• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2011.05a
• /
• pp.27-28
• /
• 2011

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 2000.06a
• /
• pp.54-61
• /
• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.05a
• /
• pp.163-165
• /
• 2003

Some multivariate analyses provide configurations for variables or objects in low dimensional space because we can see easily their relation. In particular, in simple correspondence analysis(SCA), we can obtain the various configurations which are called SCA Maps based on the algebraic algorithms. Moreover, it often occur the variability among them. Therefore, in this study, we will give a comparison of variability of SCA maps using the procrustes analysis which is a technique of comparing configurations in multidimensional scaling.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.297-306
• /
• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Communications for Statistical Applications and Methods
• /
• v.22 no.3
• /
• pp.265-276
• /
• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• Communications for Statistical Applications and Methods
• /
• v.10 no.1
• /
• pp.49-60
• /
• 2003

The human sensibility product design requires information on consumer's emotions such as vision, auditory, olfactory, gustatory, or tactile perceptions. In this study, tactile sense which has not been well studied compared to other senses, is measured and statistically analysed. The emotional responses of 37 pairs of positive and negative adjectives describing tactile senses are collected and analysed through the questionnaire to find the correlation between adjectives and surface roughness of the sample. Mean ranks for 37 pairs of adjectives on four samples are obtained, and used to cluster these adjectives by factor analysis, multidimensional scaling, or cluster analysis.

• KSTLE International Journal
• /
• v.1 no.1
• /
• pp.34-42
• /
• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Journal of the Korean Society for information Management
• /
• v.23 no.4 s.62
• /
• pp.215-231
• /
• 2006

Recently there are many bibliometric studies attempting to utilize Pathfinder networks(PFNets) for examining and analyzing the intellectual structure of a scholarly field. Pathfinder network scaling has many advantages over traditional multidimensional scaling, including its ability to represent local details as well as global intellectual structure. However there are some limitations in PFNets including very high time complexity. And Pathfinder network scaling cannot be combined with cluster analysis, which has been combined well with traditional multidimensional scaling method. In this paper, a new method named as Parallel Nearest Neighbor Clustering (PNNC) are proposed for complementing those weak points of PFNets. Comparing the clustering performance with traditional hierarchical agglomerative clustering methods shows that PNNC is not only a complement to PFNets but also a fast and powerful clustering method for organizing informations.

• Journal of The Korean Association For Science Education
• /
• v.33 no.3
• /
• pp.650-663
• /
• 2013

Multidimensional scaling can be used to identify relationships among concepts, revealing the structure of the cognitive framework by measuring distances within perceptual maps. The current study sought to examine the relationships among concepts related to photosynthesis in 2,844 $3^{rd}-11^{th}$ grade science students. The questionnaire included items on 'location,' 'products,' 'reactants,' and 'environmental factors', presenting images related to each theme. Students provided responses corresponding to particular topics, and reported the extent to which the concept was related to the topic on a scale from 1 to 30. The survey results were as follows: first, students were not able to clearly distinguish between or understand the four main topics. Second, students organized their cognitive structures by closely associating related concepts after learning. Third, the presented concepts revealed a mixture of scientific and non-scientific concepts, suggesting that students needed to clearly distinguish the preconceptions through which they organized concepts, so that they are suitable for cognitive structures based on learning. Furthermore, non-scientific concepts within perceptions were consistently maintained throughout learning, affecting the proximity of scientific concepts.

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