• The Korean Journal of Pain
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• 제8권1호
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• pp.152-155
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• 1995

Meralgia paresthetica is a disorder characterized by a pain or dysaesthesia, or both, in the anterolateral aspect of the thigh caused by entrapment or neurinoma formation of the lateral femoral cutaneous nerve. Currently available modes of therapy include conservative treatment, lateral femoral cutaneous nerve block with steroids and local anesthetics, and surgery. At our neuro-pain clinic, w recently encountered three cases of meralgia paresthetica, all of which were treated by lateral femoral cutaneous nerve block. In which of them, two cases were successfully treated but one case was associate with pain relapse due to entrapment of lateral femoral cutaneous nerve by a retroperitoneal mass, schwannoma. In this paper we report our experience along with a review of the current literatures.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2879-2881
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• 1999

This paper presents the adaptive Active Noise Control(ANC) system using the Neuro-Fuzzy controller. In general, the character of noise is time-varing and nonlinear Thus controller must have the adaptivness so that applied in Active Noise Control system to cancel the noise. This paper propose the Neuro-Fuzzy controller trained with back-propagation teaming algorithm to optimize the parameters of controller The objects of this paper are cancel the noise, extract the original(speech) signal polluted by noise and design the Neuro-Fuzzy controller.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.262-266
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• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.

• The Korean Journal of Pain
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• 제7권1호
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• pp.65-68
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• 1994

연세대학교 원주의과대학 신경통증과에 의뢰된 5명의 수근관 증후군 환자에게 비수술적인 요법인 정중 신경 차단과 성상 신경절 차단을 시행하여 5예중 4예에서 치료효과가 20개월 이상 지속되는 우수한 성적을 얻었기에 문헌적 고찰과 함께 보고하는 바이다.

• 전기학회논문지P
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• 제56권3호
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• pp.129-135
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• 2007

This paper deals with speed-sensorless control of DC servo motor using Neuro-Fuzzy Observer. DC servo motor has very low rotor inertia and excellent response characteristic and it is very useful to control torque and speed. It is easy to detect the voltage and current and resolver or encoder is used to measure a rotor speed. But it has a limit as a driving speed to detect speed precisely. So it is problem to improve the performance of the driving system. To solve this problem, it is studied to detect a speed of DC servo motor without sensor. In particular, study on the method to estimate the speed using the observer is performed a lot. In this paper, the gain of the observer is properly set up using the Neuro-Fuzzy control and Neuro-Fuzzy Observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. It calculates the differentiation of the rotor current directly using the rotor current measured in the DC servo motor and estimates the speed of the rotor using the differentiation. Proposed speed sensorless control method is performed using the estimated speed. Also, it is proved feasibility of the proposed observer from the comparison tested a case with a speed sensor and a case without a speed sensor which used a highly efficient drive and 200[w] DC servo motor starting system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.474-476
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• 1998

In this paper, a multi variable neuro-fuzzy controller for a boiler-turbine system is designed. Two architectures are used. The first consists of boiler-turbine system identification and the second is designing a controller. A generalized backpropagation algorithm is developed and used to train the neuro-fuzzy controller. Designed controller is good tracking property and rejects the input and output disturbances. The results of the proposed design method is verified through simulation.

• Composites Research
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• 제14권1호
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• pp.39-46
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• 2001

본 논문에서는 신경망 제어기를 이용하여 복합재 보의 적응 다중 모드 진동 제어에 관한 실험적 연구를 수행하였다. 신경망 제어기는 계산량이 많기 때문에 실시간 적용에 어려움이 따른다. 본 논문에서는 진동 신호를 모드별로 분리하기 위한 적응 노치 필터를 제안하였다. 연결 강도의 개수가 적어서 계산량이 적은 두 개의 신경망 제어기를 이용하여 각 모드의 제어력을 계산하였다. 끝단 질량의 위치의 차이로 인해 고유 진동수가 다른 두 시편 A, B에 대하여 적응 노치 필터와 신경망 제어기를 이용한 적응 진동 제어를 수행한 결과, 두 경우 모두 효과적으로 진동 제어가 이루어졌다. 이러한 결과로 시스템 파라미터의 변환에 대한 신경망 제어기의 적응 진동 제어 성능을 확인할 수 있다.