• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.404-404
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• 2017

이상기후현상으로 인해 극치 수문 사상들이 빈번히 발생함에 따라 상대적으로 높은 재현기간에 해당하는 극치 수문 사상해석에 대한 관심이 높아지고 있다. 그러나 우리나라의 경우 이러한 극치 수문 사상을 추정하기 위한 표본의 수가 부족한 실정이다. 지역빈도해석은 지점의 표본 수가 적거나 수문자료의 수집이 불가능한 미계측지점인 경우, 해당 지점과 수문학적으로 동질하다고 여겨지는 주변 지점들의 자료를 확보하여 확률수문량을 추정함으로써 상대적으로 지점빈도해석 보다 roubst한 추정값을 얻을 수 있다는 장점을 가지고 있다. 따라서 최근 확률수문량 산정 기법으로 지역빈도해석 방법에 관한 관심이 높아지고 있다. 지역구분은 지역빈도해석이 지점빈도해석과 구분될 수 있는 큰 특징이고 지역구분 결과 따라 지역의 표본 크기가 결정되기 때문에 수문학적으로 동질한 지역을 나누는 방법은 매우 중요하다고 볼 수 있다. 인공신경망은 인간의 뇌가 학습하는 방식을 모사한 통계적 모델링 기법이다. 즉, 인간의 뇌가 일정한 반복 학습을 통해 어떠한 문제의 해법을 추론하거나 예측, 또는 패턴을 인식하는 일련의 과정을 알고리즘화 하여 목적함수의 해를 찾는 방식이다. 특히, 주어진 자료들로 부터 특징을 추출하고 그 특징을 학습하여 전체 자료의 분류나 군집화를 이루는데 널리 이용되고 있다. 본 연구에서는 낙동강유역을 대상으로 인공신경망을 이용한 군집분석을 수행하고 구분된 지역을 이용하여 지역빈도해석을 수행하였다.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.707-719
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• 2018

Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.148-152
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• 2007

본 연구에서는 LIDAR 포인트 자료로부터 분리된 포인트 군집의 윤곽 포인트 추출을 위하여，가상격자를 이용한 검색 영역의 제한을 통한 윤곽 포인트 추출 방식을 제안하였으며 성능을 평가하기 위해 보편적으로 사용되는 TIN을 이용한 방식과 비교하였다. 실제 건물 포인트 자료에 대하여 적용한 결과 TIN을 이용한 방식보다 빠른 처리가 가능하며 시각적인 평가를 통해 결과물의 품질 면에서도 두 가지 방식이 거의 유사함을 확인할 수 있었다.

• Korean Journal of Environment and Ecology
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• v.10 no.2
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• pp.270-282
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• 1997

This investgation was performed to provide basic data for the management program of the Soraksan national park. Sixty plots were set up and surveyed in the Sinhungsa-Wasondae Area. The vegetation was divided into two communities, Pinus densiflora community and Quercus serrata community, according to the analysis of DCA ordination. It was found out that the succession of the Pinus densiflora comunity would proceed to Quercus serrata community of which components was dominant in understory layer and shrub layer. And it was found out that in the Quercus serrata community the Pinus densiflora being mainly big size in DBH would becomr dominant comtinuously. Shannon's diversity of both community were 1.2554 and 1.1134 respectively. The numbers of woody species per 100m$^{2}$ ranged 7 to 26, and the average number of woody species was 13.

• Spatial Information Research
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• v.20 no.6
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• pp.13-21
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• 2012

In this study, homogeneous regions for regional frequency analysis were identified using rainfall data from 61 observation points in Korea. The used data were gathered from 1980 to 2010. Self organizing map and K-means clustering based on Davies-Bouldin Index were used to make clusters showing similar rainfall patterns and to decide the optimum number of the homogeneous regions. The results from this analysis showed that the 61 observation points can be optimally grouped into 6 geographical clusters. Finally, the 61 observations points grouped into 6 clusters were mapped regionally using Thiessen polygon method.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.315-328
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• 2018

In this paper, we examine the methodology to predict consumer preferences using several groups of attributes of products and application to real data. In the food industry, studies are in progress to investigate the relationship between product attributes and consumer preferences; consequently, various methodologies are proposed. Among these methodologies, we consider multiple factor analysis (MFA). The result of the MFA enable the division of consumers into four clusters with similar liking and the defining of preference characteristics for each cluster. Also, using the results of multiple factor analysis, we find the partial least squares path model to predict consumer preferences through the characteristics of the product and the characteristics evaluated by consumers. We can understand the relationship between the cluster of consumers and the preferred/undesirable characteristics of products through the partial least squares path model applied to two clusters with different liking. When multiple factor analysis is used in the partial least squares path model, it is possible to investigate relationships between products and consumers by analyzing product characteristics and consumer preferences simultaneously. The results can be applied to product developments and sales which makes this methodology important and useful.

