نتایج جستجو برای: mean algorithm

تعداد نتایج: 1306753  

Journal: :Journal of Communication and Information Systems 2003

Due to the extensive use of composites in various industries and the fact that defects reduce ultimate strength and efficiency during operation, detection of failures in composite parts is very important. The aim of this paper is to use Acoustic Emission (AE) non-destructive method in four-point bending test of carbon/epoxy composite to analyze and examine the failure mechanisms. This method is...

Journal: :Simulation 1999
Claudio Gennaro Peter J. B. King

Volume 16, No. 1 TRANSACTIONS 1

2014
Ya-Zhou Ren Carlotta Domeniconi Guoji Zhang Guo-Xian Yu

The mean shift algorithm is a nonparametric clustering technique that does not make assumptions on the number of clusters and on their shapes. It achieves this goal by performing kernel density estimation, and iteratively locating the local maxima of the kernel mixture. The set of points that converge to the same mode defines a cluster. While appealing, the performance of the mean shift algorit...

2010
Themos Stafylakis Vassilios Katsouros George Carayannis

In this paper, we investigate the use of the mean shift algorithm with respect to speaker clustering. The algorithm is an elegant nonparametric technique that has become very popular in image segmentation, video tracking and other image processing and computer vision tasks. Its primary aim is to detect the modes of the underlying density and consequently merge those observations being attracted...

2009
Jakub Chaloupka Lubos Brim

We study some existing techniques for solving mean-payoff games (MPGs), improve them, and design a randomized algorithm for solving MPGs with currently the best expected complexity.

Journal: :CoRR 2016
Lampros Flokas Petros Maragos

This work presents a new variation of the commonly used Least Mean Squares Algorithm (LMS) for the identification of sparse signals with an a-priori known sparsity using a hard threshold operator in every iteration. It examines some useful properties of the algorithm and compares it with the traditional LMS and other sparsity aware variations of the same algorithm. It goes on to examine the app...

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