• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• 지식경영연구
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• 제16권3호
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• pp.81-102
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• 2015

With the rapid development of digital media, there has been a huge change in a way of communication, a process of information diffusion and a role of traditional media. Not like mass media, social media enables users to generate and tap into the opinions of a larger world. From that reason, social media is impacting marketing strategies. However, still social media marketing researches just focus on case study, analysis of users motivation or analysis of power user's usage pattern. Word-of-mouth has always been important especially in marketing area. In social media, word-of-mouth depends on each user that's why this research focuses on individual user's activity in SNS. I defined 4 factors (produce, diffusion, network size, activity of network size enlarge) that are effect on activity and verified hypothesis by multiple regression analysis, hierarchical regression analysis and moderated multiple regression.

• 한국콘텐츠학회논문지
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• 제17권2호
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• pp.404-414
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• 2017

스마트폰의 보급과 온라인 소셜 네트워크 서비스의 발전으로 사용자들은 많은 콘텐츠를 생산하거나 서로 공유한다. 이로 인해 사용자는 자신이 원하지 않는 콘텐츠를 받아보거나 소비함으로써 많은 시간을 소요하게 된다. 이와 같은 문제를 해결하기 위해 소셜 네트워크 사용자에게 적합한 콘텐츠를 추천하기 위한 기법들이 활발하게 연구되고 있다. 본 논문에서는 온라인 소셜 네트워크 사용자에게 협업 필터링을 이용하여 적합한 콘텐츠를 추천하는 기법을 제안한다. 제안하는 기법은 추천의 정확성을 낮추는 사용자의 데이터를 제거하기 위해서 사용자 신뢰도를 고려한다. 사용자의 신뢰도는 온라인 소셜 네트워크의 사용자 행위를 분석해서 도출한다. 사용자의 신뢰도를 다양한 관점에서 평가하기 위해서 기존기법에서 사용하지 않았던 사용자 행위들을 수집해서 사용한다. 다양한 성능평가를 통해 제안하는 기법이 기존 기법보다 우수함을 보인다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.368-372
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• 2009

기존 사용자의 행동 패턴을 인식하는 연구들이 몇 개의 특정 행동을 설정, 사용자 독립적인 인식 결과를 낼 수 있는 특징 추출 방법들을 제안해왔다. 그러나 이러한 연구는 실험실 차원의 결과에 그치고 사용자 독립적인 일반성 획득이나 특정 행동만을 인식 대상으로 삼음으로써 구현상에서 많은 어려움을 초래한다. 이러한 문제점을 개선하고자 본 논문에서는 사용자의 일정 기간 동안의 행동 패턴에 대해 반복성과 지속성을 기준으로 새로 입력되는 행동패턴의 정상/비정상 여부를 검출한다. 기존 연구에서 사용한 교사학습 방법이 아닌 비교사학습 방법을 적용, 일정 기간 동안 수집된 데이터를 클러스터링하여 반복성을 평가하는 기준으로 삼는다. 실험을 통해 반복적으로 발생하는 데이터를 근거로 하여 처음 나타난 행동을 비정상 행동으로 검출할 수 있음을 입증했다.

• Journal of Multimedia Information System
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• 제2권2호
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• pp.233-240
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• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.

• 품질경영학회지
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• 제44권1호
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• pp.199-214
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• 2016

Purpose: The present study was designed to examine the casual relationships among performance and arts information service quality, initial trust, user satisfaction, reuse intention and prosumer activity in social network service(SNS). Also, we intended to explore significant factors on use performance of SNS through causal model analysis in the viewpoint of total effect. Methods: As a survey tool, questionnaire has obtained validity and reliability through literature survey, exploratory survey and pretest and sample 403 was selected. For statistical treatment of pretest and main analysis, SPSS18.0 and AMOS18.0 were employed and structural equation model was employed as analysis method. Results: Result of this study shows as follows. Two factors (precision and reciprocal action) have an effect on user satisfaction, initial trust, reuse intention and prosumer activity. We found that with an importance of initial trust, prosumer activity can be a useful and significant factor in causal relationship of SNS. Conclusion: The present study shows that two factors(precision and reciprocal action) in via of initial trust, were important factors that related companies have to emphasize to raise performance, And also we confirmed new factor 'prosumer activity' through this study. However, the present study has some limitations to be studied in the future.

• 한국산학기술학회논문지
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• 제11권9호
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• pp.3524-3531
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• 2010

유비쿼터스 컴퓨팅 환경에서 사용자에게 최적의 서비스를 제공하기 위해서는 객체 그리고 사용자의 행위와 밀접한 연관이 있는 시공간 정보를 고려하는 것이 중요하다. 이를 위해 이 논문에서는 사용자의 상황을 고려하기 위한 시공간 온톨로지를 설계하고 이를 이용하여 사용자의 행동 및 서비스 패턴을 능동적으로 마이닝할 수 있는 시스템 구조를 제안한다. 제안된 시스템은 사용자의 시간에 따른 위치 및 객체와의 연관성을 고려하여 사용자의 행동과 서비스 패턴을 지능적으로 마이닝 하기 위한 프레임워크이고 트리거 시스템을 기반으로 한다.

• 대한임베디드공학회논문지
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• 제10권3호
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• pp.129-137
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• 2015

Various personalized services are provided based on user context these days, and IoT(Internet of Things) devices provides effective ways to collect user context. For example, user's activity such as walking steps, calories, and sleeping hours can be collected using smart activity tracker. Smart scale can sense change of user's weight or body fat percentage. However, these services are independent to each other and not easy to make them collaborate. Many standard bodies are working on the documents for this issue, but due to diversity of IoT use case scenarios, it seems that multiple IoT technologies co-exist for the time being. This paper propose a framework to collaborate heterogeneous IoT services. The proposed framework provides methods to build application for heterogeneous IoT devices and user context management in more intuitive way using HTTP. To improve compatibility and usability, gathered user contexts are based on MPEG-UD. Implementation of framework and service with real-world devices are also presented.

• 한국컴퓨터정보학회논문지
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• 제20권9호
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• pp.67-72
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• 2015

Recently, studies that interact with human and things through motion recognition are increasing due to the expansion of IoT(Internet of Things). This paper proposed the system that recognizes the user's logical activity in home environment by attaching some sensors to various objects. We employ Arduino sensors and appreciate the logical activity by using the physical activitymodel that we processed in the previous researches. In this System, we can cognize the activities such as watching TV, listening music, talking, eating, cooking, sleeping and using computer. After we produce experimental data through setting virtual scenario, then the average result of recognition rate was 95% but depending on experiment sensor situation and physical activity errors the consequence could be changed. To provide the recognized results to user, we visualized diverse graphs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.