• 지식경영연구
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• 제15권2호
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• pp.23-41
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• 2014

Firms increasingly utilize Social Networking Service(SNS) to lead user's voluntary behavior. In the web-based environment, users show coexist loyal behavior which is represented by 'web-based pro-organization citizenship behavior' and 'anti-citizenship behavior'. To measure genuine performance of web-activity, we separated degree of compliance based on credibility, 'deep-level' and 'surface-level' to comprehend different behavior after compliance. The analysis result shows that contents credibility is important to enhance deep-level of compliance which has significant influence on web-based pro-organization citizenship behavior. Contrastively, surface-level of compliance has influence on anti-citizenship behavior. Based on the results of these analyses, the directions of web-based activities for the common good and self-interests of the stakeholders of the web-based activities will be proposed.

• Journal of Information Processing Systems
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• 제7권1호
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• pp.173-186
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• 2011

Due to the disadvantages of signature-based computer virus detection techniques, behavior-based detection methods have developed rapidly in recent years. However, current popular behavior-based detection methods only take API call sequences as program behavior features and the difference between API calls in the detection is not taken into consideration. This paper divides virus behaviors into separate function modules by introducing DLLs into detection. APIs in different modules have different importance. DLLs and APIs are both considered program calling resources. Based on the calling relationships between DLLs and APIs, program calling resources can be pictured as a tree named program behavior resource tree. Important block structures are selected from the tree as program behavior features. Finally, a virus detection model based on behavior the resource tree is proposed and verified by experiment which provides a helpful reference to virus detection.

• International journal of advanced smart convergence
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• 제6권3호
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• pp.29-37
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• 2017

The current behavior recognition system don't match data formats between sensor data measured by user's sensor module or device. Therefore, it is necessary to support data processing, sharing and collaboration services between users and behavior recognition system in order to process sensor data of a large capacity, which is another formats. It is also necessary for real time interaction with users and behavior recognition system. To solve this problem, we propose fog cloud based behavior recognition system for human body sensor data processing. Fog cloud based behavior recognition system solve data standard formats in DbaaS (Database as a System) cloud by servicing fog cloud to solve heterogeneity of sensor data measured in user's sensor module or device. In addition, by placing fog cloud between users and cloud, proximity between users and servers is increased, allowing for real time interaction. Based on this, we propose behavior recognition system for user's behavior recognition and service to observers in collaborative environment. Based on the proposed system, it solves the problem of servers overload due to large sensor data and the inability of real time interaction due to non-proximity between users and servers. This shows the process of delivering behavior recognition services that are consistent and capable of real time interaction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2337-2341
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• 2005

This paper presents the target detection method using Support Vector Machines(SVMs) and the navigation system using behavior-based fuzzy controller. SVM is a machine-learning method based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate detection of target objects as a supervised-learning problem and apply SVM to detect at each location in the image whether a target object is present or not. The behavior-based fuzzy controller is implemented as an individual priority behavior: the highest level behavior is target-seeking, the middle level behavior is obstacle-avoidance, the lowest level is an emergency behavior. We have implemented and tested the proposed method in our mobile robot "Pioneer2-AT". Comparing with a neural-network based detection method, a SVM illustrate the excellence of the proposed method.

• Asia Marketing Journal
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• 제11권2호
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• pp.37-51
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• 2009

Although previous studies have made significant progress in adaptive selling behavior (ASB), few studies have considered salesperson's customer orientation (CO) and learning behavior as determinants of effective sales management (ASB and relationship-making efforts), despite the discussion of important roles of these constructs. The authors test not only the relationships of salesperson's CO and market-based learning behavior to ASB and relationship-making efforts, but also the effects of ASB on relationship-making efforts and performance. The results of the study, which is done with samples of salespeople from Korean companies, indicate that salesperson's CO and market-based learning behavior are identified as significant determinants of ASB. Moreover, both salesperson's ASB and relationship-making efforts have significant effects on sales performance. On the other hand, as per salesperson's relationship-making efforts, salesperson's CO has a positive effect, but salesperson's market-based learning behavior and ASB do not influence his or her relationship-making efforts, which suggest a provocative possibility of conceptualization regarding the relationship between ASB and relationship management efforts.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• 한국콘텐츠학회논문지
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• 제13권2호
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• pp.451-459
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• 2013

