• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.403-407
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• 2005

자궁 경부 세포진 영상의 핵 영역 분할은 자궁 경부암 자동화 검색 시스템의 가장 어렵고도 중요한 분야로 알려져 있다. 본 논문에서는 자궁 경부 세포진 영상에서 HSI 모델을 이용하여 세포진 핵 영역을 추출한다. 추출된 세포진 핵 영역은 형태학적 정보(morphometric feature)와 명암 정보(densitometric feature), 색상 정보(colorimetric feature), 질감 정보(textural features)를 분석하여 핵의 특징을 추출한다. 또한 Bethesda System에서의 분류 기준에 따라 핵의 분류 기준을 정하고 추출된 핵의 특징들을 퍼지 c-Means 클러스터링 알고리즘에 적용하여 실험한 결과, 제안된 방법이 자궁 세포진 핵 추출과 인식에 있어서 효율적임을 확인하였다.

• 융합신호처리학회논문지
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• 제16권1호
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• pp.9-14
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• 2015

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.195-197
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• 2009

The role of environment-friendly and energy-efficient rail transportation is on the rise as a "sustainable transportation means" to cope with environmental changes that are major concerns around the world. Along with the environmental problems, the CO2 inhibition issue became critical for mankind to prepare global warming and high oil prices. It has come to a point where an alternate means are needed to revitalize plans including renewable energy, bicycle utilization, and prepare new solutions for decreasing number of cars within the city The personal rapid Transit(PRT) is the revolutionary future transportation means that can replace cars to deal with ever-increasing traffic congestions, vehicles, and environmental/energy problems. Expected as an efficient means, technology development has already taken place in developed countries such as U.S., England, and Germany. To meet the future demands, PRT installation around the nation's new and existing towns is being examined to produce important factors. The factors are produced for examining the availability of system requirements during design and construction practice.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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• pp.201-202
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• 2007

In this paper we investigated the partial discharge distribution using the K-means clustering according to the needle of tilt and void at the cross linked polyethylene(XLPE) insulators. As a result, the specimen with tilt $45^{\circ}$ has highest breakdown voltage and the specimen with air void has lower breakdown voltage than the specimen with on void. In K-menas clustering distribution of clusters concentrates at inception condition, but the distribution spreads widely at breakdown.

• 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.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.497-508
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• 2005

This paper aims at studying on K-means Clustering focusing on initialization which affect the clustering results in K-means cluster analysis. The four different methods(the MA method, the KA method, the Max-Min method and the Space Partition method) were compared and the clustering result shows that there were some differences among these methods, especially that the MA method sometimes leads to incorrect clustering due to the inappropriate initialization depending on the types of data and the Max-Min method is shown to be more effective than other methods especially when the data size is large.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.531-538
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• 2005

In k-means clustering, we standardize variables before clustering and iterate two steps: units allocation by Euclidean sense and centroids updating. In applications to DB marketing where clusters are to be used as customer segments with similar consumption behaviors, we frequently acquire additional variables on the customers or the units through marketing campaigns a posteriori. Hence we need to modify the clusters originally formed after each campaign. The aim of this study is to propose a revision method of k-means clusters, incorporating added information by weighting clustering variables. We illustrate the proposed method in an empirical case.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.360-362
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• 2019

본 논문에서는 k-Means 클러스터링을 활용한 컬러 기반 이미지 추출을 통한 색각 검사 방안 연구를 진행한다. 이를 위해, RGB 컬러스페이스 기반의 이미지를 특별한 컬러스페이스 이미지로 변환 후 컬러 패턴 분포에 따라 k-Means 클러스터링을 적용하여 다양한 형태의 이미지를 추출하는 실험을 수행한다. 위의 실험을 통해 하나의 이미지를 컬러 분포 패턴을 통해 클러스터링하여 이미지를 추출을 통하여 정상인과 색각 이상자를 판별할 수 있었다. 실험 결과, 다양한 형태와 색을 가진 이미지를 추출하여 정상인이 보는 이미지와 색각 이상자가 보는 이미지가 다른 것을 확인하였다.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1438-1444
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• 2018

Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest information. However, microblogs have word limits, and it has there is not enough information to analyze for content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only measures the similarity between the data represented as a vector space model, but also measures the semantic similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering algorithm.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.153-159
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• 2022

Plain radiographic analysis is the initial imaging modality for suspected small bowel obstruction. Among the many features that affect the diagnosis of small bowel obstruction (SBO), the presence of gas-filled or fluid-filled small bowel loops is the most salient feature that can be automatized by computer vision algorithms. In this study, we compare three frequently applied pixel-clustering algorithms for extracting gas-filled areas without human intervention. In a comparison involving 40 suspected SBO cases, the Possibilistic C-Means and Fuzzy C-Means algorithms exhibited initialization-sensitivity problems and difficulties coping with low intensity contrast, achieving low 72.5% and 85% success rates in extraction. The Adaptive Resonance Theory 2 algorithm is the most suitable algorithm for gas-filled region detection, achieving a 100% success rate on 40 tested images, largely owing to its dynamic control of the number of clusters.