• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.55-70
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• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1017-1021
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• 2013

The traffic signal has been widely used in the transport system with a fixed time interval currently. This kind of setting time was determined based on experience for vehicles to generate a waiting time while allowing pedestrians crossing the street. However, this strict setting causes inefficient problems in terms of economic and safety crossing. In this research, we propose a monitoring algorithm to detect, track and check pedestrian crossing the crosswalk by the patterns of behavior. This monitoring system ensures the safety for pedestrian and keeps the traffic flow in efficient. In this algorithm, pedestrians are detected by using HOG feature which is robust to illumination changes in outdoor environment. According to a complex computation, the parallel process with the GPU as well as CPU is adopted for real-time processing. Therefore, pedestrians are tracked by the relationship of hue channel in image sequence according to the predefined pedestrian zone. Finally, the system checks the pedestrians' crossing on the crosswalk by its HOG based behavior patterns. In experiments, the parallel processing by both GPU and CPU was performed so that the result reaches 16 FPS (Frame Per Second). The accuracy of detection and tracking was 93.7% and 91.2%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5062-5079
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• 2017

Malicious insider threats have increased recently, and methods of the threats are diversifying every day. These insider threats are becoming a significant problem in corporations and governments today. From a technology standpoint, detecting potential insider threats is difficult in early stage because it is unpredictable. In order to prevent insider threats in early stage, it is necessary to collect all of insiders' data which flow in network systems, and then analyze whether the data are potential threat or not. However, analyzing all of data makes us spend too much time and cost. In addition, we need a large repository in order to collect and manage these data. To resolve this problem, we develop an indicator-based behavior ontology (IB2O) that allows us to understand and interpret insiders' data packets, and then to detect potential threats in early stage in network systems including social networks and company networks. To show feasibility of the behavior ontology, we developed a prototype platform called Insider Threat Detecting Extractor (ITDE) for detecting potential insider threats in early stage based on the behavior ontology. Finally, we showed how the behavior ontology would help detect potential inside threats in network system. We expect that the behavior ontology will be able to contribute to detecting malicious insider threats in early stage.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.190-194
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• 1998

In this paper, we introduce the micro robot soccer playing system as a standard test bench for the study on the multiple agent system. Our method is based on following viewpoints. They are (1) any complex behavior such as cooperation among agents must be completed by sequential basic actions of concerned agents. (2) those basic actions can be well defined, but (3) how to organize those actions in current time point so as to result in a new stale beneficial to the end aim ought to be achieved by a kind of self-learning self-organization strategy.

• Health Policy and Management
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• v.24 no.3
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• pp.291-300
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• 2014

Background: The efficient knowledge management in hospital organization is generally known as the important activities relevant to employees' knowledge sharing behavior and work performance. This research examined factors affecting employees' knowledge sharing behavior and work performance in top 4 university hospitals. This study is based on individual factors such as incentives, reciprocity, behavioral control, and subjective norms. Also, there are organizational factors such as CEO support, learning climate, IT system, rewards system, and trust. Methods: Data was collected from employees who are working at 3 hospitals university in Seoul and 1 university hospital in Gyeonggi-Do through the self-administered questionnaires. A total of 779 questionnaires were analyzed by PASW SPSS ver. 18.0. (SPSS Inc., Chicago, IL, USA). Results: The significant variables affecting knowledge sharing behavior are behavioral control (in individual factor) and CEO, IT system, and trust (in organization factor). Also the significant variables affecting work performance are incentives, reciprocity, subjective norms, and behavioral control (in individual factor) and CEO support, IT system, reward system, and trust (in organization factor). Conclusion: The personality and organization characteristics factors is important to improve knowledge sharing behavior and work performance of hospital employees. Therefore, to make more efficient knowledge management is to build and system knowledge sharing culture, system, and leadership and to develop practical strategies.

• Knowledge Management Research
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• v.13 no.3
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• pp.105-125
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• 2012

Previous studies have focused on individual and organizational learning. Amid an increasingly complex business environment, a team system designed to improve flexibility and adaptability constitutes the most basic part of an organization. Still, team learning has rarely been discussed. In addition, team learning behavior, despite being an important part of a team process, is often mentioned as a team-level outcome variable. Given that team learning behavior involves constant changes in thinking and behavior, a shared belief among team members is needed in order to positively influence innovative performance of a team. In spite of that, there has been only limited discussion of it. Besides, few domestic studies have dealt with R&D teams that can clearly demonstrate team learning behavior and team innovative performance. This study is an empirical analysis of the impact of team efficacy on team innovative performance and the mediating role of team learning behavior based on materials collected from team leaders and their immediate subordinates in 268 R&D teams. The analysis showed that team learning behavior actually has a positive effect on team innovative performance. Team efficacy also turned out to have a positive influence on team learning behavior. Lastly, the study found that team learning behavior played a mediating role in the relationship between team efficacy and team innovative performance. Based on those results, the study has identified implications and suggested directions for future research.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.261-266
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• 2014

The current status of domestic monitoring centers was reviewed and the pattern-based security monitoring system and the centralized security monitoring system, both of which are the characteristics of security monitoring systems, were analyzed together with their advantages and disadvantages. In addition, as for a development plan of domestic security monitoring systems, in order to improve the problems of the existing pattern-based centralized monitoring system, Honeynet and Darknet, which are based on anomalous behavior detection, were analyzed and their application plans were described.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1525-1530
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• 2003

In this paper, a model of adaptive individual life support system is proposed from the viewpoint of cybernetics. This model is derived based on the relation between human behavior and human action, static and dynamic in processing speed, and abstract/concrete. In applications, task and information of human which includes in this system analyzed by paying attention to cybernetics. This paper shows a few actual example of modeling by fundamental adaptive individual life support model such as medical diagnosis, health care and education support. Finally as an example, design and implementation are concretely carried out for health care support system. This is also a method to design a information support system which is involved in human.

• Korean Journal of Computational Design and Engineering
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• v.13 no.3
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• pp.209-216
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• 2008

This paper presents a novel approach to design evaluation of portable consumer electronic (PCE) products using augmented reality (AR) based tangible interaction and functional behavior simulation. In the approach, the realistic visualization is acquired by overlaying the rendered image of a PCE product on the real world environment in real-time using computer vision based augmented reality. For tangible user interaction in an AR environment, the user creates input events by touching specified regions of the product-type tangible object with the pointer-type tangible object. For functional behavior simulation, we adopt state transition methodology to capture the functional behavior of the product into a markup language-based information model, and build a finite state machine (FSM) to controls the transition between states of the product based on the information model. The FSM is combined with AR-based tangible objects whose operation in the AR environment facilitates the realistic visualization and functional simulation of the product, and thus realizes faster product design and development. Based on the proposed approach, a product design evaluation system has been developed and applied for the design evaluation of various PCE products with highly encouraging feedbacks from users.