• Korean Journal of Environmental Biology
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• v.26 no.1
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• pp.42-46
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• 2008

The community size of culturable heterotrophic bacteria and community level physiological profiles (CLPP) in the rhizosphere of Brassica rapa subsp. pekinensis (Chinese cabbage) were analyzed in two different sites. The average community size of culturable heterotrophic bacteria ranged between $2.65\times10^6CFU\;g^{-1}$ soil (Suwon) and $3.75\times10^6CFU\;g^{-1}$ soil (Yesan), whereas those of bulk soils ranged between $2.45\times10^6CFU\;g^{-1}$ soil (Suwon) and $2.97\times10^6CFU\;g^{-1}$ soil (Yesan). The average functional richness of Suwon rhizoshpere was 90.8, whereas that of Yesan rhizosphere was 154.1. High level of correlation was found between the community size and functional richness. The most actively utilized substrates in both rhizospheres were adonitol, L-asparagine, D-gluconic acid, L-glutamic acid and D-galacturonic acid. Clear differences were seen in the utilization patterns between the two sites. Differences were also observed for the patterns of bulk soils between the two sites, although D-raffinose and D-mannose were found as the commonly utilized substrates.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.113-129
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• 2021

Recently, traffic congestion in the city is continuously increasing due to the expansion of the living area centered in the metropolitan area and the concentration of population in large cities. New road construction has become impossible due to the increase in land prices in downtown areas and limited sites, and the importance of efficient data-based road operation is increasingly emerging. For efficient road operation, it is essential to classify appropriate scenarios according to changes in traffic conditions and to operate optimal signals for each scenario. In this study, the Dynamic Time Warping model for cluster analysis of time series data was applied to traffic volume and speed data collected at continuous intersections for optimal scenario classification. We propose a methodology for composing an optimal signal operation scenario by analyzing the characteristics of the scenarios for each data used for classification.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.97-99
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• 2004

본 논문에서는 다차원 데이터의 유사도 검색을 효과적으로 수행하기 위한 색인 구조를 제안한다. 제안하는 색인 구조는 차원의 저주 현상을 극복하기 위한 벡터 근사 기반의 색인 구조이다. 제안하는 색인 구조는 부모 노드를 기준으로 KDB-트리와 유사한 영역 분할 방식으로 분할하고 분할된 각 영역은 데이터의 분포 특성에 따라 동적 비트를 할당하여 벡터 근사화된 영역을 표현한다. 따라서, 하나의 노드 안에 않은 영역 정보를 저장하여 트리의 깊이를 줄일 수 있다. 또한 다차원의 특징 벡터 공간에 상대적인 비트를 할당하기 때문에 군집화되어 있는 데이터에 대해서 효과적이다 제안하는 색인 구조의 우수성을 보이기 위해 다양한 실험을 통하여 성능의 우수성을 입증한다.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.491-493
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• 1998

영상인식은 최근 시각정보의 중요성과 영상을 취득장비의 발달, 처리기술의 향상으로 여러 분야에서 그 중요성과 활용도가 급격히 증가하고 있다. 본 논문에서는 도심지 항공 영상에서 자동표적인식에 관한 문제에서 탐색 물체 주변에 건물들이 밀집되어 있고, 배경이 존재하는 경우에서 fused 분할 방법을 이용하여 기존의 에지 기준 방법인 허프 변환, 에지연결 등에서 발생하는 군집화 문제점을 해결하다. 취득환경의 차이에 다른 농도치 차이를 BCT 방법으로 정규화하여 유사도 기준치로 편차오차를 계산하여 인식하였다. 실험에서는 다양한 탐색물체를 대상으로 회전, 이동, 신축 등의 복합적인 변형에 대하여 불변적으로 인식한 결과를 보였으며, 영상 정합, 컴퓨터 비전, 영상 분석, 영상 이해등의 분야에 적용 가능성을 제시하였다.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.4
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• pp.273-279
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• 2018

This experiment was conducted to investigate the effects of individual management and group management of Jeju cross-bred horses on the physiological characteristics, productivity, and characteristics of the loin muscle of the horse meat. Sixteen herds of Jeju cross-bred horses older than 44 months were selected as experimental subjects. The experiment was conducted by dividing the herds into eight herds for individual management and another eight herds for group management. Herds were fed with concentrated feed of 2.5% of the body weight for four times a day and roughage and water were fed ad libitum. In the 12th week, the intestinal pH was statistically significantly lower with group management than with individual management (p<0.05). However, no symptoms of colic were observed. With regard to the general composition of the loin muscle, fat content was a statistically significant difference between the 7.83% with individual management and 5.65% with group management (p<0.05), indicating that individual management is more effective than group management in terms of fattening. In conclusion, feeding a concentrated feed at a level of 2.5% of body weight with individual management during fattening of Jeju cross-bred horses could be utilized as a fattening method.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.547-553
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• 2008

We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.435-442
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• 2007

This paper is concerned with the mesh segmentation problem that can be applied to diverse applications such as texture mapping, simplification, morphing, compression, and shape matching for 3D mesh models. The mesh segmentation is the process of dividing a given mesh into the disjoint set of sub-meshes. We propose a method for segmenting meshes by simultaneously reflecting global and local geometric characteristics of the meshes. First, we extract sharp vertices over mesh vertices by interpreting the curvatures and convexity of a given mesh, which are respectively contained in the local and global geometric characteristics of the mesh. Next, we partition the sharp vertices into the $\kappa$ number of clusters by adopting the $\kappa$-means clustering method [29] based on the Euclidean distances between all pairs of the sharp vertices. Other vertices excluding the sharp vertices are merged into the nearest clusters by Euclidean distances. Also we implement the proposed method and visualize its experimental results on several 3D mesh models.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.414-417
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• 2007

Citation matching (CM) is a method for clustering citation records that refer to the same paper. Normally, CM is preceded by citation field segmentation (CFS) which divides a citation record into its fields such as author(s), a title, a title of publication, year, etc. Although many studies have attacked CFS and CM, the relationship between CFS and CM was not sufficiently explored. Among many aspects of the effect of CFS on CM, this study concentrates on what citation fields should identify for CM. As its first attempt, we compared CM performances over different sets of citation fields manually segmented, and confirmed that the use of more citation fields help CM to cluster citation records better.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.285-292
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• 2013

This study proposes a network matching method based on a network division technique. The proposed method generates polygons surrounded by links of the original network dataset, and detects corresponding polygon group pairs using a intersection-based graph clustering. Then corresponding sub-network pairs are obtained from the polygon group pairs. To perform the geometric correction between them, the Iterative Closest Points algorithm is applied to the nodes of each corresponding sub-networks pair. Finally, Hausdorff distance analysis is applied to find link pairs of networks. To assess the feasibility of the algorithm, we apply it to the networks from the KTDB center and commercial CNS company. In the experiments, several Hausdorff distance thresholds from 3m to 18m with 3m intervals are tested and, finally, we can get the F-measure of 0.99 when using the threshold of 15m.