• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2123-2125
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• 2001

In thermal power plant, the efficiency of a combined power plant with a gas turbine increases, exceeding 50%, while the efficiency of traditional steam turbine plants is approximately 35% to 40%. Up to the present time, the PID controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain without any experience, since the gain of the PID controller has to be manually tuned by trial and error procedures. This paper focuses on the neural network tuning of the 2-DOF PID controller with a separated 2-DOF parameter (NN-Tuning 2-DOF PID controller), for optimal control of the Gun-san gas turbine generating plant in Seoul. Korea. In order to attain optimal control, transfer function and operating data from start-up, running, and stop procedures of the Gun-san gas turbine have been acquired, and a designed controller has been applied to this system. The results of the NN-Tuning 2-DOF PID are compared with the PID controller and the conventional 2-DOF PID controller tuned by the Ziegler-Nichols method through experimentation. The experimental results of the NN-Tuning 2-DOF PID controller represent a more satisfactory response than those of the previously-mentioned two controller.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.399-400
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• 2020

본 논문은 Sensored BLDC 모터의 제어방법을 설명하고, 효율적인 최적 제어를 하기위해 Lead Angle 제어를 하는 방법에 대하여 설명하였다. Lead Angle 값을 실시간 Auto Tuning 하는 기법을 설계 및 구현하였다. 실제 Microchip 사의 DSC를 사용하여 Software로 동작을 구현하였으며, PWM 스위칭 하기 위한 반도체 소자는 IGBT 모듈인 IPM을 사용하여 BLDC 모터의 구동회로인 인버터를 구성하였다. 실제 사용하는 400W급 BLDC 모터를 사용하여 Lead Angle 제어와 Auto Tuning 실험결과를 비교 검증하였다.

• Journal of IKEEE
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• v.27 no.3
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• pp.313-318
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• 2023

This paper proposes a neural network applying fine-tuning as a way to improve the performance of Video Classification based on Vision Transformer. Recently, the need for real-time video image analysis based on deep learning has emerged. Due to the characteristics of the existing CNN model used in Image Classification, it is difficult to analyze the association of consecutive frames. We want to find and solve the optimal model by comparing and analyzing the Vision Transformer and Non-local neural network models with the Attention mechanism. In addition, we propose an optimal fine-tuning neural network model by applying various methods of fine-tuning as a transfer learning method. The experiment trained the model with the UCF101 dataset and then verified the performance of the model by applying a transfer learning method to the UTA-RLDD dataset.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.351-354
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• 2001

In this paper, we present self-tuning control of switched reluctance motor for maximum torque with phase current and shaft position sensor. Determination method of turn-on/off angle is realized by using self-tuning control method. During the sampling time, micro-controller checks the number of pulse from encoder and compare with the number of pre-checked pulse. After micro-controller calculates between two data, it moves forward or backward turn-off angle. When the turn-off angle is fixed optimal turn-off angle, the turn-on angle automatically moves forward or backward by a step using self-tuning control method. And then, optimal turn-off angle is searched once again. As such a repeating process, turn-on/off angle is moved automatically to obtain the maximum torque. The experimental results are presented to validate the self-tuning algorithm.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.3
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• pp.40-46
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• 2008

This paper presents the optimal motion control for 3-axis SCARA robot by using $LabVIEW^{(R)}$. Specifically, for optimal motion control of 3-axis SCARA robot, we study velocity profile based on finite jerk(the first derivative of acceleration) and optimal gain tunig based on frequency response method by using $LabVIEW^{(R)}$. The velocity optimization with finite jerk aims at generating the smooth velocity profile of robot. Velocity profile based on finite jerk is acquired and applied to 3-axis SCARA robot by using $LabVIEW^{(R)}$. DSA(Dynamic Signal Analyzer) for frequency response method is programed by using $LabVIEW^{(R)}$. We obtain the bode plot of transfer function about 3-axis SCARA robot by using DSA, and perform the gain tuning considering dynamic characteristic based on the bode plot. These experiments have shown that the proposed motion control can reduce vibration displacement and response error rate each 33.7% and 51.7% of 3-axis SCARA robot.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.401-411
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• 2003

Important results about two-degree-of-freedom PID controllers are surveyed for the tutorial purpose, including equivalent transformations, various explanations about the effect of the two-degree-of-freedom structure, relation to the preceded-derivative PID and the I-PD controllers, and an optimal tuning method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1008-1011
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• 1996

Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.4
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• pp.448-454
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• 2007

The new tuning rules of the PID controller for the rejection of load disturbance are proposed incorporating with real-coded genetic algorithms (RCGAs). The optimal gain parameters of the PID controller for a first-order plus time delay model are obtained based on a RCGA. Then tuning formula are derived using the tuned parameters sets potential tuning rule models and another RCGA. The performance criteria of the controller are adopted as ISE, IAE and ITAE. A series of simulation are carried out to verify the effectiveness of the proposed tuning rules.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.186-189
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• 2003

This paper presents an optimal tuning method of the decentralized PI controller of two-input, two-output(TITO) second order system to be formulated as LQR. The tuning method of proposed decentralized PI controller is developed by establishing the relationships between the closed-loop state equation including decentralized PI control factors and the closed-loop state equation of LQR, which can be guaranteed the performance and stability-robustness by selecting the weighting factors Q and R of the cost function in order to satisfy design specifications in frequency domain.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.321-322
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• 2008

Co-optimization of Gen. Speed Governor & AGC are essential to control proper Frequency control & Economic Dispatch. Improper AGC Control result in the decrease of Electricity frequency quality & Generator Life cycle by over-regulating each Generator. This paper presents a number of AGC Area/PLC parameter tuning technique & better performance results. This optimal tuning was studied & implemented by System Operation Dept Korea Power Exchange in 2007.