• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.175-184
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• 2007

LOD scores and a permutation test for detecting and locating quantitative trait loci (QTL) from the Hanwoo economic trait have been described and we selected a considerable major BMS1167 locus for further analysis. K-means clustering analysis, for the major DNA marker mining of BMS1167 microsatellite loci in Hanwoo chromosome17, has been tried and three cluster groups divide four traits. The three cluster groups are classified according to eight DNA marker bps. Finally, we employed the bootstrap test method to calculate confidence intervals using the resampling method to find major DNA markers. We conclude that the major marker of BMS1167 locus in Hanwoo chromosome17 is only DNA marker 100bp.

• 스마트미디어저널
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• 제1권4호
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.389-398
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• 2012

Texture is an important visual feature for image analysis. Many approaches have been proposed to model and analyze texture features. Although these approaches significantly contribute to various image-based applications, most of these methods are sensitive to the changes in the scale and orientation of the texture pattern. Because textures vary in scale and orientations frequently, this easily leads to pattern mismatching if the features are compared to each other without considering the scale and/or orientation of textures. This paper suggests an Orientation Matching Scheme (OMS) to ease the problem of mismatching rotated patterns. In OMS, a pair of texture features will be compared to each other at various orientations to identify the best matched direction for comparison. A database including rotated texture images was generated for experiments. A synthetic retrieving experiment was conducted on the generated database to examine the performance of the proposed scheme. We also applied OMS to the similarity computation in a K-means clustering algorithm. The purpose of using K-means is to examine the scheme exhaustively in unpromising conditions, where initialized seeds are randomly selected and algorithms work heuristically. Results from both types of experiments show that the proposed OMS can help improve the performance when dealing with rotated patterns.

• 전기전자학회논문지
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• 제4권2호
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• pp.233-240
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• 2000

This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

• Journal of Platform Technology
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• 제11권5호
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• pp.110-125
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• 2023

본 논문에서는 2019년 국내 공항을 기준으로 측정된 시계열 항공기 위치 데이터를 활용하여 국내 공항에 이착륙 시 접근 단계에서의 항공 위험상황 중 Go-Around 및 UOC_D 를 분석하고, 다양한 클러스터링 기반 머신 러닝 기법을 적용하여 국내 항공 데이터에서 가장 알맞은 분석 기법이 무엇인지를 실험을 통해 제시한다. 항공기 위치를 측정하기 위한 센서는 ADS-B를 단일로 사용하였으며, 클러스터링 기법들 중 K-Means, GMM, DBSCAN 등의 알고리즘을 사용하여 이상상황을 분류하기 위한 모델을 설계하였다. 그 중 해외에서는 RF 모델이 가장 나은 성능을 보였으나, 국내 항공 데이터에 대해서는 국내 지형에 특화된 부분을 반영한 GMM이 가장 높은 분류 성능을 보이는 것으로 실험을 통해 확인하였다.

• 대한원격탐사학회지
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• 제37권5_3호
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• pp.1373-1387
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• 2021

산불은 지표 에너지 균형, 사회 및 환경에 중대한 위협을 미치며, 사회경제적 손실을 일으킨다. 한편, 현재까지 널리 사용되고 있는 다중분광 위성 영상 기반 산불 피해 탐지 알고리즘은 구름으로 인한 반사도 오염으로 인해 시의적절한 산불 정보를 얻기 어려운 문제가 있다. 따라서 본 연구에서는 구름에 영향을 받지 않는 유럽우주국의 Sentinel-1 SAR (Synthetic Aperture Radar) 자료로부터 2019년 4월 초에 발생한 남한 강원도의 강릉·동해, 고성·속초 및 인접한 북한의 두 산불 발생 지역을 대상으로 주성분분석(Principal Component Analysis; PCA)을 포함하는 일련의 전 처리 및 K-means clustering을 이용하여 산불 피해 면적을 탐지하였다. 추정된 산불 면적은 국립산림과학원에서 남한의 두 산불에 대해 제공한 산불 피해 면적 및 강도 참조자료 및 산불 피해 탐지에 널리 사용되는 dNBR (differenced Normalized Burn Ratio)을 사용하여 검증하였다. 국립산림과학원의 참조자료 기반 검증에서 강릉·동해와 고성·속초 산불에 대해 평균 약 86%의 정확도를 보였다. dNBR을 사용한 검증에서는 남한 및 북한의 지역 모두에 대해 평균 약 84%의 정확도를 보였다. 이때, 산불 강도가 강할수록 산불 면적 탐지 성능이 높고 반대로 산불 강도가 약할수록 산불 면적 탐지 성능이 낮은 것을 확인할 수 있었다. 본 연구를 통해 검증된 SAR 영상을 이용한 PCA 및 K-means clustering 기반 탐지 알고리즘이 추후 구름의 영향이 크고 작은 산불이 빈번하게 발생하는 한반도에 대하여 신속한 산불 피해 면적 탐지에 활용될 수 있을 것으로 기대된다.

