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1.
一类非线性滤波器--UKF综述   总被引:87,自引:3,他引:84  
潘泉  杨峰  叶亮  梁彦  程咏梅 《控制与决策》2005,20(5):481-489,494
回顾了UKF算法的发展,从一般意义讨论了UT变换算法和采样策略的选择依据,并给出了UKF算法描述.从条件函数和代价函数入手,在给出多种采样策略的基础上对UKF采样策略进行了分析和比较.最后对UKF算法未来可能的研究方向进行了探讨.  相似文献
2.
改进的概率路径图法   总被引:2,自引:0,他引:2       下载免费PDF全文
概率路径图法(PRM)是最主要的运动规划算法之一,针对概率路径图法在复杂环境中规划效率低下的缺点提出了一种改进的概率路径图法,新方法的采样采用了分阶段混合采样策略。最后通过与传统的PRM方法进行仿真实验比较,结果表明改进后的PRM方法能够较大的提高规划效率。  相似文献
3.
三相SPWM波的软件生成及应用研究   总被引:1,自引:0,他引:1  
提出了一种软件生成三相SPWM波的新方法,首先,阐述了对称规则采样法和基准正弦函数法;然后,介绍了一个通用变频器的应用实例及生成三相SPWM的程序,实验结果显示,该变频器能满足实际应用的要求。  相似文献
4.
三角平面Halton点采样策略及其性能分析   总被引:1,自引:0,他引:1  
针对一般随机采样的局限性,提出了基于Halton点采样的原理与方法.给出了Halton点的定义,分析了其算法复杂度,在此基础上给出了三角平面的采样策略,包括几何区域的划分、三角平面与矩形平面的坐标映射、采样点坐标计算原理;并将该方法与Jittered采样法、改进的LHS采样法进行了比较.实验结果表明,Halton点采样策略比一般的随机采样策略具有更好的采样点分布.  相似文献
5.
基于采样策略的主动学习算法研究进展   总被引:1,自引:0,他引:1  
主动学习算法通过选择信息含量大的未标记样例交由专家进行标记,多次循环使分类器的正确率逐步提高,进而在标记总代价最小的情况下获得分类器的强泛化能力,这一技术引起了国内外研究人员的关注.侧重从采样策略的角度,详细介绍了主动学习中学习引擎和采样引擎的工作过程,总结了主动学习算法的理论研究成果,详细评述了主动学习的研究现状和发展动态.首先,针对采样策略选择样例的不同方式将主动学习算法划分为不同类型,进而,对基于不同采样策略的主动学习算法进行了深入地分析和比较,讨论了各种算法适用的应用领域及其优缺点.最后指出了存在的开放性问题和进一步的研究方向.  相似文献
6.
In this paper we consider (hierarchical, La-grange)reduced basis approximation anda posteriori error estimation for linear functional outputs of affinely parametrized elliptic coercive partial differential equa-tions. The essential ingredients are (primal-dual)Galer-kin projection onto a low-dimensional space associated with a smooth “parametric manifold” - dimension re-duction; efficient and effective greedy sampling meth-ods for identification of optimal and numerically stable approximations - rapid convergence;a posteriori er-ror estimation procedures - rigorous and sharp bounds for the linear-functional outputs of interest; and Offine-Online computational decomposition strategies - min-imummarginal cost for high performance in the real-time/embedded (e.g., parameter-estimation, control)and many-query (e.g., design optimization, multi-model/ scale)contexts. We present illustrative results for heat conduction and convection-diffusion,inviscid flow, and linear elasticity; outputs include transport rates, added mass,and stress intensity factors. This work was supported by DARPA/AFOSR Grants FA9550-05-1-0114 and FA-9550-07-1-0425,the Singapore-MIT Alliance,the Pappalardo MIT Mechanical Engineering Graduate Monograph Fund,and the Progetto Roberto Rocca Politecnico di Milano-MIT.We acknowledge many helpful discussions with Professor Yvon Maday of University Paris6.  相似文献
7.
