• Title/Summary/Keyword: artifical neural network

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Prediction of Deep Excavation-induced Ground surface movements using Artifical Neural Network (인공신경망기법을 이용한 굴착에 따른 지표침하평가)

  • 유충식;최병석
    • Proceedings of the Korean Geotechical Society Conference
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    • 2003.03a
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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.

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Typical Models of Artificial Neural Network and Their Application Fields to the Power System (인공신경회로망의 대표적 모델과 전력계통적용에 대한 조사연구)

  • Ko, Yun-Seok;Kim, Ho-Yong
    • Proceedings of the KIEE Conference
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    • 1990.07a
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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.

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Dynamic Characteristics Modeling for A MR Damper using Artifical Neural Network (인공신경망을 이용한 MR댐퍼의 동특성 모델링)

  • 백운경;이종석;손정현
    • Transactions of the Korean Society of Automotive Engineers
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    • v.12 no.3
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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.

Artifical Neural Network for In-Vitro Thrombosis Detection of Mechanical Valve

  • Lee, Hyuk-Soo;Lee, Sang-Hoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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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.

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Development of Model of Shear Strength Estimative for Steel Fiber Reinforced Concrete Using Neural Network (신경망 기법을 이용한 강섬유 혼입 콘크리트의 전단강도 추정 모형 개발)

  • Kwak, Kae-Hwan;Hwang, Hae-Sung;Kim, Woo-Jong;Jang, Hwa-Sup;Kang, Shin-Muk
    • Journal of The Korean Society of Agricultural Engineers
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    • v.49 no.2
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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.

Forceseeability and Decision for Moving Condition of the Machine Driving System by Artificial Neural Network (인공신경망에 의한 기계구동계의 작동상태 예지 및 판정)

  • Park, H. S.;Seo, Y. B.;Lee, C. Y.;Cho, Y. S.
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.7 no.5
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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.

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Prediction of Deep-Excavation induced Ground surface movements using Artifical Neural Network (인공신경망기법을 이용한 깊은 굴착에 따른 지표변위 예측)

  • 유충식;최병석
    • Proceedings of the Korean Geotechical Society Conference
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    • 2002.10a
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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.

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Structure optimization of neural network using co-evolution (공진화를 이용한 신경회로망의 구조 최적화)

  • 전효병;김대준;심귀보
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.4
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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.

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Fuzzy Rule Identification System using Artifical Neural Networks (인공신경망을 이용한 퍼지 규칙 인식 시스템)

  • Jang, Mun-Seok;Jang, Deok-Cheol
    • The Transactions of the Korea Information Processing Society
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    • v.2 no.2
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    • pp.209-214
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    • 1995
  • It is very hard to identify the fuzzy rules and tune the membership functions of the fuzzy reasoning in fuzzy systems modeling .We propose a method which canautomatically identify the fuzzy rules and tune the membership functions of fuzzy reasoning simultaneously using artifical neural network. In this model,fuzzy rules are identified by backpropagation algorithm. The feasibility of the method is simulated by a simple robot manipulator.

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