基于凸多面体碰撞检测的虚拟砂轮建模研究

虚拟砂轮建模时大多采用包围球对磨粒进行碰撞检测,而包围球相互接触时凸多面体磨粒之间仍存在间隙,导致虚拟砂轮表面与实际砂轮表面差异较大,影响后续磨削过程仿真的准确性.针对这一问题,提出了一种基于凸多面体碰撞检测的虚拟砂轮建模方法.推导了砂轮表面磨粒随机位置的数学模型,基于凸多面体碰撞检测判断磨粒干涉状况,最终生成虚拟砂轮...     

松软突出煤层新型钻进技术研究

低螺旋钻杆是为解决松软突出煤层钻进难题而发明的一种新型钻杆.为充分发挥低螺旋钻杆应用于松软煤层钻进的优势,根据实际应用情况,建立相应的排渣数值模型,分析低螺旋钻杆螺旋凸棱的宽度和螺距的变化对排渣效果的影响情况;根据在实际应用中配套钻机的动力参数,对低螺旋钻杆进行强度分析.研究结果表明,螺旋凸棱宽度l为14 mm,螺距s为80 mm,排渣效果最优,且钻杆强度的最小安全系数为1.87,钻杆加工参数优化设计合理.通过松软突出煤层钻进工业性试验,钻进深度和钻进效率提高显著.     

基于混合POCS的视频图像超分辨率重建

提出混合凸集投影算法HPOCS对视频图像进行超分辨率重建,利用连续视频图像间的互异信息生成更高分辨率的视频图像.该算法首先采用图像匹配估计视频图像间的运动位移;然后进行基于的APEX盲解卷积,估计点扩散函数和理想视频图像;最后在凸集投影的理论框架下进行图像重建.实验表明,HPOCS重建后,视频图像的分辨率相对于原始图像、双线性内插图像和POCS重建图像明显提高,图像边缘更加清晰,细节信息更加突出.     

论二维点集或线段集凸壳生成算法改进与优化的同构化方向

本文指出了迄今为止的现行二维点集或线段集（包括：多边形、封闭折线、半封闭折线、开放线段集等）凸壳生成算法的共同弱点;提出了可改进与优化凸壳算法的同构化凸壳构造基本定理。进而,基于同构化凸壳构造基本定理,阐明了有限二维点集或线段集凸壳生成算法改进与优化的同构化方向,应当是：第一,使凸壳极点（或称顶点）分布域极小化,即让包含凸壳极点的判定区域尽可能小;使极点判定对象直接化,即让所判定对象尽可能接近当前所寻极点。第二,尽力对有可改造潜力的优秀串行凸壳算法施以并行化改造和创新。     

不确定切换系统的可靠控制

研究一类结构参数不确定性的切换系统的可靠控制问题.这种可靠系统可以抵御执行器出现故障给系统带来的影响.利用凸组合技术,设计可靠控制器和相应的切换策略,使得不仅当执行器正常时而且当一些执行器出现故障时仍保证闭环系统是全局渐近稳定的,最后通过仿真算例验证所设计方法的可行性、有效性.     

图像目标外接多边形及凸壳的一种构造方法

对二值图像进行Hough变换后,在(ρ,θ)空间中选取了一组边界对应点,通过计算与这些边界对应点对应的图像空间中直线的交点,构造了图像目标的外接多边形;通过比较相距π/2 rad的投影区间长度是否相等,或区间长度的乘积是否为最小,得到了形状外接正方形和外接最小面积矩形;利用构造形状外接多边形的方法并通过增加边的数目,构造了形状的近似凸壳.实验和理论分析表明,文中算法具有好的抗噪性能和广泛的适用范围.     

全息离子束摆动刻蚀凸面闪耀光栅制备技术

高衍射效率的凸面闪耀光栅是高光谱分辨率成像光谱仪的核心分光元件,其制作方法包括机械刻划法、电子束直写法、X射线光刻法、全息离子束刻蚀法等,其中全息离子束刻蚀法因为具备良好的各向异性,不受尺寸与曲面形状限制,杂散光低,完全没有鬼线,制造时间短等优点成为现今光栅制造领域常用方法之一.传统全息离子束刻蚀凸面光栅时基底的弯曲会...     

Mixed norm regularized recursive total least squares for group sparse system identification

A mixed l_p,0‐regularized recursive total least squares (RTLS) algorithm is considered for group sparse system identification. Regularized recursive least squares (RLS) has been successfully applied to group sparse system identification; however, the estimation performance in regularized RLS‐based algorithms deteriorates when both input and output are contaminated by noise (the error‐in‐variables problem). We propose an l_p,0‐RTLS algorithm to handle group sparse system identification with errors‐in‐variables. The proposed algorithm is an RLS‐like solution that utilizes l_p,0‐regularization. The proposed algorithm provides excellent performance as well as reduces the required complexity by effective inversion matrix handling. Simulations demonstrate the superiority of the proposed l_p,0‐regularized RTLS for a group sparse system identification setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Clipping algorithms for solving the nearest point problem over reduced convex hulls

The nearest point problem (NPP), i.e., finding the closest points between two disjoint convex hulls, has two classical solutions, the Gilbert-Schlesinger-Kozinec (GSK) and Mitchell-Dem’yanov-Malozemov (MDM) algorithms. When the convex hulls do intersect, NPP has to be stated in terms of reduced convex hulls (RCHs), made up of convex pattern combinations whose coefficients are bound by a μ<1 value and that are disjoint for suitable μ. The GSK and MDM methods have recently been extended to solve NPP for RCHs using the particular structure of the extreme points of a RCH. While effective, their reliance on extreme points may make them computationally costly, particularly when applied in a kernel setting. In this work we propose an alternative clipped extension of classical MDM that results in a simpler algorithm with the same classification accuracy than that of the extensions already mentioned, but also with a much faster numerical convergence.     

System identification techniques based on support vector machines without bias term

The intention of this article is to utilize support vector machines (SVMs) as process models, which are the basis for most controller designs as well as simulation and monitoring tasks. SVMs are data‐driven models comparable with regularization networks, which merge elements from robust statistics, statistical learning, and kernel theory. The presentation is focused on the ‘no‐bias‐term’ variant, accounts for several peculiarities specific to SVM regression and derives an active‐set algorithm to solve the resulting large‐scale quadratic programming problem. For linear systems, SVMs are combined with multi‐stage methods for estimating output error and ARMAX models. Finally, two real‐world processes serve as test cases to evaluate the SVMs’ properties as nonlinear dynamic models. Copyright © 2013 John Wiley & Sons, Ltd.