• Communications of the Korean Mathematical Society
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• v.32 no.3
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• pp.709-714
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• 2017

For every interval [a, b], we denote by (${\bar{x}}_A,{\bar{y}}_A$) and (${\bar{x}}_L,{\bar{y}}_L$) the geometric centroid of the area under a catenary y = k cosh((x - c)/k) defined on this interval and the centroid of the curve itself, respectively. Then, it is well-known that ${\bar{x}}_L={\bar{x}}_A$ and ${\bar{y}}_L=2{\bar{y}}_A$. In this paper, we show that one of ${\bar{x}}_L={\bar{x}}_A$ and ${\bar{y}}_L=2{\bar{y}}_A$ characterizes the family of catenaries among nonconstant $C^2$ functions. Furthermore, we show that among nonconstant and nonlinear $C^2$ functions, ${\bar{y}}_L/{\bar{x}}_L=2{\bar{y}}_A/{\bar{x}}_A$ is also a characteristic property of catenaries.

• Steel and Composite Structures
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• v.22 no.5
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• pp.1073-1084
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• 2016

Rectangular concrete-filled steel tubular columns with unequal wall thickness were investigated in the paper. The physical centroid, the centroidal principal axes of inertia, and the section core were given. The generalized bending formula and the generalized eccentric compression formula were deduced, and the equation of the neutral axis was also provided. The two rectangular concrete-filled steel tubular stub specimens subjected to the compression load on the physical centroid and the geometric centroid respectively were tested to verify the theoretical formulas.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.114-123
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• 1996

In this paper, we propose an automatic recognition algorithm using the object centroid of a facial image. First, we separate the facial image from the background image using the chroma-key technique and we find the centroid of the separated facial image. Second, we search nose in the facial image based on knowledge of human faces and the coordinate of the object centroid and, we calculate 17 feature parameters automatically. Finally, we recognize the facial image by using feature parameters in the neural networks which are trained through error backpropagation algorithm. It is illustrated by experiments by experiments using the proposed recogniton system that facial images can be recognized in spite of the variation of the size and the position of images.

• East Asian mathematical journal
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• v.29 no.4
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• pp.425-447
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• 2013

Since the center of mass of mathematics curriculum in middle school is dealt with only on triangle and it is defined as just an intersection point of median lines without any physical experiments, students sometimes have misconception of the centroid as well as it is difficult to promote divergent thinking that enables students to think the centroids of various figures. To overcome these problems and to instruct effectively the centroid unit in middle school mathematics classroom, this study suggests a teaching and learning method for the unit which uses physical experiments, drawing, and calculation methods sequentially based on the investigation of students' understanding on the centroid of triangle and the analysis of the mathematics textbooks.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.203-204
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• 2007

This paper investigated the influence on partial discharge distribution of various defects at the model power cable joints interface using K-means clustering. As the result of analyzing discharge number distribution of ${\Phi}-n$ cluster, clusters shifted to $0^{\circ}\;and\;180^{\circ}$ with increasing applying voltage. It was confirmed that discharge quantity and euclidean distance between centroids were increased with applying voltage from the analyzing centroid distribution of ${\Phi}-q$ cluster. The degree of dispersion was increased with calculating standard deviation of ${\Phi}-q$ cluster centroid. The tendency both number of discharge and mean value of ${\Phi}-q$ cluster centroid were some different with defect types.

• Communications of the Korean Mathematical Society
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• v.33 no.1
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• pp.237-245
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• 2018

For every interval [a, b], we denote by $({\bar{x}}_A,{\bar{y}}_A)$ and $({\bar{x}}_L,{\bar{y}}_L)$ the geometric centroid of the area under a catenary curve y = k cosh((x-c)/k) defined on this interval and the centroid of the curve itself, respectively. Then, it is well-known that ${\bar{x}}_L={\bar{x}}_A$ and ${\bar{y}}_L=2{\bar{y}}_A$. In this paper, we fix an end point, say 0, and we show that one of ${\bar{x}}_L={\bar{x}}_A$ and ${\bar{y}}_L=2{\bar{y}}_A$ for every interval with an end point 0 characterizes the family of catenaries among nonconstant $C^2$ functions.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.156-165
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• 2021

In this paper, we propose an histogram weighted centroid based Siamese region proposal network for object tracking. The original Siamese region proposal network uses two identical artificial neural networks which take two different images as the inputs and decide whether the same object exist in both input images based on a similarity measure. However, as the Siamese network is pre-trained offline, it experiences many difficulties in the adaptation to various online environments. Therefore, in this paper we propose to incorporate the histogram weighted centroid feature into the Siamese network method to enhance the accuracy of the object tracking. The proposed method uses both the histogram information and the weighted centroid location of the top 10 color regions to decide which of the proposed region should become the next predicted object region.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• Journal of IKEEE
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• v.26 no.1
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• pp.67-76
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• 2022

In this paper, we propose a new algorithm to improve the accuracy of the Wi-Fi access point (AP) positioning technique. The proposed algorithm based on evaluating the trustworthiness of the signal strength quality of each measurement node is superior to other existing AP positioning algorithms, such as the centroid, weighted centroid, multilateration, and radio distance ratio methods, owing to advantages such as reduction of distance errors during positioning, reduction of complexity, and ease of implementation. To validate the performance of the proposed algorithm, we conducted experiments in a complex indoor environment with multiple walls and obstacles, multiple office rooms, corridors, and lobby, and measured the corresponding AP signal strength value at several specific points based on their coordinates. Using the proposed algorithm, we can obtain more accurate positioning results of the APs for use in research or industrial applications, such as finding rogue APs, creating radio maps, or estimating the radio frequency propagation properties in an area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.