• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.897-899
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• 1999

This paper presents a scaling factor tuning method to improve the performance of fuzzy logic controller. Tuning rules and reasoning are utilized off-line to determine the scaling factors based on absolute value of the error and its difference. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a dc-servo motor control system. The performance of this control system is demonstrated higher than a conventional fuzzy logic controller(FLC).

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.782-784
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• 1999

This paper deals with speed control of DC-servo motor using a Back-Propagation(BP) Learning Algorism and a PID controller Conventionally in the industrial control, PID controller has been used. But the PID controller produced suitable parameter of each system and also variable of PID controller should be changed enviroment, disturbance, load. So this paper revealed for experimental, a neural network and a PID controller combined system using developed speed characters of a Variable Load DC-servo motor. The parameters of the plant are determined by neural network perform on on-line system after training the neural network on off-line system.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.170-173
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• 2005

This paper presents an Fuzzy-Neuro Observer system for an ac servo motor dirve to track periodic commands using a neuro-fuzzy observer. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2304-2306
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• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2356-2358
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• 2004

This thesis aims to investigate new approaches to the control strategies of human arm movements and its application for the human interface. By analyzing myoelectric signal(MES) from the arm movements of the normal human subjects, neurological informations obtained patterned could be used to identify different movement patterns of the arm movement. In this paper Artificial neural network for separation of the contraction patterns of four kinds of arm movements, i.e. and flexion and extension of the elbow and adduction and abduction of the forearm were adopted through computer simulation and experiments results were compared with the experimental added-load arm movements.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2362-2364
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• 2004

최근 여러 가지 자동화 서비스를 제공하기 위하여 이동로봇이 스스로 장애들을 감지하여 회피하고 이동경로를 생성할 수 있는 능력을 필요로 하고 있다. 이동 경로상에 장애물을 회피하는 방법에는 여러 가지가 있으나, 본 논문에서는 이동 경로상에 존재할 수 있는 장애들과의 충돌을 회피하여 원하는 목적지에 도달하는 방법에 대하여 연구하였다. 이를 위해 포텐셜 함수방법을 사용하여 장애물과 목표점에 인공적인 포텐셜을 부여하여 이를 실시간 충돌 회피 문제에 적용해 보았다.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.468-472
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• 1992

In this paper, we classified electromyographic(EMG) signal for prothesis control using neural network. For this study fast Fourier transform(FFT) with ensemble averaged spectrum is applied to two-channeI EMG signal for biceps and triceps. We used the three layer network. And a cumulative back-propagation algorithm is used for classification of six arm functions, flexion and extension of elbow and pronation and supination of the forearm and abduction and adduction of wrist.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.14 no.1
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• pp.29-34
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• 2001

구조물설계에 있어서 영향선은 최대반력, 최대전단력, 최대휨모멘트 등을 계산하는데 아주 유용하게 사용된다. 모멘트분배법, 인도행렬법, 전달행렬법, 그리고 Muller-Breslau 원리에 의한 단순보와 연속보의 영향선은 잘 알려져 있고 또 교량공학에서 널리 사용되고 있다. 그러나 변위를 허용하는 특별한 구조물의 영향선을 계산할 경우에는 약간의 어려움이 있다. 이 연구에서는 절점변위를 허용하는 문형라멘의 영향선을 전달행렬법에 의하여 구하고 유한요소법에 의하여 얻은 영향선과 비교하였고 그 결과는 좋은 일치를 보이고 있다.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.473-478
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• 1987

The purpose of this study is to design a phoneme-class recognition system for Korean language using filter-bank analysis and zero crossing rate method. First, the speech signals are separated in 16 bandpass filters to obtain short-time spectrum of speech signals, and digitized by 16-ch A/D converter. And then, with the set of features which extracted from patterns of ratios of each channel energy level to overall energy level, the decision rules are made for recognize unknown speech signal. In this experiment, the recognition rate was about 93.1 percent for 7 vowels under multitalker environment and 74.4 percent for 10 initial sounds at single speaker.