• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2003년도 봄 학술발표회 논문집
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• pp.69-76
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• 2003

This paper presents the prediction of deep excavation-induced ground surface movements using artifical neural network(ANN) technique, which is of prime importance in the perspective of damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Arificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effecting used for a first-order prediction of ground movements associated with deep-excavation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 하계학술대회 논문집
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• pp.143-146
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• 1990

The human brain has the most powerful capabilities in thinking, interpreting, remembering, and problem-solving. Artificial neural network is appeared by scientists who have tried to simulate such a human brain. The artificial neural network has the capability of learning, massive parallelism capability and robustness for disturbance which are necessary for power system application. In this paper, We reviewed the typical topologies and learning algorithms of artifical neural networks which can be used for pattern classification. And we surveyed for the applications of artifical neural network to the power system.

• 한국자동차공학회논문집
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• 제12권3호
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• pp.170-176
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• 2004

MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.762-766
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• 1998

Mechanical valve is one of the most widely used implantable artificial organs, Since its failure (mechanical failures and thrombosis to name two representative example) means the death of patient, its reliability is very important and early noninvasive detection is essential requirement . This paper will explain the method to detect the thrombosis formation by spectral analysis and neural network. In order quantitatively to distinguish peak of a normal valve from that of a thrombotic valve, a 3 layer backpropagation neural network, which contains 7,000 input nodes, 20 hidden layer and 1output , was employed. The trained neural network can distinguish normal and thrombotic valve with a probability that is higher than 90% . In conclusion, the acoustical spectrum analysis coupled with a neural network algorithm lent itself to the noninvasive monitoring of implanted mechanical valves. This method will be applied to be applied to the performance evaluation of other implantable rtificial organs.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1995년도 특별강연 및 춘계학술발표 개요집
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• pp.126-129
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• 1995

• 한국농공학회논문집
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• 제49권2호
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• pp.27-36
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• 2007

This study, the present study wishes to develop a model that estimates shear strength characteristics of steel fiber reinforced concrete using artifical neural network models. Neural network models, developed as mathematical models, are being widely used not only in its original purpose of pattern recognition, but also in application fields by the function's nonlinear loaming and interpolar ability Neural network has a repetitive rotation algorithm that can cyclically and repeatedly estimate system conditions and parameter ideal values, and it can be used in the modeling of the nonlinear system by nonlinear characteristic functions that construct the system.

• 한국생산제조학회지
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• 제7권5호
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• pp.92-97
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• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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• pp.451-458
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• 2002

This paper presents the prediction of deep excavation-induced ground surface movements using artificial neural network, which is of prime importance in the perspective of damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep-excavation-induced ground movements was employed and validated against available large-scale model test results. The validated model was then used to perform a parametric study on deep excavations with emphasis on ground movements. Using the result of the finite element analysis, Artificial Neural Network(ANN) system is formed, which can be used in the prediction of deep exacavation-induced ground surface displacements. The developed ANN system can be effecting used for a first-order prediction of ground movements associated with deep-excavation.

• 전자공학회논문지S
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• 제35S권4호
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• 한국정보처리학회논문지
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• 제2권2호
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• pp.209-214
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• 1995

일반적으로 퍼지 시스템 모델링에 있어서, 퍼지 규칙을 인식하고 퍼지 추론의 소속함수를 조정하기란 매우 어렵다.본 논문에서는 인공신경망을 이용함으로써,자동으로 퍼지 규칙을 인식하고 동시에 퍼지 추론의 소속함수를 조정할수 있는 방법을 제시하였다. 본 모델은 역전파를 기본으로 한 알고리즘으로 학습하며,이 방법의 타당성을 로보트 매니퓰레이터를 통해 검증한다.