体可视化的快速光线投射算法

对规则标量数据场的体绘制算法进行了讨论。在此基础上给出了一种快速的自前向后的光线投射算法,利用多边形扫描转换减少了投射光线的数量。通过光线的参数化表示方法减少了光线与数据场求交的计算量。并且通过将Bresenham算法扩展至三维,对光线通过三维扫描转换以进行体素化,从而避免了对空间的采样从而加速了光线投射的效率,而且通过利用OpenGL提供的函数接口,简化了算法的实现过程。     

一个矢量形式的双圆弧插值算法

本文在分析数控插补最常用的双圆弧插值算法特点的基础上，将以局部坐标系中的三角运算为核心的传统双圆弧插值算法，改造成为以整体坐标系中的线性运算为核心的矢量形式的双圆弧插值算法，从而减少了计算的复杂性和程序的复杂性，提高了效率和可靠性。     

八卦限式的圆弧插补算法

本文基于最小偏差插补算法的主要思路，提出了一种新的八卦限式的圆弧插补递推算法，其算法简单，插补精度高，执行速度快，可以方便地应用于各种两轴CNC系统。     

圆弧半径的“多点”测算方法

本文依据“三点定径”基本原理，通过坐标旋转角和被测圆弧的切线距，建立圆弧半径的计算公式，并借助于函数计算器快速、准确求解圆弧半径。     

一种宽椭圆生成算法

计算机辅助设计领域常涉及宽椭圆生成,宽椭圆生成算法的优劣直接影响设计效果.为了生成一个圆心在原点的标准宽椭圆,每次用单像素宽的椭圆中点扫描转换算法,得到一个单像素宽椭圆上的一个点,填充一个以该点为中心,椭圆宽为直径的圆弧,扫描转换结束后,生成一个无明显视觉缺陷的第一象限1/2宽椭圆.     

非线性混合整数规划问题的改进差分进化算法

针对非线性混合整数规划问题,本文采用非固定多段映射罚函数法处理约束条件、用混合整数编码技术处理连续变量和整数变量,并在基本差分进化算法中加入一种新型的凸组合变异算子和一种指数递增交叉算子,由此构造出了一种求解非线性混合整数规划问题的改进差分进化算法。实验表明,所提出的算法全局收敛速度快,精度高,鲁棒性强。     

圆弧多点测量中粗差的特殊影响和检查

本文指出圆弧多点测量中粗差影响的特殊性，必须引起精测人员的充分重视，并介绍用微机辅助检查判别和剔除粗差的实用方法。     

机器人空间三点圆弧算法的研究与实现

本文介绍了一种针对机器人空间圆弧的插补方法。机器人TCP经历的空间任意三点,求出空间圆弧的圆心和半径,继而推导出机器人控制系统中任意空间圆弧的实现方法,该算法不仅理论上可使所有插补点均在圆弧上,而且采用了矢量算法,避免了插补方向和过象限的判断。     

基于波长移相的改进加权多步算法

在斐索干涉仪中测量光学平板受到多面反射光束的影响,而波长移相干涉技术是通过光源波长的改变来实现干涉信号的移相,原理上可以实现各个干涉信号的分离,在算法上要求能对多次谐波进行较好的抑制,加权多步移相算法可以实现这样的目的.笔者提出了加权多步移相算法的改进算法,可以提高算法对误差的抑制能力,最后用实验验证了改进算法的有效性.     

A discrete hybrid differential evolution algorithm for solving integer programming problems

Differential evolution (DE) is one of the most prominent new evolutionary algorithms for solving real-valued optimization problems. In this article, a discrete hybrid differential evolution algorithm is developed for solving global numerical optimization problems with discrete variables. Orthogonal crossover is combined with DE crossover to achieve crossover operation, and the simplified quadratic interpolation (SQI) method is employed to improve the algorithm's local search ability. A mixed truncation procedure is incorporated in the operations of DE mutation and SQI to ensure that the integer restriction is satisfied. Numerical experiments on 40 test problems including seventeen large-scale problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.     

