分维自适应稀疏网格积分非线性滤波器

针对含加性高斯噪声的非线性离散系统,提出了可分别根据各维状态及量测方程的非线性函数特性来确定采样点及其权重的积分滤波器.设计了基于嵌入式高斯采样积分和稀疏网格法则的自适应多变量采样积分方法,可在匹配函数高阶泰勒展开项时,利用低阶采样点,提出了高效的数据结构和遍历算法,便于采用该积分方法分别估计系统状态/量测的预测均值和协方差矩阵.该滤波器既能根据各维非线性函数的特性确定采样点,又实现了对采样值和权重的完全复用,保证了算法效率.理论分析和仿真表明,该滤波算法中自适应调整的运算量小于计算非线性函数采样值.该滤波器与无迹卡尔曼滤波相比,提高了滤波精度,与固定形式的稀疏网格滤波器相比,提高了采样效率,且该方法为两者的广义形式.仿真实验也验证了状态估计的精确性和函数采样的高效性.     

基于辅助变量的压缩采样匹配追踪闭环系统辨识方法

针对被控对象和反馈通道均具有未知时滞的闭环系统,提出一种基于辅助变量的压缩采样匹配追踪辨识方法.该方法利用辅助变量方法对压缩采样匹配追踪算法进行改进,获得过参数化辨识模型稀疏参数向量的估计,根据稀疏向量的结构得到前向通道的参数估计和时滞估计,进而根据模型等价原理获得反馈通道的参数估计.仿真结果表明,所提出方法仅需少量的迭代即可获得这类闭环系统参数与时滞的有效估计.     

零件点云法向量估计的多尺度特征融合网络

为了解决机械零配件点云处理中非均匀采样干扰、尖锐特征损失等难点,提出一种基于深度神经网络多尺度融合的点云法向量估计方法.该网络在不同邻域尺度下集成了采样点细节与点云块整体两种特征.为了使该多维回归输出网络的训练更稳定且能缓解梯度爆炸问题,重新设计了一个光滑的损失函数.实验结果表明,该方法性能优于传统的方法以及HoughCNN、PCPNet等方法,能够更准确地估计尖锐边缘的法向量,对点云各种噪声和采样方法鲁棒性都更强.     

单通道同频线性调制混合信号的时延估计

针对同频线性调制混合信号单通道盲分离中的时延估计问题,提出了一种基于自相关函数的估计方法.该方法利用接收基带信号的自相关函数建立关于时延的非线性方程,并通过延迟采样进一步构造适定方程组,然后利用梯度下降算法对方程组进行迭代求解,完成整个估计过程.理论分析表明,时延估计的误差正比于自相关函数估计的误差,随数据量增大不断减小.仿真结果验证了该方法的有效性.     

一种空间自适应采样和递归线性估计的光线投射算法

光线投射算法因其成像质量高而广泛地用于虚拟内窥镜系统,但成像速度非常缓慢.为此,本文提出了一种自适应采样和递归估计的成像加速算法.首先,根据查找法快速得到的梯度和光线方向信息,自适应地调整采样步长,使得该算法能够以大步长快速跳过体素值变化缓慢的区域,同时在体素值变化剧烈或快接近等值面的区域,能够以小步长进行搜索.其次,以递归线性插值的方法估计投射光线与实际等值面的交点,用于补偿大步长导致交点精度的降低,此举能够显著地提高成像质量.实验结果表明,该算法在保证绘制图像质量的前提下,提高了体绘制速度,取得了比较满意的效果.     

基于小波系数层间相关性的图像噪声方差估计

噪声的方差估计是含噪图像处理中的常见问题之一,其基本思想是通过某种方法寻找含噪图像中的"纯"噪声子图像来估计原噪声方差.传统方法是通过空域或频域采样,得到该子噪声图像,然后直接对其估计方差,它对图像信息的分布有要求.在传统频域采样方法的基础上,提出一种结合图像小波变换系数层间相关性的新方法.其过程是:对第一级有效小波分解的斜向子块进行分析,利用小波变换系数的层间相关性,去除其中的图像信息,得到更"纯"的子噪声块,再估计其方差.通过仿真实验和实际4f系统输出图像实验证明,该方法比传统方法的估计结果更准确,更适合带宽较低的系统图像和图像本身高频信息较丰富的场合.     

