• Journal of the Korea Computer Industry Society
• /
• v.2 no.7
• /
• pp.945-952
• /
• 2001

In consequence of expansion of internet users, electronic commerce is becoming a new prototype for marketing and sales, arid most of electronic commerce sites or internet shopping malls provide a rich source of information and convenient user interfaces about the organizations customers to maintain their patrons. One of the convenient interfaces for users is service to recommend products. To do this, they must exploit methods to extract and analysis specific patterns from purchasing information, behavior and market basket about customers. The methods are association rules and sequential patterns, which are widely used to extract correlation among products, and in most of on-line electronic commerce sites are executed with users information and purchased history by category-oriented. But these can't represent the diverse correlation among products and also hardly reflect users' buying patterns precisely, since the results are simple set of relations for single purchased pattern. In this paper, we propose an efficient mining technique, which allows for multiple purchased patterns that are category-independent and have relationship among items in the linked structure of single pattern items.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2018.11a
• /
• pp.19-21
• /
• 2018

선원의 행동오류는 해양사고를 야기하는 하나의 직접적인 원인이기 때문에 이를 이해하는 것은 해양사고 예방에 근본이 된다. 선원의 행동오류를 이해하기 위해서는 행동오류를 추정하고 예측할 수 있어야 한다. 본 연구에서는 은닉 마르코브 모델(Hidden Markov Model, HMM)을 이용하여 선원들의 행동오류를 추정하고 예측하였다. 아울러 5가지 선박의 종류 각각에 나타나는 선원들의 행동오류를 서로 비교 분석하였다. 모델에 사용한 데이터는 해양안전심판원의 해양사고 보고서에 기록된 내용을 SRKBB(Skill-, Rule- and Knowledge-Based Behavior) 모델을 기반으로 분류하고 관측 수열을 생성하며 라벨링 작업을 통해서 구축하였다. 구축한 데이터를 적용하여 HMM을 보정하고 파라미터를 획득하여 선원들의 행동오류에 관한 모델을 구축하였다. 실험 결과, 선박 종류별로 선원들의 행동오류의 패턴은 서로 다르고, 이를 통해서 선박종류별 해기사들의 행동오류의 추정과 예측이 가능함을 일차적으로 확인할 수 있었다. 추후 본 연구를 지속 전개하여 해양사고 예방을 위한 인적오류의 저감에 기여할 수 있는 방안을 모색할 에정이다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.5
• /
• pp.2157-2168
• /
• 2013

This study is about facilities using pattern for 'River-eco-park' that is Hwa-myeong Eco-park, Sam-rak Eco-park, Maek-do Eco-park. Through doing surveys, most patterns could be divided 5 patterns; 'Eco observation facility pattern', 'Promenade pattern', 'Bike path pattern', 'Sports facility pattern', 'Fitness center pattern'. Then we find channel of movement of user patterns by follow-up surveys. On the basis of this surveys, we are going to suggest some directions for River-eco-park design.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.112-114
• /
• 2004

오늘날 웹을 이용하는 사용자들의 웹 검색 형태를 저장한 웹 로그 데이터들은 데이터 마이닝을 위한 중요한 자료가 되고 있다. 이들 웹 로그들로부터 사용자의 현재 행동을 기반으로 사용자가 다음에 요청할 요구를 예측할 수 있는 예측 모델을 만들 수 있다. 하지만 이들 웹 로그들은 크기가 매우 크고 분석하기가 어렵다. 이런 문제를 해결하기 위해 이미 않은 방법이 제안되었다. 그 중에서 효과적으로 예측할 수 있도록 제안된 순차적 분류 기반에 연관법칙을 적용한 예측 기법이 있다. 본 논문에서는 전방향 참조 경로 탐사 패턴 알고리즘을 적용하여 연관규칙에 기반 한 웹 문서 예측 기법을 향상시키는 모델을 제안한다.

• Annual Conference of KIPS
• /
• 2005.11a
• /
• pp.163-166
• /
• 2005

홈 네트워크 환경에서의 다양한 어플리케이션 서비스를 개발하기 위한 필수적인 요소인 멀티미디어 개념을 기반으로 본 논문에서는 사용자가 홈 내 상황에 대한 정보를 얻기 위해 사용자의 행동 패턴을 분석하여 개인적 성향을 가미한 서비스를 구성원 각자에게 제공함으로써 보다 빠르게 원하는 정보를 얻을 수 있도록 하였다. 또한 12byte의 salt 함수를 해쉬화 알고리즘에 이용하여 자체 인증 기법을 통한 정상적인 인증을 거쳐 홈 서버에 접속을 하게 되는데 이러한 방식은 기존 시스템보다 가벼우면서도 강인한 인증 절차를 가져오도록 보안적인 접근을 꾀했다.

