• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.405-408
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• 2003

This paper proposes a new algorithm for cloudy area detection using K-Means and GHA (Generalized Hebbian Algorithm). K-Means is one of simple classification algorithm, and GHA is unsupervised neural network for data compression and pattern classification. Proposed algorithm is based on block based image processing that size is l6$\times$l6. Experimental results shows good performance of cloudy area detection except blur cloudy areas.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.295-306
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• 2006

When X and Y have independent Poisson distributions, we develop a Bayesian one-sided testing procedures for the ratio of two Poisson means. We propose the objective Bayesian one-sided testing procedures for the ratio of two Poisson means based on the fractional Bayes factor and the intrinsic Bayes factor. Some real examples are provided.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.179-184
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• 2001

Schafer and Shenker(2000) mentioned the one of analytic imputation technique involving conditional means. We derive an approximate moments of a variance estimate with imputed conditional means.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.275-277
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• 2014

본 논문에서는 세라믹 소재 영상에 가우시안 필터링 기법을 적용하여 잡음을 제거하고, K-means 알고리즘을 적용하여 결함 영역을 세분화 한 뒤, 세분화된 결함 영역에 Max-Min 이진화 기법을 이용하여 결함 영역을 추출한 후, 형태학적 기법을 이용하여 잡음을 제거하고 결함을 추출한다. 제안된 방법을 세라믹 소재 영상을 대상으로 실험한 결과, 기존의 방법보다 효율적으로 결함이 검출되는 것을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.211-214
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• 2006

Fuzzy C-Means(FCM) 알고리즘은 probabilitic 멤버쉽을 사용하는 클러스터링 방법으로서 널리 쓰이고 있다. 하지만 이 방법은 노이즈에 대하여 민감한 성질을 가진다는 단점이 있다. 따라서 본 논문에서는 이러한 노이즈에 민감한 성질을 보완하기 위해서 데이터의 밀도추정을 이용하여 새로운 FCM 알고리즘을 제안한다. 본 논문에서 제안된 알고리즘은 FCM과 비슷한 성능의 클러스터링 수행이 가능하며, 노이즈가 포함된 데이터에서는 FCM보다 더 나은 성능을 보여준다.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.905-922
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• 2021

Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.87-101
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• 2002

In this paper, we proposes the car plate recognition and describe the parking control system using the proposed car plate recognition algorithm. The car plate recognition system using color information and fuzzy c-means algorithm consists of the extraction part of a car plate from a car image and the recognition part of characters in the extracted car plate. This paper eliminates green noise from car image using the mode smoothing and extract plate region using green and white information of RGB color. The codes of extracted plate region is extracted by histogram based approach method and is recognized by fuzzy c-means algorithm. For experimental, we tested 80 car images. We shows that the proposed extraction method is better than that from the color information of RGB and HSI, respectively. So, we can know that the proposed car plate recognition method using fuzzy c-means algorithm was very efficient. We develop the parking control system using the proposed car plate recognition method, which showed performance improvement by the experimental results.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.57-64
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• 2012

In this paper, the multi-objective genetic algorithm is proposed for the document clustering which is important in the text mining field. The most important function in the document clustering algorithm is to group the similar documents in a corpus. So far, the k-means clustering and genetic algorithms are much in progress in this field. However, the k-means clustering depends too much on the initial centroid, the genetic algorithm has the disadvantage of coming off in the local optimal value easily according to the fitness function. In this paper, the multi-objective genetic algorithm is applied to the document clustering in order to complement these disadvantages while its accuracy is analyzed and compared to the existing algorithms. In our experimental results, the multi-objective genetic algorithm introduced in this paper shows the accuracy improvement which is superior to the k-means clustering(about 20 %) and the general genetic algorithm (about 17 %) for the document clustering.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.2
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• pp.150-156
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• 2008

Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means reduces a time complexity. Think of the factor of simplify, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system with hierarchical structure based on document clustering using K-means algorithm. Evaluated the performance on different hierarchy depth and initial uncertain centroid number based on variational relative document amount correspond to given queries. Comparing with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

• Journal of the Korean Wood Science and Technology
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• v.14 no.1
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• pp.55-60
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• 1986

Discoloration sensitivities of woods grown in this country haven't reported yet. Therefore we examined discoloration sensitivities of domestic wood specimens to iron (0.1 %, $FeCl_3.6H_2O$), alkali (pH 12.0, NaOH). acid (pH 1.0, $C_2H_2O_4$) and exposing to sunlight (40 hrs), Thirty-six hardwood species were collected and examined. All specimens were prepared from heartwoods of the collected species. But the specimens of 4 Betula species were divided into sapwoods and heartwoods. By iron stain, the color differences (${\Delta}E$) of 21 wood specimens including one Betula sapwood showed above 12.0, which means strong discoloration sensitivities, and of 3 specimens including one Betula sapwood showed below 2.5, which means weak discolorations. The most strong iron discoloration species was Jungkukgulpi-namu (Pterocarya stenoptera). By alkali stain, the color differences (${\Delta}E$) of 3 wood specimens showed above 9.0, which means strong discoloration sensitivities, and of 18 wood specimens including 4 Berula sapwoods showed below 2.5, which means weak discolorations. By acid stain, the color differences (${\Delta}E$) of 6 wood specimens showed above 10.0 which means strong discoloration sensitivities, and of 12 wood specimens including one Betula sapwoods showed below 2.5, which means weak discolorations. By exposing to sunlight, the color differences (${\Delta}E$) of 31 wood specimens including one Betula sapwoods showed below 6.5, which means, strong discoloration sensitivities, and of only one specimens showed below 2.5, which means weak discoloration. The most strong discoloration species by exposing to sunlight was Guirung-namu (Prunus padus). In general, it was shown that hardwoods grown in Korea were most subject to change of color by exposing to sunlight and next were by iron stain. Domestic hardwoods showed some differences in discoloration sensitivities from domestic softwoods previously reported.