基于随机梯度的双率系统自校正控制方法

针对输入更新频率是输出刷新频率整数倍的未知参数双率系统,设计一个损失输出估计器计算采样间输出,再根据随机梯度算法设计参数估计器并得到系统模型的估计参数,基于最小方差控制原则设计出双率系统的自适应控制器。通过与基于最小二乘方法辨识系统参数的自适应控制算法进行比较,可以看出该算法的计算量较小,尤其是在输入数据更新频率与输出数据刷新频率相差较大时,计算量的差距更加明显。最后用仿真例子说明了该算法的有效性。     

MISO-FIR系统的梯度追踪辨识算法

针对含有未知时滞的多输入单输出有限脉冲响应系统,根据系统参数化后具有的稀疏特性,基于压缩感知原理,将匹配追踪方法和梯度搜索原理相结合,在有限采样数据下,提出了可以同时估计系统参数和时滞的梯度追踪算法．该算法同正交匹配追踪算法相比,梯度追踪算法具有较小的计算量．最后通过仿真验证了算法的有效性．     

基于量子系综分类的量子系统哈密顿量辨识

针对量子系统中的哈密顿量辨识问题,提出了一种基于量子系综分类的量子系统参数辨识方法.首先,所采用的量子系综分类方法结合了基于采样的学习控制方法和梯度流算法,可利用所设计的控制场有效区分具有不同哈密顿量参数的量子系统;其次,以交叉验证的方式对于所需估计的哈密顿量参数值进行区间判定,提高估计可靠性;再次,采用逐次细化判定区间的方法,辨识出最终的哈密顿量参数;最后,通过数值仿真验证了所提出的量子系统哈密顿量辨识方法的有效性和实用性.     

基于分值函数估计的自然梯度盲分离算法

分值函数估计的准确性是影响自然梯度盲分离算法收敛速度和稳定性的一个重要因素.提出了一种通过函数逼近直接估计分值函数的方法.用一组正交多项式的线性组合来逼近分值函数,其性能用均方误差来衡量.线性组合的系数向量可运用分值函数的性质,通过最小化均方误差自适应学习得到.将估计到的分值函数代入自然梯度迭代公式中就得到新算法.实验...     

Discontinuous perturbation analysis of discrete-event dynamicsystems

The discontinuous perturbation analysis (DPA) method is presented to deal with discontinuous sample performance functions. This method is applicable to derivative estimation with respect to most threshold-type problems in discrete-event dynamic systems (DEDS). Through modeling of discontinuities by step function, we are able to provide a unified framework for constructing the derivative estimation of a DEDS. As a result, we offer an alternate approach to derive derivative estimation in DEDS's. Finally, some relationships with other PA techniques are discussed, and a numerical example is presented     

Integration of Petri nets and moment generating function approaches for system performance evaluation

This article integrates arbitrary stochastic Petri nets (ASPN) and moment generating function approaches for performance evaluation of discrete event dynamic systems (DEDS). These systems include computer-integrated manufacturing systems, resource-shared distributed systems, and communication networks. ASPN can describe various DEDS in which the time duration for activities may be a random variable of arbitrary distributions. In ASPN models, transitions with firing delays of general distributions are used to model these activities. Using our proposed performance analysis methodology, we first represent a system as an ASPN model, then generate its reachability graph and convert it into a state machine Petri net, derive the transfer functions of interesting performance measures through stepwise reductions, and finally obtain the analysis results. This method makes it possible to obtain analytical solutions of important performance indices. We use a robotic assembly system to illustrate the method. We obtain several important performance measures of a closed-form. Finally, we discuss the limitations of this approach and future research.     

Modeling operator/workstation interference in asynchronous automatic assembly systems

In several instances ofdiscrete event dynamic systems (DEDS), jobs sometimes require service from two or more resources at the same time. When queueing network models are used to study DEDS, this feature ofsimultaneous resource possession is often ignored because it is difficult for the models to handle. In some DEDS, this feature of a job demanding several resources simultaneously can have a significant effect on system performance, especially if there is a limited amount of one or more of these resources. For example, in an asynchronous automatic assembly system, an assembly at a workstation needs an operator when it experiences a jam (a random phenomenon) in order to clear the jam. Due to the presence of a limited (small) number of operators, an assembly may have to wait for an operator. This waiting orinterference time has a significant effect on the system production rate. This paper develops an analytical approximation method that can be used to determine the steady-state performance of automatic assembly systems for a given assignment of operators. The analytical method involves the simultaneous solution of two coupled queueing models; one of the models calculates the waiting time for an operator resource, while the other computes the waiting time for a workstation resource. The solution technique developed can be adapted to study instances of simultaneous resource possession in other DEDS, such as flexible manufacturing systems and computer/communication networks.This research was supported in part by a grant from the General Motors Research Laboratories and NSF grant no. DMC 8608409.     

