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

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

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.3-12
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• 2024

This study examined affective representation by analyzing physiological responses measured using wearable devices and affective ratings in response to emotional videos. To achieve this aim, a published dataset was reanalyzed using multidimensional scaling to demonstrate affective representation in two dimensions. Cross-participant classification was also conducted to identify the consistency of emotional responses across participants. The accuracy and misclassification in each emotional condition were described by exploring the confusion matrix derived from the classification analysis. Multidimensional scaling revealed that the represented objects, namely, emotional videos, were positioned along the rated valence and arousal vectors, supporting the core affect theory (Russell, 1980). Vector fittings of physiological responses also showed the associations between heart rate acceleration and low arousal, increased heart rate variability and negative and high arousal, and increased electrodermal activity and negative and low arousal. Using the data of behavioral and physiological responses across participants, the classification results revealed that emotional videos were more accurately classified than the chance level of classification. The confusion matrix showed that awe, enthusiasm, and liking, which were categorized as positive, low arousal emotions in this study, were less accurately classified than the other emotions and were misclassified for each other. Through multivariate analyses, this study confirms the core affect theory using physiological responses measured through wearable devices and affective ratings in response to emotional videos.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.15-28
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• 2023

This study examined the affective representation and response consistency among individuals using affective videos, a naturalistic stimulus inducing emotional experiences most similar to those in daily life. In this study, multidimensional scaling was conducted to investigate whether the various affective representations induced through video stimuli are located in the core affect dimensions. A cross-participant classification analysis was also performed to verify whether the video stimuli are well classified. Additionally, the newly developed intersubject correlation analysis was conducted to assess the consistency of affective representations across participant responses. Multidimensional scaling revealed that the video stimuli are represented well in the valence dimension, partially supporting Russell (1980)'s core affect theory. The classification results showed that affective conditions were successfully classified across participant responses. Moreover, the intersubject correlation analysis showed that the consistency of affective representations to video stimuli differed with respect to the condition. This study suggests that the affective representations and consistency of individual responses to affective videos varied across different affective conditions.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.39-52
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• 2023

According to the core affect theory, affect consists of two independent dimensions of valence and arousal. Previous studies have found that various types of stimuli, such as pictures, videos, and music, are mapped onto the core affect space. However, the research on affect using gustatory stimuli has not been explored sufficiently. This study investigated whether the affects elicited by tastes could be mapped onto the core affect space. Stimuli were selected based on two factors (taste types and intensity). Participants were presented with each stimulus, evaluated the tastes, and rated their affective responses on taste and emotion scales. The data were analyzed using repeated-measures ANOVAs and multivariate analyses (multidimensional scaling and classification). The results of univariate analyses indicated that participants felt positive for sweet stimuli but negative for bitter and salty. Furthermore, participants reported high arousal with high intensity. Multidimensional scaling revealed that taste stimuli are also represented on the core affect dimensions. Specifically, it was confirmed that in the first dimension, sweetness was represented as a positive affect, while bitter and salty tastes were represented as a negative affect. In the second dimension, bitterness was represented as low arousal and sourness as high arousal. Classification analyses confirmed that the taste was identified consistently based on the affective responses within and across participants. This study showed that the taste stimuli in daily life are also located on core affect dimensions of valence and arousal.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.125-160
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• 2007

Data Warehousing and OLAP (On-Line Analytical Processing) have turned into the key technology for comprehensive data analysis. Originally developed for the needs of decision support in business, data warehouses have proven to be an adequate solution for a variety of non-business applications and domains, such as government, research, and medicine. Analytical power of the OLAP technology comes from its underlying multidimensional data model, which allows users to see data from different perspectives. However, this model displays a number of deficiencies when applied to non-conventional scenarios and analysis tasks. This paper presents an attempt to systematically summarize various extensions of the original multidimensional data model that have been proposed by researchers and practitioners in the recent years. Presented concepts are arranged into a formal classification consisting of fact types, factual and fact-dimensional relationships, and dimension types, supplied with explanatory examples from real-world usage scenarios. Both the static elements of the model, such as types of fact and dimension hierarchy schemes, and dynamic features, such as support for advanced operators and derived elements. We also propose a semantically rich graphical notation called X-DFM that extends the popular Dimensional Fact Model by refining and modifying the set of constructs as to make it coherent with the formal model. An evaluation of our framework against a set of common modeling requirements summarizes the contribution.

• The Korean Journal of Ecology
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• v.16 no.1
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• pp.1-15
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• 1993

Ordination and classification techiques were used to analyze the forest communities and to examine the integration problem of community-to-ecological species group in mt. moak provincial park of korea. phytosociological classiication based on floristic composition produced seven commuities of zelkova serrata, carpinus densiflora. These seven communities were well discriminated in the two-dimensional analyses of soil moisture, soil organic matter content and temperature(elevation), eciprocally, and in three-dimensional space of the three environmental factors also. They corresponded to seven ecological groups derived from the distribution pattern analysis of species populations in this mountain.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.5 s.153
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• pp.699-710
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• 2006

The objective of this study was to identify the texture-related components of woven fabrics and to develop a multidimensional perceptual structure map to represent the tactile textures. Eighty subjects in clothing and tektite industries were selected for multivariate data on each fabric of 30 using the questionnaire with 9 pointed semantic differential scales of 20 texture-related adjectives. Data were analyzed by factor analysis, hierarchical cluster analysis, and multidimensional scaling(MDS) using SPSS statistical package. The results showed that the five factors were selected and composed of density/warmth-coolness, stiffness, extensibility, drapeability, and surface/slipperiness. As a result of hierarchical cluster analysis, 30 fabrics were grouped by four clusters; each cluster was named with density/warmth-coolness, surface/slipperiness, stiffness, and extensibility, respectively. By MDS, three dimensions of tactile texture were obtained and a 3-dimensional perceptual structure map was suggested. The three dimensions were named as surface/slipperiness, extensibility, and stiffness. We proposed a positioning perceptual map of fabrics related to texture naming system(TNS). To classify the textural features of the woven fabrics, hierarchical cluster analysis containing all the data variations, even though it includes the errors, may be more desirable than texture-related multidimensional data analysis based on factor loading values in respect of the effective variables reduction without losing the critical variations.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.415-424
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• 2021

The life of studying abroad includes not only school life, but also various areas such as economy, social relationship, and culture, so the level of satisfaction in each area could be differently shown in each individual. Based on this critical mind, this study aims to analyze the satisfaction with life of studying abroad in the multidimensional perspective. To analyze this, a latent class analysis was applied to identify subgroups, and a multinomial logistic regression model was applied to verify factors influencing group classification. The results of the analysis could be summarized into two. First, there were sub-groups showing different satisfaction with life of studying abroad. The sub-groups showed different levels of satisfaction in five areas such as housing, economy, social relationship, study, and culture, which were not discerned in single dimension. Second, the classification of group was complexly influenced by academic factor, psychological/emotional factor, and environmental factor. Especially, the predictive factor had different influences on each sub-factor. Based on such results of this study, this study aims to seek for the practical and policy-level suggestions for improving foreign students' satisfaction with life of studying abroad.