• The Journal of the Korea Contents Association
• /
• v.19 no.11
• /
• pp.247-261
• /
• 2019

Demand/withdraw communication is an important dysfunctional pattern of serial arguing. This study aims at addressing factors that affect the ways in which self-demand/partner-withdraw pattern increases the likelihood of persistence of serial arguing. We posit that sensitivity to rejection is positively related to the degree to which individuals perceive a partners' behavior as generally disconfirming, which is positively related to enactment of self-demand/partner-withdraw during an argumentative episode. This sequence is positively related to perception of arguments as unresolved. In addition, among those who reported their argument was resolved, this sequence is positively related to the likelihood that the argument is resolved without mutual agreement. Serial mediation analysis confirmed that the likelihood of resolution without mutual agreement were positively associated with rejection sensitivity partially because high RS individuals are likely to perceive their partner to be generally disconfirming and to enact self-demand/partner-withdraw communication during the episode. However, this pattern did not apply to perception of the argument as resolved.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.85-107
• /
• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Design Convergence Study
• /
• v.16 no.5
• /
• pp.203-217
• /
• 2017

This study proposes and demonstrates the value of user - centered design methodology based on linguistic analysis. The results of the proposed user behavioural analysis framework suggested that the syntactic structure between the sentence structure and its components could be a logical basis for explaining the user's situation and behavior. Based on this, the definitions and classifications of user interactions and user contexts were conducted in a microscopically context. User behavior has also been established to identify pattern structures of purposeful nature and constitutes a user behavior sequence that prioritizes them. Next, the User Experience Analysis Framework was derived by defining the relationship between User Behavior and User Behavior and User Context and User Context. To verify the framework of the framework, a professional assessment was conducted to conduct a review of the user's experience and conduct a study of the framework of the framework and conduct of the framework of the framework of the framework and practical utility of the framework. Through this, it was possible to identify the value of the qualitative and quantitative framework of the framework and the future direction of development.

• Journal of Nutrition and Health
• /
• v.52 no.1
• /
• pp.36-46
• /
• 2019

Purpose: This study evaluated amounts of physical activity and sedentary behavior patterns in older adults using an accelerometer and physical activity diary. Methods: Forty-nine older adults (male 26, female 23) participated in this study. They wore a triaxial accelerometer (ActiGraph wGT3X-BT) for one week and wrote a physical activity diary concurrently for three days. Amounts of physical activity, sedentary behavior patterns, and percentage of meeting the World health organization (WHO) physical activity guidelines were analyzed using an accelerometer. In addition, the contents recorded in the physical activity diary were reclassified to 18 levels and the average daily times spent on each level and physical activity level (PAL) were calculated. Results: The subjects were sitting more than half of the day except for bedtime and shower time (59.2%). The numbers of prolonged ${\geq}30$, 40 minutes sedentary bouts were significantly higher in males ($3.10{\pm}1.34$, $1.78{\pm}1.09$, respectively) than in females ($2.34{\pm}1.22$, $1.32{\pm}1.07$, respectively) and the number of breaks per sedentary hour was significantly less in males ($5.74{\pm}0.89$) than in females ($6.44{\pm}0.71$). Among the activities corresponding to sedentary behavior surveyed by the physical activity diary, only the amount of time spent 'resting, speaking and watching TV' showed a significant correlation with the sedentary behavior pattern measured by the accelerometer. The persistence of sedentary behavior was interrupted primarily when low intensity activity was performed. Only 22.4% of the subjects met WHO physical activity guidelines. Conclusion: Based on these results, the physical activity guidelines for older adults should be developed that reflects the appropriate strength, including low activity level and maintenance time of moderate to vigorous physical activity.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.9
• /
• pp.1266-1271
• /
• 2022

Cats have strong wildness so they have a characteristic of hiding diseases well. The disease may have already worsened when the guardian finds out that the cat has a disease. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia, polyuria, and frequent urination more quickly. In this paper, 1) Efficient version of DeepLabCut for pose estimation, 2) YOLO v4 for object detection, 3) LSTM is used for behavior prediction, and 4) BoT-SORT is used for object tracking running on an artificial intelligence device. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the server system.