机器人焊缝中心三维定位的解析方法

机器人焊接过程中焊缝三维空间中心位置的确定是焊缝自动跟踪系统重要的一环.由于多数焊缝为直线,且实际的焊缝在小范围内可近似为直线,因而特征点的空间坐标确定以后如何确定直线是问题的关键所在.在焊缝特征点确定以后,根据空间解析几何的定位原理,结合最小二乘法,首先给出了焊缝中心线的确定原则,其次,按直线的点向式方程,利用焊缝特征点的全息,给出了中心直线的点和方向向量确定的解析方法.     

模糊环境下部分权重信息的多属性群决策方法

针对决策过程中信息是模糊不确定的,属性的权重信息是部分可知的,我们给出了一个新的确定不同专家给出的属性值的权重的方法.该方法能将靠近中心值的属性值分配较大的权重,而给远离中心值的属性值分配较小的权重,从而得到更加合理的综合属性值.为了确定属性的权重信息,我们建立了一个线性规划模型.利用所给的新方法,我们给出了一个新的模糊多属性群决策方法.最后的实例说明方法的有效性和合理性.     

基于FCM-GRNN的驾驶倾向性识别方法

为提高对驾驶倾向性的辨识准确率,进行驾驶倾向性问卷表调查、模拟驾驶、人因工程测试,考虑了驾驶员的心理、生理信息,以及环境、车辆和操作信息的基础上,提出用广义神经网络确定聚类中心,优化模糊c均值聚类算法,实现目标识别级信息融合的方法,对驾驶倾向性进行预测.利用实验数据对识别方法进行验证,结果表明,该算法对驾驶倾向性的预测准确率达到了85.83%,为进一步研究驾驶员倾向的动态特性提供了依据.     

基于SPA的不确定推理模型

利用集对分析 (SPA)的思想 ,提出了一个不确定的推理模型 SPABARM—基于集对的不确定推理模型 .该模型给出了不确定信息的传播算法 ,能够把肯定信息 ,否定信息以及未知信息同一处理 .     

自适应的Monte Carlo方法对逼近问题的信息基复杂性

研究由有界混合偏导数确定的Sobolev空间上用自适应的Monte Carlo方法逼近的信息基复杂性. 利用离散化方法及拟s数(pseudo-s-scale)的性质, 确定了这个问题的渐近精确阶.     

一类均匀康托集的Hausdorff中心测度

本文研究了均匀2n部分康托集的Hausdorff中心测度.利用极大中心密度与Hausdorff 中心测度之间的关系,确定了均匀2n部分康托集Hausdorff中心测度的精确值.     

基于正交试验设计的Ω焊缝焊接工艺参数的确定

为了确定CRDM焊接机焊接工艺参数,应用正交试验设计法进行了Ω焊缝焊接工艺评定试验,用数理统计方法分析了对焊缝质量产生影响的各焊接参数的主次顺序,得到了最优生产条件。     

基于可能度排序法的区间信息指派方法

针对具有区间数信息的多目标指派问题,利用区间数可能度排序方法,给出了一种新的指派方法.该方法充分利用实际所给的区间信息进行求解,克服了以往这类指派问题最后由多目标问题转换为单目标问题时权数确定主观性大的缺陷.最后给出了该方法的一个算例.     

改进DEMATEL方法在旅游移动APP竞争力影响因素中的应用

随着大众旅游的兴起,众多旅游企业推出旅游移动APP以满足用户的旅游信息需求,如何提升旅游移动APP竞争力,对于旅游企业的可持续发展至关重要.鉴于影响旅游移动APP竞争力的因素众多且交互关系复杂,提出一种改进的DEMATEL方法,以便更好地识别分析旅游移动APP竞争力的关键影响因素.其基本思路是利用粗糙集方法计算各影响因素的客观权重,以此对DEMATEL方法确定的中心度进行加权平均,并通过得到的加权中心度来确定影响旅游移动A-PP竞争力的关键因素.相比于传统DEMATEL方法,方法能更客观地分析各影响因素指标间的相互作用关系,准确区分原因因素与结果因素,从而揭示它们对旅游移动APP竞争力的实际影响作用,为合理选择竞争力提升路径提供理论参考.     

诱导有序加权平均算子在水资源状况评估中的应用

水资源短缺风险状况评价是一项多目标的复杂决策过程,在水资源短缺风险评价的层次分析决策中应用协商评价确定各属性的权重.对评价者给出的区间赋值信息进行分类,确定各类信息的协商区间和协商值;然后利用所导出的有序加权平均(IOWA)算子对各类信息的协商区间和协商值进行集成,在此基础上提出了一种确定协商评价问题属性权重的方法,实例分析证明该方法具有实用性和可操作性是可行的.     

