• Korea Petroleum Association Journal
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• no.5 s.254
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• pp.30-35
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• 2006

정유사들이 오는 7월부터 2년 동안 바이오디젤을 자발적으로 구매해 경유에 혼합 공급한다. 바이오디젤이란 콩이나 유채, 팜 등에서 추출한 식물성 유지나 폐식용유 등을 원료로 생산한 경우 대체연료로 정부가 지난 2002년부터 시범보급사업이 진행중이다. 그간의 시범보급사업에서 정부는 바이오디젤을 경유에 2:8로 혼합한 BD20 형태를 유지했지만 올해부터는 사정이 달라졌다. 주유소에서 판매가 허용되던 BD20 형태를 유지했지만 올해부터는 사정이 달라졌다. 주유소에서 판매가 허용되던 BD20은 7월 이후 자가 정비시설 등을 갖춘 연료 자가소비처로 유통을 한정하고 그 대신 모든 경유에 5% 이하의 바이오디젤을 혼합, 공급하는 'BD5' 방식을 선택했다. 하지만 'BD5'가 의무적인 것은 아니다. 현재 경유의 법정 품질기준에는 바이오디젤을 지칭하는 '지방산메틸에스테르 함량이 5% 이하까지 혼합할 수 있도록 하는 'BD5'기준이 설정되어 있지만 최저 혼합비율을 설정하지 않아 정유사 마음먹기에 따라서 전혀 혼합하지 않아도 된다. 혼합 가능한 상한선만 정해 있을 뿐 하한선은 없기 때문이다. 이와 관련해 정부는 2008년 월 이후부터 법으로 최저 혼합비율을 명시해 정유사들이 의무적으로 바이오디젤을 섞어 판매할 수 있도록 하겠다는 복안이다. 이에 앞서 정유사들은 지난 3월 산업자원부와 협약을 맺고 올해 7월부터 2008년 6월까지 2년동안 바이오디젤을 자발적으로 구매해 경유에 혼합 공급하겠다고 선언했다. 구매 물량은 연간 9만톤 수준으로 정부가 의도중인 2008년 7월 이후의 본 보급에 앞선 사실상의 마지막 실험 무대가 될 수 있다는 측면에서 중요한 의미를 갖게 된다. 바이오디젤이 대중적으로 보급되기 위해서 필수적으로 갖춰야 하는 원료의 안정적인 수급이나 품질의 안정성, 가격경쟁력 등이 향후 2년간의 과정에서 충분히 검증돼야만 본 보급에 착수할 수 있기 때문이다. 이처럼 중요한 시점에서 바이오디젤시장이 안고 있는 문제점을 점검해 보고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.179-189
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• 2007

Every purchase a customer makes builds patterns about how products are purchased together. The process of finding these patterns, called market basket analysis, is composed of two steps in the Microsoft Association Algorithm. The first step is to find frequent item-sets. The second step which requires much less time than the first step does is to generate association rules based on frequent item-sets. Even though the first step, finding frequent item-sets, is the core part of market basket analysis, when applied to Online Analytical Processing(OLAP) cubes it always raises several points such as longitudinal analysis becomes impossible and many unpractical transactions are built up. In this paper, a new OLAP cube structures designing method which makes longitudinal analysis be possible and also makes only real customers' purchase patterns be identified is proposed for market basket analysis.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.23-30
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• 2006

A recommender system for E-commerce site receives information from customers about which products they are interested in, and recommends products that are likely to fit their needs. In this paper, we investigate several methods for large-scale product purchase data for the purpose of producing useful recommendations to customers. We apply the traditional data mining techniques of cluster analysis and collaborative filtering(CF), and CF with reduction of product-dimensionality by use of latent semantic indexing(LSI). If reduced product-dimensionality obtained from LSI shows a similar latent trend of customers for buying products to that based on original customer-product purchase data, we expect less computational effort for obtaining the nearest-neighbor for target customer may improve the efficiency of recommendation performance. From simulation experiments on synthetic customer-product purchase data, CF-based method with reduction of product-dimensionality presents a better performance than the traditional CF methods with respect to the recall, precision and F1 measure. In general, the recommendation quality increases as the size of the neighborhood increases. However, our simulation results shows that, after a certain point, the improvement gain diminish. Also we find, as a number of products of recommendation increases, the precision becomes worse, but the improvement gain of recall is relatively small after a certain point. We consider these informations may be useful in applying recommender system.

• Land and Housing Review
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• v.4 no.4
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• pp.425-434
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• 2013

The Small and Medium Business Administration specified 123 construction materials mandatory to purchase directly and forced the public owners to provide the contractors with materials as required by related law. This study extensively reviewed various characteristics and management factors of the owner-providing materials consumed in the public apartment housing under Direct Buying System(DBS) from the public owner's perspective. Subsequently, the major managed materials were identified, and the proper response plan was developed along the material procurement process. The Procurement Management Load Indices (PMLI) of 43 materials were evaluated according to rating criteria with procurement path, project-specified level, user requirement level, supplier's responsibility, on-site work requirement, additional parts, and inspection standards. The tile and aluminum windows were classified in the group needing high-level procurement efforts to reduce the errors and ensure the efficiency. The accurate quantity estimation method, definite purchase details, management activity definition before and after production, additional quantity for rework, interference coordination were defined as the essential activities for effectively responding to DBS.