• 대한자기공명의과학회:학술대회논문집
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• 대한자기공명의과학회 2002년도 제7차 학술대회 초록집
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• pp.126-126
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• 2002

Purpose： For quantitative analysis of the cardiac diseases, it is necessary to segment the left-ventricle(LV) in MR cardiac images. Snake or active contour model has been used to segment LV boundary. In using these models, however, the contour of the LV may not converge to the desirable one because the contour may fall into local minimum value due to image artifact in inner region of the LV Therefore, in this paper, we propose the new preprocessing method using K-means clustering and merging algorithms that can improve the performance of the active contour model.

• 인터넷정보학회논문지
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• 제23권1호
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• pp.11-17
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• 2022

Data mining is the task of accessing useful information from a large capacity of data. It can also be referred to as searching for correlations that can provide clues about the future in large data warehouses by using computer algorithms. It has been used in the tourism field for marketing, analysis, and business improvement purposes. This study aims to analyze the tourist profile in Turkey through data mining methods. The reason relies behind the selection of Turkey is the fact that Turkey welcomes millions of tourist every year which can be a role model for other touristic countries. In this study, an anonymous and large-scale data set was used under the law on the protection of personal data. The dataset was taken from a leading tourism company that is still active in Turkey. By using the k-means clustering algorithm on this data, key parameters of profiles were obtained and people were clustered into groups according to their characteristics. According to the outcomes, distinguishing characteristics are gathered under three main titles. These are the age of the tourists, the frequency of their vacations and the period between the reservation and the vacation itself. The results obtained show that the frequency of tourist vacations, the time between bookings and vacations, and age are the most important and characteristic parameters for a tourist's profile. Finally, planning future investments, events and campaign packages can make tourism companies more competitive and improve quality of service. For both businesses and tourists, it is advantageous to prepare individual events and offers for the three major groups of tourists.

• 한국전기전자재료학회논문지
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• 제21권4호
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• pp.348-353
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• 2008

Most of the high voltage insulation systems, such as the power cable joint having hetero interface, are composed of more than two different insulators to improve insulating performance. The partial discharge(PD) in these hetero interface is expected to affect the total insulation performance. Thus, it is important to study electrical properties on these interfaces. This study described the influence of copper and semiconductive substance defects on $\Phi$-q-n distribution between the interface of the model cable joints to classify PD source. PD was sequentially detected for 600 cycles of the applied voltage. The K-means cluster analysis has been analyzed to investigate the $\Phi$-q-n distribution. The skewness-kurtosis(Sk-Ku) plot from K-means clustering results was defined to quantify cluster distribution and classify distribution patterns. The Sk-Ku plot is composed of skewness and kurtosis along abscissa and ordinate which indicate the asymmetry and the sharpness of distribution. As a result of the Sk-Ku plot, it was confirmed that the data was distributed in 1st 2nd and 3rd quadrant at copper foreign substance defect, but in case of semiconductive foreign substance, the data was distributed in 2nd quadrant only.

• 한국기계가공학회지
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• 제20권1호
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• pp.95-102
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• 2021

Analysis of the fracture surface is one of the most important methods for determining the cause of equipment structural failure. Whether structural failure is caused by impact or fatigue is necessary information in industrial fields. For ferrous and non-ferrous metal materials, two fracture phenomena are generated on the fracture surface: ductile and brittle fractures. In this study, machine learning predicts whether the fracture is based on ductile or brittle when structurural failure is caused by impact. The K-means algorithm calculates this ratio by clustering the brittle and ductile fracture data from a photograph of the impact fracture surface, unlike the existing method, which calculates the fracture surface ratio by comparison with the grid type or the reference fracture surface shape.