• 한국터널지하공간학회 논문집
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• 제4권2호
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• pp.101-111
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• 2002

본 연구의 목적은 교란상태개념 (DSC) 모델을 이용하여 터널 암반절리면의 거동특성을 모델링 할 수 있는 개선된 구성방정식 모델을 개발하는데 있다. 교란상태 개념 (DSC) 모델은 이미 다른 접촉면 거동 모델링을 통해서 그 신뢰성을 검증 받아왔다. 이런 DSC 모델을 암반 절리면 거동 특성에 맞도록 수정한 후에, Schneider가 수행한 합리적인 실내 전단 시험 결과 및 역 해석 결과를 이용하여 DSC모델의 절리면 적용성을 검증하고자 한다. 본 연구결과로부터 DSC모델은 화강암 절리면의 변형률 연화 및 부피팽창 거동특성을 규명할 수 있다고 판단된다.

• 생물환경조절학회지
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• 제1권2호
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• pp.162-168
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• 1992

As the automation of nutrient solution management proceeds in the field of hydroponics, effective supporting systems to manage the nutrient solution by computer become needed. This study was attempt to predict the EC of nutrient solution using the neural networks. The multilayer perceptron consisting of 3 layers with the back propagation learning algorithm was selected for EC prediction, of which nine variables in the input layer were the concentrations of each ion and one variable in the output layer the EC of nutrient solution. The meq unit in ion concentration was selected fir input variable in the input layer. After the 10,000 learning sweeps with 108 sample data, the comparison of predicted and measured ECs for 72 test data showed good agreements with the correlation coefficient of 0.998. In addition, the predicted ECs by neural network showed relatively equal or closer to the measured ones than those by current complicated models.

• 한국지반공학회지:지반
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• 제13권2호
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• pp.77-90
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• 1997

지하수 거동에 대한 불확실성을 극복하기 위해서 암반 지반의 투수계수를 예측할 수 있는 신뢰적이고 경제적인 방법이 필요하다. 이러한 목적을 위하여 암반의 투수계수 예측 방법에 대한 연구가 수행되어졌다. 인공 신경망 이론을 적용한 투수계수 예측 방법에 대한 일환으로 오차역 전파 학습알고리즘을 이용한 투수계수 예측 방법에 대하여 연구를 수행하였으며, 이 방법의 타당성 검토를 위하여 현장투수시험 결과와 지반물성치들에 적용하여 검증을 실시하였다. 검증결과 평균오차 범위가 작아 비교적 정착한 투수계수 예측방법임을 보여주었다.

• 응용통계연구
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• 제21권3호
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• pp.537-546
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• 2008

시계열자료 분석에 있어 주요 목적 중에 하나는 미래에 대한 예측 값을 추정하는 것이다. 이 논문에서는 정상자기회귀이동평균 모형에서 변환-역변환 방법을 이용하여 예측값을 구하는 과정에서 발생하는 문제에 대해 알아보고 회귀분석에서 제안되었던 smearing 추정방법을 시계열분석에서 사용할 수 있도록 붓스트랩을 이용하여 수정한 추정법을 소개한다. Yeo-Johnson 변환 (2000)을 이용한 KOSDAQ지수의 수익률 실증분석을 통해 기존에 사용되고 있는 방법의 문제점과 제안된 방법의 적절성에 대해 고찰해 보았다.

• 소성∙가공
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• 제29권4호
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Geomechanics and Engineering
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• 제30권3호
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• pp.273-280
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• 2022

A certain amount of random vibration excitation to subway track is caused by subway operation. This excitation is transmitted through track foundation, tunnel, soil medium, and ground building to the ground and ground structure, causing vibration. The vibration affects ground building. In this study, the results of ANSYS numerical simulation was used to establish back-propagation (BP) neural network model. Moreover, a back-propagation neural network model consisting of five input neurons, one hidden layer, 11 hidden-layer neurons, and three output neurons was used to analyze and calculate the vertical Z-vibration level of New Capital's ground buildings of Qingdao Metro phase I Project (Line M3). The Z-vibration level under different working conditions was calculated from monolithic roadbed, steel-spring floating slab roadbed, and rubber-pad floating slab roadbed under the working condition of center point of 0-100 m. The steel-spring floating slab roadbed was used in the New Capital area to monitor the subway operation vibration in this area. Comparing the monitoring and prediction results, it was found that the prediction results have a good linear relationship with lower error. The research results have good reference and guiding significance for predicting vibration caused by subway operation.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1999년도 특별강연 및 춘계학술발표대회 개요집
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• pp.203-206
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• 1999

• 한국정밀공학회지
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• 제34권1호
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• pp.23-27
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• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2005년도 지반공학 공동 학술발표회
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• pp.417-424
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• 2005

Observed field behaviors are frequently different from the behaviors predicted in the design state due to several uncertainties involved in soil properties, numerical modelling, and error of measuring system even though a sophisticated numerical analysis technique is applied to solve the consolidation behavior of drainage-installed soft deposits. In this study, genetic algorithms are applied to back-analyze the soil properties using the observed behavior of soft clay deposit composed of multi layers that shows complex consolidation characteristics. Utilizing the program, one might be able to appropriately predict the subsequent consolidation behavior from the measured data in an early stage of consolidation of multi layered soft deposits. Example analyses for drainage-installed multi-layered soft deposits are performed to examine the applicability of proposed back-analysis method.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1999년도 제3회 압연심포지엄 논문집 압연기술의 미래개척 (Exploitation of Future Rolling Technologies)
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• pp.349-355
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• 1999

The texture inhomogeniety during rolling is one of the greatest problems. Especially, shear texture develops more easily during ferritic rolling of steel sheets at high temperatures due to friction between rolls and the material. In this study, the influence of front and back tensions on the texture development during ferritic rolling has been studied. The rolling textures were simulated using the full constrains Taylor-Bishiop-Hill model with the strain history obtained from finite element analysis. The calculated textures showed that the back tension rolling could reduce the shear component more effectively than front tension or rolling without tension. However, the experimental results showed that the lension effect was very small compared to our prediction. It might be attributed to initial texture and difference in frictions between simulation and experiments.