基于FPGA的实时Hough变换

为解决Hough变换实时性差的问题,提出一种基于现场可编程门阵列的实时Hough变换方法。采用分角度映射方法使Hough变换在空间上并行运算。设计一种基于流水线的计算结构,实现Hough空间参数计算与存储的时间并行性。通过双口RAM,在单个时钟内完成参数空间的一次投票操作,提出一种适合在FPGA中实现的快速寻找极值的二次搜索算法。与DSP进行效率对比实验,结果表明该方法效率较高,能满足实时性处理要求。     

检测不规则图形的改进广义Hough变换

广义Hough变换作为一种检测不规则图形的有效方法，具有抗噪、不怕遮挡等优点。由于R表的计算对断点和变形非常敏感，影响了广义Hough变换抗断点和变形的特性。针对这一问题，提出了一种计算图形上每个点的法线方向的新方法。利用法线方向作为R表索引项，对点进行分类，使得广义Hough变换不仅具有抗噪、不怕遮挡的优点，还具有抗断点、受变形影响小的特点。实验结果证明，该方法在检测不规则图形时具有很好的抗干扰能力，并在鞋样设计CAD系统中有效解决了鞋样匹配的问题。     

一种新的用于检测直线的快速Hough变换

在分析现有常用的改进Hough变换算法思想的基础上,设计了一种新的用于直线检测的快速Hough变换,通过与现有改进Hough变换算法的比较实验,新设计算法具有更好的实时性。     

OMR图像倾斜矫正与分割

提出一种采用Hough变换进行OMR图像倾斜矫正的方法,该方法不必识别定位标记位置,具有很好的抗噪能力。为克服Hough变换计算量大的缺点,采用图像子抽样生成低辨率图像进行Hough变换,提高了算法效率。同时,提出一种快速游程段中心迭代算法分割图像,结合Hough变换,可快速准确地实现OMR图像的倾斜矫正与分割。     

硅太阳能电池纹理缺陷检测

为实现硅太阳能电池纹理缺陷检测,提出一种采用方向可变滤波器组并结合Hough变换的检测方法。通过方向可变滤波器提取图像边缘并采用Hough变换确定纹理方向,采用角度与纹理方向一致的方向可变滤波器滤波,实现消除规则直线纹理,保留纹理缺陷特征。对滤波后的纹理缺陷结果图像采用双阈值法,以确定纹理缺陷所在的位置。和Gabor滤波器及小波滤波器的比较实验结果表明：该方法比前两种方法能更有效地进行硅太阳能电池纹理缺陷检测。     

宽线段Hough变换及其在箭靶识别上的应用

Hough变换是用于检测图像中直线段的有力工具。论文提出的宽线段Hough变换针对传统Hough变换进行了改进,使之适用于多条宽线段同时存在的情况,并且解决了端点提取的问题。该方法应用于箭靶识别取得了很好的效果,实验表明对比传统方法具有较大优势。     

改进随机Hough变换在心肌纤维图像直线检测中的应用

利用改进的随机Hough变换被用来对心肌纤维方向进行了检测。针对心肌纤维边缘的特点，采用窗口对投票点对加以筛选，同时利用模糊映射对Hough变换投票在变换空间加以修正，使随机Hough变换能够较好的用于复杂非规则直线段的检测，提高了利用Hough变换检测心肌纤维方向的正确率。     

基于线特征的机场跑道定位算法

提出了一种基于线特征的机场跑道识别定位算法。首先,将方向小波变换用于目标的边缘检测,得到了定位准确、边缘连续、背景较为干净的机场边缘图;然后,利用标定了梯度方向的边缘点作为Hough变换的基本元素,将Hough变换的全局"投票"过程变为目标方向上的局部投票,并且将直线段的检测和定位过程融为一体,从而得到跑道的描述信息。实验结果验证了该算法的有效性和稳健性。     

改进的随机Hough变换在检测多圆中的应用

提出了一种在视觉检测中用于多圆提取的改进随机Hough变换算法，该算法采用一种新的取点方法来进行计算，避免了随机Hough变换由于随机采样造成的大量无效累积。并在计算圆的几何参数时充分利用了圆的几何特征，因此检测速度快，占用内存空间小，精度高，具有良好的抗噪和抗干扰能力。最后文中还分别给出了人工合成图和实物图的实验结果。     

基于最小二乘预测的动态快速椭圆检测

对Hough变换及其改进方法进行了分析比较.提出了一种改进的Hough表存储结构,并采用随机Hough变换的取样方法和用启发函数来搜索椭圆区域,大大降低了椭圆检测过程的计算量和对存储空间的占用.为了实现运动图像中的椭圆参数检测,提出了一种最小二乘预测椭圆区域的方法.该方法将椭圆的检 测限定在一个很小的图像子区域内.几种方法的结合使用实现了动态图像中椭圆的快速检测.     

Optimal control of viscous Burgers equation via an adaptive nonmonotone Barzilai–Borwein gradient method

An adaptive nonmonotone spectral gradient method for the solution of distributed optimal control problem (OCP) for the viscous Burgers equation is presented in a black-box framework. Regarding the implicit function theorem, the OCP is transformed into an unconstrained nonlinear optimization problem (UNOP). For solving UNOP, an adaptive nonmonotone Barzilai–Borwein gradient method is proposed in which to make a globalization strategy, first an adaptive nonmonotone strategy which properly controls the degree of nonmonotonicity is presented and then is incorporated into an inexact line search approach to construct a more relaxed line search procedure. Also an adjoint technique is used to effectively evaluate the gradient. The low memory requirement and the guaranteed convergence property make the proposed method quite useful for large-scale OCPs. The efficiency of the presented method is supported by numerical experiments.     

