• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1752-1767
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• 2015

As a special type of mobile ad-hoc network, Vehicular Ad-hoc Network (VANET) is considered as an attractive topic by many researchers. In VANETs, vehicles act as routers and clients, which are connected with each other through unreliable wireless links. Due to the dynamic nature of vehicles, developing communication protocols for VANETs is a challenging task. In this paper, we tackle the problem of real-world VANET operations and propose a new dual-level communication scheme through the combination of power and rate control algorithms. Based on the game theoretic approach, the proposed scheme effectively formulates the interactive situation among several vehicles. With a simulation study, it is confirmed that the proposed scheme can achieve better performance than other existing schemes under widely diverse VANET environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.337-339
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• 2020

Speed-run is a gameplay style in what the player tries to finish the game as quickly as possible. The purpose of this study is to observe and analyze the difference between a normal player and a speed runner in a speed run to better understand their interaction with the game and to extrapolate the applications of the analyzed frameworks to scenarios that extent normal gameplay. For this purpose, MDA (Mechanic, Dynamic, Aesthetics) and DPE (Experience, Design, Play) were used. As a result, the average player was found to focus on aesthetics or affects while the speed runner focused on mechanics and gameplay.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• The Journal of the Korea Contents Association
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• v.12 no.1
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• pp.103-111
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• 2012

This paper is a basic study for making a system that can predict the success and failure of entertainment contents at the initial stage of production. It proposes the user's emotion modeling of dynamic narrative on entertainment contents. To make this possible, 1) dynamic narrative emotion model is proposed based on theoretical research of narrative structure and cognitive emotion model. 2) configuring the emotion types and emotion value, proposed model of three emotion parameter(desire, expectation, emotion type) are derived. 3)To measure user's emotion in each story event of dynamic narrative, cognitive behavior and description of user(film, game) is established. The earlier studies on the user research of conceptual, analytic approach is aimed of predicting on review of the media and user's attitude, and consequently these results is delineated purely descriptive. In contrast, this paper is proposed the method of user's emotion modeling on dynamic narrative. It would be able to contributed to the emotional evaluation of entertainment contents using specific information.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.143-152
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• 2021

It is very important to learn behavioral policies that allow multiple agents to work together organically for common goals in various real-world applications. In this multi-agent reinforcement learning (MARL) environment, most existing studies have adopted centralized training with decentralized execution (CTDE) methods as in effect standard frameworks. However, this multi-agent reinforcement learning method is difficult to effectively cope with in a dynamic environment in which new environmental changes that are not experienced during training time may constantly occur in real life situations. In order to effectively cope with this dynamic environment, this paper proposes a novel multi-agent reinforcement learning system, C-COMA. C-COMA is a continual learning model that assumes actual situations from the beginning and continuously learns the cooperative behavior policies of agents without dividing the training time and execution time of the agents separately. In this paper, we demonstrate the effectiveness and excellence of the proposed model C-COMA by implementing a dynamic mini-game based on Starcraft II, a representative real-time strategy game, and conducting various experiments using this environment.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.36-43
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• 2020

Many countries and international organizations have carried out rover missions to explore planetary surfaces. Accordingly, the demand for mechatronics education, which is closely related to building exploratory robots, is also steadily increasing. However, due to the complexity in understanding the background information needed for mechatronics, it is hard for pre-college students to study such process. In this study, we suggest an educational platform for mechatronics using a combined robot kit with a spherical robot and a smartphone application. To provide a visual understanding, the dynamic model of the robot is constructed while analyzing the error between actual driving and a simulation, and the educational algorithm of the game and a feedback method are proposed to improve the learning efficacy by considering the user's level of knowledge of mechatronics. We use this educational spherical robot to develop a curling game platform that can impart engineering education even when students lack significant knowledge.

• Journal of KIISE
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• v.42 no.11
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• pp.1423-1429
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• 2015

The Internet of Things (IoT) is a new concept associated with the future Internet, and it has recently become a popular concept to build a dynamic, global network infrastructure. However, the deployment of IoT creates difficulties in satisfying different Quality of Service (QoS) requirements and achieving rapid service composition and deployment. In this paper, we propose a new QoS control scheme for IoT systems. The Markov game model is applied in our proposed scheme to effectively allocate IoT resources while maximizing system performance. The results of our study are validated by running a simulation to prove that the proposed scheme can promptly evaluate current IoT situations and select the best action. Thus, our scheme approximates the optimum system performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.797-799
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• 2016

It is difficult for user to use arcade shooting games because patten of the missiles patterns are not dynamic. And users are hard to access to high level because spped of missiles is very fast. In this paper, we propose rhythm based shooting game. The game give us pleasant environment and use can play the game easily.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.893-903
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• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.

• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.73-80
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• 2018

The purpose of this study was to investigate the effect of motion-based dance game on static and dynamic balance in elderly women. The participants were randomly divided into 2 group, Motion Based dance game group(n=10), control group(n=10). After they were divided into an Motion Based dance game group and a control group the Motion Based dance game group participated in six-week period of time, three times a week, 60 minutes each, and the control group didn't exercise outside of their daily lives. To evaluated the balance ability of exercise, it was evaluated by using Berg Balance Scale(BBS), Functional Reach Test(FRT), Timed Up and Go test(TUG). The data was analysis using a paired t-test and independent t-test to determine the statistical significance. The results of this study between BBS, FRT, TUG and Motion Based dance game group had statistically significant difference rather than control group(p<.05). In conclusion, the Motion based Dance Game showed improvement on the balance ability in the elderly. Regular maintenance of the Dance Game "Dance Central" program for the elderly will assistance improve the balance. Consequently, studies on the development of dance games suitable for the elderly are believed to be necessary.