• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.909-912
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• 2012

An automated cell tracking system is automatically to analyze and track changes of cell behaviors in time-lapse cell images acquired from microscope in the cell culture. In this paper, we proposed and developed an ellipse fitting based algorithm for separating very small size overlapping cells in a cell image consisted of thousands or ten thousands cells. We were extracted contours of clusters and divided them into line segments and then produced their fitted ellipses for each line segment. By experimentations, our algorithm was separated clusters with average 91% precision for two overlapping cells and average 84% precision for three overlapping cells respectively.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.3
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• pp.259-266
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• 2013

An automated cell tracking system is used to automatically analyze and track the changes in cell behavior in time-lapse cell images acquired using a microscope with a cell culture. Clustering is the partial overlapping of neighboring cells in the process of cell change. Separating clusters into individual cells is very important for cell tracking. In this study, we proposed an algorithm for separating clusters by using ellipse fitting based on a direct least square method. We extracted the contours of clusters, divided them into line segments, and then produced their fitted ellipses using a direct least square method for each line segment. All of the fitted ellipses could be used to separate their corresponding clusters. In experiments, our algorithm separated clusters with average precisions of 91% for two overlapping cells, 84% for three overlapping cells, and about 73% for four overlapping cells.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.482-482
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• 2022

하천의 합류부에서는 수질이 다른 유체가 혼합하여 합류 전과 다른 특성을 보인다. 하천의 합류부에서 수질을 효율적으로 관리하기 위해서는 수질의 공간적인 혼합 특성을 규명하는 것이 중요하다. 합류부에서 수질의 공간적인 혼합 특성을 분석하기 위해 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기 조직화 지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하였다. 세 가지 기법을 비교하여 어떤 알고리즘이 합류부의 수질 변화 특성을 더 뚜렷하게 나타내는지 분석하였다. 수질 변화 비교 인자들은 pH, chlorophyll, DO, Turbidity 등이 있고, 수질 인자들은 YSI를 활용해 측정하였다. 자료의 측정 지역은 낙동강과 황강이 합류하는 지역이며, 보트에 YSI 장비를 부착하고 횡단하여 측정하였다. 측정한 데이터를 R 프로그램을 통해 세 가지 기법을 적용시켜 수질 변화 비교를 분석한다. 토폴로지 데이터 분석(topological data analysis, TDA)은 거대하고 복잡한 데이터로부터 유의미한 정보를 추출하는 데 사용하고, 자기조직화지도(Self-Organizing Map, SOM) 기법은 차원 축소와 군집화를 동시에 수행한다. k-평균 알고리즘(K-means clustering algorithm) 기법은 주어진 데이터를 k개의 클러스터로 묶는 머신러닝 비지도학습에 속하는 알고리즘이다. 세 가지 방법들의 주목적은 클러스터링이다. 클러스터 분석(Cluster analysis)이란 주어진 데이터들의 특성을 고려해 동일한 성격을 가진 여러 개의 그룹으로 대상을 분류하는 데이터 마이닝의 한 방법이다. 군집화 방법들인 TDA, SOM, K-means를 이용해 합류 지역의 수질 특성들을 클러스터링하여 수질 패턴들을 분석해 하천 수질 오염을 방지할 수 있을 것이다. 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기조직화지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하여 합류부에서의 수질 특성을 비교하며 어떤 기법이 합류의 특성을 더욱 뚜렷하게 나타내는지 규명했다. 합류의 특성을 군집화 방법을 이용해 알게 된다면, 합류부의 수질 변화 패턴을 다른 합류 지역에서도 적용할 수 있을 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.217-220
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• 2002

본 논문은 클러스터 기반의 VOD 서버에서 동적 버퍼 분할을 이용한 새로운 부하 분산 기법을 제안한다. 제안된 기법은 사용자 요청을 처리하는 서비스 노드간의 버퍼 성능과 디스크 접근 빈도를 고려하여 전체 부하를 고르게 분산하도록 한다. 또한 동적 버퍼 분할 기법은 통일한 연속매체에 접근하려는 여러 사용자에게 평균 대기시간을 감소시킬 수 있도록 버퍼를 동적으로 분할한다. 시뮬레이션을 통한 성능분석 결과에서 제안된 기법은 기존의 기법보다 부하량을 적절히 조절하면서 평균 대기시간을 감소시키고 각 노드의 처리량과 병행 사용자 수를 증가시킴을 보인다.

