• 대한한의학원전학회지
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• 제23권1호
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• pp.133-142
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• 2010

Preconditions for the separation of prescribing and dispensing medicinal herbs in Traditional Korean Medicine are classification of medicinal herbs for general public and special medical uses, establishment of national medicinal herb distribution company of governmental base, restriction in purchase of medicinal herbs for special medical use, partnership between doctors and pharmacists of Traditional Korean Medicine, and coverage of herbal medicine-based medication in national health insurance, etc. The number of Traditional Korean Medicine Pharmacists which was born during 'the herbal medicine conflict' initiated in 1993, goes over 1,000 and will increase by 120 annually. The number of Traditional Korean Medical Doctors is over 17,000 and increases by 850 annually. So in order to engage partnership between two groups, the government have to arrange the number of outputs of each group. Standardization and classification of diagnosis and diseases in Traditional Korean Medicine is a matter of course in the separation of prescribing and dispensing medicinal herbs. Related societies and academies need to do researches with governmental fund first. After these works, we can launch a task force team for implementation of process for the separation of prescribing and dispensing medicinal herbs in Traditional Korean Medicine properly. Entering the national health insurance system for full coverage of Korean Medicine care service will be essential for the patients. Implementation the separation of prescribing and dispensing medicinal herbs in Traditional Korean Medicine would be the core of health insurance coverage for medication.

• 패션비즈니스
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• 제6권5호
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• 디지털융복합연구
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• 제11권3호
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• pp.389-395
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• 2013

최근 SNS를 활용한 소셜커머스를 통해 식음료 쿠폰의 거래가 활발하게 이루어지고 있다. 소셜커머스 상에서 식음료 쿠폰을 구매하여 사용할 경우 원하는 상품을 할인된 가격으로 이용할 수 있으나, 쿠폰 구입 시 매장의 위치, 이용 가능 기간 및 시간, 할인율 등을 구매자가 직접 비교하여 선택해야 하는 어려움이 있다. 따라서 본 논문에서는 사용자의 위치 및 시간과 구매 이력 등의 상황정보를 고려하여 사용자에게 적합한 소셜커머스 상의 식음료 쿠폰을 추천하는 시스템을 제안한다. 이를 위해 사용자의 상황 인지 및 지속적인 사용자 성향 반영을 위해 베이지안 네트워크 기반의 쿠폰 추천 방법을 제안한다. 또한 사용자가 선호하는 쿠폰 선택 기준에 대한 개인화된 가중치를 반영하기 위해 AHP를 이용하여 선호도의 가중치를 측정하고 분류를 수행한다. 시스템의 효율성을 검증을 하기 위해 12명의 학생을 대상으로 1개월간 20회에 걸쳐 실험을 수행하였으며 그 결과 80%의 추천 만족도를 얻을 수 있었다.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• 한국융합학회논문지
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• 제9권3호
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• pp.231-240
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• 2018

최근 들어 증가하고 있는 온라인 및 모바일 플랫폼 기반의 행태 광고에서 개인정보 처리에 대한 논의가 중요하게 부각되고 있다. 행태 광고는 자동수집장치를 이용하여 수집된 개인의 행태 정보를 기반으로 개인의 검색 구매이력, 취미 성향 등을 분석하여 활용한다. 따라서 개인정보 보호법에 의거하여 정의된 개인 식별정보나 민감정보는 아니라 할지라도 개인의 특성을 분석할 수 있는 다른 형태의 개인 정보를 수집 저장하게 되며 이로 인해 개인정보 유출사고 및 침해사고에 노출될 수 있다는 문제점을 내재하고 있다. 본 논문에서는 급격하게 성장하고 있는 온라인 및 모바일 플랫폼 기반의 행태 광고 관련 사업자들의 개인정보 처리방침 실태를 조사 분석해보고, 행태 정보에 적합한 개인정보 수집 저장 이용 과정에서의 조치사항에 대해 논의해 보고자 한다.

• 한국지능시스템학회논문지
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• 제16권2호
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• pp.185-190
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• 2006

유비쿼터스 컴퓨팅에서 대부분의 시스템들이 개인화된 추천을 위하여 사용자와 성향이 비슷한 사람들의 컨텍스트 정보를 분석하는데 인구통계학적 방법이나 협력적 필터링을 주로 사용한다. 서비스 추천 시스템들은 컨텍스트 정보 중에서 성별, 나이, 직업, 구매이력 등의 정적 컨텍스트를 주로 사용하고 있다. 그러나 이러한 시스템은 이동경로 같은 사용자의 상황을 고려하기가 어렵기 때문에 개인의 성향을 정확하게 분석하여 실시간으로 개인화된 추천 서비스를 제공하는데 한계가 있다. 본 논문에서는 사용자의 상황을 고려하기 위해 동적 컨텍스트 중에서 사용자의 이동경로를 이용한다. 이동경로의 예측 정확도를 높이기 위해 RSOM의 입력으로 들어가는 이동경로를 경로보정 알고리즘을 사용하여 보정한다. 그리고 보정된 경로를 RSOM으로 학습시켜 사용자의 이동패턴을 분석하고 향후 이동경로를 예측한 후, 사용자의 선호도가 높은 상품들 중에서 예측 경로 상에 있는 가장 가까운 상품을 실시간으로 추천한다. 제안한 방법의 예측 정확도를 측정한 결과 MAE가 평균 0.5 이하로 측정됨으로써 사용자의 이동경로를 올바르게 예측할 수 있음을 확인하였다.

