• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.59-62
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• 2004

이동객체 색인에 관한 기존 연구는 실외 환경의 빠른 속도로 이동하는 많은 이동객체들을 대상으로 과거 궤적과 현재 위치 및 미래 위치를 검색하는 색인 기법들이었다. 그러나 쇼핑몰이나 대형 박물관과 같은 건물의 실내나 놀이동산과 같은 한정된 영역의 이동객체들은 실외 환경에서처럼 빠른 이동속도를 가지고 있지 않다. 또한 이동객체들의 이동하는 영역이 한정되어 있을 뿐만 아니라 최대 수용인원 등이 정해져 있다. 본 논문에서는 이러한 특성을 갖는 실내 환경에 적합한 색인 기법으로 현재 그리고 미래 위치 질의 처리를 위한 공간색인과 시간 색인을 분리한 두 개의 색인 구조를 갖는 새로운 색인 기법과 가까운 미래 시간의 특정 영역에 있는 이동객체들의 수에 대한 질의응답을 위한 시공간 히스토그램을 구성하고, 적용하는 방법을 제안한다.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.296-298
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• 2010

본 논문은 유비쿼터스 핵심기술인 RFID(Radio Frequency IDentification)를 이용하여 대형마트와 같은 오프라인 쇼핑몰에서 고객의 실시간 위치 파악과 쇼핑 동선을 분석하여 고객의 선호상품을 예측하여 적시에 효율적으로 선호 상품 정보를 서비스 할 수 있는 쇼핑동선 분석 시스템을 제안한다. 제안하는 시스템은 RFID 태그가 부착된 쇼핑카트를 이용하여 개별 고객들의 쇼핑 동선 및 쇼핑 패턴을 지속적으로 학습하여 이를 기반으로 각 고객들의 쇼핑패턴을 분석하고 분석된 쇼핑패턴 정보로 이용하여 선호 구역 및 선호 상품을 예측한다. 예측된 선호상품 정보는 고객의 휴대 단말기를 통해 실시간으로 전송된다

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.652-654
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• 2012

본 논문에서는 다중 심도 카메라 기반의 실시간 피플 카운팅 시스템을 제안 한다. 카메라 영상으로부터 사람을 감지하고 추적하는 시스템 및 그 방법에 관한 것으로, 피플 카운팅 시스템은 쇼핑몰이나 대형건물의 출입구 등과 같은 다양한 환경에 적용될 수 있다. 기존 피플 카운팅 시스템에서의 급격한 조명의 변화나 겹침 현상, 가림 현상에 대한 해결 방법으로, 다중 심도 카메라 환경에서 동일 객체 추적을 위해 RLM(Range Laser Method)를 적용하고, 조명 등 환경 변화에 강인한 배경 제거 및 물체 검출 기법으로 가우시안 혼합 모델(Gaussian Mixture Model)을 적용해 객체인식에 대한 정확도를 높인다. 또한, 객체를 블랍(Blob)으로 지정해 확장 칼만 필터(Extended Kalman Filter, EKF) 방법으로 객체를 추적한다. 본 제안은 피플 카운팅 시스템에의 객체 검출 및 인식에 대한 정확도를 향상시킬 수 있으리라 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.111-112
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• 2023

학교, 대형 쇼핑몰, 공항 등과 같은 큰 실내 공간에서는 군중의 동선과 밀도를 파악하고 관리하는 것은 안전사고와 연관되어 있어 매우 중요하다. 와이파이 센싱은 기존에 존재하던 CCTV 카메라나 센서를 활용한 혼잡도 관리보다 효율적이고 정확한 방식으로 추정하는 데 도움이 되며, 설치 및 유지보수 측면에서도 효율적이다. 본 논문에서는 실내 환경에서 군중 수를 추정하기 위해 딥 러닝을 이용한 무선랜 신호 분석 기법을 제안한다. 송수신기가 같은 공간에 위치했던 기존 연구들과는 달리 본 논문에서는 송신기와 수신기가 서로 다른 공간에 배치된 환경에서도 무선랜 수신 신호를 통해 다른 공간의 군중 수를 정확히 예측할 수 있다는 것을 실험으로 검증하였다.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.734-743
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• 2007

