• 제목/요약/키워드: Residual Network

검색결과 460건 처리시간 0.024초

신경회로망을 이용한 용접잔류응력 예측에 관한 연구 (A Study on the Predict of Residual Stress Using a Neural Network)

  • 김일수;이연신;박창언;정영재;안영호
    • 대한용접접합학회:학술대회논문집
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    • 대한용접접합학회 2000년도 특별강연 및 춘계학술발표대회 개요집
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    • pp.251-255
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    • 2000
  • Recently, the improvement of computer capacities and artificial intelligence ware caused to employ for prediction of residual stresses and strength evaluation. There are a lot of researches regarding the measurement and prediction of residual stresses for weldment using a neural network in the advanced countries, but in our country, a neural network as a technical part, has only been used on the possibilities of employment for welding area. Furthermore, the relationship between residual stress and process parameters using a neural network was wholly lacking. Therefore development of a new technical method for the optimized process parameters on the reduction of residual stress and applyment of real-time production line should be developed. The objectives of this paper is to measure the residual stress of butt welded specimen using strain gage sectioning method and to apply them to a neural network for prediction of residual stresses on a given process parameter. Also, the assessment of the developed system using a neural network was carried out

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인공신경회로망을 이용한 탄산가스 아크 용접의 잔류응력 예측에 관한 연구 (A Study of Predicting Method of Residual Stress Using Artificial Neural Network in $CO_2$ Arc Welding)

  • 조용준;이세헌;엄기원
    • Journal of Welding and Joining
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    • 제13권3호
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    • pp.77-88
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    • 1995
  • A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO$_{2}$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a backpropagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the ailure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

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A Study of Predicting Method of Residual Stress Using Artificial Neural Network in $CO_2$Arc welding

  • Cho, Y.;Rhee, S.;Kim, J.H.
    • International Journal of Korean Welding Society
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    • 제1권2호
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    • pp.51-60
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    • 2001
  • A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

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신경망 기법을 이용한 1축 잔류응력장에서 구멍뚫기법의 구멍편심 오차 보정 (Compensation of the Error due to Hole Eccentricity of Hole-drilling Method in Uniaxile Residual Stress Field Using Neural Network)

  • 김철;양원호;조명래
    • 대한기계학회논문집A
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    • 제26권12호
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    • pp.2475-2482
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    • 2002
  • The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is compensated using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could compensated the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for compensation of the error due to hole eccentricity in hole-drilling method.

Comparison of Convolutional Neural Network Models for Image Super Resolution

  • Jian, Chen;Yu, Songhyun;Jeong, Jechang
    • 한국방송∙미디어공학회:학술대회논문집
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    • 한국방송∙미디어공학회 2018년도 하계학술대회
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    • pp.63-66
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    • 2018
  • Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

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인공신경망을 이용한 탄산가스 아크용접의 잔류응력 예측 (Predicting Method of Rosidual Stress Using Artificial Neural Network In $CO_2$ Are Weldling)

  • 조용준;이세현;엄기원
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1993년도 추계학술대회 논문집
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    • pp.482-487
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    • 1993
  • A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO $_{2}$ Arc Welding using the finite element method. The validity of the above results is demonstrated by experimental elastic stress relief method which is called Holl Drilling Method. The first part of numarical analysis performs a three-dimensional transient heat transfer anslysis, and the second part then uses results of the first part and performs a three-dimensional transient thermo-clasto-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method were used to train a backpropagation neural network to predict residual stress. Architecturally, the finite element method were used to train a backpropagation voltage and the current, a hidden layer to accommodate failure mechanism mapping, and an output layer for residual stress. The trained network was then applied to the prediction of residual stress in the four specimens. The results of predicted residual stress have been very encouraging.

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신경회로망을 이용한 용접잔류응력 예측에 관한 연구 (A Study on the Prediction of Welded Residual Stresses using Neural Network)

  • 차용훈;김일수;김하식;이연신;김덕중;성백섭;서준열
    • 한국생산제조학회지
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    • 제9권6호
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    • pp.89-95
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    • 2000
  • In order to achieve effective prediction of residual stresses, the series experiment were carried out and the residual stresses were measured using the backgpropagation algorithm from the neural network and the sectional method. Using the experimental results, the optimal control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on residual stresses during GMA welding processes. The results obtained from the comparison between the measured and calculated results, showed that the neural network based on backpropagation algorithm can be sued in order to control weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also, improve the quantity control for welded structures. The development of the system is goal in this study.

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신경회로망을 이용한 평판 맞대기용접의 잔류응력 예측시스템 개발 (Predictive System Evaluation of Residual Stresses of Plate Butt Welding Using Neural Network)

  • 차용훈;성백섭;이연신
    • Journal of Welding and Joining
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    • 제21권1호
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    • pp.80-86
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    • 2003
  • This study develops a system for effective prediction of residual stresses by the backpropagation algorithm using the neural network. To achieve this goal, a series of experiments were carried out to and measured the residual stresses using the sectional method. With the experimental results, the optional control algorithms using a neural network could be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm.. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

신경회로망을 이용한 용접잔류응력 예측 및 최적의 용접조건 선정에 관한 연구 (A Study on the Prediction of Welding Residual Stresses and the Selection of Optimal Welding Condition using Neural Network)

  • 차용훈;이연신;성백섭
    • 한국안전학회지
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    • 제16권4호
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    • pp.58-64
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    • 2001
  • In this study, it is developed that the system for effective prediction of residual stresses by the back-propagation algorithm using the neural network. To achieve This goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm. This system can no only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

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잔류 합성 곱 신경망 기반의 코골이 식별 방식 (Snoring identification method based on residual convolutional neural network)

  • 신승수;김형국
    • 한국음향학회지
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    • 제38권5호
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    • pp.574-579
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    • 2019
  • 코골이는 전형적인 수면장애 증상이며 수면 무호흡증을 유발하기 때문에 코골이의 발생을 확인하는 것이 중요하다. 이에 본 논문에서는 효율적인 코골이 식별 알고리즘으로 잔류 합성 곱 신경망을 제안한다. 잔류 학습과 합성곱 신경망을 결합한 구조인 잔류 합성 곱 신경망은 기존의 신경망보다 데이터에 존재하는 특징을 효과적으로 추출하여 코골이 식별 정확도를 향상한다. 실험 결과는 제안한 코골이 식별 알고리즘의 성능이 기존 방식보다 더 우수하다는 것을 보여준다.