• Korean Journal of Microbiology
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• v.26 no.3
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• pp.256-261
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• 1988

Samples from Naktong Estuary had been taken for the characterization of heterotrophic bacterial communities and of the effects of environmental factors on their distribution in estuarine ecosystem. Bacterial communities isolated from seawater region were composed of more euryhalone groups than those from freshwater region, and the bacterial communities of summer were composed of more eurythermal groups than those of winter. Bacterial commnities became more diverse by the input of allochthonous bacteria from terrestrial and freshwater ecosystem, but less diverse by worse environmental conditions such as nutrient load, high salinity, low temperature, and so on.

• Korean Journal of Environment and Ecology
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• v.13 no.1
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• pp.49-60
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• 1999

월출산 국립공원 및 공원보호구역의 현존식생과 식물군집구조를 파악하기 위하여 1997년 7월에 27개의 조사구를 설치하여 조사하였다. 조사대상지의 식생은 난대기후대의 수종과 온대기후대의 수종이 함께 출현하는 추이대 특성을 나타내고, 주로 졸참나무군락, 소나무군락, 낙엽참나무군락이 많이 분포하고 있었다. 총 27개 조사구에 대한 TWINSPAN의 분석결과 낙엽활엽수군집, 굴참나무-졸참나무군집, 신갈나무군집, 신갈나무-낙엽활엽수군집, 굴참나무군집, 소나무-붉가시나무군집, 소나무군집, 조록싸리-조릿대군집으로 8개 군집으로 대별되었다. 월출산국립공원의 식생은 수종간 경쟁이 활발한 천이도중상의 식생구조를 나타내고 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.3-5
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• 2000

현존하는 다양한 군집화 알고리즘들이 개체들을 군집화하기 위하여 사용하는 기준들은 일반적으로 인위적으로 설정된 것들이다. 이러한 기준들은 개체들 자체로부터 나오는 자연스러운 기준이라기 보다는 군집을 위하여 임의로 선정된 것이므로 군집화의 기본 목적인 개체들을 자연스러운 그룹들로 분할하고자 하는데 있어 한계를 갖게 된다. 본 논문에서는 이러한 점에 주목하여 현존하는 자연계의 군집 법칙으로 대표되는 만유인력의 법칙을 사용한 개체 군집화 알고리즘을 제안함으로써 기본적인 목적에 충실한 군집화를 실현하고자 한다. 이 방법은 기존의 방법론들에서 찾아볼 수 없었던 자연 법칙에 근거한 새로운 군집화 시도일 뿐만 아니라, 초기조건에 관계없이 안정적인 성능을 보이고 또한 군집의 수가 자연 법칙에 따라 자동으로 결정되는 특성을 지니고 있어 다양한 실질적인 응용 분야에서 효과적으로 사용될 수 있는 새로운 군집화 도구가 될 수 있을 것으로 보인다.

• Journal of Wetlands Research
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• v.9 no.3
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• pp.13-24
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• 2007

This study was conducted to confirm if spatial distribution of macrobenthic fauna were related with sedimentary characteristics. Sedimentary characteristics were compared in each community after carrying of community analysis based upon species composition. Macrobenthic animals were distinguished with 4 communities based on the tidal level, of which 3 communities were divided into two groups on the sediment composition. Common species which were presented in all communities, Nephtys californiensis Hartman 1938, Haploscoloplos elongatus Johnson 1901, Glycera sp., Heteromastus sp. Notomastus sp. Timarete amtarctica Mono 1930, Glysinde sp., Lumbrineris japonica Marenzeller 1879 were having different appearance densities at each community area caused by different sedimentary characteristics. But only two species among the common species have a correlation significantly. Temporal variation of common species could not be recognised because of 4 times of seasonal investigation was not enough know their life cycles. So, in the future more frequently sampling method should be required.

