• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.537-551
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• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.13-20
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• 2006

In the advanced country, It is forecasting farm prices using intelligence style of farming technique. In our country, It is offering basis research to prevent the prices rising and falling, But, It is impossible that no one can predict exactly for farming price. In this paper to improve forecasting farming price using neural network as a preprocessing. Also, we developed a fuzzy algorithm for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results preyed reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.3
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• pp.79-87
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• 1998

The purpose of this paper is to develope a neurofuzzy system for a ship design which can determine efficiently design values e.g. principal dimensions and hull factors in a conceptual design. The neurofuzzy system for a ship design(NeFHull) applies a information about given input-output data to fuzzy theories and deals these fuzzificated values with neural networks, e.g. first, redefines normalized input-output data ad membership functions and then executes these fuzzficated information with backpropagation neural networks. We use a hybrid learning algorithm in the training of neural networks and examine the usefulness of suggested method through mathematical and mechanical examples.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1975-1976
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• 2006

이족 보행 로봇의 연구는 평지에서의 보행에 관한 연구가 주를 이루고 있다. 현재는 평지뿐만 아니라 비평지, 경사면, 계단 등과 같은 특수한 상황에서의 보행이 연구되고 있지만 대부분 여러 가지 제약조건이 뒤따르고 있다. 그래서 본 논문에서는 퍼지 시스템과 신경망을 이용하여 이러한 제약 조건을 극복하였다. 신경망을 이용하여 이족 보행 로봇의 보행궤적을 생성하였으며, 퍼지 시스템은 로봇의 자세제어를 하는데 이용되었다. 또한 이족 보행 로봇이 로봇 내부 및 외부의 상황을 스스로 인지하기 위해서 자이로, 압력센서, 적외선, 초음파 센서를 이용하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.334-337
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• 2004

차세대 생산 시스템(Next Generation Manufacturing System： NGMS)의 핵심 개념은 분산 생산 시스템과 다품종 소량의 유연 생산 시스템의 지원이다. 이러한 시스템의 구성을 위하여 실시간 데이터에 기반한 예측 모델이 필수적인데, 이러한 예측 기능을 통하여 생산공정의 관리와 운영, 특히 전체 공정관리를 효율적으로 수행할 수 있다. 한편, 공정으로부터 전송된 데이터는 특정한 형태의 지식으로 표현된다. 이러한 지식들은 시스템에 대한 다양한 정보를 가지고 있으므로 정보를 이용하여 시스템 상태를 빠르고 쉽게 진단할 수 있다. 공정 진단은 현재 공정 상태에서 생산되는 제품의 품질을 추정할 수 있는 정보로 활용된다. 본 논문에서는 이러한 개념이 바탕이 되어 공정관리 시스템을 설계하였다. 제안된 시스템의 적용 대상은 반도체 제조 공정의 단위 공정인 에칭 공정이다. 에칭 공정은 공정 중에 연속적인 검사가 수행되지 않고 최종 제품에 대한 검사가 수행되므로 불량 원인을 찾는 것이 쉽지 않다. 따라서 본 논문에서는 공정관리를 위한 의사지원시스템을 통해 공정의 연속적인 간접진단을 수행하고자 하였다. 본 연구에서 사용된 의사지원시스템은 각 공정에서 얻어지는 데이터와 경험적 지식을 토대로 공정시스템의 해석과 진단이 가능한 시스템이다.

• The Journal of Information Technology
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• v.4 no.2
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• pp.117-127
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• 2001

A self-structured neural network algorithm that finds optimal fuzzy membership functions and nile base to fuzzy model is proposed and a fuzzy-neural network controller is designed to get more accurate position and velocity control of wheeled mobile robot. This procedure that is composed of three steps has its own unique process at each step. The elements of output term set are increased at first step and then the rule base Is varied according to increase of the elements. The adjusted controller is in competition with controller which doesn't include any increased elements. The adjusted controller will be removed if the control-law lost. Otherwise, the controller is replaced with the adjusted system. After finished regulation of output term set and rule base, searching for input membership functions is processed with constraints and fine tuning of output membership functions is done.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.331-339
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• 2008

Nowadays with advancement in technology and aging society, the number of disabled citizens is increasing. The disabled citizens always need a caretaker for daily life routines especially for mobility. In future, the need is considered to increase more. To reduce the burden from the disabled, various devices for healthcare are introduced using computer technology. The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path. User has a handheld bio-sensor monitoring system for get user's bio-signal. If user detects unusual signal, alarm send to protector.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.439-447
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• 2011

It is a main concern for sustainable development in water resources management to evaluate adaptation capability of water resources structures under the future climate conditions. This study introduced the Fuzzy Inference System (FIS) to represent the change of release and storage of reservoirs in the Han River basin corresponding to various inflows. Defining the adaptation capability of reservoirs as the change of maximum and/or minimum of storage corresponding to the change of inflow, the study showed that Gangdong Dam has the worst adaptation capability on the variation of inflow, while Soyanggang Dam has the best capability. This study also constructed an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the more accurate and efficient simulation of the adaptation capability of the Soyanggang Dam. Nine Inflow scenarios were generated using historical data from frequency analysis and synthetic data from two general circulation models with different climate change scenarios. The ANFIS showed significantly different consequences of the release and reservoir storage upon inflow scenarios of Soyanggang Dam, whilst it provides stable reservoir operations despite the variability of rainfall pattern.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.141-144
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• 2001

In this paper, we proposed a neural network controller with a fuzzy compensator which compensate a output of neural network controller. Even if learn by neural network controller, it can occur a bad results from disturbance or load variations. So in order to adjust above case. we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning an inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. In order to confirm a performance of the proposed controller, we implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.