• Title, Summary, Keyword: 인공 신경 회로망

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Application of Artificial Neural Networks to Predict Ultimate Shear Capacity of PC Vertical Joints (PC 수직 접합부의 극한 전단 내력 예측에 대한 인공 신경 회로망의 적용)

  • 김택완;이승창;이병해
    • Computational Structural Engineering
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    • v.9 no.2
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    • pp.93-101
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    • 1996
  • An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

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Implementation of ME8P Learning Circuitry With Simple Nonlinear Synapse Circuit (간단한 비선형 시냅스 회로를 이용한 MEBP 학습 회로의 구현)

  • Cho, Hwa-Hyun;Chae, Jong-Seok;Lee, Eum-Sang;Park, Jin-Sung;Choi, Myung-Ryul
    • Proceedings of the KIEE Conference
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    • pp.2977-2979
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    • 1999
  • 본 논문에서는 MEBP(Modified Error Back-Propagation) 학습 규칙을 간단한 비선형 회로를 이용하여 구현하였다. 인공 신경 회로망(ANNs : Artificial Neural Networks)은 많은 수의 뉴런을 필요하기 때문에 표준 CMOS 기술을 이용하는 간단한 비선형 시냅스(synapse) 회로는 인공 신경 회로망 구현에 적합하다. 학습회로는 비선형 시냅스 회로. 시그모이드(sigmoid) 회로. 그리고 선형 곱셈기로 구성되어 있다. 학습 회로의 출력은 각 입력 패턴에 따라 유일한 값으로 결정되어진다. 제안한 학술회로를 $2{\times}2{\times}1$$2{\times}3{\times}1$ 다층 feedforward 신경 회로망 모델에 적용하였다. MEBP 하드웨어 구현은 HSPICE 회로 시뮬레이터를 이용하여 검증하였다. 제안한 학술 회로는 on-chip 학습회로를 포함한 대규모 신경회로망 구현에 매우 적합하리라 예상된다.

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Prediction of Ozone Formation Based on Neural Network and Stochastic Method (인공신경망 및 통계적 방법을 이용한 오존 형성의 예측)

  • Oh, Sea Cheon;Yeo, Yeong-Koo
    • Clean Technology
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    • v.7 no.2
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    • pp.119-126
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    • 2001
  • The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

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Research Trend of Cellular Automata in Brain Science Research (뇌과학 연구에서 셀룰라 오토마타의 연구 현황)

  • Kang, Hoon
    • Proceedings of the IEEK Conference
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    • pp.441-447
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    • 1999
  • 본 논문은 복잡 적응 시스템의 분석 및 모델링을 위해, 인공생명의 기본 패러다임인 셀룰라 오토마타를 선택하여, 무정형의 구조를 가지며 투명한 자료 전파 특성을 갖는 셀룰라 신경 회로망의 설계하고 개발하는데 중점을 두었다. 우선, 신경 회로망의 불규칙한 구조를 발생학적으로 다루어 무정형의 은닉층을 생성하고, 다윈의 진화론을 적용하여 구조적 진화 및 선택을 통해 최적화된 신경 회로망을 설계하였다. 주변 셀의 상태를 감지하여 자신의 상태를 수정해나가는 방식의 셀룰라 오토마타의 투명한 신호 전파 모델로 자료 및 오차의 역전파에 적용하도록 고안하였고, 라마르크의 용불용설을 활용한 오차의역전파 학습 알고리즘을 유도하였다. 이러한 복잡 적응계의 학습 과정을 유도하여 시뮬레이션에서 그 타당성을 입증하였다. 시뮬레이션에서는 신경 회로망의 XOR 문제와 다중 입력 다중 출력 함수에 대한 근사화 문제를 풀었다.