• Journal of radiological science and technology
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• v.27 no.3
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• pp.31-41
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• 2004

Attention deficit hyperactivity disorder(ADHD)is one of the most common psychiatric disorders in childhood, especially school age children and persisting into adult. ADHD is affected 7.6% in our children, Korea. and persisting into $15{\sim}20%$ in adult. And it is characterized by hyperactivity, inattention and impulsivity. Brain imaging is one of way to diagnosis for ADHD. Brain imaging studies may be provide information two types - structural and functional imaging. Structural and functional images of the brain play an important role in management of neurologic and psyciatric disorders. Brain SPECT, with perfusion imaging radiopharmaceuticals is one of the appropriate test to diagnosis of neurologic and psychiatric diseases. Ther are a few studies about separated analysis between boys and girls ADHD SPECT brain images. Selection of Probability level(P-value) is very important to determind the abnormalities when analysis a data by SPM. SPM is a statistical method used for image analysis and determine statistical different between two groups-normal and ADHD. Commonly used P-value is P<0.05 in statistical analysis. The purpose of this study is to evaluation of blood flow clusters distribution, between boys and girls ADHD. The number of normal boys are 8(6-7y, average : $9.6{\pm}3.9y$) and 51(4-11y, average : $9.0{\pm}2.4$) ADHD patients, and normal girls are 4(6-12y, average : $9{\pm}2.4y$) and 13(2-13y, average $10{\pm}3.5y$) ADHD patiens. Blood flow tracer $^{99m}Tc-ethylcysteinate$ dimer(ECD) injected as rCBF agent and take blood flow images after 30 min. during sleeping by SPECT camera. The anatomical region of hyperperfusion of rCBF in boys ADHD group is posterior cingulate gyrus and hyperperfusion rate is 15.39-15.77% according to p-value. And girls ADHD group appears at posterior cerebellum, Lt. cerbral limbic lobe and Lt. Rt. cerebral temporal lobe. These areas hyperperfusion rate are 24.68-31.25%. Hypoperfusion areas in boys ADHD,s brain are Lt. cerebral insular gyrus, Lt. Rt. frontal lobe and mid-prefrontal lobe, these areas decresed blood flow as 15.21-15.64%. Girls ADHD decreased blood flow regions are Lt. cerebral insular gyrus, Lt. cerebral frontal and temporal lobe, Lt. Rt. lentiform nucleus and Lt. parietal lobe. And hypoperfusion rate is 30.57-30.85% in girls ADHD. The girls ADHD group's perfusion rate is more variable than boys. The studies about rCBF in ADHD, should be separate with boys and girls.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.160-170
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• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.9
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• pp.1388-1393
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• 2014

To enzymatically prepare amylopectin cluster (APC), cyclodextrin glucanotransferase (CGTase I-5) and its mutant enzyme from alkalophilic Bacillus sp. I-5 were employed, after which the hydrolysis patterns of CGTase wild-type and its mutant enzyme toward amylopectin were investigated using multi-angle laser light scattering. CGTase wild-type dramatically reduced the molecular weight of waxy rice starch at the initial reaction, whereas the mutant enzyme degraded waxy rice starch relatively slowly. Based on the results, the molecular weight of one cluster of amylopectin could be about $10^4{\sim}10^5g/mol$. To determine production of cyclic glucans from amylopectin, matrix-assisted laser desorption ionization time-of-flight mass spectrometry was performed. CGTase I-5 produced various types of cyclic maltooligosaccharides from amylopectin, whereas the mutant enzyme hardly produced any.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.7-17
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• 2019

Machine learning is one of the most accurate techniques to predict and analyze various phenomena. K-means clustering is a kind of machine learning technique that classifies given data into clusters of similar data. Because it is desirable to perform an analysis based on a lot of data for better performance, K-means clustering can be performed in a model with a server that calculates the centroids of the clusters, and a number of clients that provide data to server. However, this model has the problem that if the clients' data are associated with private information, the server can infringe clients' privacy. In this paper, to solve this problem in a model with a number of clients, we propose a privacy-preserving K-means clustering method that can perform machine learning, concealing private information using homomorphic encryption.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.155-164
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• 2024

This study proposes an unsupervised learning-based clustering model to estimate the ESG ratings of domestic public institutions. To achieve this, the optimal number of clusters was determined by comparing spectral clustering and k-means clustering. These results are guaranteed by calculating the Davies-Bouldin Index (DBI), a model performance index. The DBI values were 0.734 for spectral clustering and 1.715 for k-means clustering, indicating lower values showed better performance. Thus, the superiority of spectral clustering was confirmed. Furthermore, T-test and ANOVA were used to reveal statistically significant differences between ESG non-financial data, and correlation coefficients were used to confirm the relationships between ESG indicators. Based on these results, this study suggests the possibility of estimating the ESG performance ranking of each public institution without existing ESG ratings. This is achieved by calculating the optimal number of clusters, and then determining the sum of averages of the ESG data within each cluster. Therefore, the proposed model can be employed to evaluate the ESG ratings of various domestic public institutions, and it is expected to be useful in domestic sustainable management practice and performance management.