• 한국학교수학회논문집
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• 제9권1호
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• pp.93-104
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• 2006

삼각형의 무게중심은 물리적인 성질이지만 대부분의 교사와 학생들은 실험단계를 거치지 않기 때문에 수학적인 정의와 물리적인 성질의 관계에서 많은 오개념을 가지고 있다. 본 연구에서는 무게중심에 대한 교사의 실험정도와 교사의 이해정도를 조사하고 수학영재반 기초과정 학생들이 무게중심에 대한 원리를 이해했을 때 어느 정도 무게중심의 개념을 일반화하는지를 연구하였다.

• The Korean Journal of Ecology
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• 제26권3호
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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.

• 농업생명과학연구
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• 제43권1호
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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.

• 천문학회지
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• 제33권2호
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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.

• 방송공학회논문지
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• 제7권2호
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• pp.126-135
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• 2002

영상은 색상, 형태, 위치, 질감 같은 다양한 특성을 갖고 있기 때문에 하나의 특성만을 이용하여 일괄적으로 영상을 검색할 경우, 만족할 만한 검색효율을 얻기가 어렵다. 특히, 대용량의 영상 데이터베이스일수록 그 같은 현상은 빈번하게 일어나기 때문에 기존의 내용 기반 영상 검색 시스템들은 대부분 하나 이상의 특성을 이용하여 검색효율 향상을 죄하고 있다. 본 논문에서는 Centroid 위치벡터를 이용하여 영상 내의 색상 정보뿐만 아니라, 특정 색상에 대한 위치정보를 고려하는 기법을 제안한다. 질의영상의 한 색상에 대해 Centroid 위치벡터를 추출하고 비교영상의 같은 색상의 Centroid 위치벡터와의 거리를 비교하여 그 거리가 짧을수록 각 색상의 위치 유사도를 높게 책정하는 방식을 제안한다. 제안된 검색 기법은 기존의 색상 분포만을 이용하는 검색 기법에 비해, 원근 처리된 영상에 강인하고, 회전되거나 뒤집힌 영상의 변별력이 향상되었다. 또한, 제안된 방식은 색상정보와 위치정보의 추출을 이원화시키지 않고 동시에 추출함으로써 계산량을 줄이고, 효율적인 색인 파일을 생성하여 검색속도를 향상시켰다.

• 한국측량학회지
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• 제15권2호
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• pp.245-252
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• 1997

디지털 카메라의 개발과 컴퓨터 프로세서의 발달은 수치영상 획득과 분석과정을 단순화시켜 수치사진측량의 실시간 처리를 가능케 하고 있다. 본 연구에서는 centroid기법을 이용한 상 측정 정확도의 향상을 위해 sub-pixel 측정시스템을 개발하고 이미지의 준 자동 측정을 실현하므로서 보다 효과적인 centroid측정기법과 이에 적합한 타겟의 형상을 결정할 수 있었다. 그리고 수치 영상의 기하학적 내부정확도 향상을 위해 Kodak DCS200 카메라의 렌즈 왜곡보정을 실시하므로서 비측정용 카메라의 단점을 보완할 수 있었다.

• 한국통신학회논문지
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• 제32권9C호
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• pp.861-866
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• 2007

본 논문은 가우시안 확률분포함수 (Gaussian Probability Distribution Function) 데이터 군집화를 위해 중심신경망 (Centroid Neural Network, CNN)에 Bhattacharyya 커널을 적용한 군집화 알고리즘 (Bhattacharyya Kernel based CNN, BK-CNN)을 제안한다. 제안된 BK-CNN은 무감독 알고리즘인 중심신경망을 기반으로 하고 있으며, 커널 방법을 이용하여 데이터를 특징공간에서 투영한다. 입력공간의 비선형 문제를 선형적으로 해결하기 위해 제안한 커널 방법인데, 확률분포 사이의 거리측정을 위해 Bhattacharyya 거리를 이용한 커널방법을 사용하였다. 제안된 BK-CNN을 영상데이터 분류의 문제에 적용했을 때, 제안된 BK-CNN 알고리즘이 Bhattacharyya 커널을 적용한 k-means, 자기조직지도(Self-Organizing Map)와 중심 신경망등의 기존 알고리즘보다 1.7% - 4.3%의 평균 분류정확도 향상을 가져옴을 확인할 수 있었다.

• East Asian mathematical journal
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• 제29권4호
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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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• 제13권2호
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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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• 제30권3호
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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.