• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.399-404
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• 2008

사용자 중심의 메뉴 기반 인터페이스를 설계하기 위해서는 인간의 지식 구조를 이해하는 것이 중요하다. 인간의 지식 구조를 이해하게 되면, 인터페이스를 통해서 전달된 자극들이 만들어낸 개념들이 어떠한 관계를 가지고 정신 모형(mental model)을 형성하고 있는지 알 수 있다. 인간의 지식 구조는 MDS (Multidimensional Scaling)과 Trajectory Mapping을 이용하여 Visual Concept Map 으로 나타낼 수 있고, 이것을 바탕으로 인간의 지식구조를 시각적으로 이해할 수 있다. MDS 는 인간의 머릿속에 자리잡고 있는 개념들의 상대적 위치를 알려주고, Trajectory Mapping 은 개념들 간의 연결 상태를 보여준다. 즉, Trajectory Mapping 을 통하여 개념들 간악 인지적 정보를 알 수 있다. 본 연구에서는 MDS 와 Trajectory Mapping 을 이용하여 핸드폰 메뉴로부터 전달 받은 시각적 자극들에 악해 형성된 개념들에 대한 인간의 지식 구조를 Visual Concept Map 으로 시각화하였다. 그리고 이렇게 시각화된 지식 구조를 바탕으로 메뉴 구조를 개발하였다. 본 연구 결과, MDS 와 Trajectory Mapping 을 이용한 인간의 지식 구조의 시각화는 사용자 중심의 메뉴 기반 인터페이스를 설계하는데 유용하게 쓰일 수 있을 것으로 보인다.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.567-578
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• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.648-653
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• 2010

The non-metric multidimensional scaling (nMDS) is a method for analyzing the relation among objects by mapping them onto the Euclidean space. The nMDS is useful when it is difficult to use the concept of distance between pairs of objects due to non-metric dissimilarities between objects. The nMDS can be regarded as an optimization problem in which there are many local optima. Since the conventional nMDS algorithm utilizes the steepest descent method, it has a drawback in that the method can hardly find a better solution once it falls into a local optimum. To remedy this problem, in this paper, we applied the simulated annealing to the nMDS and proposed a new optimization algorithm which could search for a global optimum more effectively. We examined the algorithm using benchmarking problems and found that improvement rate of the proposed algorithm against the conventional algorithm ranged from 0.7% to 3.2%. In addition, the statistical hypothesis test also showed that the proposed algorithm outperformed the conventional one.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.357-361
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• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Journal of the Korean Institute of Landscape Architecture
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• v.32 no.1
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• pp.47-56
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• 2004

Rapid economic development in Korea caused functions of city functions such as concentration of population, deterioration of the quality of living environment and traffic congestion. Korean cities have lost their identity becausr they are merged functionally and physically with neighboring cities, forming one mesa-city. Unified shape and disorganized streets of cities often cause confusion among foreigners and visitors. It is very difficult for them to find their image in strange cities. It is, however, important to correctly analyze the image and meaning of cities for understanding its identity. The purpose of this study is to develop a method to analyze the city image by focusing on some of the main cities in Korea. For this purpose, the adjective questionnaire and multi-dimension scaling (MDS) are applied to the analysis of city image. Image analysis graph by MDS can visually present the general and integrate images. The results of this study are summarized as follows: The important factors for interpretation of city image are historical and industrial character. Seoul, Taegu and Pusan have industrial and complex city images. Kongju has historical city image, while Changwon has a modern image. Chuncheon belongs to a soft and small image. Each city has an alternative solution against a negative image, according to the image analysis graph.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1171-1180
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• 2015

Multidimensional scaling (MDS) is a graphical technique of multivariate analysis to display dissimilarities among individuals into low-dimensional space. We often have two kinds of MDS which are metric MDS and non-metric MDS. Metric MDS can be applied to quantitative data; however, we need additional information about variables because it only shows relationships among individuals. Gower (1992) proposed a method that can represent variable information using trajectories of the pseudo-points for quantitative variables on the metric MDS space. We will call his method a 'replacement method'. However, the trajectory can not be represented even though metric MDS can be applied to binary data when we apply his method to binary data. Therefore, we propose a method to represent information of binary variables using pseudo-points called a 'partition method'. The proposed method partitions pseudo-points, accounting both the rate of zeroes and ones. Our metric MDS using the proposed partition method can show the relationship between individuals and variables for binary data.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1009-1018
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• 2012

Persistence homology (a type of methodology in computational algebraic topology) can be used to capture the topological characteristics of functional data. To visualize the characteristics, a persistence diagram is adopted by plotting baseline and the pairs that consist of local minimum and local maximum. We use the bottleneck distance to measure the topological distance between two different functions; in addition, this distance can be applied to multidimensional scaling(MDS) that visualizes the imaginary position based on the distance between functions. In this study, we use handwriting data (which has functional forms) to get persistence diagram and check differences between the observations by using bottleneck distance and the MDS.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.517-528
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• 2018

We provide R scripts to detect outliers in multivariate data and visualization. Detecting outliers is provided using three approaches 1) Robust Mahalanobis distance, 2) High Dimensional data, 3) density-based approach methods. We use the following techniques to visualize detected potential outliers 1) multidimensional scaling (MDS) and minimal spanning tree (MST) with k-means clustering, 2) MDS with fviz cluster, 3) principal component analysis (PCA) with fviz cluster. For real data sets, we use MLB pitching data including Ryu, Hyun-jin in 2013 and 2014. The developed R scripts can be downloaded at "http://www.knou.ac.kr/~sskim/ddpoutlier.html" (R scripts and also R package can be downloaded here).

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• ALGAE
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• v.31 no.1
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• pp.1-16
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• 2016

Paralia species have been frequently reported as P. sulcata in Korea, despite the species diversity within the genus. To understand the species diversity of Paralia in Korea, we collected phytoplankton samples at 79 sites from April 2006 to April 2015. Five Paralia species, P. fenestrata, P. guyana, P. marina, P. cf. obscura, and P. sulcata, were observed during this study, and we described their fine structure in terms of quantitative and qualitative morphological characteristics. To provide additional criteria to identify Paralia species more clearly, we morphometrically analysed four quantitative characteristics on valve diameter: pervalvar axis / diameter, internal linking spines / diameter, marginal linking spines / diameter, and fenestrae/diameter using non-metric multidimensional scaling (MDS). MDS analysis distinguished four Paralia species: P. guyana, P. marina, P. cf. obscura, and P. sulcata, with the exception of P. fenestrata. This new approach in using morphometric analysis is useful for the accurate identification of Paralia species.