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A Study on Adaptation of Neural Network to Warren Truss Design (와렌 트러스 설계에의 신경망 적용에 관한 연구)

  • Shin, Dong Cheol;Lee, Seung Chang;Cho, Young Sang
    • Journal of Korean Society of Steel Construction
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    • v.15 no.4
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    • pp.413-422
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    • 2003
  • Most engineers tend to rely on their intuition or existing data in formulating structural design or preliminary estimate of various conditions. Because of these variations, the artificial neural network is used as an alternative design model of the warren truss since it can handle uncertainty through the probability method. This research validated the approximate structural design model of the warren truss, with its proper parameter values of the neural network and design process falling within 10 percent torrence of the different designs that resulted between this model and the MIDAS program. The suggested model for the process was adapted for the truss design using the member section table, while time saving and efficiency are based on the allowed range of torrence.

Development of Adaptive AE Signal Pattern Recognition Program and Application to Classification of Defects in Metal Contact Regions of Rotating Component (적응형 AE신호 형상 인식 프로그램 개발자 회전체 금속 접촉부 이상 분류에 관한 적용 연구)

  • Lee, K.Y.;Lee, C.M.;Kim, J.S.
    • Journal of the Korean Society for Nondestructive Testing
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    • v.15 no.4
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    • pp.520-530
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    • 1996
  • In this study, the artificial defects in rotary compressor are classified using pattern recognition of acoustic emission signal. For this purpose the computer program is developed. The neural network classifier is compared with the statistical classifier such as the linear discriminant function classifier and empirical Bayesian classifier. It is concluded that the former is better. It is possible to acquire the recognition rate of above 99% by neural network classifier.

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Classification of Schizophrenia Using an ANN and Wavelet Coefficients of Multichannel EEG (다채널 뇌파의 웨이블릿 계수와 신경망을 이용한 정신분열증의 판별)

  • 정주영;박일용;강병조;조진호;김명남
    • Journal of Biomedical Engineering Research
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    • v.24 no.2
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    • pp.99-106
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    • 2003
  • In this paper, a method of discriminating EEG for diagnoses of mental activity is proposed. The proposed method for classification of schizophrenia and normal EEG is based on the wavelet transform and the artificial neural network. The wavelet coefficients of $\alpha$ band, $\beta$ band, $\theta$ band, and $\delta$ band are obtained using the wavelet transform. The magnitude, mean, and variance of wavelet coefficients for each EEG band are applied to the input data of the system's ANN. The architecture of the ANN s a four layered feedforward network with two hidden layer which implements the error back propagation learning algorithm. Through the classification of schizophrenia composed of 19 ANNs corresponding to 19 channels, the classifying system show that it can classify the 100% of the normal EEG group and the 86.67% of the schizophrenia EEG group.

Continuous Surveillance and Diagnostics System Using Neural Network (인공 신경 회로망을 이용한 핵물질 거동 감시 시스템 개발)

  • 최재형;한명철;박영수;김호동;홍종숙
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.1182-1185
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    • 1995
  • This paper presents a novel technology for unattented continuous monitoring of radioactive material in hot cell environments. In this monitoring system, the surveillance camera data and NDA data are time synchronized and integrated into the same dimension through data processing. The integrated information is then fed into a neural network to generate diagnostics through data processing. the integrated information of the concept is tested for a spent nuclear fuel transprotation in an operational hot cell at KAERI. The presented integral part of the multi-sensory system and the analytical paradigm may provide an effective technologyical alternative for safeguarding new conceptual hot cell facilities, namely the Dupic facility.

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A Development on the Fault Prognosis of Bearing with Empirical Mode Decomposition and Artificial Neural Network (경험적 모드 분해법과 인공 신경 회로망을 적용한 베어링 상태 분류 기법)

  • Park, Byeonghui;Lee, Changwoo
    • Journal of the Korean Society for Precision Engineering
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    • v.33 no.12
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    • pp.985-992
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    • 2016
  • Bearings have various uses in industrial equipment. The lifetime of bearings is often lesser than anticipated at the time of purchase, due to environmental wear, processing, and machining errors. Bearing conditions are important, since defects and damage can lead to significant issues in production processes. In this study, we developed a method to diagnose faults in the bearing conditions. The faults were determined using kurtosis, average, and standard deviation. An intrinsic mode function for the data from the selected axis was extracted using empirical mode decomposition. The intrinsic mode function was obtained based on the frequency, and the learning data of ANN (Artificial Neural Network) was concluded, following which the normal and fault conditions of the bearing were classified.