• Journal of applied mathematics & informatics
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• v.4 no.1
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• pp.167-178
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• 1997

In this paper we present an application to airfoil design of an optimum design method based on optimal control theory. The method used here transforms the design problem by way of a change of variable into an optimal control problem for a distributed system with Neumann boundary control. This results in a set of variational inequalities which is solved by adding a penalty term to the differential equation. This si inturn solved by a finite element method.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1155-1158
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• 1999

In this paper, we consider the optimal control of detection threshold to minimize the conditional mean-square state estimation error for the probabilistic data association (PDA) filter. Earlier works on this problem involved the cumbersome graphical optimization algorithm or time-consuming numerical optimization algorithm. Using the numerical approximation of information reduction factor, we obtained the closed-form optimal detection threshold. This results are very useful for real-time implemenation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1258-1261
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• 1993

This paper presents a methodology of path planning and navigation for an autonomous mobile robot. A fast algorithm using decomposition technique, which computes the optimal paths between all pairs of nodes, is proposed for real-time calculation. The robot is controlled by fuzzy approximation reasoning. Our new methodology has been implemented on a mobile robot. The results show that the robot successfully navigates to its destination following the optimal path.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.7_8
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• pp.387-396
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• 2003

This paper provides an approximation algorithm for STP-MSP(Steiner Tree Problem with minimum number of Steiner Points).Because it seems to be impossible to have a PTAS(Polynomial Time Approximation Schemes), which gives the near optimal solutions, for the problem, the algorithm of this paper is an alternative that has the approximation ratio 2 with $n^{O(1)}$ run time. The importance of this paper is the potential to solve other related unsolved problems. The idea of this paper is to distribute the error allowance over the problem instance so that we may reduce the run time to polynomial bound out of infinitely many cases. There are earlier works[1,2] that show the approximations that have practical run times with the ratio of bigger than 2, but this paper shows the existence of a poly time approximation algorithm with the ratio 2.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2303-2308
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• 2012

In this paper, we deal with the problem of scheduling parallel tasks with deadlines. Parallel tasks can be simultaneously executed on various machines and specially, we consider the malleable tasks, that is, the tasks whose execution time is given by a function of the number of machines on which they are executed. The goal of the problem is to maximize the throughput of tasks completed within their deadlines. This problem is well-known as NP-hard problem. Thus we will find an approximation algorithm, and its performance is compared with that of the optimal algorithm and analyzed by finding the approximation ratio. In particular, the algorithm has more resources, that is, more machines, than the optimal algorithm. This is called the resource augmentation analysis. We propose an algorithm to guarantee the approximation ratio of 3.67 using 1.5 times machines.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.4
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• pp.351-365
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• 2019

This article presents a new approach for conformal flattening with optimal cone singularity. The algorithm here takes an optimal control for selecting optimal cones and uses the Ricci flow to force the flattening. This work is considered as a modification to the work of Soliman et al. [1] in the sense that they make use of the Yamabe equation for the flattening, which is an approximation of the Ricci flow. We present a numerical algorithm based on the optimal control with the mathematical background. Several numerical results validate that our method is optimal in total cone angle and usage of the Ricci flow ensures the conformal flattening while selecting optimal cones.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.134-147
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• 2009

Tracking and exploiting instantaneous spectrum opportunities are fundamental challenges in opportunistic spectrum access (OSA) in presence of the bursty traffic of primary users and the limited spectrum sensing capability of secondary users. In order to take advantage of the history of spectrum sensing and access decisions, a sequential decision framework is widely used to design optimal policies. However, many existing schemes, based on a partially observed Markov decision process (POMDP) framework, reveal that optimal policies are non-stationary in nature which renders them difficult to calculate and implement. Therefore, this work pursues stationary OSA policies, which are thereby efficient yet low-complexity, while still incorporating many practical factors, such as spectrum sensing errors and a priori unknown statistical spectrum knowledge. First, with an approximation on channel evolution, OSA is formulated in a multi-armed bandit (MAB) framework. As a result, the optimal policy is specified by the wellknown Gittins index rule, where the channel with the largest Gittins index is always selected. Then, closed-form formulas are derived for the Gittins indices with tunable approximation, and the design of a reinforcement learning algorithm is presented for calculating the Gittins indices, depending on whether the Markovian channel parameters are available a priori or not. Finally, the superiority of the scheme is presented via extensive experiments compared to other existing schemes in terms of the quality of policies and optimality.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.15-15
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• 2003

We provide some recent results of approximation algorithms for solving Markov Games and discuss their applications to problems that arise in Computer Science. We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with an infinite horizon discounted cost criterion. We present error bounds from the optimal equilibrium value of the game when both players take “correlated” receding horizon policies that are based on exact or approximate solutions of receding finite horizon subgames. Motivated by the worst-case optimal control of queueing systems by Altman, we then analyze error bounds when the minimizer plays the (approximate) receding horizon control and the maximizer plays the worst case policy. We give two heuristic examples of the approximate receding horizon control. We extend “parallel rollout” and “hindsight optimization” into the Markov game setting within the framework of the approximate receding horizon approach and analyze their performances. From the parallel rollout approach, the minimizing player seeks to combine dynamically multiple heuristic policies in a set to improve the performances of all of the heuristic policies simultaneously under the guess that the maximizing player has chosen a fixed worst-case policy. Given $\varepsilon$>0, we give the value of the receding horizon which guarantees that the parallel rollout policy with the horizon played by the minimizer “dominates” any heuristic policy in the set by $\varepsilon$, From the hindsight optimization approach, the minimizing player makes a decision based on his expected optimal hindsight performance over a finite horizon. We finally discuss practical implementations of the receding horizon approaches via simulation and applications.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.8
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• pp.678-684
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• 2008

A general treatment is to restore abfraction lesions with dental filler materials to reduce stress concentration. A material should be selected from various dental products based on long term experiences of dentist or personal preference concerning filler methods. A quantitative criterion is necessary to make an evaluation of the results as dentists decide treatment methods and dental materials relying on their clinical experiences. The purpose of this study is to find an optimal restoration method and material for noncarious cervical lesions using the finite element method. An objective function was defined to minimize the sum of tension or compression stress. Trial-and-error and approximation were used to find an optimal restoration method. An optimal solution was to fill TetricFlow inside the lesion and Z100 in the remaining region. The most desirable thickness ratio of the two filler materials was 0.125 with trial-and-error and it was similar to the results of approximation, 0.121 and 0.132.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.3
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• pp.21-32
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• 1994

The Problem of mechanical vibration is investigated for an automotive exhaust system. The vibrational reduction effect is systematically evaluated according to the attachment of the exhaust system. Moreover, the optimal attachment position of bellows is determined from the viewpoint of vibration isolation. The structure is analysed by the finite element technique where the geometry, the mass, the stiffness and the damping properties of the exhaust pipe are modeled. The validity of the developed model is verified by comparing with the experimental results. An optimization is carried out by the quadratic approximation algorithm. The reaction transferred to an automobile body by the hanger is considered ad the objective function. It is shown that the exhaust system which has the bellows at the optimal position is more effective for the vibrational characteristics than the others. It is also proved that this analytical method is quite useful in the design stage of the exhaust system.