• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.166-174
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• 2010

As the speed of social change consumers' buying patterns change, with an effective and competitive means of communication is an effort to find ads world. Characters have to maximize its own image, the character font used for the situation fits perfectly expressed in the formative Calligraphy differentiated pay plan as part of Calligraphy is considered. People feel in the hand by writing letters that can not be standardized as a strong symbol and symbolism, and the appeal and beauty, dynamic motion, mystery, and can be expressed. Design disciplines across all areas of Calligraphy extensive coverage, digital coolness of the hard preparation for the soft and warm lyricism analogue availability is required to meet contemporary needs. Media ads that emphasize the use of the Calligraphy emotional representation of the contents of the ads favorable to improve cognitive function, attention, recall of positive affect and, therefore, character-driven emotional expression as an art communications capabilities with one of the media is situated in the heart of the culture.

• The Research Journal of the Costume Culture
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• v.13 no.1
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• pp.34-46
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• 2005

The purpose of this study was to research buying behavior and home fashion preferences based on housing size. The target consumers were Korean women, aged 20~40s who reside in the Seoul & Kyunggido areas. We distributed questionnaires to 650 women. However, only 600 questionnaires were used for the statistical analysis. Data analyses were conducted with SPSS program on the frequency, Chi-square test, cluster analysis, t-test and ANOVA. The results of this study are as follows: 1. The considering factors for purchasing such as brand, trends coordinating existing furnishings with new products and functionality were significantly associated with housing size. The buyers who reside in bigger size homes. over 40 pyung place higher value on brand name, trends. or coordinating existing furnishing with new products than residents in smaller units. However, women who live in smaller units place higher value on functionality when purchasing home fashion products. Considering factors such as brand, trend, and materials were also significantly associated with segmented age group; 40~49 age group considered brand, trend, and materials more than 20~29 age group. The group who are highly interested in home fashion considered design/color, rand, coordination, and functionality than the group who are low interested in home fashion. 2. Residents in over 40 pyung homes buy home fashion products at department stores, while residents in less than 39 pyung homes buy them at discounted store. 3. Respondents preferred solid colors more than patterns. However, they favored character pattern for textile bedding products for their children. 4. there is also a statistical difference in preferences for types of window treatments between large ad small housing sizes. Residents living in over 40 pyung preferred tie-back/cottage curtain, while residents living in less than 29 pyung preferred Roman shade style.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.625-632
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• 2001

The electronic stores have realized that they need to understand their customers and to quickly response their wants and needs. To be successful in increasingly competitive Internet marketplace, recommender systems are adapting data mining techniques. One of most successful recommender technologies is collaborative filtering (CF) algorithm which recommends products to a target customer based on the information of other customers and employ statistical techniques to find a set of customers known as neighbors. However, the application of the systems, however, is not very suitable for seasonal products which are sensitive to time or season such as refrigerator or seasonal clothes. In this paper, we propose a new adjusted item-based recommendation generation algorithms called the exponentially weighted collaborative filtering recommendation (EWCFR) one that computes item-item similarities regarding seasonal products. Finally, we suggest the recommendation system with relatively high quality computing time on main memory database (MMDB) in XML since the collaborative filtering systems are needed that can quickly produce high quality recommendations with very large-scale problems.

• Proceedings of the KSCN Conference
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• 2003.11a
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• pp.1055-1056
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• 2003

