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排序方式: 共有4491条查询结果,搜索用时 15 毫秒
91.
This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed. 相似文献
92.
93.
Z.A. Aboeleneen 《Mathematics and computers in simulation》2010,81(1):26-36
Based on generalized order statistics from Weibull distribution the approach of Bayesian and non-Bayesian estimation are discussed. We present a simple and efficient simulational algorithm for generating a generalized order statistics sample from any continuous distribution. Specializations to Bayesian and non-Bayesian estimators, some lifetime parameters and confidence intervals of progressive II censoring and record values are obtained and compared with the existing results. Two examples are given to illustrate the proposed estimators and the simulation algorithm. 相似文献
94.
95.
96.
基于购物网站用户搜索日志的商品词发现 总被引:1,自引:0,他引:1
商品词是电子商务领域描述商品的新词。主要介绍基于购物网站用户搜索日志的商品词发现的方法。该方法从搜索日志中提取用户查询,对查询进行分词,采用N元递增分步算法和串频统计,计算候选串的条件概率,选择候选商品词。为了降低人工审核的成本,只对产出商品词的准确率进行评价。利用该方法在手机、面霜和香水三类商品的搜索日志上进行了实验,最高准确率达到92.58%。 相似文献
97.
非参数回归模型均值函数结构变点的检测与应用 总被引:1,自引:0,他引:1
本文将一类系统参数变点检测问题转化为非参数回归模型均值函数结构变点的检测问题.针对当非参数模型均值函数跃度的长期均值为零时,残量累积和(cumulative sum,CUSUM)统计量无效的问题,首先利用均值函数的核估计构造新统计量,给出了原假设和备择假设下统计量的极限分布;进一步构造Bootstrap检验,证明了Bootstrap检验的一致性;最后以模拟结果表明新方法明显优于已有的方法,并应用于两类实际数据分析,说明方法的有效性. 相似文献
98.
Kostadin Koroutchev Author Vitae Elka Korutcheva Author Vitae 《Pattern recognition》2009,42(8):1684-1692
The purpose of this paper is to introduce an algorithm that can detect the most unusual part of a digital image in probabilistic setting. The most unusual part of a given shape is defined as a part of the image that has the maximal distance to all non-intersecting shapes with the same form. The method is tested on two- and three-dimensional images and has shown very good results without any predefined model. A version of the method independent of the contrast of the image is considered and is found to be useful for finding the most unusual part (and the most similar part) of the image conditioned on given image.The results can be used to scan large image databases, as for example medical databases. 相似文献
99.
Zheng Yin Author Vitae Xiaobo Zhou Author Vitae Youxian Sun Author Vitae Author Vitae 《Pattern recognition》2009,42(4):509-522
Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels. 相似文献
100.
This paper focuses on the performance evaluation of complex man-made systems, such as assembly lines, electric power grid,
traffic systems, and various paper processing bureaucracies, etc. For such problems, applying the traditional optimization
tool of mathematical programming and gradient descent procedures of continuous variables optimization are often inappropriate
or infeasible, as the design variables are usually discrete and the accurate evaluation of the system performance via a simulation
model can take too much calculation. General search type and heuristic methods are the only two methods to tackle the problems.
However, the “goodness” of heuristic methods is generally difficult to quantify while search methods often involve extensive
evaluation of systems at many design choices in a large search space using a simulation model resulting in an infeasible computation
burden. The purpose of this paper is to address these difficulties simultaneously by extending the recently developed methodology
of Ordinal Optimization (OO). Uniform samples are taken out from the whole search space and evaluated with a crude but computationally
easy model when applying OO. And, we argue, after ordering via the crude performance estimates, that the lined-up uniform
samples can be seen as an approximate ruler. By comparing the heuristic design with such a ruler, we can quantify the heuristic
design, just as we measure the length of an object with a ruler. In a previous paper we showed how to quantify a heuristic
design for a special case but we did not have the OO ruler idea at that time. In this paper we propose the OO ruler idea and
extend the quantifying method to the general case and the multiple independent results case. Experimental results of applying
the ruler are also given to illustrate the utility of this approach.
Zhen Shen received the B.E. degree from Department of Automation, Tsinghua University, Beijing, China in 2004. Currently, he is a Ph.D. candidate of Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar from Oct. 2007 to Apr. 2008 at Department of Manufacturing Engineering and Center for Information and Systems Engineering, Boston University, MA, USA. He specializes in the area of the discrete event dynamic systems (DEDS) theory and applications, and the optimization of complex systems. He is a student member of IEEE. Yu-Chi Ho received his S.B. and S.M. degrees in Electrical Engineering from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. Except for three years of full time industrial work he has been on the Harvard faculty. Since 1969 he has been Gordon McKay Professor of Engineering and Applied Mathematics. In 1988, he was appointed to the T. Jefferson Coolidge Chair in Applied Mathematics and Gordon McKay Professor of Systems Engineering at Harvard and as visiting professor to the Cockrell Family Regent’s Chair in Engineering at the University of Texas, Austin. In 2001, he retired from teaching duties at Harvard and became a Research Professor (2001–2006) and also was appointed to be a chair professor and chief scientist (part time), at the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing China. Qian-Chuan Zhao received the B.E. degree in automatic control in July 1992, the B.S. degree in applied mathematics in July 1992, and the Ph.D. degree in control theory and its applications in July 1996, all from Tsinghua University, Beijing, China. He is currently a Professor and Associate Director of the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar at Carnegie Mellon University, Pittsburgh, PA, and Harvard University, Cambridge, MA, in 2000 and 2002, respectively. He was a Visiting Professor at Cornell University, Ithaca, NY, in 2006. His research interests include discrete event dynamic systems (DEDS) theory and applications, optimization of complex systems, and wireless sensor networks. Dr. Zhao is an associate editor for the Journal of Optimization Theory and Applications. 相似文献
Zhen ShenEmail: |
Zhen Shen received the B.E. degree from Department of Automation, Tsinghua University, Beijing, China in 2004. Currently, he is a Ph.D. candidate of Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar from Oct. 2007 to Apr. 2008 at Department of Manufacturing Engineering and Center for Information and Systems Engineering, Boston University, MA, USA. He specializes in the area of the discrete event dynamic systems (DEDS) theory and applications, and the optimization of complex systems. He is a student member of IEEE. Yu-Chi Ho received his S.B. and S.M. degrees in Electrical Engineering from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. Except for three years of full time industrial work he has been on the Harvard faculty. Since 1969 he has been Gordon McKay Professor of Engineering and Applied Mathematics. In 1988, he was appointed to the T. Jefferson Coolidge Chair in Applied Mathematics and Gordon McKay Professor of Systems Engineering at Harvard and as visiting professor to the Cockrell Family Regent’s Chair in Engineering at the University of Texas, Austin. In 2001, he retired from teaching duties at Harvard and became a Research Professor (2001–2006) and also was appointed to be a chair professor and chief scientist (part time), at the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing China. Qian-Chuan Zhao received the B.E. degree in automatic control in July 1992, the B.S. degree in applied mathematics in July 1992, and the Ph.D. degree in control theory and its applications in July 1996, all from Tsinghua University, Beijing, China. He is currently a Professor and Associate Director of the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar at Carnegie Mellon University, Pittsburgh, PA, and Harvard University, Cambridge, MA, in 2000 and 2002, respectively. He was a Visiting Professor at Cornell University, Ithaca, NY, in 2006. His research interests include discrete event dynamic systems (DEDS) theory and applications, optimization of complex systems, and wireless sensor networks. Dr. Zhao is an associate editor for the Journal of Optimization Theory and Applications. 相似文献