• Korean Business Review
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• v.21 no.1
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• pp.1-17
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• 2008

This article examines the establishment of the game theoretic model for the cleaner vehicles and analyzes the established model. We discuss the way to represent the players' preferences over the outcomes to make the model applicable in real practice. In this article we employ the real data to represent the preferences. In the analysis of the model we consider various scenarios and discuss how we can use GAMBIT, which is a game theory analysis software, to find solutions in each proposed scenario.

• KOREAN POULTRY JOURNAL
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• v.15 s.168
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• pp.33-36
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• 1983

브로일러의 소비신장이 눈에 띠게 증가하고 있다. 금년상반기에 육용계 배합사료 생산량은 약 30만톤으로 작년 같은 기간에 비해서 37.25$\%$나 증가되었다. 정부에서도 육류소비구조 개선사업으로 닭고기의 소비증대를 위해 수입쇠고기 포장육을 20$\%$ 의무화 하였다. 우리는 아직도 포장이나 부분육에 대한 기준이 없어 규격과 품질이 다양해 불편하며 하루속히 그 기준을 정하는 것이 필요하다. 본지는 일본의 도계품기준을 이미 소개한 바 있으며 이번 호에서는 미국 BNC (National Broiler Council)자료를 입수하여 미국의 포장방법과 부분별 용도를 소개하고 최근 TV, 라디오, 신문$\cdot$잡지 등에서 닭고기구매방법에 대한 문의가 많아지고 있으나 아직 국내에서는 요리법이나 부분육제품이 이에 맞게 생산되지 않고 있다. 그러나 앞으로를 위해서 소개하고 그동안 가정에서 주부가 할 수 있는 방법을 소개한다. 중량단위가 우리와 달라 이해에 어려운 점도 있는데 이는 우리 현실에 맞게 앞으로 조정될 것으로 믿는다.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.409-416
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• 2004

동적 공급사슬망은 복잡하고 다양한 이해관계를 가진 기업들로 구성되어 있다. 다수의 구매자로부터 주문 의뢰가 동시다발적으로 발생하므로 하위 구성원들은 경쟁적 관계에 놓이게 된다. 그러므로 최적의 공급사슬구성을 위해서는 수평적 경쟁 관계를 고려하여 구성주체들간의 협력관계를 통해 이를 해결하여야 한다. 지금까지의 스케줄링 문제에서는 상위의 구성원이 하위 구성원들을 일방적으로 선택하는 의사결정이 이루어졌으나 본 문제에서는 구성원간의 협력관계에서 에이전트를 통한 다자간 협상을 통해 공급사슬 전체의 최적화를 구성하는 방법론을 제시한다. 본 협상방법론은 단일기계에서 상이한 납기일, 조기생산(earliness), 지연생산(tardiness)을 동시에 고려하였으며 전체 공급사슬의 평균절대편차(Mean Absolute Deviation)의 최소화를 목적으로 하고 있다. 본 협상방법론의 효과성을 증명하기 위해 분지한계법(Branch & Bound)과 비교하고, 알고리즘 구현을 통해 구매자 협상방법론의 최적화 여부를 실험을 통해 증명하였다.

• Annual Conference of KIPS
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• 2007.11a
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• pp.1123-1126
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• 2007

인터넷을 통한 서비스 제공은 법규 준수, 사용 권한 확인, 요금 부과, 차별화된 서비스 제공 등의 다양한 이유로 사용자 인증을 필요로 한다. 이러한 확인 과정은 인증만 되면 언제 어디서든 서비스를 이용할 수 있다는 측면에서 사용자에게도 편리성을 제공해 주지만 사용자의 서비스 이용 정보가 쉽게 기록되고 노출될 수 있는 문제점을 가지고 있다. 이를 해결하기 위한 방법으로 사용자 정보를 보호하면서도 불법적인 사용자에게 악용되지 않도록 하기 위해 추적 가능한 익명성을 보장하는 방안이 제안되고 있다. 하지만 이러한 방법으로는 법 준수를 위한 서비스 제한 규정이나 사용자 별 차별화를 필요로 하는 서비스 모델을 지원하지 못한다. 본 연구에서는 사용자에게는 익명성을 보장하고 적법한 절차를 통한 추후 구매자 추적이 가능하면서도 서비스 제공자에게는 서비스 그룹별로 차별화된 서비스 제공이 가능한 새로운 익명 생성 방안과 이를 적용하는 디지털 콘텐트 구매 프로토콜을 제안하고 있다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.143-150
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• 2023

This paper aims to investigate the relationship between consumption value of sports products, upward consumption propensity, and post-purchase happiness among adolescents. The subjects of the study were extracted from adolescents in Chungcheong-do by convenience sampling method, and 257 people were used in the final analysis. Statistical methods were frequency analysis, correlation analysis, and regression analysis. The research results are as follows. First, social value and exploratory value, which are sub-factors of consumption value, were shown to have a significant effect on upward consumption propensity, but functional value and self-value were found to be statistically insignificant. Second, social value, self-value, and functional value, which are sub-factors of consumption value, had a significant effect on post-purchase happiness, but exploratory value was found to be statistically insignificant. Third, the upward comparison propensity was found to have a significant effect on post-purchase happiness.