• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.75-82
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• 1985

The fuel pump system is developed with the Model Reference Adaptive Control (M.R.A.C.) algorithm based on the Weight Least Square (W.L.S.) algorithm for the parameter Identification and the one step ahead dead-beat control with the reference model. The value of some parameters as the sampling period 7, the weighting coefficient L, and the State Variable Filter (5.V,F.) coefficient f which a(fects the system performance are selected through computer simulation. For the variation of the plante dynamics rspecially due to the change of the fuel viscosity with the ambient temperature condition, the adaptability of the control system is studied in the case of regulation and tracking.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.5
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• pp.605-612
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• 1994

The methods to determine the hydrodynamic coefficients for the fixed type artificial reefs which were constructed to control ecological system in coastal waters are compared and discussed by model test results. To calculate the wave forces, least square method show good agreement with the experimental results and more stability than maximum force component method or Fourier decomposition method. This modified least square method of weighting the square of measured force turned out to be the most feasible method for maximum force. Using the feasible method, hydrodynamic characteristics for artificial reefs on uniform slopes offshore and breaking zone were studied. They were properly related to Keulegan-Carpenter's number and found larger than previous results. Wave force coefficients for artificial reefs around breaking zone were distributed from 1.5 to 2.5, and the mean value was 2.0. Drag force components were more in evidence than inertia force in maximum force which is important parameter to evaluate stability for high-permeability structures. A formula for the calculation of the maximum force for artificial reefs design is proposed, using structural dimension, water particle velocity and Keulegan-Carpenter's number.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• Journal of Korean Port Research
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• v.14 no.2
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• pp.187-197
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.301-309
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• 2019

A methodology to assess seismic fragility of a nuclear power plant (NPP) using a conditional mean spectrum is proposed as an alternative to using a uniform hazard response spectrum. Rather than the single-scenario conditional mean spectrum, which is the conventional conditional mean spectrum based on a single scenario, a multi-scenario conditional mean spectrum is proposed for the case in which no single scenario is dominant. The multi-scenario conditional mean spectrum is defined as the weighted average of different conditional mean spectra, each one of which corresponds to an individual scenario. The weighting factors for scenarios are obtained from a deaggregation of seismic hazards. As a validation example, a seismic fragility assessment of an NPP containment structure is performed using a uniform hazard response spectrum and different single-scenario conditional mean spectra and multi-scenario conditional mean spectra. In the example, the number of scenarios primarily influences the median capacity of the evaluated structure. Meanwhile, the control frequency, a key parameter of a conditional mean spectrum, plays an important role in reducing logarithmic standard deviation of the corresponding fragility curves and corresponding high confidence of low probability of failure (HCLPF) capacity.

• Structural Engineering and Mechanics
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• v.10 no.6
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• pp.517-532
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• 2000

A direct output feedback control scheme was recently proposed by the authors for single-story building structures resting on flexible soil body. In this paper, the control scheme is extended to mitigate the seismic responses of multi-story buildings. Soil-structure interaction is taken into account in two parts: input at the soil-structure interface/foundation and control algorithm. The former reflects the effect on ground motions and is monitored in real time with accelerometers at foundation. The latter includes the effect on the dynamic characteristics of structures, which is formulated by modifying the classical linear quadratic regulator based on the fundamental mode shape of the soil-structure system. Numerical result on the study of a $\frac{1}{4}$-scale three-story structure, supported by a viscoelastic half-space of soil mass, have demonstrated that the proposed algorithm is robust and very effective in suppressing the earthquake-induced vibration in building structures even supported on a flexible soil mass. Parametric studies are performed to understand how soil damping and flexibility affect the effectiveness of active tendon control. The selection of weighting matrix and effect of soil property uncertainty are investigated in detail for practical applications.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.383-391
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• 2010

The groundwater recharge was assessed by using both SWAT and HELP models in Bocheong-cheon watershed. The SWAT model is a comprehensive surface and subsurface model, but it lacks the physical basis for simulating a soil water percolation process. The HELP model which has a drawback in simulating subsurface lateral flow and groundwater flow component can simulate soil water percolation process by considering the unsaturated flow effect of soil layers. The SWAT model has been successfully applied for estimating groundwater recharge in a number of watersheds in Korea, while the application of HELP model has been very limited. The subsurface lateral flow parameter was proposed in order to consider the subsurface lateral flow effect in HELP model and the groundwater recharge was simulated by the modified exponential decay weighting function in HELP model. The simulation results indicate that the recharge of HELP model significantly depends on the values of lateral flow parameter. The recharge errors between SWAT and HELP are the smallest when the lateral flow parameter is about 0.6 and the recharge rates between two models are shown to be reasonably comparable for daily, monthly, and yearly time scales. The HELP model is useful for estimating groundwater recharge at watershed scale because the model structure and input parameters of HELP model are simpler than that of SWAT model. The accuracy of assessing the groundwater recharge might be improved by the concurrent application of SWAT model and HELP model.