• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.61-72
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• 2011

In order to assess hydrologic and nonpoint source pollutant behaviors in a watershed with Soil and Water Assessment Tool (SWAT) model, the accuracy evaluation of SWAT model should be conducted prior to the application of it to a watershed. When calibrating and validating hydrological components of SWAT model, the Nash-Sutcliffe efficiency coefficient (EI) has been widely used. However, the EI value has been known as it is affected sensitively by big numbers among the range of numbers. In this study, a Web-based flow clustering EI estimation system using K-means clustering algorithm was developed and used for SWAT hydrology evaluation. Even though the EI of total streamflow was high, the EI values of hydrologic components (i.e., direct runoff and baseflow) were not high. Also when the EI values of flow group I and II (i.e., low and high value group) clustered from direct runoff and baseflow were computed, respectively, the EI values of them were much lower with negative EI values for some flow group comparison. The SWAT auto-calibration tool estimated values also showed negative EI values for most flow group I and II of direct runoff and baseflow although EI value of total streamflow was high. The result obtained in this study indicates that the SWAT hydrology component should be calibrated until all four positive EI values for each flow group of direct runoff and baseflow are obtained for better accuracy both in direct runoff and baseflow.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.43-57
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• 2022

Advances in measurement techniques have reduced measurement costs and enhanced safety resulting in less uncertainty. For example, an acoustic doppler current profiler (ADCP) based suspended sediment concentration (SSC) measurement technique is being accepted as an alternative to the conventional data collection method. In Korean rivers, horizontal ADCPs (H-ADCPs) are mounted on the automatic discharge monitoring stations, where SSC can be measured using the backscatter of ADCPs. However, automatic discharge monitoring stations and sediment monitoring stations do not always coincide which hinders the application of the new techniques that are not feasible to some stations. This work presents and analyzes H-ADCP-SSC models for 9 discharge monitoring stations in Korean rivers. In application of the Gaussian mixture model (GMM) to sediment-related variables (catchment area, particle size distributions of suspended sediment and bed material, water discharge-sediment discharge curves) from 44 sediment monitoring stations, it is revealed that those characteristics can distinguish sediment monitoring stations regionally. Linking the two results, we propose a protocol determining the H-ADCP-SSC model where no H-ADCP-SSC model is available.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.1
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• pp.25-32
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• 2022

In this study, basic skin characteristics data were collected by measuring skin hydration, skin melanin, skin redness, and skin torsion elasticity from Korean women in from 20's to 40's ages, and then, age and correlation analysis were conducted. This was used to create a skin index, and cluster analysis was performed to classify the groups into 4 clusters, and the skin characteristics of each cluster were confirmed. Then, two prototypes were used for two weeks to confirm the improvement effect on skin moisture, skin redness, and skin dead mass reduction, and then analyzed which product was more effective in which cluster of subjects participated in the skin characteristics test. As a result of the study, the possibility of preparing for the customized cosmetics market was confirmed by applying the skin index and cluster analysis results to product efficacy evaluation.

• 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.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.177-181
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• 2009

Developing u-Public facilities for application u-City is to combine both the state-of-the art of the construction and ubiquitous computing and must be flexibly comprised of the facilities for the basic service of the building such as air conditioning, heating, lighting and electric equipments to materialize a new format of spatial planning and the public facilities inside or outside. Accordingly, in this paper we suggested the time pattern system for predicting the most basic power system loads for the basic service. To application the tim e pattern we applied SOM algorithm and k-means method and then clustered the data each weekday and each time respectively. The performance evaluation results of suggestion system showed that the forecasting system better the ARIMA model than the exponential smoothing method. It has been assumed that the plan for power supply depending on demand and system operation could be performed efficiently by means of using such power load forecasting.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.100-106
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• 2022

A longitudinal study refers to a research method based on longitudinal data repeatedly measured on the same object. Most of the longitudinal analysis methods are suitable for prediction or inference, and are often not suitable for use in exploratory study. In this study, an exploratory method to analyze longitudinal data is presented, which is to find the longitudinal trajectory after determining the best number of clusters by clustering longitudinal data using self-organizing map technique. The proposed methodology was applied to the longitudinal data of the Employment Information Service, and a total of 2,610 samples were analyzed. As a result of applying the methodology to the actual data applied, time-series clustering results were obtained for each panel. This indicates that it is more effective to cluster longitudinal data in advance and perform multilevel longitudinal analysis.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.107-143
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• 2017

