• Journal of the Korean School Mathematics Society
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• v.9 no.1
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• pp.93-104
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• 2006

The centroid of triangle is physical property but almost mathematics teachers do not teach centroid by the help of experiments an so they have misconception on principle of centroid. In this paper we investigate whether teachers have made an experiment on centroid of triangle, and we check up on the level of understanding on centroid for mathematics teachers. We introduce the method of teaching centroid and study the process of generalization about centroid of triangle for gifted students.

• The Korean Journal of Ecology
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• v.26 no.3
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• pp.123-127
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• 2003

Cubic-meter volumes estimated from two proxy taper functions were compared to observed volumes of Japanese red cedar trees (Cryptomeria japonica D. Don) to evaluate accuracy and precision in the centroid method. Centroid volume estimates were also compared to volume estimates from existing whole-tree volume equations developed for another geographic region. This study found that one proxy function produced unbiased volume estimates while the other was biased. Volume estimates from the whole-tree equations were also biased. However, the volume estimates from the whole-tree equations were more precise than those from the centroid method. These results support previous studies that the centroid method can produce reliable volumes of trees when no other reliable volume equations exist.

• Journal of agriculture & life science
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• v.43 no.1
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• pp.17-23
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• 2009

This study was conducted to compare volumes estimated from two taper functions and observed volumes of Chamaecyparis obtusa trees to evaluate accuracy and precision of centroid method. Centroid volume estimates were also compared with volume estimates from existing Forest Resources Evaluation and Prediction Program. The results of this study showed that Gregoire's simple taper function produced unbiased volume estimates while the others were biased. Volume estimates from the Forest Resources Evaluation and Prediction Program were also biased when applied in the Jangseong National Forest regions. These results suggested that the centroid method could produce reliable stem volumes of trees when no other reliable stem volume equations exist.

• Journal of The Korean Astronomical Society
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• v.33 no.2
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• pp.89-95
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• 2000

As an efficient method to detect blending of general gravitational microlensing events, it is proposed to measure the shift of source star image centroid caused by microlensing. The conventional method to detect blending by this method is measuring the difference between the positions of the source star image point spread function measured on the images taken before and during the event (the PSF centroid shift, ${\delta}{\theta}$c,PSF). In this paper, we investigate the difference between the centroid positions measured on the reference and the subtracted images obtained by using the difference image analysis method (DIA centroid shift, ${\delta}{\theta}$c.DIA), and evaluate its relative usefulness in detecting blending over the conventional method based on ${\delta}{\theta}$c,PSF measurements. From this investigation, we find that the DIA centroid shift of an event is always larger than the PSF centroid shift. We also find that while ${\delta}{\theta}$c,PSF becomes smaller as the event amplification decreases, ${\delta}{\theta}$c.DIA remains constant regardless of the amplification. In addition, while ${\delta}{\theta}$c,DIA linearly increases with the increasing value of the blended light fraction, ${\delta}{\theta}$c,PSF peaks at a certain value of the blended light fraction and then eventually decreases as the fraction further increases. Therefore, measurements of ${\delta}{\theta}$c,DIA instead of ${\delta}{\theta}$c,PSF will be an even more efficient method to detect the blending effect of especially of highly blended events, for which the uncertainties in the determined time scales are high, as well as of low amplification events, for which the current method is highly inefficient.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.126-135
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• 2002

An image contains various features such as color, shape, texture and location information. When only one of those features is used to retrieve an image, it is difficult to acquire satisfactory retrieval efficiency. Especially, in the database with huge capacity, such phenomenon happens frequently. Therefore, by using moi·e features, efficiency of the contents-based image retrieval (CBIR) system can be improved. This paper proposes a technique to consider location information about specific color as well as color information in image using centroid situation vector. Centroid situation vectors are calculated for specific color of the query image. Then, location similarity is determined through comparing distances between extracted centroid situation vectors of query image and target image in the database. Simulation results show that the proposed method is robust in zoom-in or zoom-out processed images and improves discrimination ability in fliped or rotated images. In addition, the suggested method reduced computational complexity by overlapping information extraction, and that improved the retrieval speed using an efficient index file.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.245-252
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• 1997

The development of digital camera and advancement of computer processor could simplify the acquisition and the analysis of digial image, and be the real-time processing by the digital photogrammetry. This study is about to enhancement of the image measuring accuracy by the centroid methods. We were able to determine more effective centroid measuring methods and suitable target shape as the development of analysis system and actualize semi-automatic measuring of digital image. And we can supply the weakness of non-metric camera for the geometric internal accuracy of digital image as the correct of Kodak DCS200 camera 8008s lens distortion.

• 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.

• 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.

• 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.