• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.11a
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• pp.493-503
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• 2000

인터넷은 "정보의 바다"라는 말처럼 날마다 방대한 양의 데이터가 쏟아져 나오고 있으며, 이런 방대한 데이터 속에서 정보를 찾기란 쉽지 않다. 그러나 인터넷 사용자들은 정확한 정보를 신속하게 언제 어디서나 획득할 수 있기를 희망한다. 현재 정보의 창고인 도서관은 더 이상의 Paper 정보가 아닌 Digital 정보로의 변화가 필요하며 각 분야에서 많은 연구가 이루어지고 있다. 연구개발정보센터(KORDIC)에서는 과학기술지역인프라 구축사업을 통해서 광주ㆍ전남권역을 시범 권역으로 선정하여 13개 대학의 도서관, 산업단지, 연구소(KORDIC)를 하나로 묶는 즉, 산ㆍ학ㆍ연이 하나가 되어 상호대차 원문 서비스를 할 수 있도록 기반을 구축하는데 목적이 두고있다. 본 시스템은 상호대차 원문 서비스를 위해 대학 간 분담수서를 통해서 분담된 도서를 원문서비스 하는 상호 협력 체제를 구축하는데 필요한 시스템이며 사용자들은 해외 저널 원문을 신청하게되고, 서비스 담당자는 신청된 해외저널원문을 Digital화해서 사용자에게 보내주는 체제를 구축하기 위해 열악한 대학에는 시스템과 통신 인프라를 지원하여 상호대차원문서비스가 가능하게 하는 것을 전제로 하고 있다. 분담수서를 통해 서비스하게 될 해외저널에 대해서는 각 대학에서 책임감을 가지고 서비스를 해야하므로 서비스의 질과 업무의 자립도를 높일 수 있으며 상호대차를 통해 산ㆍ학ㆍ연이 하나처럼 연계가 된다. 또한 해외 학술 저널 DB를 공동으로 구축하여 국내 지역별 대학소장 해외 학술 전문자료를 데이터베이스화, 전문정보망을 구축 회원기관 간 정보교류의 활성화, 대학 소장 해외 학술 전문자료의 정보 표준화 효과를 창출할 수 있으며, 분담수서를 통해 얻어지는 예산 절감으로 새로운 분야에 재투자한다면 정보의 질적, 양적 향상을 도모하게 될 것이다.것으로 나타났다.본 규격은 키, 총장, 어깨길이, 등길이, 머리길이, 머리둘레, 진동둘레, 목둘레, 가슴둘레, 허리둘레, 배둘레, 엉덩이둘레, 앞품, 뒤품, drop치를 포함하고 있고, 각 규격에서 호칭간 치수 간격도 함께 제시하고 있다. 본 연구 결과에서 보듯, 현행 8규격의 무진복의 각 호칭간 적정 허용범위를 고려해 합리적인 치수체계를 정립한다면 치수에 대한 적합도가 상당히 증가할 뿐 아니라 생산비용도 상당히 감축할 것으로 생각된다.나타났다. 4) 호감적 서비스능력 차원에서 세 독립변수간에 유의한 3원 상호작용이 존재하는 것으로 나타나( $F_{2,228}$=15.62, P<.001) 20대에 적합한 의복 착용시( $F_{2,228}$=3.98, P<.05)와 60대에 적합한 의복 착용시( $F_{2,228}$=16.55, P<.001) 점포유형과 격식차림간에는 유의한 상호작용이 존재하는 것으로 나타났다. 5) 호감을 구성하는 세 요인들이 구매의도에 미치는 영향을 조사한 결과 호감적 인상차원은 29%(P<.001), 호감적 서비스능력차원은 6%(P<.001)의 구매의도를 설명해 주는 것으로 나타났다. 본 연구결과 노년 소비자에게 호감을 주는 판매원의 외모는 구매의도에 영향을 주어 실버의류산업의 이익증대와 밀접한 연관을 갖는 서비스품질의 중요한 요인으로 밝혀졌다.중요한 요인으로 밝혀졌다.로운 단백질 EPSPS가 다른 여러 식물에 이미 존재하고 있는 단백질로서 우리가 이미 이러한 식품을 섭취할 때 이 단백질도 같이 섭취해오고 있었다는 점, 둘째. 이 단백질이 소화액 분해 실험에서 짧은 시간내에 분해가 되었다는 점, 셋째. 재조합 된 콩과 자연 콩이 성분 분석에서 차이를 나타내지 않았다는 점, 네 번째. 쥐를 통한 다양섭취

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.847-855
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• 2014

In recent years, women's monetary rewards in golf increased and their performances have improved significantly compared to other sports. Sports marketing has become more active in Asia and the number of Korean players in LPGA with good scores are increasing. For these reasons, golf is becoming increasingly popular. The prize money is higher than in other sports and the economic benefits are increasing due to the financial incentives such as sponsorships. Many of these prospects actively affect women's golf. Certain rookies continue to increase and their performances improve day by day. In this study, I analyze the changes in performance over time of last 5 years from 2009 using growth curve analysis. According to the results of analysis, driving distance and average putting skills developed but green in regulation decreased.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.447-454
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• 2022

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.