An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines

The buffer allocation problem, i.e. how much buffer storage to allow and where to place it within the line, is an important research issue in designing production lines. In this study, a novel adaptive tabu search approach is proposed for solving buffer allocation problem in unreliable and non-homogeneous production lines. The objective is to maximize the throughput of the line, which is constrained by the capacity of each buffer space and also the total buffer capacity to allocate to these spaces. Besides proposing a new strategy to tune the parameters of tabu search adaptively during the search, an experimental study is carried out to select an intelligent initial solution scheme among three alternatives so as to decrease the search effort to obtain the best solutions. The performance of the proposed approach is evaluated by computational tests and very promising results are obtained.     

A new cubic convergent method for solving a system of nonlinear equations

We present a new cubic convergent method for solving a system of nonlinear equations. The new method can be viewed as a modified Chebyshev's method in which the difference of Jacobian matrixes replaces three order tensor. Therefore, the new method reduces the storage and computational cost. The new method possesses the local cubic convergence as well as Chebyshev's method. A rule is deduced to ensure the descent property of the search direction, and a nonmonotone line search technique is used to guarantee the global convergence. Numerical results indicate that the new method is competitive and efficient for some classical test problems.     

未知环境中多移动机器人协作围捕的研究

为了实现多移动机器人在未知环境中的围捕,本文将任务建模为排队、随机搜索、包围、 捕捉和预测五种状态.提出了排队、搜索、包抄、捕捉、预测和方向优化策略,结合状态转换条件保 证了任务的顺利实现.同时,赋予被捕捉对象(下用Invader表示)一种安全运动策略,增加了围捕 的难度.仿真结果表明了所提方法的可行性.     

连铸-轧制生产中带有批决策的排序问题的建模与优化方法

以长材产线为背景, 研究了炉次在连铸及轧制阶段的组批及批排序问题. 与以往将连铸、轧制分开研究不同, 本文同时考虑连铸和轧制阶段对组批及批排序的要求, 还考虑了下游工序精整机组负荷均衡生产的要求. 为该问题建立了新的混合整数规划(Mixed integer programming, MIP) 模型. 由于问题的NP-hard 属性和模型的大规模特征, 以及工业应用的实际要求, 本文提出了改进的分散搜索(Scatter search, SS) 算法用于求解该问题. 在改进的SS 算法中, 利用解的相关性质来限制搜索空间, 并将变邻域搜索策略引入, 从而结合解的多样性及邻域互补性特点, 充分发挥算法混合的优势. 实际数据的计算结果验证了改进SS 算法的有效性.     

一种改进的机器人路径规划的蚁群算法

针对具有复杂回旋地形结构的机器人路径规划问题, 提出了一种改进的蚁群算法. 该算法引入自适应迁移概率函数实现蚁群具有正、反向运动的能力, 改善了算法的曲折迂回能力; 能见度信息中引入距离启发因素和障碍相交检测机制, 完成路径搜索与避障过程有机结合, 提高算法的搜索效率; 引入贪婪信息素更新策略和节点信息素分布, 降低了数据存储量, 改善了路径规划的效果和算法的收敛速度. 基于不同算法的比较仿真实验, 数值结果证实了该算法的有效性.     

Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches

A popular strategy for dealing with large parameter estimation problems is to split the problem into manageable subproblems and solve them cyclically one by one until convergence. A well-known drawback of this strategy is slow convergence in low noise conditions. We propose using so-called pattern searches which consist of an exploratory phase followed by a line search. During the exploratory phase, a search direction is determined by combining the individual updates of all subproblems. The approach can be used to speed up several well-known learning methods such as variational Bayesian learning (ensemble learning) and expectation-maximization algorithm with modest algorithmic modifications. Experimental results show that the proposed method is able to reduce the required convergence time by 60–85% in realistic variational Bayesian learning problems.     

Randomized line search techniques in combined GA for discrete sizing optimization of truss structures

This paper presents two randomized line search techniques, each combined with a genetic algorithm (GA), to improve the convergence and the accuracy ratio for discrete sizing optimization of truss structures. The first technique is a simple one-dimensional line search in which design variable axes are selected randomly as search directions. The second is a line search technique whose search direction is determined randomly by fitness function values and differences in the genotypes of individuals. To apply the above-mentioned line search techniques without difficulty, real coding is adopted for discrete problems. The line search techniques are applied to discrete optimization problems of minimum-weight truss structures subjected to stress and displacement constraints. The proposed techniques provide convergence to better solutions than a conventional GA.     

广义霍夫变换的改进

提出基于广义霍夫变换（Generalized Hough Transformation，GHT）的改进算法．与传统方法比较，新方法将参考点设在形状边界上，可以减少内存的需要，并且用于寻找峰值的速度也大大提高．理论上，改进后的算法对内存的需要是一个基于形状描述复杂度的函数，越是精确和高级的形状和特征描述，意味着节省的内存空间越大．最后，将改进的GHT应用于物体形状识别，取得一些实验性效果．     

一种广义霍夫变换的改进

本文提出了基于广义霍夫变换(GHT)的一种改进。通过将参考点设在形状边界上,新方法与传统方法比较,霍夫变换可以减少内存的需要。与此同时,用于寻找峰值的速度也大大提高了。从理论上说,改进后的算法对内存的需要是一个基于形状描述复杂度的函数。越是精确和高级的形状和特征描述,意味着更大的内存空间减少。最后,将改进的GHT用于物体形状识别,取得一些实验性效果。