• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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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.

• 한국전기전자재료학회논문지
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• 제20권11호
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• pp.959-964
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• 2007

To investigate the influence of partial discharge(PD) distribution characteristics due to various defects on the power cable joints interface, we used the K-means clustering method. As the result of PD number(n) distribution analyzing on $\Phi-n$ graph, the phase angle($\Phi$) of cluster centroid shifted to $0^{\circ}\;and\;180^{\circ}$ increasing with applying voltage. It was confirmed that the PD quantify(q) and euclidean distance of centroid were increased with applying voltage from the centroid distribution analyzing of $\Phi-q$ plane. The dispersion degree was increased with calculated standard deviation of the $\Phi-q$ cluster centroid. The PD number and mean value on $\Phi-q$ graph were some different by electric field concentration with defect types.

• 천문학회보
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• 제42권1호
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• pp.29.4-30
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• 2017

Korea Astronomy and Space Science Institute We present a weak-lensing study of the galaxy cluster SPT-CL J2106-5844 at z=1.132 discovered in the South Pole Telescope Sunyaev-Zel'dovich (SPT-SZ) survey. The cluster is claimed to be the most massive system at z > 1 in the SPT-SZ survey. The inferred mass ($M_{200c}=(1.27{\pm}0.21){\times}10^{15}M_{sun}$) is somewhat unusual at such a high redshift given the current ΛCDM prediction. The mass estimates, however, may be biased because the hydrostatic assumption may not hold when the universe was about 40% of the current age. In this work, we reconstruct the dark matter distribution and measure the mass of this interesting cluster using weak-lensing analysis based on the images from the Advanced Camera for Surveys and Wide Field Camera 3 on-board the Hubble Space Telescope. We find that the mass distribution of the cluster is unimodal with no significant substructures. The centroid of the dark matter agrees with both galaxy luminosity and number density distributions, as well as the hot gas centroid. We confirm that the cluster is indeed extremely massive ($M_{200c}=(1.81{\pm}0.47){\times}10^{15}M_{sun}$) supporting the previous non-lensing measurements. We also discuss the rarity of the cluster in the ΛCDM cosmology, comparing with the expected abundance of similarly massive clusters.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.450-453
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 steps. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional (3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.119-122
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• 2002

The research Interest of this paper is focused on the efficient clustering task for an arbitrary color data. In order to tackle this problem, we have tiled to model the inherent uncertainty and vagueness of color data using fuzzy color model. By laking a fuzzy approach to color modeling, we could make a soft decision for the vague regions between neighboring colors. The proposed fuzzy color model defined a three dimensional fuzzy color ball and color membership computation method with the two inter-color distance measures. With the fuzzy color model, we developed a new fuzzy clustering algorithm for an efficient partition of color data. Each fuzzy cluster set has a cluster prototype which is represented by fuzzy color centroid.

• 한국통신학회논문지
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• 제31권9C호
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• pp.853-858
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• 2006

음성인식에서 TMHMM(Tied Mixture Hidden Markov Model)은 자유 매개변수의 수를 감소시키기 위한 좋은 접근이지만, GPDF(Gaussian Probability Density Function) 군집화 오류에 의해 음성인식의 오류를 발생시켰다. 본 논문은 TMHMM에서 발생하는 군집화 오류를 최소화하기 위하여 HCNN(Homogeneous Centroid Neural Network) 군집화 알고리즘을 제안한다. 제안된 알고리즘은 CNN(Centroid Neural Network)을 TMHMM상의 음향 특징벡터에 활용하였으며, 다른 상태에 소속된 확률밀도가 서로 겹쳐진 형태의 이질군집 지역에 더 많은 코드벡터를 할당하기 위해서 본 논문에서 새로 제안이 제안되는 이질성 거리척도를 사용 하였다. 제안된 알고리즘을 한국어 고립 숫자단어의 인식문제에 적용한 결과, 기존 K-means 알고리즘이나 CNN보다 각각 14.63%, 9,39%의 오인식률의 감소를 얻을 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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 하계학술대회 논문집 Vol.7
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• pp.83-84
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• 2006

In this paper, we examine discharge characteristics of cross-linked polyethylene (since then; XLPE) according to thickness. Voltage was applied to power frequency by step method, and calibration of discharge was set to 50[pC] (slope=8.333). After the voltage was applied, for 10 [sec] (600 [cycle]), occurring discharge and number were detected. Determine of input pattern is difficult because discharge pattern is irregular. Therefore we investigated pattern using the K-means Analysis and Weibull function. Also we investigated variation of centroid and cluster.

• 한국멀티미디어학회논문지
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• 제3권4호
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• pp.339-346
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• 2000

의료 영상은 환자에 대한 해부학적인 진단 정보를 얻기 위한 영상으로 정확한 병변 인식과 판단을 위해서는 조직별 분할이 선행되어야 한다. 본 논문에서는 T1 강조 영상 그리고 T2 강조 영상, PD 영상의 특징을 상호보완적으로 이용한 자동적인 영상 분할 방법을 제안한다. 제안한 분할 알고리듬은 PD 영상으로부터 대뇌마스크를 획득하고, 대뇌마스크를 T1 과 T2, PD의 입력 영상에 씌워 각각의 대뇌 영상을 획득하여 T1과 T2, PD를 축으로 하는 3차원 공간상에서 스케일 스페이스 필터링과, 3차원 클러스터링을 이용하여 대뇌 내부조직에 해당하는 클러스터를 찾아서 분할에 이용한다. 대뇌 영상분할은 이들 클러스터의 중심 값을 FCM 알고리듬의 초기 중심 값으로 두고 FCM 알고리듬을 이용하여 분할한다. 제안한 분할 알고리듬은 정확한 클러스터의 중심 값을 계산함으로 초기 값의 영향을 많이 받는 FCM 알고리듬의 단점을 보완하였고 다중 스펙트럼 영상의 특성을 조합하여 분할에 이용함으로 단일 스펙트럼 영상만을 이용하는 방법보다 향상된 분할 결과를 얻을 수 있었다.

• 대기
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• 제16권4호
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• pp.259-267
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• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.