This study aims to understand the impact of internal and external environments and innovation efforts on a company's performance. First, the relationships and patterns between variables were determined through an exploratory factor analysis. Afterwards, a cluster analysis was conducted, in which the influential factors summarized in the factor analysis were classified. Finally, structural equation modeling was used to carry out an empirical analysis of the structural relationship between innovation efforts and the company's performance in the classified clusters. 7 factors were derived from the exploratory factor analysis of 40 input variables from external and internal environments. 4 clusters (n=1,022) were formed based on the 7 factors. Empirical analysis of the 4 clusters using structural equation modelling showed the following: Only independent technology development had a positive impact on the company's performance for Cluster 1, which is characterized by sensitivity to a technological/competitive environment and innovativeness. Only independent technology development and joint research had positive impacts on the company's performance for Cluster 2, which is characterized by sensitivity to a market environment and internal orientation. Joint research and the mediating variable of government support program utilization had positive impacts, while the introduction of technology had a negative impact on the company's performance for Cluster 3, which is characterized by sensitivity to a competitive environment, innovativeness, and willingness to cooperate with the government and related institutions. Independent technology development as well as the mediating variables of network utilization and government support program utilization had positive impacts on the company's performance for Cluster 4, which is characterized by openness and external cooperation.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.479-479
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• 2017

본 연구는 벤처기업의 매출액의 영향요인에 대하여 기업 내 자원을 범주화하여 그 영향력을 검증하고, 나아가 기업이 보유한 역량을 2개의 차원으로 구분하여 각 차원에 따른 집단 간 벤처기업성과 영향요인의 차이를 확인하고자 하였다. 즉, 기업이 보유하는 역량인 기술, 가격, 품질, 디자인, 조직관리, 마케팅 경쟁력에 대하여 요인분석을 통해 기술역량과 경영역량 차원을 도출하였고, 군집화 과정을 거쳐 각 역량 수준에 따른 네 개의 벤처기업 집단을 분류하였다. 네 개의 집단은 기술-경영 우위, 기술주도, 경영주도, 기술-경영 열위로 나뉘었다. 이후 성과 영향요인들을 자원기반이론의 관점에서 기업현황, 기업가자원, 인적자원, 재무자원, 기술자원, 외부자원의 여섯 개의 상위 범주로 구분하였고, 각 범주에 포함되는 세부 요인들이 매출액에 미치는 영향을 '벤처기업정밀실태조사'의 2개년 데이터를 활용하여 분석하였다. 분석 결과 첫 번째, 전체 벤처기업을 대상으로 분석한 결과 모형에 투입된 33개의 변수들 중 22개의 변수가 벤처기업 매출액에 유의한 영향을 미치는 것이 확인되었다. 두 번째, 기업 역량 차원 분류에 따른 기업 집단 간 영향요인 차이를 분석한 결과, 매출액 영향요인은 입력된 33개의 독립변수 중 최대 13개에서 최소 8개로, 기업 역량 차원에 따른 매출액 영향요인의 차이가 확인되었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.111-111
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• 2020

하천환경과 기후의 변화로 인해 수질오염 과정의 메커니즘이 더욱 복잡해짐에 따라 다양한 요인을 고려한 불확실성 평가 연구가 요구되고 있다. 하천 수질 중에서도 부영양화 문제는 특히 개발로 인한 하천환경 변화 이후 사회 정치적 논점이 되어왔다. 본 연구에서는 지난 7년 동안의 수질 변화의 전반적인 양상을 조사하였으며, 클로로필-a(Chl-a, chlorophyll-a) 농도의 시공간적 의존성의 효과적으로 고려하기 위해 기계학습 기반 분류(classification) 접근법인 다변량 은닉 마코프 모형(MHMM, multivariate hidden Markov model)을 사용하였다. 월 단위 수질 및 수문 자료를 사용하여 Chl-a의 변동성을 군집화하여 수질 상태의 익월 천이확률을 효과적으로 추정하였다. Chl-a와 수질 및 수문기상 조건의 관계를 평가하였으며, 결과적으로 수질 상태의 시공간적 전이가 정확하게 식별되었고 이의 잠재적 원인에 대하여 논의하였다.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.237-249
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• 2023

This study was carried to establish a new landscape-oriented gardening model based on climate, vegetation, and forest landscape characteristics. In addition, innovative forest garden models were suggested through an integrated approach to the ecological characteristics of forest vegetation communities and existing garden planting types. For the study, the key landscape elements that make up the main forest vegetation community were identified. And the vertical layers and horizontal distribution patterns of the community structure were typified by diagnostic species and their growth forms & habits such as dominant species, character species, and differential species, and degree of dominance-sociability. Based on this, a standardized vegetation structure and formation was developed by stratifying the landscape into main features, minor features, and detailed features according to visual dominant elements. Also, the applicability of the forest garden model was examined by applying the concept of borrowing landscape to representative deciduous broadleaf forests in the temperate northern region of Korea. Additionally, an integrated forest garden models based on the conceptual definition and typology of forest gardens, and a strategic approach to forest vegetation were proposed