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

This article proposes an efficient metaheuristic based on hybridization of teaching–learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching–learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.     

A hybrid homogeneously weighted moving average control chart for process monitoring: Discussion

The sensitivity of a monitoring scheme depends on many factors including the variance of the charting statistic which is very important in the computation of the control limits. This paper discusses the computation of the variance of the recently proposed hybrid homogeneously weighted moving average (HHWMA) $\overline{X}$ scheme which was based on an incorrect assumption. The correct variance is used to evaluate the run-length characteristics of the HHWMA $\overline{X}$ scheme. It is observed that the incorrect variance has a significant impact on the sensitivity (or performance) of the HHWMA $\overline{X}$ scheme.     

Hybrid genetic algorithm for test bed scheduling problems

In this paper, we address the scheduling problem for a heavy industry company which provides ship engines for shipbuilding companies. Before being delivered to customers, ship engines are assembled, tested and disassembled on the test beds. Because of limited test bed facilities, it is impossible for the ship engine company to satisfy all customers’ orders. Therefore, they must select the orders that can be feasibly scheduled to maximise profit. An integer programming model is developed for order selection and test bed scheduling but it cannot handle large problems in a reasonable amount of time. Consequently, a hybrid genetic algorithm (GA) is suggested to solve the developed model. Several experiments have been carried out to demonstrate the performance of the proposed hybrid GA in scheduling test beds. The results show that the hybrid GA performs with an outstanding run-time and small errors in comparison with the integer programming model.     

A hybrid topology optimization algorithm for structural design

A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.     

Threshold automatic selection hybrid phase unwrapping algorithm for digital holographic microscopy

Conventional quality-guided (QG) phase unwrapping algorithm is hard to be applied to digital holographic microscopy because of the long execution time. In this paper, we present a threshold automatic selection hybrid phase unwrapping algorithm that combines the existing QG algorithm and the flood-filled (FF) algorithm to solve this problem. The original wrapped phase map is divided into high- and low-quality sub-maps by selecting a threshold automatically, and then the FF and QG unwrapping algorithms are used in each level to unwrap the phase, respectively. The feasibility of the proposed method is proved by experimental results, and the execution speed is shown to be much faster than that of the original QG unwrapping algorithm.     

金属拉深件拉深过程微裂纹AE信号特征参数的优化及状态识别

本文通过对拉深件成型状态的声发射测试,进行了拉深过程AE特征参数信号的提取。对采集到的信号进行局域波分解后提取各IMF(Intrinsic Mode Function)的能量值作为初始特征参数,应用遗传算法对初始特征参数进行优化,生成最优特征参数。采用简单的马氏距离方法,将正常状态和微裂纹状态两种质量状态下的实验数据进行计算,比较两种状态下马氏距离的大小,取其中最小判别距离对应的状态为测试样本的状态类型。研究结果说明了该方法可以有效地识别出拉深件的微裂纹AE信号,从而判断出拉深件的初始裂纹状态,实现AE信号特征参数的优化及对金属拉深件成型质量状态的识别。     

车身制件拉延模数字设计研究

为完善车身冲模结构设计的理论体系,提出了制件在冲模设计坐标系中定位的概念,并应用六点定位原理对这一问题进行了研究,探讨了冲压方向的选择原则与工序件的设计方法,根据数字设计的特点对拉延模零件及其特征进行了分类,在此基础上提出了拉延模数字设计的工作策略与方法.结果表明:制件在冲模设计坐标系中的定位是通过设计基准点与冲压方向实现的;拉延模数字设计的工作策略与方法经工程实例验证是可行的.     

改进的混合粒子群优化算法

针对粒子群算法后期收敛速度较慢,易陷入局部最优的缺点,提出了改进的混合粒子群算法.通过更改现有的速度更新公式,加入扰动项,以及引入交叉和变异算子等措施,改进了粒子群算法的性能.数值试验表明,改进后的粒子群算法在全局寻优和局部寻优能力上均得到提高,是一种有效的优化算法.     

An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.