본 연구의 목적은 근무시간 중 스마트폰의 인터넷 기반 일탈행동에 영향을 미치는 요인들과 일탈행동이 성과에 미치는 영향에 대하여 조사하는 것이다. 본 연구는 이를 위하여 스마트폰 인터넷을 이용한 일탈행동, 성과, 그리고 일탈행동 영향요인들인 의향, 습관 등으로 구성된 이론모형을 개발하였다. 제안된 이론모형은 업무영역에서 스마트폰을 사용하는 직장인들을 대상으로 설문조사를 실시하여 분석하였다. 분석결과 첫째, 스마트폰 인터넷을 이용한 일탈행동은 성과에 부정적인 영향을 미치는 것으로 밝혀졌다. 둘째, 스마트폰 인터넷을 이용한 일탈행동 의향 및 습관은 그 행동을 예측할 수 있는 요인들로 확인되었다. 본 연구의 결과는 직장에서의 스마트폰 인터넷을 이용한 일탈행동에 대한 분석틀을 제시하였다는 점에서 의의가 있다.

• 한국보건간호학회지
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• 제28권2호
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• pp.241-257
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• 2014

Purpose: The purpose of this study was to investigate the combined influence of screen based sedentary behavior and sleep duration on obesity, depression, and suicidal ideation in Korean Adolescents. Methods: This cross sectional study used secondary data from the 2013 Adolescent Health Behavior Online Survey. A total of 27,929 high school students were included in the study. Logistic regression was used for analysis of the combined influence of screen based sedentary behavior and sleep duration on obesity, depression, and suicidal ideation. Results: Obesity was more prevalent among adolescents who had ${\geq}$ 4h of screen based sedentary behavior and <7h of sleep duration compared to the reference group, defined as < 2h of screen time and > 7h of sleep duration. Depression and suicidal ideation were more prevalent among adolescents who had 2-4h of screen based sedentary behavior and <7h of sleep duration compared to the reference group. Conclusion: Based on the results of this study, maintaining a low (< 2h) level of screen based sedentary behavior and appropriate sleep duration over 7 hours per day is essential for prevention of adolescent obesity, depression, and suicidal ideation.

• 산업융합연구
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• 제14권1호
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• pp.1-13
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• 2016

I have done a case study on individual level based-organizational behavior textbooks published in Korea and USA. The objects are 'organizational behavior' by Su Yong Jung and 'organizational behavior' by Im Chang Hee in Korea, and 'organizational behavior' by Schermerhorn, Osborn, Uhl-Bien, & Hunt and 'organizational behavior' by Robbins, & Judge. The goal of this study is to identify the differences of the perspectives on the individual level based-organizational behavior between Korean and American textbooks. This research finding shows that it is valid to classify organizational behavior into such categories as individual differences, attitude, emotion & stress, perception & learning, and motivation in the Korean textbooks for the better future study.

• 산업융합연구
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• 제14권1호
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• pp.27-37
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• 2016

I have done a case study on organization level based-organizational behavior textbooks published in Korea and USA. The objects are 'organizational behavior' by Su Yong Jung and 'organizational behavior' by Im Chang Hee in Korea, and 'organizational behavior' by Schermerhorn, Osborn, Uhl-Bien, & Hunt and 'organizational behavior' by Robbins, & Judge. The goal of this study is to identify the differences of the perspectives on the organization level based-organizational behavior between Korean and American textbooks. This research finding shows that it is valid to classify organizational behavior into such categories as organizational structure, organizational culture, and organizational change in the Korean textbooks for the better future study.