A new approach to the analysis of discrete event dynamic systems

We present a new, time domain approach to the study of discrete event dynamical systems (DEDS), typified by queueing networks and production systems. A general state-space representation is developed and perturbation analysis is carried out. Observation of a single sample realization of such a system can be used to predict behavior over other sample realizations, when some parameter is perturbed, without having to make additional observations. Conditions under which this is always possible are investigated and explicit results for some special cases are included.     

The score function approach for sensitivity analysis of computer simulation models

Some theoretical and practical aspect of the score function (SF) approach for estimating the sensitivities of computer simulation models and solving the so-called “what if” problem (performance extrapolation) are considered. It is shown that both the sensitivities (gradients, Hessians, etc.) and the performance extrapolation can be derived simultaneously by simulating only a single sample path from the nominal system. It is also shown that the SF approach can be efficiently applied for DESS (discrete event static systems, example: reliability models and stochastic networks) and for DEDS (discrete events dynamic systems, example: queuing networks) under light traffics. Control variates procedure for variance reduction is presented as well     

参数摄动时一类离散事件动态系统的渐近性能估计和鲁棒性条件*

本文针对一类在极大代数上可表示为线性模型的离散事件动态系统,讨论了参数摄动对系统周期稳态性能的影响,给出了定量估计参数摄动下性能改变量的一个关系式,建立了性能对参数摄动不敏感的鲁棒性条件。本文的结果,对确定系统参数的允许摄动范围,使之不破坏离散生产过程的有序和协调运行,是有指导意义的。     

On the efficient generation of discrete event sample paths under different system parameter values

The techniques of perturbation analysis (PA) are generalized from the viewpoint of efficiently generating sample paths of discrete event dynamic systems (DEDS) operating under different parameter values. The central idea, denoted as ‘cut-and-paste’, is simply to utilize one segment of a sample path generated under one parameter as legitimate sample paths of as many different parameter values of the DEDS as possible. Viewed in this light, many new ways to perform discrete event simulation for parametric study appear possible. Both analytical and experimental results are offered to substantiate our case.     

一类DEDS状态反馈控制器综合的新方法

对具有无穷状态空间的并发离散事件动态系统提出了一种基于Petri网图示的矩阵代数 综合方法.该方法借助于对Petri网的结构分解可以用于结构无竞争Petri网描述的一类离 散事件动态系统状态反馈控制器的离线综合.     

关键路径与随机串行生产线的灵敏度分析

扰动分析是研究离散事件动态系统的有效方法,性能函数的可微性是应用该方法 的前提条件之一.利用关键路径的概念,证明了随机串行生产线稳态性能函数可微的充要条 件为系统的关键路径以概率1唯一;而且,当系统的关键路径以正概率不唯一时性能函数的方 向导数存在,进而给出了其方向导数的无偏估计量.最后指出应用扰动分析和非光滑分析方 法研究这类性能函数不可微系统的思路.     

Illumination robust single sample face recognition using multi-directional orthogonal gradient phase faces

Due to the limitation of the storage space in the real-world face recognition application systems, only one sample image per person is often stored in the system, which is the so-called single sample problem. Moreover, real-world illumination has impact on recognition performance. This paper presents an illumination robust single sample face recognition approach, which utilizes multi-directional orthogonal gradient phase faces to solve the above limitations. In the proposed approach, an illumination insensitive orthogonal gradient phase face is obtained by using two vertical directional gradient values of the original image. Multi-directional orthogonal gradient phase faces can be used to extend samples for single sample face recognition. Simulated experiments and comparisons on a subset of Yale B database, Yale database, a subset of PIE database and VALID face database show that the proposed approach is not only an outstanding method for single sample face recognition under illumination but also more effective when addressing illumination, expression, decoration, etc.