Probabilistic Roadmaps (PRM) have been successfully used to plan complex robot motions in configuration spaces of small and large dimensionalities. However, their efficiency decreases dramatically in spaces with narrow passages. This paper presents a new method—small-step retraction—that helps PRM planners find paths through such passages. This method consists of slightly “fattening” robot's free space, constructing a roadmap in fattened free space, and finally repairing portions of this roadmap by retracting them out of collision into actual free space. Fattened free space is not explicitly computed. Instead, the geometric models of workspace objects (robot links and/or obstacles) are “thinned” around their medial axis. A robot configuration lies in fattened free space if the thinned objects do not collide at this configuration. Two repair strategies are proposed. The “optimist” strategy waits until a complete path has been found in fattened free space before repairing it. Instead, the “pessimist” strategy repairs the roadmap as it is being built. The former is usually very fast, but may fail in some pathological cases. The latter is more reliable, but not as fast. A simple combination of the two strategies yields an integrated planner that is both fast and reliable. This planner was implemented as an extension of a pre-existing single-query PRM planner. Comparative tests show that it is significantly faster (sometimes by several orders of magnitude) than the pre-existing planner. Mitul Saha received the B.S. degree from the Indian Institute of Technology, Kanpur, India, in 2001 and the M.S. degree from the Computer Science Department at Stanford University, Stanford, CA, in 2005. He is currently pursuing the Ph.D. degree in mechanical engineering at Stanford University. His research interests include motion planning, computer vision, graphics, and structural biology. Jean-Claude Latombe graduated in electrical and computer engineering from the National Polytechnic Institute of Grenoble, France, in 1970. He received the M.S. degree in electrical engineering from the National Polytechnic Institute of Grenoble in 1972, and the PhD degree in computer science from the University of Grenoble in 1977. He joined the Department of Computer Science at Stanford University in 1987, where he currently is the Kumagai Professor in the School of Engineering. He does research in the general areas of artificial intelligence, robotics, and geometric computing. He is particularly interested in motion planning, computational biology, and computer-assisted surgery. Yu-Chi Chang is a Ph.D. candidate in the Mechanical Engineering at Stanford University. Yu-Chi received the B.Sc. in Mechanical Engineering and the M.Sc. in Material Science from National Taiwan University, Taiwan, and the M.Sc. in Mechanical Engineering from Stanford University, United States. His current research interests include robust design and statistical analysis for manufacturing system. Friedrich Prinz is the Rodney H. Adams Professor of Engineering and Professor of Mechanical Engineering and Materials Science and Engineering, Stanford University. Professor Prinz received his Ph.D. degree in Physics from the University of Vienna in 1975. He has been active in synergistic activities with organizations like the National Research Council Committees, the Japanese Technology Evaluation Center and World Technology Evaluation Center, as well as Portuguese Science and Technology Foundation. He was elected to the Austrian Academy of Science (foreign member), Vienna, Austria in 1996. Dr. Prinz's current research activities address a wide range of problems related to design and rapid prototyping of organic and inorganic devices. His current work focuses on the fabrication and physics of fuel cells as well as the creation of biological cell structures. His group uses atomic force microscopy and impedance spectroscopy to characterize the behavior of electrochemical systems with micro and nano-scale dimensions.  相似文献
8.
Circle detection is a critical issue in image analysis and object detection. Although Hough transform based solvers are largely used, randomized approaches, based on the iterative sampling of the edge pixels, are object of research in order to provide solutions less computationally expensive. This work presents a randomized iterative work-flow, which exploits geometrical properties of isophotes in the image to select the most meaningful edge pixels and to classify them in subsets of equal isophote curvature. The analysis of candidate circles is then performed with a kernel density estimation based voting strategy, followed by a refinement algorithm based on linear error compensation. The method has been applied to a set of real images on which it has also been compared with two leading state of the art approaches and Hough transform based solutions. The achieved results show how, discarding up to 57% of unnecessary edge pixels, it is able to accurately detect circles within a limited number of iterations, maintaining a sub-pixel accuracy even in the presence of high level of noise.  相似文献
9.
针对底层局部时空特征数量少以及中层特征表达能力弱的问题,结合时空深度特征,提出一种人体行为识别算法。依据运动剧烈区域在行为识别中提供更多判别信息的思想,利用视频图像的深度信息确定人体运动显著性区域,通过计算区域内光流特征作为度量区域活跃度的能量函数,依据能量函数对运动显著性区域进行高斯取样,使样本点分布于运动剧烈区域。将采集到的样本点作为动作底层特征描述人体行为,结合词袋模型,采用支持向量机分类器对行为进行识别。实验结果表明,在SwustDepth数据集中,基于时空深度特征的人体行为识别算法的平均行为识别准确率达到92%,且具有较高的鲁棒性。  相似文献
10.
为了克服训练样本不足、获取大量标注样本费时费力的问题,在基于不确定选择策略的基础上,提出了一种新的基于分层选择策略的主动学习方法。使用新提出的选择策略从大量无标注的样本中选择最有价值的样例,进行标注后加入到训练集中来训练分词器。最后在 PKU、MSR 和山西大学数据集上进行测试,并与不确定选择策略进行比较。结果表明提出的分层选择策略在相同大小的训练语料下可以获得更高的分词准确率,同时还降低了人工标注的代价。  相似文献
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