基于中心平面的聚类模型及在电商中的应用

因为k-平面聚类算法(kPC)和k-中心平面聚类算法(kPPC)构建的聚类中心平面是无限延伸的,这会影响聚类的性能,所以提出了局部的k-中心平面聚类(L-kPPC)算法.此算法在kPPC中引入了k-均值聚类算法(k-mean),这样使得样本点都聚集在类中心周围.L-kPPC利用了各聚类中心平面的局部特征构建类中心平面,使同一类的数据点到此类的聚类中心或平面尽可能的近,离其他类中心或平面尽量远,这导致求解特征值问题.在此,利用拉普拉斯图建立初始化的数据点,而不是随机选择的初始数据点.最后从电商平台ebay提供的Web Service接口提得数据进行实验,实验结果分析表明,L-KPPC算法有较好的表现.     

On Properties of Different Notions of Centers for Convex Cones

The points on the revolution axis of a circular cone are somewhat special: they are the “most interior” elements of the cone. This paper addresses the issue of formalizing the concept of center for a convex cone that is not circular. Four distinct proposals are studied in detail: the incenter, the circumcenter, the inner center, and the outer center. The discussion takes place in the context of a reflexive Banach space.     

An algorithm for the minimax weber problem

The paper presents an algorithm for finding the weighted absolute center of a given set of points in the Euclidean plane.The algorithm is a specialized implementation of the method of feasible directions.The presentation includes a proof of convergence as well as computational experience.     

Fitting a Set of Points by a Circle

Given a set of points S={p ₁ ,. . ., p _n } in Euclidean d -dimensional space, we address the problem of computing the d -dimensional annulus of smallest width containing the set. We give a complete characterization of the centers of annuli which are locally minimal in arbitrary dimension and we show that, for d=2 , a locally minimal annulus has two points on the inner circle and two points on the outer circle that interlace anglewise as seen from the center of the annulus. Using this characterization, we show that, given a circular order of the points, there is at most one locally minimal annulus consistent with that order and it can be computed in time O(n log n) using a simple algorithm. Furthermore, when points are in convex position, the problem can be solved in optimal Θ(n) time. Received June 25, 1997, and in revised form March 5, 1998.     

A bundle modification strategy for convex minimization

We present a new bundle algorithm for minimizing convex not necessarily smooth functions. The novelty of our approach is based on a bundle modification strategy that we apply whenever the stability center is updated and which is aimed at substituting the points of the bundle by new points characterized by possibly better values of the objective function. Convergence of the algorithm is proved and numerical results are presented.     

圆形区域分散布局问题研究

针对圆形区域分散布局问题, 文中给出了一个带约束的非线性规划模型. 当布局点数量较少时, 通过将模型转化为无约束优化问题, 利用梯度方法进行求解; 对于布局点数量较多的情况, 提出了一个界为1/2的多项式时间的近似算法, 并进行了相应的算例分析, 进一步来验证算法解的合理性. 研究的结论及方法一定程度上丰富和完善了圆形区域的分散布局理论.     

Uniform Big Bang–Chaotic Big Crunch optimization

This study proposes methods to improve the convergence of the novel optimization method, Big Bang–Big Crunch (BB–BC). Uniform population method has been used to generate uniformly distributed random points in the Big Bang phase. Chaos has been utilized to rapidly shrink those points to a single representative point via a center of mass in the Big Crunch phase. The proposed algorithm has been named as Uniform Big Bang–Chaotic Big Crunch (UBB–CBC). The performance of the UBB–CBC optimization algorithm demonstrates superiority over the BB–BC optimization for the benchmark functions.     

Offset approximation based on reparameterizing the path of a moving point along the base circle

This paper presents a novel algorithm for planar curve offsetting. The basic idea is to regard the locus relative to initial base circle, which is formed by moving the unit normal vectors of the base curve, as a unit circular arc first, then accurately to represent it as a rational curve, and finally to reparameterize it in a particular way to approximate the offset. Examples illustrated that the algorithm yields fewer curve segments and control points as well as C^1 continuity, and so has much significance in terms of saving computing time, reducing the data storage and smoothing curves entirely.     

An efficient simulation algorithm for the generalized von Mises distribution of order two

In this article we propose an exact efficient simulation algorithm for the generalized von Mises circular distribution of order two. It is an acceptance-rejection algorithm with a piecewise linear envelope based on the local extrema and the inflexion points of the generalized von Mises density of order two. We show that these points can be obtained from the roots of polynomials and degrees four and eight, which can be easily obtained by the methods of Ferrari and Weierstrass. A comparative study with the von Neumann acceptance-rejection, with the ratio-of-uniforms and with a Markov chain Monte Carlo algorithms shows that this new method is generally the most efficient.