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Coplanar Force Systems
Published in M. Rashad Islam, Md Abdullah Al Faruque, Bahar Zoghi, Sylvester A. Kalevela, Engineering Statics, 2020
M. Rashad Islam, Md Abdullah Al Faruque, Bahar Zoghi, Sylvester A. Kalevela
‘Centroid’ means the center of gravity of an area. The centroids of two regular geometric areas (rectangle and triangle) are studied in this chapter; they are shown in Figure 2.20. If the sides of a rectangle are b and h, respectively, then the centroid is located at (b/2, h/2), as shown in Figure 2.20.
Machine Learning Basics
Published in Peter Wlodarczak, Machine Learning and its Applications, 2019
k-means clustering is one of the most popular cluster and machine learning algorithm. k-means clustering falls into the category of centroid-based algorithms. A centroid is the geometric center of a geometric plane figure. The centroid is also called barycenter. In centroid-based clustering, n observations are grouped into k clusters in such a way that each observation belongs to the cluster with the nearest centroid. Here, the criterion for clustering is distance. The centroid itself does not need to be an observation point. Figure 2.7 shows k-means clustering with 3 clusters.
Statics
Published in Quamrul H. Mazumder, Introduction to Engineering, 2018
Centroid is the geometric center of a shape. In a homogeneous shape, the centroid is the same as the center of gravity. The center of gravity is the point of balance. In a free-body diagram, this is the point where gravity is drawn as an acting force. The weight W⃗ and the coordinates x̄, ȳ of the equations W=∫dW;x¯W=∫xdW;y¯W=∫ydW define the center of gravity G of a two-dimensional structure.
Potential conflicts between individual preferences and city color planning: a case study of Busan, South Korea
Published in Journal of Asian Architecture and Building Engineering, 2023
Yajun Wen, Yuka Himeno, Jaehoon Chung
The interpretation of which variables are related, and the extent to which they are related is based on the centroid plots. This plot shows how well the variables separate groups of objects. A centroid represents the center of gravity of an object (Ayşe Canan et al. 2010). In this method, it is estimated that there is a correlation between orthogonal factors or factors existing in the same quadrant (Chung and Song 2018). Furthermore, in a quadrant, the close variables have more similarities than variables that are far apart. Figure 6, and Table S4, and S5 show the centroid plots diagram of each individual attribute and color attribute. These centroid plots illustrate the comprehensive relationship between seven individual attributes and the three color elements.
Scalable Feature Tracking for Finite Element Meshes Demonstrated with a Novel Phase-Field Grain Subdivision Model
Published in Nuclear Technology, 2021
Cody J. Permann, Andrea M. Jokisaari, Michael R. Tonks, Daniel Schwen, Derek R. Gaston, Fande Kong, Robert Hiromoto, Richard C. Martineau
On each step, the feature information from the previous time step is compared against all of the features from the current time step and organized such that the best matches for all features are determined correctly. The comparison criterion is the global minimization of the centroid distances of all features simultaneously. The centroid is calculated by averaging the element centroids making up each feature. As we iterate over the new list of features, we select the feature in the previous list that is closest by centroid distance. This pairing is saved into a “best match” data structure while the remaining features are being processed. It is possible for features to compete for the same best match feature on the previous time step. This indicates that a feature has been absorbed or has otherwise disappeared on the current step and that its corresponding feature from the previous step is incorrectly identifying an unrelated feature as the best match. This case is handled by marking the feature with the greater centroid distance mismatch as inactive.
Application of Image Processing Techniques and Artificial Neural Network for Detection of Diseases on Brinjal Leaf
Published in IETE Journal of Research, 2022
Area: Area of the region can be calculated by finding the ROI. In general, the area of the region is defined as where Perimeter: Perimeter is one of the structural properties of the region which is obtained by finding the sum of the distances from each coordinate and to the next. The perimeter measurement can be distorted due to the infected nature of certain boundaries. Let us suppose, a(t) and b(t) denote the parametric coordinates of a curve enclosing a region S, then the perimeter of the region is given by Centroid: The geometrical centre of a body is known as centroid, which gives the centre mass of an infected part of the leaf image. The centroid represents a point that can be obtained by finding the mean of the coordinates. The centroid of a non-self-intersecting closed region is defined by n vertices , … , the point is given by the following equations where A is the regions of the affected area Diameter: The distance around an infected region of the leaf image is called the circumference. The distance across a circle through the centre is called the diameter. The radius of the circumference of an affected area, , where C is the circumference and is the diameter. Even though it is possible to extract a large set of features, only a small subset of them is used for classification. As the dimensionality of the feature vector increases, the amount of training data required also increases exponentially. Furthermore, there may be a strong correlation between different types of features and thus by fusing these features can produce a feature vector. The features are grouped into three following categories based on the prescribed information. The important challenge in this step is to find the most appropriate representation and select a subset of the features extracted from the infected part of the leaf. The intensity-based features provide information on the intensity (grey-level or colour) and histogram of the pixels located in the infected part of the image. The textural features provide information about the variation in the intensity of a surface and structure of the leaf. The structural features provide information about the size and shape of the infected region found on the leaf image. The feature vector contains combined feature of fusing: intensity, textural and structural data of the images.