• Journal of Korea Water Resources Association
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• v.43 no.5
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• pp.471-482
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• 2010

Rainfall events in the hydrologic circulation are closely related with various meteorological factors. Therefore, in this research, correlation relationship was analyzed between sea surface temperature of typical meteorological factor and monthly rainfall on Korean peninsula. The cluster analysis was performed monthly average rainfall data, longitude and latitude observed by rainfall observatory in Korea. Results from cluster analysis using monthly rainfall data in South Korea were divided into 4 regions. The principal components of monthly rainfall data were extracted from rainfall stations separated cluster regions. A correlation analysis was performed with extracted principal components and sea surface temperatures. At the results of correlation analysis, positive correlation coefficients were larger than negative correlation coefficients. In addition, The 3 month of principal components on monthly rainfall predicted by locally weighted polynomial regression using observed data of sea surface temperature where biggest correlation coefficients have. The result of forecasting through the locally weighted polynomial regression was revealed differences in accuracy. But, this methods in the research can be analyzed for forecasting about monthly rainfall data. Therefore, continuous research need through hydrological meteorological factors like a sea surface temperature about forecasting of the rainfall events.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.151-165
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• 2004

The purpose of the study was to explore the potential of the Cluster Analysis and the Community Analysis of Social Network Analyses family in job-task analysis for curriculum design. These two multivariate analysis techniques were expected to bring us relevant and scientific information as well as inspiration in investigating the structure and nature of job system, which are critical in developing relevant curriculum. To pursue the purpose mentioned above, qualitative and quantitative data were collected from "S" Corporate, a major large high-tech manufacturing company, and analyzed by relevant analytic procedures. Results indicate that there are discrepancies between formal job structures and actual ones. Following Community analysis showed that the presence of center-marginal structure along with clustering structure in the current job formation. Interpretations of the results of the study are provided in light of past research and additional data collected from the study. Implications of the study are also discussed along with suggestions for future research.

• Korean Business Review
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• v.7
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• pp.165-196
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• 1994

Harris(1986)[22]는 주식가격에 있어서의 요일효과(曜日效果)(day-of-the-week effect)의 증거는 광범위한 시장지수에서의 일별(日別) 종가(終價) 대 종가(終價)수익률(收益率)에 대한 연구들에서 나타난다고 한다. 이러한 연구들은 결론적으로 체계적 수익률 행태를, 특히 음(陰)의 월요일 수익률을 증명한다. Harris(1990)[24]는 군집현상(群集現象)은 가격이산성(價格離散性)이 추정량(推定量)에 미치는 영향을 분석할 때 고려되어야 한다고 주장한다. 특히, 군집현상(群集現象)이 거래자가 규정된 최소가격변동에 기초한 집합보다 더 큰 이산적(離散的)가격집합(價格集合)을 사용하기 때문에 결과한다면, Gottlieb 와 Kalay(1985)[21] 및 Harris(1990)[24]에서 확인된 분산(分散)과 시계열공분산(時系列共分散) 추정량(推定量) 편의(偏倚)는 훨씬 더 심각할 것이라고 한다. 또한 모든 연구들은 이산성(離散性)이 거래가격의 유의한 특성이기 때문에 군집현상(群集現象)을 고려하여야 한다고 한다. 주식시장의 경우 요일효과가 존재한다면, 관찰주가의 이산성(離散性)으로 인한 요일별 주가의 끝자리가격의 분포가 월요일과 다른 요일에 있어 차이가 있는지와 요일별 가격결정의 정도가 (1) 주가의 수준, (2) 주가수익률의 기복 및 (3) 시장에서의 주식거래량에 있어 차이가 있는지 둥에 대하여 의문을 갖게 한다. 따라서 본 연구는 이산성으로 인한 요일별 관찰주가의 군집현상에 관한 거래자료를 연구하기 위하여 한국 주식시장에서의 입수가능한 최근년도인 1990년 1월 4일에서 1994년 6월 30일까지의 4년 6개월 동안의 일별주가변동(日別株價變動) 거래자료(去來資料)를 조사하고 실증분석을 수행하였다. 본 연구의 결과에 의하면 주식가격에 있어서의 요일효과는 관찰가격의 이산성 특히, 호가(呼價)의 가격단위(價格單位)에 기인하는 것 같지는 않다. 그러나 본 연구의 결과에 의하면 최돈일(1993)[7]의 연구 결과에서와 같이 Gottlieb 와 Kalay(1985) [21] 및 Ball(1988)[9]의 주장을 받아들이기 어렵다. 최돈일(1993)[7]의 연구를 확장한 본 연구의 결과는 최돈일(1993)의 연구 결과와도 상이하다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.347-356
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• 2009

This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.