The KTX station is one of special generators that produce a lot of trips caused by special land use such as university, airport, and super shopping mall. Special generators need special attention in developing travel demand models since the standard trip generation and distribution model in the conventional four-step approach do not provide reliable estimates of their travel patterns. New modeling approach, activity-based model, considering travel behavior of person, seem to be more appropriate for those special generators. Thus, this study introduces a spatial-temporal activity-based approach and how activity-based approach can be applied to estimation of travel demand at KTX stations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.819-828
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• 2011

Due to emergence of large discount mart and on-line shopping, and the resulting shrinkage of market share, traditional market that is typically having operational disadvantages is facing a predicament. However, considering the total sales volume and the consumer traffic, it is true that traditional market still plays an important role. In this context, traditional market should be vitalized through an improved management system as well as restructuring, and seek a balanced growth in the distribution industry. In order to provide competitiveness to the local traditional market of Jeung-Pyung Province and to vitalize the local economy, an analysis was preformed for the local market environment, and the vitalization plans, which can be referenced in determining the local economic policies, are suggested in this paper.

• 정보화사회
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• s.181
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• pp.29-31
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• 2006

데이콤(대표 박종응 www.dacom.net)은 지난 5월 29일, 국내 최초로 무선 인터넷 전화 서비스인 와이파이(Wi-Fi)폰 서비스를 6월부터 일반 기업을 대상으로 상용화한다고 밝혔다.이 서비스는 기존 무선랜 환경을 기반으로 이동형 단말기인 Wi-Fi폰을 통해 구내.시내.시외.국제.이동전화 등을 제공하는 서비스. 기업내 구축돼 있는 무선랜스위치와 AP(Access Point)를 이용, 무선으로 VolP 서비스를 제공하는 방식이다. 데이콤에 따르면, 지금까지 070 등 유선을 기반으로 한 서비스 제공은 있었지만, 이동형 무선단말기를 통해 인터넷전화(VolP)서비스를 상용화하기는 이번이 처음이다. 지금까지 인터넷전화를 이용하기 위해선 별도의 070번호를 새로 부여받아야 했지만, 데이콤의 이번 와이파이 서비스는 시내번호 이동시 기존 시내전화 번호를 유지하면서 인터넷전화로 전환이 가능, 인터넷전화 서비스 활성화에도 큰 몫을 할 것으로 데이콤은 기대했다. 데이콤은 앞으로 $\Delta$무선랜 서비스를 이용 중이며, $\Delta$본.지사간 통화요금을 절감하고자 하는 기업$\Delta$IP기반의 사무실 환경개선을 희망하는 중대형 기업 $\Delta$호텔이나 병원, 대형 쇼핑몰처럼 선 없는 쾌적한 통신환경을 필요로하는 고객 중심으로 와이파이 서비스를 적극 확대해 나갈 계획이다.데이콤은 앞으로 Wi-Fi폰을 통해 음성 뿐 아니라 향후 영상 멀티미디어까지도 송수신 할 수 있는 서비스 개발에 박차를 가하기로 했다.

• Journal of Distribution Research
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• v.15 no.5
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• pp.257-272
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• 2010

The DongDeaMoon fashion market was carried out the core role of low price distribution as production and selling illuviation of fashion merchandise. However, the unique and successful fashion merchandising system of DongDeaMoon fashion market has been seriously threatened by consumer need changes as well as by an increased competition from new types of retail stores such as fast fashion brands and online shopping malls. Therefore, this study reviewed the policies for DongDeaMoon fashion market carried out by Seoul Metropolitan City and formulated the policies for strengthening competitiveness of DongDeaMoon fashion market.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.