• Knowledge Management Research
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• v.15 no.2
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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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• v.7 no.1
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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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• v.6 no.3
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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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• 2005.06a
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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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• v.11 no.2
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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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• v.9 no.4
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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.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.451-459
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• 2013

The purpose of this paper is to investigate the role of the internet based deviant behavior of smart phone during work hours on performance and the antecedents to deviant behavior mechanism. To do this, this paper proposes a theoretical model consisting of factors such as internet-based deviant behavior, work performance and intention, habit as it's influencing factors. It was analyzed via a survey of employees using smart phone at workplace. The results of this empirical study is summarized as followings. First, internet-based deviant behavior using smart phone has a negative effect on work performance. Second, intention and habit toward internet-based deviant behavior using smart phone are identified as the determinants of it's behavior. The findings have significant implications for the framework of internet-based deviant behavior using smart phone in the workplace.

• Journal of Korean Public Health Nursing
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• v.28 no.2
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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.

• Journal of Industrial Convergence
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• v.14 no.1
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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.

• Journal of Industrial Convergence
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• v.14 no.1
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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.