• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.4
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• pp.287-295
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• 2022

As low altitude long-range artillery threat has been strengthened, the development of anti-artillery interception system to protect assets against its attacks will be kicked off. We view the defense of long-range artillery attacks as a typical dynamic weapon target assignment (DWTA) problem. DWTA is a sequential decision process in which decision making under future uncertain attacks affects the subsequent decision processes and its results. These are typical characteristics of Markov decision process (MDP) model. We formulate the problem as a MDP model to examine the assignment policy for the defender. The proximity of the capital of South Korea to North Korea border limits the computation time for its solution to a few second. Within the allowed time interval, it is impossible to compute the exact optimal solution. We apply approximate dynamic programming (ADP) approach to check if ADP approach solve the MDP model within processing time limit. We employ Shoot-Shoot-Look policy as a baseline strategy and compare it with ADP approach for three scenarios. Simulation results show that ADP approach provide better solution than the baseline strategy.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.65-78
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• 2013

Circular shareholding is established when at least three member firms in a business group hold stock in other member firms and form a series of ownership in a circular way. Although there have been many studies which investigated a negative effect of circular shareholding on firm's value, few studies have discussed how to resolve the problem given complicated ownership structures of large business groups. This paper is based on a mixed integer programming model, which was proposed in the author's previous research and can identify the ownership share divested in order to resolve circular shareholding. Since the optimization model becomes too complicated for large business groups and requires a sophisticated software to solve it, we propose a simple heuristic method that can find a good approximate solution to the model. Its applications to twelve Korean large business groups show that the heuristic method is not just computationally attractive but also provides near-optimal solutions in most cases.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.8 s.173
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• pp.34-41
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• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1119-1131
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• 2001

In this paper we formulate an optimization based approach to determining the maximally inscribed rectangle in a robots workspace. The size and location of the maximally inscribed rectangle is an effective index for evaluating the size and quality of a robots workspace. Such information is useful for, e. g., optimal worktable placement, and the placement of cooperating robots. For general robot workspaces we show how the problem can be formulated as a constrained nonlinear optimization problem possessing a special structure, to which standard numerical algorithms can be applied. Key to the rapid convergence of these algorithms is the choice of a starting point; in this paper we develop an efficient computational geometric algorithm for rapidly obtaining an approximate solution suitable as an initial starting point. We also develop an improved version of the algorithm of Haug et al. for calculating a robots workspace boundary.

• Journal of Korean Society of Transportation
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• v.5 no.1
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• pp.3-23
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• 1987

Traffic signal control problems in urban street networks are formulated in two ways. In the formulations network flows are assumed to satisfy the user route choice criterion. the first formulation which is called implicit substiuation incorporates user route behavior implicitly in the objective function by recognizing the dependence of the link flows on the signal variables. On the other hands, the second one which is called 'penalty formulation' consists in expressing the route choice conditions in the form of a single nonlinear constraint. Approximate solution algorithm for each of the formulations was investigated in detail and computer codes were written to examine key aspects of each algorithm. A test was done on a network which is small in size but sufficiently complex in representing real-world traffic conditions and the test result shows that both algorithms produce converged solutions. It is recommended, however, that further studies should be done in order to compare the performance of each algorithm more in depth.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.490-491
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• 2004

Recently, sectionalizing switches have been coming to be operated by remote control through the distribution SCADA system. However, the problem of determining the optimal switching sequence is a combinatorial optimization problem, and is quite difficult to solve. Hence, it is imperative to develop practically applicable solution algorithms for this problem. Several efficient algorithms have been developed for finding approximate solutions to such problems. these algorithms create a new arbitral distribution system configuration from an initial configuration, and some of these algorithms do not show a load transfer sequence to reach the objective system.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1303-1311
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• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.2
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• pp.76-107
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• 2022

In this paper, we study the temporal decay of global weak solutions to the inhomogeneous Hall-magnetohydrodynamic (Hall-MHD) equations. First, an approximation problem and its weak solutions are obtained via the Caffarelli-Kohn-Nirenberg retarded mollification technique. Then, we prove that the approximate solutions satisfy uniform decay estimates. Finally, using the weak convergence method, we construct weak solutions with optimal decay rates to the inhomogeneous Hall-MHD equations.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.207-223
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• 1996

These days, customers have attached great importance to the function of product liability and quality assurance. In Korea, the single rectifying sampling inspection for attribute (KS A 3105) has been used. But this inspection plan given by tables (KS A 3105) has some defects. There are limitations in the range of applications and irrationality of approximate probability and the double rectifying sampling inspection is not mentioned. Moreover, ATI (average total inspection) does not reflect sampling costs and the loss of nonconforming item. Therefore, the objectives of this study is to develope new algorithms and computer program that provide the optimal sampling inspection plan based on minimum linear costs (single & double inspection plan). The result of this study revealed that the new algorithm is less than KS A 3105 in ATI and basically, double inspection plan is more economical. Also it comes over restrictions in KS A 3105. So, it is definite that the optimal solution can be obtained considering cost factors in manufacturing and sampling process, and costs can be saved in the long term.

• Bulletin of the Korean Mathematical Society
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• v.48 no.4
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• pp.791-812
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• 2011

This is a survey on American options. An American option allows its owner the privilege of early exercise, whereas a European option can be exercised only at expiration. Because of this early exercise privilege American option pricing involves an optimal stopping problem; the price of an American option is given as a free boundary value problem associated with a Black-Scholes type partial differential equation. Up until now there is no simple closed-form solution to the problem, but there have been a variety of approaches which contribute to the understanding of the properties of the price and the early exercise boundary. These approaches typically provide numerical or approximate analytic methods to find the price and the boundary. Topics included in this survey are early approaches(trees, finite difference schemes, and quasi-analytic methods), an analytic method of lines and randomization, a homotopy method, analytic approximation of early exercise boundaries, Monte Carlo methods, and relatively recent topics such as model uncertainty, backward stochastic differential equations, and real options. We also provide open problems whose answers are expected to contribute to American option pricing.