• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2114-2116
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• 2002

본 논문은 개선된 Complex 방법을 이용한 하이브리드 퍼지 PID 제어기의 최적 자동동조 알고리즘을 제안한다. 제어응답은 퍼지제어기의 환산계수 값에 의해 여러 종류, 여러 형태로 변화하기 때문에 해당하는 제어계의 평가 기준을 만족하도록 제어 파라미터 값을 정하는 것이 중요하다. PID 파라미터 조정법에는 많은 방법이 제안되어 왔었다. 대표적인 예로서 Ziegler-Nichols, Cohen-Coon, Chien-Hrones-Reswick(CHR) 등에 의해 제안된 방법들이 있다. 본 논문에서는 개선된 Complex 방법을 이용한 강력한 자동동조 알고리즘이 하이브리드 퍼지 PID 제어기의 성능을 자동적으로 개선하기 위해 사용된다. 이 알고리즘은 하이브리드 퍼지 PID 파라미터와 환산계수를 제어출력 변화율과 제한조건에 따라 자동으로 추정한다. 지연시간을 갖는 1계, 2계 공정에 적용하고. 공정출력 기준치는 단위 입력으로 한다. 제어 결과의 성능평가를 위해 ITAE(Integral of Time multiplied by the Absolute value of Error)가 사용되며, 또한 제어기의 오버슈트도 토의된다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.3
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• pp.11-15
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• 1985

A Novel design of MRAC for achieving independent tracking and regulation objectives by applying Pad e-Type approximation in the controller parameter estimation is presented. The design of the controller is done with the unknown plant parameters from the first. The result of the design is a simple control scheme with the reduction of estimation para-meters. The performance of rho proprosed contiol structure in tracking and regulation is compared with the other method by computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1326-1331
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• 2006

Weld root gap is a important fact of a falling-off weld quality in various kind of weld defect. The welding quality can be controlled by monitoring important parameters, such as, the Arc voltage, welding current and welding speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using neural networks for detecting and monitoring of weld root gap and bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and detect the welding defects.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.4
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• pp.223-231
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• 2022

Residual stress that can occur during the metal additive manufacturing process is an important factor that must be properly controlled for the precise production of metal parts through 3D printing. Therefore, in this study, the factors affecting these residual stresses were investigated using an experimental method. For the experiment, a specimen was manufactured through an additive manufacturing process, and the amount of deformation was measured by cutting it. By appropriately calibrating the measured data using methods such as curve fitting, it was possible to quantitatively analyze the effect of process parameters and metal powder reuse on deformation due to residual stress. From this result, it was confirmed that the factor that has the greatest influence on the magnitude of deformation due to residual stress in the metal additive manufacturing process is whether the metal powder is reused. In addition, it was confirmed that process parameters such as laser pattern and laser scan angle can also affect the deformation.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.150-151
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• 2019

리튬 이온 배터리가 전기 자동차 및 다양한 어플리케이션에 적용됨에 따라 폐배터리의 수요 또한 증가하고 있다. 내부 화학적 상태가 상이한 배터리의 전기적 특성실험을 통해 파라미터를 선정할 수 있으며 전기적 특성 실험 전 후의 시간차에 따른 파라미터 변화를 반영하는 것이 필수적이다. 제조 공정과정의 파라미터의 측정값과 특성실험 후의 파라미터 재측정값을 비교함으로써 이를 3-D Kmeans Clustering 알고리즘에 반영하여 더욱 정밀한 셀 선별을 실시하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.71-74
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• 2002

본 논문에서는 유전알고리즘(GA)을 이용한 PID계수 자동조정기법에 관한 DC 서보 모터 속도 제어기 설계를 목적으로 한다. DC 서보 모터는 수많은 제어용 기계나 로봇 등의 응용분야에 사용되고 있고 이러한 분야에서 제어기 파라미터들의 선택이 사용자의 전문적인 지식을 요구하게 된다. 따라서 일반적인 공정 기술자들은 시행착오에 의해 제어기 파라미터들을 계속적으로 반복 조절해 나가야 한다. 이와 같이 동적 시스템의 변화나 외란에 대하여 파라미터 계수를 자동 조정해야 할 경우 유전알고리즘을 사용함으로써 보다 정밀하고 최적화된 파라미터 계수값을 추종함에 따라 그 성능을 높일 수 있다. 본 논문에서는 DC 모터의 동특성을 분석하여 얻은 동특성 모델링으로부터 응답 특성이 빠르고 속도 정밀도가 향상된 구동제어가 가능한 제어기를 설계하고 이를 PID제어기와 비교 평가하였다.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.363-370
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• 2002

A substrate network model characterizing substrate effect of submicron MOS transistors for RF operation and its parameter extraction with physically meaningful values are presented. The proposed substrate network model includes a single resistance and inductance originated from ring-type substrate contacts around active devices. Model parameters are extracted from S-parameter data measured from common-bulk configured MOS transistors with floating gate and use where needed with out any optimization. The proposed modeling technique has been applied to various-sized MOS transistors. Excellent agreement the measurement data and the simulation results using extracted substrate network model up to 30GHz.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.363-370
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• 2002

A substrate network model characterizing substrate effect of submicron MOS transistors for RF operation and its parameter extraction with physically meaningful values are presented. The proposed substrate network model includes a single resistance and inductance originated from ring-type substrate contacts around active devices. Model parameters are extracted from S-parameter data measured from common-bulk configured MOS transistors with floating gate and use where needed with out any optimization. The proposed modeling technique has been applied to various-sized MOS transistors. Excellent agreement the measurement data and the simulation results using extracted substrate network model up to 30㎓

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.62-67
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• 2011

Grinding process is executed in the final machining stage to meet the quality requirements. In generally the ground surface of workpiece is affected by dressing condition as well as grinding condition. In order to estimate the roughness of workpiece, the several roughness models have been researched. These models defined the specific parameters and considered the several parameters which affect to roughness as multiply relationship among them. However, the multiply relationship among parameters is not enough to show the complicated grinding mechanism. Therefore, the neural network algorithm is used in this paper to predict the ground roughness for the plunge grinding. The proposed structure is composed of the initial roughness as well as final roughness model. The input parameters of proposed neural network are referred with the existing roughness model's. The performance of the proposed model is verified through experiments.