• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.2
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• pp.287-304
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• 2016

This study examines the influence of product classification standards and structure on user perception as well as their attitude towards online shopping sites. The causal relationships of variables are also examined. The analysis was based on an online survey with 247 responses. Four types of internet shopping sites were developed and used as a stimulus. The results of the mean comparison analysis indicated that perceived variety, information overload, perceived shopping value and attitude towards the site varies significantly with product classification standards and structure. There was also of a marginally significant interaction between the classification standard and structure on perceived variety and information overload. The causal relationship analysis revealed that perceived variety positively influenced hedonic and utilitarian shopping value. However, information overload had a negative effect on hedonic and utilitarian shopping value. Both the hedonic and utilitarian shopping value positively influenced attitudes towards the sites. This study demonstrates that classification method influences customer perception and attitude. It offers interesting insights on a product classification method as a strategic tool for online shopping.

• Journal of Information Management
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• v.35 no.1
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• pp.1-27
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• 2004

To share knowledge efficiently among our members on the basis of knowledge management system, first of all, we need to systematically design the knowledge classification scheme that we can classify these knowledge well. The objective of this study is to construct the most suitable knowledge classification scheme that all of us can share them in Korea Atomic Energy Research Institute (KAERI). To construct the knowledge classification scheme all over the our organization, we established a few principles to design it and examined related many classification schemes. And I carried out 3 steps to complete the best desirable KAERI's knowledge classification scheme, that is, (1) the step to design a draft of the knowledge classification scheme, (2) the step to revise a draft of the knowledge classification scheme, (3) the step to verify the revised scheme and to decide its scheme. The scheme completed as a results of this study is consisted of total 218 items : sections of 8 items, classes of 43 items and sub-classes of 167 items. I expect that the knowledge classification scheme designed as the results of this study can be played an important role as the frame to efficiently share knowledge among our members when we introduce knowledge management system in our organization. In addition, I expect that steps to design its scheme as well as this scheme itself can be applied when design a knowledge classification scheme at the other R&D institutes and enterprises.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.309-319
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• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.92-93
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• 2004

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.188-189
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• 2002

• Tunnel and Underground Space
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• v.11 no.2
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• pp.182-189
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• 2001

Rockmass properties have great influence on blasting performance so that it cannot be overemphasized to analyze rockmass properties and to perform blast design based on them. Up to the present, however blast design is performed either considering only uniaxial compressive strength of intact rock or using RMR classification as a blast ability classification scheme. In this paper Ashby's approach is adopted to evaluate blast index. In addition. rockmass classification for the blast design based on joint survey results and pattern design procedure are added to Ashby's original approach. With this extended approach, blastability can be classified considering joint properties and objectiveness of evaluated blast index can be confirmed. This approach is anticipated to enhance the tunnel blast design by considering joint properties and classifying the rockmass for blast design.

• Explosives and Blasting
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• v.24 no.1
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• pp.63-69
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• 2006

Building tunnels means dealing with what rock is encountered. Relocation of the site of the underground structure is rarely possible. Tunneling engineers and miners have to cope with the quality of the rock mass as it is. Different tunneling philosophies and different rock classification methods have been developed in various countries. Most of the rock classification methods are based on the response of the rock mass to the excavation. Tunnel support requirements could be assessed analytically, supplemented by rock mass classification predictions, and verified by measurements during construction. Rock mass classifications on their own should only be used for preliminary, planning purposes and not for final tunnel support. Design of blast pattern in tunneling projects in Korea is also mostly prepared according to the general rock classification methods such as RMR or Q. They, however, do not take into account the blast performance, and as a consequence, produce poor blasting results. In this paper, the methods of general rock classification and blast design for tunnel excavation in Korea are reviewed, and efforts to develop a new classification method, reflecting the blasting performance, are presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.229-232
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• 2008

A Neural network classification of human activity data is presented. The data acquisition system involves a tri-axial accelerometer in wireless sensor network environment. The wireless ad-hoc system has the advantage of small size, convenience for wearability and cost effectiveness. The system can further improve the range of user mobility with the inclusion of ad-hoc environment. The classification is based on the frequencies of the involved activities. The most significant Fast Fourier coefficients, of the acceleration of the body movement, are used for classification of the daily activities like, Rest walk and Run. A supervised learning approach is used. The work presents classification accuracy with the available fast batch training algorithms i.e. Levenberg-Marquardt and Resilient back propagation scheme is used for training and calculation of accuracy.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.571-580
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• 2017

Recently, there are many communication softwares based on text on various smart devices. Unlike traditional print publishing, mobile publishing and SNS tools tends to utilize more decorative or more emotional fonts so that users can pass some feelings from contents. So font providers have released new fonts which deal with the requirements of the market. Nevertheless being released lots of new fonts, general users have not used them because they searched only by font name or font provider's name. It means that there is no way for users to know and find new things. In this study, we suggest font shape classification rules for font registration system based on font design features. We proved the validity of classification standard study through some experiments with 50 commercial fonts. Also the result of this study was provided for Korea Telecommunication Technology Association and adopted by the Korea industrial standard.