• Journal of Game and Entertainment
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• v.2 no.1
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• pp.35-44
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• 2006

Objective of this study is to develop a 3D car navigation system that shows the driving direction to a destination through real-time 3-D panoramic views of the route. For the purpose, a new searching process was established to find the optimal driving direction based on the driver's current location and the real-time traffic situation and the TIP (tour into the picture) method was extended to implement a wide virtual environment. A virtual environment was built up by applying the extended TIP method to the panoramic images taken at a constant distance from a real road, and then, displayed 3-D navigation as clear as the real images. The car navigation system developed in this study provides the optimal driving direction and real-time traffic situation using 2-D navigation module and 3D navigation module.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.463-468
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• 2016

The overall performance of multiuser systems significantly depends on how effectively and fairly manage resources shared by them. The efficient resource management strategies are even more important for multimedia users since multimedia data is delay-sensitive and massive. In this paper, we focus on resource allocation based on a game-theoretic approach, referred to as Nash bargaining solution (NBS), to provide a quality of service (QoS) guarantee for each user. While the NBS has been known as a fair and optimal resource management strategy, it is challenging to find the NBS efficiently due to the computationally-intensive task. In order to reduce the computation requirements for NBS, we propose an approach that requires significantly low complexity even when networks consist of a large number of users and a large amount of resources. The proposed approach linearizes utility functions of each user and formulates the problem of finding NBS as a convex optimization, leading to nearly-optimal solution with significantly reduced computation complexity. Simulation results confirm the effectiveness of the proposed approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.651-658
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• 2020

In the fifth generation (5G) mobile networks, the interactive mobile game users have increased tremendously, which induces correlated information sources (CIS). One of the promising 5G technologies is non-orthogonal multiple access (NOMA). In NOMA, the users share the channel resources, so that CIS affect each user's bit-error rate (BER) performance, which is not the case for orthogonal multiple access (OMA). In this paper, we derive the optimal receiver for NOMA with CIS, and then investigate the impact of CIS on each user's BER performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.399-406
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• 2020

In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.105-117
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• 2010

In recent years, dynamic collaboration (DC) among cloud providers (CPs) is becoming an inevitable approach for the widely use of cloud computing and to realize the greatest value of it. In our previous paper, we proposed a combinatorial auction (CA) based cloud market model called CACM that enables a DC platform among different CPs. The CACM model allows any CP to dynamically collaborate with suitable partner CPs to form a group before joining an auction and thus addresses the issue of conflicts minimization that may occur when negotiating among providers. But how to determine optimal group bidding prices, how to obtain the stability condition of the group and how to distribute the winning prices/profits among the group members in the CACM model have not been studied thoroughly. In this paper, we propose to formulate the above problems of cooperative negotiation in the CACM model as a bankruptcy game which is a special type of N-person cooperative game. The stability of the group is analyzed by using the concept of the core and the amount of allocationsto each member of the group is obtained by using Shapley value. Numerical results are presented to demonstrate the behaviors of the proposed approaches.

• Journal of the Korea Society for Simulation
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• v.15 no.2
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• pp.31-39
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• 2006

The visibility graph is a well-known method for efficient path-finding with the minimum search space modelling the game world. The generalized visibility graph is constructed on the expanded obstacle boundaries to eliminate the "wall-hugging" problem which is a major disadvantage of using the visibility graph. The paths generated by the generalized visibility graph are guaranteed to be near optimal and natural-looking. In this paper we propose the method to apply the generalized visibility graph efficiently for game characters who moves among static obstacles between varying start and goal points. Even though the space is minimal once the generalized visibility graph is constructed, the construction itself is time-consuming in checking the intersection between every two links connecting nodes. The idea is that we build the map for static obstacles first and then incorporate start and goal nodes quickly. The incorporation of start and goal nodes is the part that must be executed repeatedly. Therefore we propose to use the rotational plane-sweep algorithm in the computational geometry for incorporating start and goal nodes efficiently. The simulation result shows that the execution time has been improved by 39%-68% according to running times in the game environment with multiple static obstacles.

