新型8极9槽永磁同步电机静态场的分析

针对采用分布式绕组的缺点,提出一种采用集中式绕组的新型8极9槽永磁同步电机,并在此基础上改变磁钢的参数,对电机的结构进行优化。     

任意裂纹面荷载作用下界面断裂分析

为研究裂纹面上作用的荷载对裂纹稳定性的影响,本文基于比例边界有限元方法提出裂纹面作用有任意方向、任意大小面荷载的界面应力强度因子求解模型.界面裂纹具有复数形式的应力奇异性指数,在任意裂纹面荷载作用下其奇异应力场更为复杂.应用本模型,径向的位移和应力可解析求解,无需网格细分即可自动反映裂尖的应力奇异性.裂纹面上的任意荷载首先可分解成平行于裂纹面以及垂直于裂纹面的分量,并进一步分解成有限项幂函数的和.对每个幂函数荷载解析求解,基于线性叠加原理获得结构在全部荷载作用下的解.该模型对各向同性材料和各向异性材料均适用.文中通过板承受裂缝面荷载时的应力强度因子求解的多个算例对该模型进行了验证和应用,对板的几何尺寸和双材料参数进行了敏感性分析,并应用于重力坝坝踵界面裂缝在水压力作用下的应力强度因子求解.     

Coordinate descent with arbitrary sampling I: algorithms and complexity†

We study the problem of minimizing the sum of a smooth convex function and a convex block-separable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a very flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordinate descent and distributed coordinate descent—both in nonaccelerated and accelerated variants. The variants with arbitrary (or importance) sampling are new. We provide a complexity analysis of ALPHA, from which we deduce as a direct corollary complexity bounds for its many variants, all matching or improving best known bounds.     

三杆六脚体对桥墩周围流场影响的试验研究

为寻求保护桥墩周围河床避受冲刷的有效方式,采用物理模型试验的方法,对三杆六脚透水架体对桥墩周围流场水力特性的影响进行了研究分析。结果表明,三杆六脚透水架体不但能削减穿越其内部的水流流速,在床面附近形成流速减速区域,将墩周马蹄形旋涡和墩后尾流漩涡的作用能量有效地转移到远离床面的水体中,而且还能利用自身扰流消能和实体抗冲的双重特性有效抑制墩前向下水流对床面的冲击。     

基于LabVIEW的虚拟任意信号发生器设计

为了解决传统的专用仪器开发难度大、周期长、成本高的问题,提出并实现了采用虚拟仪器技术实现的任意信号发生器,不但降低了开发成本,节省了开发时间,并且可以和其他虚拟仪器构成一个完整的实验系统而无需添加其他硬件产生信号输出供其他仪器使用.主要利用LabVIEW编程,在计算机内产生信号的数据,通过D/A模块将产生的数据按照一定的速率输出,从而得到连续的模拟信号波形.用户可以通过此虚拟任意信号发生器自己编辑波形,从而在真正意义上实现任意波形发生器功能.     

Accurate evaluation of Green’s functions in a layered medium by SDP-FLAM

Based on local Taylor expansions on the complex plane, a method for fast locating all modes (FLAM) of spectral-domain Green’s Functions in a planar layered medium is developed in this paper. SDP-FLAM, a combination of FLAM with the steepest descent path algorithm (SDP), is employed to accurately evaluate the spatial-domain Green’s functions in a layered medium. According to the theory of complex analysis, the relationship among the poles, branch points and Riemann sheets is also analyzed rigorously. To inverse the Green’s functions from spectral to spatial domain, SDP-FLAM method and discrete complex image method (DCIM) are applied to the non-near field region and the near filed region, respectively. The significant advantage of SDP-FLAM lies in its capability of calculating Green’s functions in a layered medium of moderate thickness with loss or without loss. Some numerical examples are presented to validate SDP-FLAM method. Supported by the National Natural Science Foundation of China (Grant No. 60621002), and the State Key Development Program for Basic Research of China (Grant No. 2009CB320200)     