• Korean Journal of Environment and Ecology
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• v.11 no.2
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• pp.149-159
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• 1997

Eleven plots(10m*10m) were established in Pinus densiflora forests and analized in with TWINSPAN and DCA techniques, to study the comparison of the structure of plant community by soil depth in Ulsan Grand Park. As a result of this analyusis, the communities were divided into two groups : Community B which have shallow one. Community A showed climatic climax which have proceeded from Pinus densiflora to Quercus variabilis, Castanea crenata, Platycarya strobilacea, and at last to Carpinus laxiflora. Community A showed climatic climax which have proceeded fro, Pimus densiflora to Quercus variabilis, Castanea crenata, Platycarya strobilacea, and at last to carpenus laxiflora. Community B showed edaphic climax in sere of Pinus desiflora. The diffrernce of the growth of tree height was showed significantly between two communities. The sample tree aged 30 in Community A was 8.50m high and the sample tree aged 35 in Community B was 3.80m high. Community A was better than Community B in soil characteristics and species diversity indicies.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.23-38
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• 2007

Corporations adopt a segmentation depends on the existence of target variables, in general. In this paper, for the case of no target variables, a strategy through segmentation is proposed for corporations having a special status based on the management index. In case of segmentation using cluster analysis, however, if one classify according to many variables then he will be in face of difficulties in characterizing. Therefore, after extracting representative factors by factor analysis, a segmentation method through 2 step cluster analysis is employed on the basis of these representative factors. As a result, six segmentation groups are found and the resulting strategy is proposed which strengthens prominent factors and makes up defective factors for each group.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.87-97
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• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.459-469
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• 2016

The purpose of this study is to classify the subtypes of perpetrators of school violence based on their psychological characteristics. In order to classify the students, questionnaires/interviews which consist of 6 factors (Cognitive Impulsivity, Unplanned Impulsivity, Depression, Anxiety, Peer Conformity, and Self assertion) and 19 questions were administered to 86 perpetrators of school violence. Then, a two-step cluster analysis was performed with the survey results of 74 perpetrators. As a result, three clusters were identified and named as follows: 1) Impulsive Vulnerability, 2) Emotional Vulnerability, and 3) Social Vulnerability. Scrutinizing the detailed characteristics of each cluster, the first cluster, Impulsive Vulnerability, showed higher scores on Cognitive Impulsivity and Unplanned Impulsivity, compared to the other two clusters, while Depression and Anxiety scores were lower. The second cluster, Emotional Vulnerability, showed higher scores on Depression and Anxiety, while Cognitive Impulsivity and Unplanned Impulsivity scores were lower. The third cluster, Social Vulnerability, showed the highest score on Peer Conformity among the three clusters. However, Self assertion scores were the lowest in this cluster, and Cognitive Impulsivity, Unplanned Impulsivity, depression, and anxiety scores were lower than in the others. This study will provide a useful insight for facilitating teachers and parents' understanding of the psychological characteristics of school violence perpetrators and thereby contributing to effective intervention.

• Land and Housing Review
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• v.2 no.4
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• pp.539-552
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• 2011

This research thesis measured potential housing development of 250 'subway station areas' around Seoul Metropolitain Region, with the use of added weighting and data of each indicator based on AHP. The analysis was conducted on the ranking of the development of 'subway station areas' on the basis of measurement results of potential housing development along with the analysis on characteristics of 'subway station areas' in daily life zone. Cluster analysis-oriented analytic measurement results suggested that 'subway station areas' in Seoul Metropolitain Region can be grouped into five clusters: Cluster 1 and Cluster 2 were populated with 'reservation areas' for outside and inside urban development, respectively scoring low all in housing development pressure and capacity. Cluster 3 was populated with 'development maintenance areas', scoring low in housing development pressure but high all in housing development capacity. Cluster 4 was populated with 'development facilitation areas', scored high all in housing development pressure and capacity. Cluster 5 was populated with 'development control areas' scoring high in housing development pressure but low in housing development capacity.