• 한국군사과학기술학회지
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• 제10권1호
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• pp.78-85
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• 2007

A gun is still one of the major weapons of a combat ship. To assess the ship's fire control capability which is influenced by tracking system, fire control algorithm, gun, the ship itself, target behavior, environment and engagement situation, simulation system for gun-oriented engagement for surface ship is needed. This paper proposes the process for designing and implementing a gun-oriented engagement simulation system using DEVS(Discrete Event Simulation Specification), which is a formalism based on the set theory. It consists of the following activities : 1) analyzing the characteristics of a gun-oriented engagement, 2) constructing the deterministic model of the combat ship of study with DEVS, 3) modeling properties of each entity showing as stochastic errors. With this process, the gun-oriented engagement simulation system is developed and applied for the combat system under development.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2004년도 춘계 학술대회논문집
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• pp.9-16
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• 2004

This study presents results of aerodynamic wing optimization under uncertainties. To consider uncertainties, an alternative strategy for reliability-based design optimization(RBDO) is developed. The strategy utilizes a single loop algorithm and a sequential approximation optimization(SAO) technique. The SAO strategy relies on the trust region-SQP framework which validates approximated functions at every iteration. Further improvement in computational efficiency is achieved by applying the same sensitivity of limit state functions in the reliability analysis and in the equivalent deterministic constraint calculation. The framework is examined by solving an analytical test problem to show that the proposed framework has the computational efficiency over existing methods. The proposed strategy enables exploiting the RBDO technique in aerodynamic design. For the aerodynamic wing design problem, the solution converges to the reliable point satisfying the probabilistic constraints.

• 한국경영과학회지
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• 제28권3호
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• pp.49-60
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• 2003

In this paper, we study the joint design of sampling inspections and service capacities for multi-products. Products of different defect rates which are either deterministic or random variables are supplied in batches after sampling inspection and rework. When supplied, all defective products that have not been inspected in batches are uncovered through total inspection and returned to service. We identify the optimal inspection policies and service capacities for multi-products reflecting the relationships between inspection rework costs and service provision costs. We also develope a marginal allocation algorithm for the optimal allocation of the limited total service capacity to products as well as inspection quantities.

• Industrial Engineering and Management Systems
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• 제12권1호
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• pp.30-35
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• 2013

Motivated by the emergency scheduling in a transportation network, this paper considers a transportation problem, in which, the truck times and transportation costs are assumed as uncertain variables. To meet the demand in the practical applications, two optimization objectives are considered, one is the total costs and another is the completion times. And then, a multi-objective optimization model is developed according to the situation in applications. Because there are commensurability and conflicting between the two objectives commonly, a solution does not necessarily exist that is best with respective to the two objectives. Therefore, the problem is reduced to a single objective model, which is an uncertain programming with a chance-constrain. After some analysis, its equivalent deterministic form is obtained, which is a nonlinear programming. Based on a stepwise optimization strategy, a solution method is developed to solve the problem. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• 산업공학
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• 제18권1호
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• pp.63-72
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• 2005

In this paper, we present a distribution planning method for a supply chain. Like a typical distribution network of manufacturing firms, we have the form of arborescence. To consider more realistic situation, we investigated that an outside supplier has limited capacity. The customer demands are given in deterministic form in finite number of discrete time periods. In this environment, we attempt to minimize the total costs, which is the sum of inventory holding and backorder costs over the distribution network during the planning horizon. To make the best of the restricted capacity, we propose the look-ahead feature. For looking ahead, we convert this problem into a single machine scheduling problem and utilize tabu search approach to solve it. Numerous simulation tests have shown that the proposed algorithm performs quite well.

• 한국경영과학회지
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• 제24권3호
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• pp.83-91
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• 1999

In order to build and manage an ATM network effectively under several types of control methods, it is necessary to estimate the performance of the equipments in various viewpoints, especially of ATM multiplexer. As for the method to model the input stream into the ATM multiplexer, many researches have been done to characterize it by, such as, fluid flow, MMPP(Markov Modulated Poisson Process), or MMDP (Markov Modulated Deterministic Process). We introduce an MRP(Markov Renewal Process) to model the input stream which has proper structure to represent the burst traffic with high correlation. In this paper, we build a model for aggregated heterogeneous ON-OFF sources of ATM traffic by MRP. We make discrete time MR/D/1/B queueing system, whose input process is the superposed MRP and present a performance analysis by finding CLP(Cell Loss Probability). A simulation is done to validate our algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.116-120
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• 1989

The optimal control problem of linear Lumped Parameter Systems (LPS) and Distributed Parameter Systems (DPS) is studied by employing the technique of Walsh functions (WF). By the using the elegant operational properties of WF, a direct computational algorithm for evaluating the optimal control and trajectory of LPS and DPS is developed. Without the need of solving the traditional matrix Riccati equation, the WF approach in shown very simple in form and convenient for use of a computer. The approximation is in the sense of least squares employing WF as the basis and the results are in the piecewise constant and discrete form.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• 제어로봇시스템학회논문지
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• 제10권1호
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• pp.10-14
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• 2004

We present a predictive control algorithm combined with the robust robot control that is constructed on the Lyapunov min-max approach. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about the model, it is an important trend to design a robust control law that guarantees the desired properties of the manipulator under uncertain elements. In the preceding robust control work, we need to tune several control parameters in the admissible set where the desired stability can be achieved. By introducing an optimal predictive control technique in robust control we can find out much more deterministic controller for both the stability and the performance of manipulators. A new class of robust control combined with an optimal predictive control is constructed. We apply it to a simple type of 2-link robot manipulator and show that a desired performance can be achieved through the computer simulation.

• 전자공학회논문지B
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• 제30B권8호
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.