• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.64-66
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• 1995

This paper presents a new approach to evaluate optimal basic reliability indices of electric distribution systems using genetic algorithm. The use of optimal reliability evaluation is an important aspect of distribution system planning and operation to determine adequacy reliability level of each area. In this paper, the reliability model is based on the analytical method, connecting component failure to load point outage in each section. The proposed method applies genetic algorithm to calculate the optimal values of basic reliability indices, ie. failure rate and repair time, for a load point in the power distribution system, subject to minimizing interruption cost. Test results for the model system are reported in the paper compared with a direct optimization method(gradient projection).

• Journal of computational fluids engineering
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• v.10 no.2
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• pp.54-59
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• 2005

For the improvement of aerodynamic performance of the turbine blade in a turbopump for the liquid rocket engine, the optimization of turbine profile shape has been studied. The turbine in a turbopump in this study is a partial admission of impulse type, which has twelve nozzles and supersonic inflow. Due to the separated nozzles and supersonic expansion, the flow field becomes complicate and shows oblique shocks and flow separation. To increase the blade power, redesign ol the blade shape using CFD and optimization methods was attempted. The turbine cascade shape was represented by four design parameters. For optimization, a genetic algorithm based upon non-gradient search hue been selected as an optimizer. As a result, the final blade has about 4 percent more blade power than the initial shape.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.61-68
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• 2002

This paper presents a new matching algorithm for fingerprint recognition, which is robust to image translation. The basic idea of this paper is to estimate the translation vector of an imput fingerprint image using N minutiae at which the gradient of the ridge direction field is large. Using the estimated translation vector we select minutiae irrelevant to the translation. We experimentally prove that the presented algorithm results in good performance even if there are large translation and pseudo-minutiae.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.182-182
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• 2000

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion. The trajectory trucking experiment of the polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding model controller provides reliable tracking performance during the polishing process.

• Journal of Astronomy and Space Sciences
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• v.30 no.1
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• pp.11-15
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• 2013

A collision-free formation reconfiguration trajectory subject to the linearized Hill's dynamics of relative motion is analytically developed by extending an algorithm for gravity-free space. Based on the initial solution without collision avoidance constraints, the final solution to minimize the designated performance index and avoid collision is found, based on a gradient method. Simple simulations confirm that satellites reconfigure their positions along the safe trajectories, while trying to spend minimum energies. The algorithm is applicable to wide range of formation flying under the Hill's dynamics.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.129-132
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• 1987

This paper presents an emergency control algorithms for viability crisis following abnormal condition as well as a sudden major supply outage and line outage. The algorithm considers the effect of voltage-reactive power control for determining the load shedding quantities and generation reallocation. The problem is decomposed into a P-problem and a Q-problem. The former minimizes system frequency deviation from nominal value and the latter minimizes voltages violation of load buses. The optimization problem is solved by a reduced gradient technique which can handle a great number of inequality constraints very efficiently. It has been found that the use of the proposed algorithm for 6-Bus system restore the abnormal system during the viability crisis to the normal state.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.11
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• pp.619-625
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• 2003

We proposed an optimum PID controller design method of the Smith Predictor It can be applied to various processes. The real process is approximated via the second order plus time delay model (SOPTD) whose parameters are specified through a model reduction algorithm. We already proposed a new model reduction method that considered four point in the Nyquist curve to reduced the steady state error between the real process model and the reduced model using the gradient decent method and the genetic algorithms. In addition, the Smith predictor is used to compensate time delay of the real process model. In this paper, the new optimum parameter tuning algorithm for PID controller of the Smith Predictor is proposed through ITAE as performance index. The Simulation results show the validity and improvement of performance for various processes.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.623-625
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• 1998

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.124-129
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• 2007

For seismic imaging and inversion, the inverted image depends on how we define the objective function. ${\ell}^1$-norm is more robust than ${\ell}^2$-norm. However, it is difficult to apply the Newton-type algorithm directly because the partial derivative for ${\ell^1$-norm has a singularity. In our paper, to overcome the difficulties of singularities, Huber function given by hybrid ${\ell}^1/{\ell}^2$-norm is used. We tested the robustness of our new object function with several noisy data set. Numerical results show that the new objective function is more robust to band limited spiky noise than the conventional object function.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.329-334
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• 1997

A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI(Korea Superconducting Tokamak Advanced Research Neutral Beam Injector) which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography.