost within 6% of the lower bound is developed for a fixed base planning period. For a variable base planning period, a 98% effective policy is provided. An extension is included for a system with price dependent holding costs. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007