• Journal of the Korean Society of Costume
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• v.62 no.8
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• pp.45-54
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• 2012

The increase in the consumption of outdoors sportswear is not because of an increase in sales from hiking enthusiasts but rather the general public's desire to wear them as everyday clothing. We expect that the market for the outdoors sportswear will grow gradually as people feel the need to wear extra outerwear for protection from wind everyday. Furthermore, as the consumers' demands for these outerwear increase, their desire for more variety increases as well. Five prominent domestic brands were chosen for the analysis. The selection method included two factors: 1) the brands with the highest sales figures in the last five years 2) brands that were mentioned most frequently in fashion articles (i.e apparel news, fashion biz) from 2009 to 2011. the goal is to analyze each of the brands' different concepts of outerwear design so that the results from the analysis can be used to develop better more diverse products in the market and satisfy with the consumers' need. In the end we have to develop better technology and more diverse designs in order to meet the increase in consumers' need. They are interested in sportswear and functional clothing; we have to satisfy their need. for diverse selections in their outerwear and this is especially the case with consumers in their teens and twenties.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.153-168
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• 2012

It has been recognized as an important issue to design optimally a firm's logistics network for minimizing logistics cost and maximizing customer service. It is, however, not easy to get an optimal solution by analyzing trade-off of cost factors, dynamic and interdependent characteristics in the logistics network decision making. Although there has been some developments in a system which helps decision making for logistics analysis, it is true that there is no system for enterprise-wise's on-site support and methodical logistics decision. Specially, E-biz process along with information technology has been made dramatic advance in a various industries, there has been much need for practical education closely resembles on-site work. The software developed by this study materializes efficient algorithm suggested by recent studies in key topics of logistics such as location and allocation problem, traveling salesman problem, and vehicle routing problem and transportation and distribution problem. It also supports executing a variety of experimental design and analysis in a way of the most user friendly based on Java. In the near future, we expect that it can be extended to integrated supply chain solution by adding decision making in production in addition to a decision in logistics.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.159-173
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• 2017

In order to provide technical financial support to small and medium-sized venture companies based on technology, the government implemented the TCB evaluation, which is a kind of technology rating evaluation, from the Kibo and a qualified private TCB. In this paper, we briefly review the current state of TCB evaluation and available indicators related to technology evaluation accumulated in the Korea Credit Information Services (TDB), and then use indicators that have a significant effect on the technology rating score. Multiple regression techniques will be explored. And the relative importance and classification accuracy of the indicators were calculated by applying the key indicators as independent features applied to the generalized boosting model, which is a representative machine learning classifier, as the class influence and the fitness of each model. As a result of the analysis, it was analyzed that the relative importance between the two models was not significantly different. However, GBM model had more weight on the InnoBiz certification, R&D department, patent registration and venture confirmation indicators than regression model.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.43-62
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• 2022

This paper is a study to analyze when ISV(independent software company) has more active participation in the platform ecosystem. According to previous studies, companies are active in technological innovation when they can appropriate the outcome of innovation and when they have complementary assets (marketing, manufacturing capabilities, etc.) that can convert the innovation into value. The effect of these two conditions to join platform ecosystem is investigated. The duplication between the ISV's product portfolio and platform service is also included as an independent variable. The two sample groups are composed of independent SW companies that signed a partner agreement with platform companies and non-participating companies in the platform. As a result of empirical study, it is found that the patent rights do not affect participation in the platform. The ISVs might have believed that the benefits from cooperation with platform companies are greater than the risks of exposure to innovative technologies and unique Biz models. On the other hand, downstream's capability and the duplication of product portfolio affect participation in the platform. If ISVs have the downstream capability to transform cooperation into value creation, ISVs are actively participating in the platform. In addition, cooperation is active when the product portfolio is complementary to platform service rather than competition. This study is the empirical study of open innovation between Korean independent software companies and digital platform companies. There are similar prior studies abroad, but there are no similar studies in Korea. It is meaningful in that the determinants of platform ecosystem participation were investigated through empirical analysis by composing a sample group of companies participating in the platform ecosystem and companies not participating in the platform ecosystem.

• Korean Journal of Materials Research
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• v.6 no.7
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• pp.684-691
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• 1996

To enhance the formation of Bi,Sr,Ca,Cu,O,(Bi-2223) single phase in a shorter reaction time, the intermediate compounds such as Bi,Sr,CuO,(Bi-2201). BizSr,CaCuz08(Bi-2212) and (Ca, Sr,, ,9)CuOz in the Bi-Sr-Ca-Cu-0 system were used as the precursors. The formation of Bi-2223 was enhanced in the mixture of Bi-2201 and (Ca, ,,Sr, 119)C~eOsZpe,c ially from the mixture with (Ca, 9I Sr, ,,)CuO,-rich composition compared to Bi, iPb, 4Sr2Ca,Cu3010-cxo mposition. The formation of Bi- 2223 essentially completed within 60h at 860$^{\circ}$C and 870$^{\circ}$C. However, a small amount of the remnant Bi-2212 phase did not disappear even after a prolonged reaction at 870'C. The merit of the proposed synthetic method using the intermediate precusors can be summarized as a shorter reaction time for the formation of Bi-2223 phase, in addition to a smaller amount of second phases compared to the conventional solid-state reaction method.