• Korean Management Science Review
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• v.32 no.1
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• pp.83-100
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• 2015

We analyze a duopoly competition when two firms face input cost increases. The objective of this study is to determine the firms' optimal strategy between a price increase and downsizing under conditions of a spatially differentiated market and consumers' diminishing utility on the product size. We develop a theoretical model of two competing firms offering homogenous products using the standard Hotelling model to determine how firms' optimal strategies change when facing input cost increases. In this paper, there are two types of duopoly competitions: symmetric and asymmetric. In the symmetric case, the two firms have the same marginal cost and are producing and selling identical products. In the asymmetric case, the two firms have different marginal costs. The results show that the optimal strategy decision depends on the size of the input cost increase and the cost differences between the two firms. We find that when two firms are asymmetric (i.e., they have different marginal costs), the two firms might choose asymmetric pairs of strategies in equilibrium under certain conditions. When the cost differences between the two firms are sufficiently large and the cost increase is sufficiently small, the cost leader chooses price increase, and the cost-disadvantaged firm chooses downsizing in equilibrium. This asymmetric strategy reduces price competition between two firms, and consumers are better off. When the cost differences between the two firms are sufficiently large, downsizing is the dominant strategy for the cost-disadvantaged firm. The cost-disadvantaged firm finds it more profitable to reduce the product size than to increase its price to reduce price competition, because consumers prefer downsizing to price increases. This paper might be a good starting point for further analytical research in this area.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.556-559
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• 2007

The notion of linearity is fundamental in science and engineering. Much of system and control theory is based on the analysis of linear system, which does not care whether it is nonlinear and complex. The dynamic programming is one of concerned technology when users are interested in choosing best choice from system operation for nonlinear or dynamic system‘s performance and control problem. In this paper, we will introduce the dynamic programming which is based on discrete system. When the discrete system is constructed with discrete state, transfer between states, and the event to induct transfer, the discrete system can describe the system operation as dynamic situation or symbolically at the logical point of view. We will introduce technologies which are related with controllable of Controlled Markov Chain as shown example of simple game. The dynamic programming will be able to apply to optimal control part which has adaptable performance in the discrete system.

• Management Science and Financial Engineering
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• v.1 no.1
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• pp.1-19
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• 1995

In this paper, we analyze an M / M / 1 queueing system where there are incentive conflicts among customers. Self-interested customers' decisions whether to join the system or not may not necessarily induce a socially optimal congestion level. As a way to alleviate the over-congestion, toll imposition was used in Naor's paper [3]. Instead of using a toll mechanism, we study the usefulness of imperfect information on system state (queue size, for example) as a way to reduce the over-congestion by self-interested customers. The main conclusion of this paper is that by purposefully giving fuzzy or imperfect information on the current queue size we can improve the congestion in the system. This result might look contradictory to rough intuition since perfect information should give better performance than imperfect information. We show how this idea is verified. In deriving this result, we use the concept of Nash equilibrium (pure and mixed strategy) as introduced in game theory. In some real situations, using imperfect information is easier to apply than imposing a toll, and thus the result of this paper has practical implications.

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.107-122
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• 2002

Multi-body spacecraft attitude and configuration control formulations based on the use of collaborative control theory are considered. The control formulations are based on two-player, nonzero-sum, differential game theory applied using a Nash strategy. It is desired that the control laws allow different components of the multi-body system to perform different tasks. For example, it may be desired that one body points toward a fixed star while another body in the system slews to track another satellite. Although similar to the linear quadratic regulator formulation, the collaborative control formulation contains a number of additional design parameters because the problem is formulated as two control problems coupled together. The use of the freedom of the partitioning of the total problem into two coupled control problems and the selection of the elements of the cross-coupling matrices are specific problems ad-dressed in this paper. Examples are used to show that significant improvement in performance, as measured by realistic criteria, of collaborative control over conventional linear quadratic regulator control can be achieved by using proposed design guidelines.