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Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.


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Journal ArticleDOI
TL;DR: Reference period collation, a method recently proposed for analysing the stochastic nature of a nominally periodic textile reinforcement, is extended to allow application to a laminate of stacked, nested plies.
Abstract: Reference period collation, a method recently proposed for analysing the stochastic nature of a nominally periodic textile reinforcement, is extended to allow application to a laminate of stacked, nested plies. The method decomposes the characteristics of the fibre reinforcement into non-stochastic periodic (or systematic) trends and non-periodic stochastic fluctuations. The stochastic character of every tow is analysed in terms of the centroid position, aspect ratio, area, and orientation of its cross-section. The collation method is tested using X-ray micro-computed tomography data for a seven-ply 2/2 twill woven carbon-epoxy composite produced by resin transfer moulding. All tow characteristics, with exception of the in-plane centroid position, exhibit systematic trends that show only mild differences between plies. They correlate most strongly with cross-over points within a single ply. Of the various parameters, the in-plane centroid position is subject to the largest tow-to-tow variability, with deviations correlated over distances exceeding the unit cell size.

85 citations

Journal ArticleDOI
16 Jun 2016
TL;DR: The results indicated that k-means has a potential to classify BCW dataset and provided extensive understanding of the computational parameters that can be used with k-Means.
Abstract: Breast cancer is one of the most common cancers found worldwide and most frequently found in women. An early detection of breast cancer provides the possibility of its cure; therefore, a large number of studies are currently going on to identify methods that can detect breast cancer in its early stages. This study was aimed to find the effects of k-means clustering algorithm with different computation measures like centroid, distance, split method, epoch, attribute, and iteration and to carefully consider and identify the combination of measures that has potential of highly accurate clustering accuracy. K-means algorithm was used to evaluate the impact of clustering using centroid initialization, distance measures, and split methods. The experiments were performed using breast cancer Wisconsin (BCW) diagnostic dataset. Foggy and random centroids were used for the centroid initialization. In foggy centroid, based on random values, the first centroid was calculated. For random centroid, the initial centroid was considered as (0, 0). The results were obtained by employing k-means algorithm and are discussed with different cases considering variable parameters. The calculations were based on the centroid (foggy/random), distance (Euclidean/Manhattan/Pearson), split (simple/variance), threshold (constant epoch/same centroid), attribute (2–9), and iteration (4–10). Approximately, 92 % average positive prediction accuracy was obtained with this approach. Better results were found for the same centroid and the highest variance. The results achieved using Euclidean and Manhattan were better than the Pearson correlation. The findings of this work provided extensive understanding of the computational parameters that can be used with k-means. The results indicated that k-means has a potential to classify BCW dataset.

85 citations

Journal ArticleDOI
TL;DR: This paper introduces a cluster level index called centroid index, which is intuitive, simple to implement, fast to compute and applicable in case of model mismatch as well as to other clustering models beyond the centroid-based k-means.

85 citations

Journal ArticleDOI
TL;DR: This work proposes a new way to solve the auxiliary problem of finding a column with negative reduced cost based on geometric arguments that greatly improves the efficiency of the whole algorithm and leads to exact solution of instances with over 2,300 entities.
Abstract: Given a set of entities associated with points in Euclidean space, minimum sum-of-squares clustering (MSSC) consists in partitioning this set into clusters such that the sum of squared distances from each point to the centroid of its cluster is minimized. A column generation algorithm for MSSC was given by du Merle et al. in SIAM Journal Scientific Computing 21:1485–1505. The bottleneck of that algorithm is the resolution of the auxiliary problem of finding a column with negative reduced cost. We propose a new way to solve this auxiliary problem based on geometric arguments. This greatly improves the efficiency of the whole algorithm and leads to exact solution of instances with over 2,300 entities, i.e., more than 10 times as much as previously done.

85 citations

Journal ArticleDOI
TL;DR: An analog aggregation network that extracts the position of a stimulus in a sensory field through the computation of the centroid of a visual image is presented and theory for the localization of a bright visual stimulus is developed.
Abstract: An analog aggregation network that extracts the position of a stimulus in a sensory field is presented. This network is integrated with photodiodes in a VLSI circuit that performs stimulus localization through the computation of the centroid of a visual image. In this implementation, bipolar transistors and global subtraction are used to produce a high-precision centroid implementation. Theory for the localization of a bright visual stimulus is developed, and the theoretical predictions are compared to experimental data taken from the 160×160-pixel centroid circuit. Finally, the applications of these circuits to more complex feature extraction and to sensorimotor feedback systems are discussed.

84 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023492
20221,001
2021184
2020202
2019269
2018271