• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.400-406
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• 2010

This paper deals with parameter tuning of the Power System Stabilizer (PSS) for 612 MVA thermal power plants in the KEPCO system and its validation in a field test. In this paper, the selection of parameters, such as lead-lag time constants for phase compensation and system gain, is optimized using linear and eigenvalue analyses. This is then verified through the time-domain transient stability analysis. In the next step, the performance of PSS is finally verified by the generator's on-line field test. After the field test, measured and simulated data are also compared to prove the effectiveness of the models used in the simulations.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2114-2116
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• 2001

Model reduction from high order systems to low order systems in frequency domain is considered four point (${\angle}$G(jw)=0, - ${\pi}/2$, ${\pi}$, and -3${\pi}$/2) instead of two point (${\angle}$G(jw) = - ${\pi}$/2,- ${\pi}$) of existing method in Nyquist curve. The Performances of reduced order model by proposed approach is similar to original model. In this paper, we proposed a new tuning algorithm for PID controller using model reduction in frequency domain. Simulations for some examples with varies dynamic characteristics are provided to show the effectiveness of the proposed tuning algorithm for PID controller using model reduction.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.794-797
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• 2024

Despite advancements in deep learning, existing semantic segmentation models exhibit suboptimal performance under adverse weather conditions, such as fog or rain, whereas they perform well in clear weather conditions. To address this issue, much of the research has focused on making image or feature-level representations weather-independent. However, disentangling the style and content of images remains a challenge. In this work, we propose a novel fine-tuning method, 'freeze-n-update.' We identify a subset of model parameters that are weather-independent and demonstrate that by freezing these parameters and fine-tuning others, segmentation performance can be significantly improved. Experiments on a test dataset confirm both the effectiveness and practicality of our approach.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.83-86
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• 1995

This paper proposes an auto-tuning method of feedforward signal in boiler control of thermal power plants by using the neural network. The neural network produces an optimal feedforward signal by tuning the weights of the network. The weights are adapted effectively by using the teaching signal of PI control output. The proposed method was evaluated based on a detailed simulator which expressed non-linear characteristics of the 600 MW actual thermal power plant at load chaning operations, showed effectiveness in the learning of the weights of the neural network, and gave an accurate control performance in the temperature control of the system. Through the evaluation, the proposed method was proved to be effectively applicable to the actual thermal plants as the automatic adjustment tool.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.191-194
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• 2002

In this paper, a new GA-based methodology with information granules is suggested for construction of the fuzzy classifier. We deal with the selection of the fuzzy region as well as two major classification problems-the feature selection and the pattern classification. The proposed method consists of three steps: the selection of the fuzzy region, the construction of the fuzzy sets, and the tuning of the fuzzy rules. The genetic algorithms (GAs) are applied to the development of the information granules so as to decide the satisfactory fuzzy regions. Finally, the GAs are also applied to the tuning procedure of the fuzzy rules in terms of the management of the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example-the classification of the Iris data, is provided.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2046-2048
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• 2003

In this paper, a neural disturbance observer(NDO) is developed and its application to the control of a nonlinear system with the internal and/or external disturbances is presented. To construct the NDO, a parameter tuning method is proposed and shown to be useful in adjusting the parameters of the NDO. The tuning method employes the disturbance observation error to guarantee that the NDO monitors unknown disturbances. Each of the nodes of the hidden layer in the NDO network is a radial basis function(RBF). In addition, the relationships between the suggested NDO-based control and the conventional adaptive controls reported in the previous literatures are discussed. And it is shown in a rigorous manner that the disturbance observation error converges to a region of which size can be kept arbitrarily small. Finally, an example and some computer simulation results are presented to illustrate the effectiveness and the applicability of the NDO.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.32-39
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• 1997

One of the most important performance criteria in tuning the gain of position loop controller for CNC machining center is the contour error. In this papre we analyze contour error in the linear and circular interpolations for the axis-matched and mismatched cases. To have small contour errors, it is necessary to set the P gain for each axis to be same. And the D gain should be much smaller than the P gain. Baded on the analysis in the frequency domain, we propose a gain tuning method for the P and PD controllers. We show that the PD controller is better than the P controller. The effectiveness of this method is demonstrated by experiments.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.136-138
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• 2005

The conventional self-tuning methods have the speed control problem of nonlinear BLDC motor which can't adapt against any kinds of noise or operation circumstances. In this paper, supposed to solve these problem to PI parameters controller algorithm using ANN. In the proposed algorithm, the parameters of the controller were adjusted to reduce by on-line system the error of the speed of BLDC motor. In this process, EBPA NN was constituted to an output error value of a BLDC motor and conspired an input and output. The performance of the self-tuning controller is compared with that of the PI controller tuned by conventional method(Z&N). The effectiveness of the proposed control method IS verified thought the Matlab Simulink.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.460-465
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• 1994

A high precision position control techinque of Brushless DC(BLDC) motor system with time varying parameters is expressed using the self tuning control method. The time varying parameters is estimated on real time by detecting voltage references from controller and mechanical motor speeds from tacho-generator. The effectiveness of the method is evaluated through the positon control experimental results of a BLDC motor system for reference change and arbitrary disturbance.