• Journal of the Korean Society for information Management
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• v.22 no.1 s.55
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• pp.65-85
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• 2005

This study proposes a new site selection model which reflects integrated opinions of several groups and identifies sites through objectivity of selection procedure. The proposed model consists of two parts, Analytic Hierarchy Process(AHP) and Geographic Information(GIS). This model was applied to Seocho-gu in Seoul. First, library site selection criteria were determined through literature study. Hierarchical relationship based on the questionnaire was determined and refined to be suited to Seocho-gu case. A survey was conducted with three groups, namely, library users, librarians, and public worker. A few inconsistent answers to the survey questionnaire were excluded and the relative importance of each criterion was measured. Next, an overlay method was used and the relative importance was used as a weight for selecting candidates. This process excluded the areas where a library was unable to be built, for example, rivers, military areas, other restricted areas by law, etc. and resulted in seventy-five sites. Five groups of candidates were identified according to the similarity of criteria. Finally, four groups, after eliminating one lowly fitted group, were determined.

• Journal of IKEEE
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• v.13 no.2
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• pp.167-174
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• 2009

Music evokes human emotions or creates music moods through various low-level musical features. Typical music clip consists of one or more moods and this can be used as an important criteria for determining the similarity between music clips. In this paper, we propose a new music retrieval scheme based on the mood change patterns of music clips. For this, we first divide music clips into segments based on low level musical features. Then, we apply K-means clustering algorithm for grouping them into clusters with similar features. By assigning a unique mood symbol for each cluster, we can represent each music clip by a sequence of mood symbols. Finally, to estimate the similarity of music clips, we measure the similarity of their musical mood sequence using the Longest Common Subsequence (LCS) algorithm. To evaluate the performance of our scheme, we carried out various experiments and measured the user evaluation. We report some of the results.

• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.95-116
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• 2009

Ontologies have provided supports in integrating heterogeneous and distributed information. More and more ontologies and tools have been developed in various domains. However, building ontologies requires much time and effort. Therefore, ontologies need to be shared and reused among users. Specifically, finding the desired ontology from an ontology repository will benefit users. In the past, most of the studies on retrieving and ranking ontologies have mainly focused on lexical level supports. In those cases, it is impossible to find an ontology that includes concepts that users want to use at the semantic level. Most ontology libraries and ontology search engines have not provided semantic matching capability. Retrieving an ontology that users want to use requires a new ontology selection and ranking mechanism based on semantic similarity matching. We propose an ontology selection and ranking model consisting of selection criteria and metrics which are enhanced in semantic matching capabilities. The model we propose presents two novel features different from the previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure.

• Journal of the Korean Society for information Management
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• v.37 no.2
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• pp.197-223
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• 2020

The purpose of this study is to analyze the issues resulted from the process of grouping KORMARC records using FRBR WORK concept and to suggest a new method. The previous studies did not sufficiently address the criteria or processes for identifying representative authors of records and their derivatives. Therefore, our study focused on devising a method of identifying the representative author when there are multiple contributors in a work. The study developed a method of identifying representative authors using an author role dictionary constructed by extracting role-terms from the statement of responsibility field (245). We also designed another way to group records as a work by calculating similarity measures of authors and titles. The accuracy rate of WORK grouping was the highest when blank spaces, parentheses, and controling processes were removed from titles and the measured similarity rates of authors and titles were higher than 80 percent. This was an experiment study where we developed an author-role dictionary that can be utilized in selecting a representative author and measured the similarity rate of authors and titles in order to achieve effective WORK grouping of KORMARC records. The future study will attempt to devise a way to improve the similarity measure of titles, incorporate FRBR Group 1 entities such as expression, manifestation and item data into the algorithm, and a method of improving the algorithm by utilizing other forms of MARC data that are widely used in Korea.

• Journal of the Korean Society of Fashion and Beauty
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• v.3 no.2 s.2
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• pp.65-71
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• 2005

The aim of this research is to investigate the demographic characteristics of the purchase tendencies and shopping trends amongst female golfers and how these are influenced by their life style and to analyse their selection criteria for purchases of golf-related items. The research methodology was through the use of questionnaires, completed by female golfers in Gyeongnam. The results are as follows: life style trends of male golfers were analysed to be categorized into one of the following: the shopping-addicted, fashion-conscious, rationalist and family oriented spenders. The characteristics of these categories are described as one of the following: utilitarian-complacent, rationalist, self-worshipping, inconsiderate. The demographic characteristics showed notable variations only in age differences. The obtained results show that the influences of the variables are minimal and there was no notable correlation. Significant differences were observed from one life style group to another, in selection criteria for purchase, which mainly depended on style, design, colour, pattern, designer-label, co-ordinated looks, similarity, ease of maintenance and functionality. Comparisons were made between the previously categorized life-style-groups and notable differences were present in such characteristics as ostentatious, trendy, aesthetically pleasing and functional.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.5
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• pp.894-909
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• 2016

This study analyzed a somatotype that was more suitable to a virtual fitting model and to improve the reality of a virtual model size. We analyzed 1,868 women 18-59 years old from the 6th Size Korea data. First, factor analysis was done for abstracting new criteria for dividing the somatotype; subsequently, we selected the waist height proportion to stature (body proportion) and drop (torso shape). Next, the cluster analysis was done with these criteria and 7 body proportion types and 11 torso shapes were distinguished. A virtual model size for the most common somatotype was also developed by a regression analysis of constituting sizes of each factor that was compared with body sizes well as with Clo's virtual model size. The model of this research showed a high similarity in sizes with body as well as improved better realisty than the Clo model which presented size problems such as longer limbs, bigger bust, smaller waist and a smaller arm circumference than the real body.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.161-169
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• 2016

This paper is intended to propose a novel approach to select an optimal site for a small-scaled artificial recharge system installation using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with geospatial data. TOPSIS is a MCDM (Multi-Criteria Decision Making) method to choose the preferred one of derived alternatives by calculating the relative closeness to an ideal solution. For applying TOPSIS, in the first, the topographic shape representing optimal recovery efficiency is defined based on a hydraulic model experiment, and then an appropriate surface slope is determined for the security of a self-purification capability with DEM (Digital Elevation Model). In the second phase, the candidate areas are extracted from an alluvial map through a morphology operation, because local alluvium with a lengthy and narrow shape could be satisfied with a primary condition for the optimal site. Thirdly, a shape file over all candidate areas was generated and criteria and their values were assigned according to hydrogeologic attributes. Finally, TOPSIS algorithm was applied to a shape file to place the order preference of candidate sites.