Communication contention in APN list scheduling algorithm

Task scheduling is an essential aspect of parallel process system. This NP-hard problem assumes fully connected homogeneous processors and ignores contention on the communication links. However, as arbitrary processor network (APN), communication contention has a strong influence on the execution time of a parallel application. This paper investigates the incorporation of contention awareness into task scheduling. The innovation is the idea of dynamically scheduling edges to links, for which we use the earliest finish communication time search algorithm based on shortest-path search method. The other novel idea proposed in this paper is scheduling priority based on recursive rank computation on heterogeneous arbitrary processor network. In the end, to reduce time complexity of algorithm, a parallel algorithm is proposed and speedup O(PPE) is achieved. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm significantly surpasses classic and static communication contention awareness algorithm, especially for high data transmission rate parallel application. Supported by the National Natural Science Foundation of China (Grant Nos. 90715029 and 60603053), the Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Edacation of China, and the Key Project of Science & Technology of Hunan Province (Grant No. 2006GK2006)     

Free-Form Deformation with Rational DMS-Spline Volumes

In this paper, we propose a novel free-form deformation （FFD） technique, RDMS-FFD （Rational DMS-FFD）, based on rational DMS-spline volumes. RDMS-FFD inherits some good properties of rational DMS-spline volumes and combines more deformation techniques than previous FFD methods in a consistent framework, such as local deformation, control lattice of arbitrary topology, smooth deformation, multiresolution deformation and direct manipulation of deformation. We first introduce the rational DMS-spline volume by directly generalizing the previous results related to DMS-splines. How to generate a tetrahedral domain that approximates the shape of the object to be deformed is also introduced in this paper. Unlike the traditional FFD techniques, we manipulate the vertices of the tetrahedral domain to achieve deformation results. Our system demonstrates that RDMS-FFD is powerful and intuitive in geometric modeling.     

基于抠像技术的图像无缝融合算法

为了更快更好的进行图像融合,在用自由融合算法对泊松图像编辑与抠像技术进行改进的基础上,实现了一套图像自由无缝融合算法。该融合算法首先利用抠像技术来提取图像中的物体边缘,然后迭代求解泊松方程,从而取得了自然的融合效果。该算法与最新的无缝融合算法相比,其优点在于:①对于前景与背景的颜色变化复杂的图像,仍可得到准确的边缘;②可以使用户能引导前景映射图的走向;③无论图像前景层中存在多少个洞,融合效果将不受任何影响。这使得该算法的应用具有相当的灵活性。对于目前的图像融合技术而言,该算法可以获得更高的融合质量、更快的处理速度以及多样的融合效果。     

一种任意分布下的隐变量因果结构学习算法

因果发现旨在通过观测数据挖掘变量间的因果关系,在实际应用中需要从观测数据中学习隐变量间的因果结构。现有方法主要利用观测变量间的协方差信息（如四分体约束）或引入非高斯假设（如三分体约束）来解决线性因果模型下的隐变量结构学习问题,但大多限定于分布明确的情况,而实际应用环境往往并不满足这种假设。给出任意分布下隐变量结构的识别性证明,指出在没有混淆因子影响的情况下,两个隐变量的因果方向可识别所需要的最小条件是仅需要其中一个隐变量的噪声服从非高斯分布。在此基础上,针对线性隐变量模型提出一种在任意分布下学习隐变量因果结构的算法,先利用四分体约束方法学习得到隐变量骨架图,再通过枚举骨架图的等价类并测量每一个等价类中的三分体约束来学习因果方向,同时将非高斯约束放宽到尽可能最小的变量子集,从而扩展线性隐变量模型的应用范围。实验结果表明,与MIMBuild和三分体约束方法相比,该算法得到了最佳的F1值,能够在任意分布下学习更多的隐变量因果结构信息,且具有更强的鲁棒性。