식습관이나 식품 기호도는 식품의 선택에 있어서 중요한 변수이며 어릴 때 형성이 되는 어린이의 식품 기호도는 부모의 태도에 크게 영향을 받는다. 특히 유아기는 사회적, 인지적 능력이 발달하고 모방을 통하여 학습되는 시기이므로 이 시기의 식품에 대한 올바른 인식과 경험은 평생의 건강을 위해서도 부모의 역할은 중요하다. 근래 과잉 영양과 관련된 비만이 우려되고 있지만 아직도 유아에서는 부적절한 식사패턴으로 인한 영양소 결핍이 문제가 되고 있다. 선행 연구에서 유아나 어린이의 식습관이 부모, 특히 어머니의 영향을 많이 받는다고 지적하고 있지만 상호 관련에 대한 연구는 미흡하다. 또한 ‘무엇’과 ‘얼마나 많이’ 등이 중점이 되었던 양적 연구에서 이제는 ‘어떻게’ ‘왜’라는 것에 관심을 두는 사례연구의 중요성이 대두되고 있으므로 본 연구에서는 취학전 아동의 식습관과 영양상태에 관한 질적 연구로써 어머니와의 상호 관련성을 알아보고자 하였다. 대구, 부산지역에 거주하는 4∼7세 유아와 그 어머니(각 15명)를 대상으로 포커스 그룹 인터뷰를 실시하고 유아와 1:1면담을 실시하였다. 어머니들의 식품구매에 영향을 미치는 주요 요인은 어머니 자신의 기호도와 편리, 가치관으로 나타났다. 유아와 어머니의 식품기호도를 조사한 결과 좋아하는 식품의 경우 두 군 모두 육류의 선호도가 높았다. 싫어하는 식품은 어머니의 경우에는 ‘없다’가 73%이었으나 유아의 경우에는 ‘야채류’가 73%이었다. 유아와 어머니의 식품 기호도 일치정도는 좋아하는 식품이 67%, 싫어하는 식품이 33%로 나타났다. 유아는 야채류를 싫어한다고 대답하면서도 건강에 좋은 식품을 ‘야채류’라고 생각하고 있었다 유아의 특정 식품의 선호도나 거부의 주된 이유는 ‘맛’이었으며, 유아의 편식습관은 어머니가 간편성 위주, 가족의 기호도 만을 고려하여 식단을 구성하는 것과 관련이 있었다. 조사대상 유아의 33%가 비타민제, 47%가 칼슘제제를 복용하고 있었으며 27%가 근래에 보약을 복용한 경험이 있었다. 어머니들은 영양제나 보약이 유아의 건강 유지에 많은 도움이 된다고 여기고 있었고 어머니의 임신 중 식품섭취나 이유기의 식품경험정도가 어린이의 식품 기호도에 영향을 미친다고 생각하고 있었다. 어머니가 식품구매시 질적으로 고려하는 부분은 건강과 관련된 무농약 식품, 인스턴트 식품 제한, 외식의 절제가 대부분 어머니의 관심사였다. 본 연구에서 어머니는 유아의 식습관과 식품기호도에 크게 영향을 미침을 알 수 있었으며, 유아 영양교육에서 어머니의 참여와 식품에 대한 인식 개선은 유아의 영양수준 개선에 필수적이라고 생각된다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.5
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• pp.213-222
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• 2008

This paper is related to color image segmentation and textile texture mapping for the 2D virtual wearing system. The proposed system is characterized as virtually wearing a new textile pattern selected by user to the clothing shape section, based on its intensity difference map, segmented from a 2D clothes model image using color image segmentation technique. Regardless of color or intensity of model clothes, the proposed system is possible to virtually change the textile pattern or color with holding the illumination and shading properties of the selected clothing shape section, and also to quickly and easily simulate, compare, and select multiple textile pattern combinations for individual styles or entire outfits. The proposed system can provide higher practicality and easy-to-use interface, as it makes real-time processing possible in various digital environment, and creates comparatively natural and realistic virtual wearing styles, and also makes semi-automatic processing possible to reduce the manual works to a minimum. According to the proposed system, it can motivate the creative activity of the designers with simulation results on the effect of textile pattern design on the appearance of clothes without manufacturing physical clothes and, as it can help the purchasers for decision-making with them, promote B2B or B2C e-commerce.

• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.733-740
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• 2010

This proposal of eco-friendly Resort wear targeting medical tourists of Jeju intend to contribute to the local economy by creating more added-values and profits. With high-quality leisure outfits, the medical tourism could provide tourists more pleasantness as well as a souvenior, which may help them to cherish the memory in Jeju. Well-developed Galot leisure outfits matched with other Galot products could result in additionary buying of other Galot items. Furthermore, by developing the size system as well as pattern grading, we can help local Galot manufacturers who currently require these standardized creation system. The summary of this research is as follows: 1. we examined the current issues and disadvantages of the local Galot through a survey. 2. We defined the Galot leisure outfit for Jeju medical tourism, conducted market research, and reflected these into representative designs for them. 3. We developed a size system as well as a pattern grading to standardize the manufacturing process.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.365-380
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• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.467-475
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• 2020

As the growth rate of IPTV VOD usage increases it is necessary to analyze VOD usage patterns systematically. This study divides VOD users into free VOD and paid VOD users, then explores how VOD usage motivations, usage patterns, and demographic factors affect the differences between two groups. The results show that social motivation, VOD satisfaction, using content after the holdback expiration, an intention to pay for ad-skip, the proportion of VOD usage, a VOD give-up experience, TV usage time, and SVOD usage time, are statistically significant. Except the VOD satisfaction factor, all of the factors analyzed are more likely to expect paid VOD users. Additionally this study found paid VOD users are more likely to use a SVOD service as an alternative one compared with free VOD users.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.