• 한국의류산업학회지
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• 제23권3호
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• pp.358-369
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• 2021

This study aims to classify the dimensions of the sales information of secondhand clothing goods on the C2C secondhand trading platform and to systematically analyze the components of each dimension. To this end, the NVivo 12.0 qualitative data analysis software was used. The content analysis showed that the sales information of secondhand clothing goods was classified into four dimensions: detailed information of the sale goods, information specific to secondhand clothing goods, seller opinion information, and service information. The components of each dimension were as follows. The detailed information of the sale goods included size, sale price, item, design, brand name, material, color, wearing season, fit, gender, etc. The information specific to secondhand clothing goods included the number of times the item was worn, its purchase history, and product condition. Seller opinion information included product review, sales motivation, notes for the transaction, coordination proposal, and usage proposal. The service information included the transaction mode, exchange·return·refund, and promotion. The frequency analysis showed that the highest frequencies were sale goods(37.47%), information specific to secondhand clothing goods(24.63%), seller opinion information(20.54%), and service information(17.37%). This study will help C2C secondhand trading platform managers or sellers establish clear standards for presenting sales information and developing ideas toward constructing differentiated platform contents.

• Agribusiness and Information Management
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• 제10권1호
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• pp.16-25
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• 2018

Fig is a fruit of which the flesh is very sweet, and it is a tree which has been grown for fruit in Korea since long time ago. However, since the flesh of fig tends to be easily softened, commercial cultivation of this fruit began later than that of other fruit trees grown for profit, however, the cultivation and demand of fig tend to be increased steadily due to the development of technology for storage and distribution since the 2000s. In addition, as the domestic dining culture is getting diversified, the dishes cooked by using fig as a food material are introduced through diverse foods including dessert, and it is possible to intake fig in diverse ways, but not through the traditional processed food. Therefore, it is necessary to establish a measure of expanding the consumption of fig as a processed food, and it will be possible to overcome the limitation of short storage period, while securing the competitiveness of the fig industry. In this research, we have studied the history of domestic fig cultivation, current status of it and status of processed foods through related documents and materials, and the characteristics of the consumers who purchase figs. Fig is a traditional fruit, however, we could find out the fact that the consumers tend not to recognize it as a traditional one. Therefore, if we could add fig to various processed foods utilizing its sweet taste, rather than increasing the consumption of fresh fruits, it may increase the consumption of it.

• 한국전자거래학회지
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• 제26권1호
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• pp.127-140
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• 2021

최근 센서 측정 데이터, 구매이력 등과 같이 시간 정보를 포함하는 시퀀스(sequence) 데이터가 다양한 응용에서 발생되고 있다. 주어진 시퀀스들 중 다른 시퀀스들과 매우 상이한 이상(anomalous) 시퀀스를 탐지하는 기법들은 지금까지 많이 연구되어왔으나 이들 대부분은 주로 시퀀스 내 원소들의 순서만을 고려하여 이상 시퀀스를 찾는다는 한계가 있다. 따라서 본 논문에서는 원소들의 순서와 원소들 간의 시간 간격 모두를 고려하는 새로운 이상 시퀀스 탐지 기법을 제안한다. 본 논문에서 제안하는 방법은 확장된 LSTM 오토인코더 모델을 사용한다. 이 모델은 시퀀스를 해당 시퀀스 내 원소들의 순서와 시간 간격 모두를 효과적으로 학습할 수 있는 형태로 변환하는 층을 추가로 가진다. 제안방법은 확장된 LSTM 오토인코더 모델로 주어진 시퀀스들의 특징을 학습한 뒤, 해당 모델이 잘 복원하지 못하는 시퀀스를 이상 시퀀스로 탐지한다. 본 논문에서는 정상 시퀀스와 이상 시퀀스를 혼합한 가상 데이터를 사용하여 제안 방법이 전통적인 LSTM 오토인코더만을 사용하는 방법과 비교하여 100%에 가까운 정확도를 나타냄을 보인다.

• Acta Via Serica
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• 제7권1호
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• pp.53-82
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• 2022

The Hebrew word for silk, meshi, is mentioned in the Bible only once and there is a possibility that the item to which it referred was made of local wild silk. Although Jewish historical sources from the Roman and Byzantine periods mention silk many times, only a few silk textiles have been discovered at a sited dated to the Byzantine period (4^th-7^th centuries CE). The word "silk" occurs in the New Testament, although only once. A turning point in the history of the Negev (Southern Israel) occurred around 400 CE when it underwent a period of prosperity related to the advent of Christianity and pilgrimage, which enabled the purchase of imported silk textiles. The Early Islamic period (7-8th centuries CE) yielded four (out of 310) silk textiles from Nahal 'Omer on the Spice Routes joining Petra, in the Edom Mountains of modern Jordan, and the mercantile outlets on the Mediterranean Sea, notably Gaza and El Arish. The most important silk textile assemblage in the Southern Levant was found near Jericho at Qarantal Cave 38 and dates to the medieval period (9th-13th centuries CE). Linen textiles decorated with silk tapestry originating in Egypt date back to the 10-11th centuries CE. Mulham textiles - silk warp with hidden cotton wefts - were discovered in the medieval fortress on Jazirat Fara'un (Coral Island) in the Red Sea, 14 kilometers south of Elat and today located in Egypt. Mulham is mentioned in literary sources of the ninth century in Iraq and Iran, whence it spread through the Islamic world. The article will present aspects of the origin, transmission, and exchange of these textiles.