• Distribution Business Review
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• no.3
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• pp.155-170
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• 2003

본 연구는 기본적으로 온라인과 오프라인의 통합마케팅을 절명하고 이에 관한 경험적 사례를 개발하기 위한 목적으로 진행되었다. 앨범서비스 영역의 공생적 기반 위에 전개되는 온 -오프라인의 경험적 사례를 개발하고 바람직한 마케팅방향 방향을 제시하였다. 본 연구는 문헌연구와 사례연구를 병행하여 연구를 진행하였다. 사례는 인터넷 기반의 앨범서비스를 제공하기 위한 (주)와이드스쿨이다. 온-오프라인의 협력적 통합마케팅의 전략을 전개하기 위해서는 무엇보다도 온 -오프라인의 뚜렷한 목표를 절정하고 성장방향에 대한 비전을 공유하고 나아가 온 -오프라인의 사명을 감당하는 것이다. 즉, 실천적으로는 \circled1 항상 고객 (customer)기반의 의사결정을 이루며 \circled2 철저한 협력적 돕는 경쟁(competition) 의식과 \circled3 구성원 자신의 일에 대한 자신감(confidence)을 지니고 \circled4 실천을 위한 용기(courage)를 가지고 \circled4 혁신하여 변화(change)를 선도하는 것이다. 온라인(on line)으로 표현되는 인터넷환경은 모든 영역에 변화를 요구하고 있다. 온라인에 관한 연구는 크게 온라인시장의 경쟁(competition)에 관한 연구와, 온라인 소비자(consumer)에 관한 연구 그리고 온라인 시장 참가기업(company)에 관한 연구로 구분된다(이석규 ; 2001). 이중 기업에 관한 연구의 중심에는 e-biz의 수익모텔에 관한 연구가 주류를 이루고 았다(David et al, 1999) 특히 오프라인기업의 경우 어떠한 형태방법으로 온라인 환경에 부응하며 기존의 마케팅활동과 연계할 것인가는 매우 중요한 문제다. 즉 기존의 오프라인기업이 온라인도구변화에 적응하고 이를 전략적으로 활용하기 위해서는 무엇보다도 오프라인과 온라인의 통합에 관한 형태와 전략 등을 명확히 이해하고 적용하는 것이 중요하다. 개수가 감소하는 것과는 상당히 다른 분포이다. 따라서 우리의 관측 결과는 2001년 사자자리 유성우의 극대 시간 전후 2시간에 적어도 0등급 이하의 밝은 유성이 상대적으로 많이 발생하였을 것으로 해석된다. 이런 밝은 유성의 빈도는 유성우 특성 연구에 중요한 의미를 가진다. 그러나 표준성만을 이용해 결정된 유성 등급은 유성의 지속 시간에 대한 불확실성과 전천 카메라 감응도의 비선형성에 의한 불확실성을 내포하고 있음을 지적해 둔다.umn chromatography)를 사용하였고 일련의 정제 과정을 통하여 배양액 중의 L-lactic acid 정제 수율은 약 85% 정도로 나타났으며 HPLC로 분석한 결과 99.7%의 순도를 확인할 수 있었다.경향을 나타내며 유입휫수와 $Dst_{min}$ 사이에는 높은 상관관계(0.83)가 있었다. 둘째, 주상기간 중 자기폭풍의 크기가 클수록 플럭스 비 ($f_{max}/f_{ave}$는 대체로 증가하는 경향을 나타냈다. 그리고 75~113keV 에너지 채널에서의 $Dst_{min}$ 값과 플럭스 비의 상관계수는 0.74로서 가장 높았으며 나머지 에너지 채널 역시 비교적 높은 상관관계를 나타냈다. 셋째, 주상기간 중 총 에너지 유입률 지수와 $Dst_{min}$ 사이에 높은 상관관계가 확인되었다. 특히 환전류를 구성하는 주요 입자의 에너지 영역(75~l13keV)에서 가장 높은(0.80) 상관계수를 기록했다. 넷째, 회복기 중에 일어나는 입자들의 유입은 자기폭풍의 지속시간을 연장시키는 경향을 보이며 큰 자기폭풍일수록 현저했다. 주상에서 관측된 이러한 특성은 서브스톰 확장기 활동이 자기폭풍의 발달과 밀접한 관계가 있음을 시사한다.se that were all low in two aspects, named "the Nonsignificant group".

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.