• Title/Summary/Keyword: 인공신경망 구조

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Piezocone Neural Network Model for Estimation of Preconsolidation Pressure of Korean Soft Soils (국내 연약지반의 선행압밀하중 추정을 위한 피에조콘 인공신경망 모델)

  • 김영상
    • Journal of the Korean Geotechnical Society
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    • v.20 no.8
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    • pp.77-87
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    • 2004
  • In this paper a back-propagation neural network model is developed to estimate the preconsolidation pressure of Korean soft soils based on 176 oedometer tests and 63 piezocone test results, which were compiled from 11 sites - western and southern parts of Korea. Only 147 data were used for the training of the neural network and 29 data, which were not used during the training phase, were used for the verification of trained network. Empirical and theoretical models were compared with the developed neural network model. A simple 4-4-9-1 multi-layered neural network has been developed. The cone tip resistance $q_T$ penetration pore pressure $u_2$, total overburden pressure $\sigma_{vo}$ and effective overburden pressure $\sigma'_{vo}$ were selected as input variables. The developed neural network model was validated by comparing the prediction results of the proposed neural network model for the new data which were not used for the training of the model with the measured preconsolidation pressures. It can also predict more precise and reliable preconsolidation pressures than the analytical and empirical model. Furthermore, it can be carefully concluded that neural network model can be used as a generalized model for prediction of preconsolidation pressure throughout Korea since developed model shows good performance for the new data which were not used in both training and testing data.

Estimation of Dynamic Vertical Displacement using Artificial Neural Network and Axial strain in Girder Bridge (인공신경망과 축방향 변형률을 이용한 거더 교량의 동적 수직 변위 추정)

  • Ok, Su Yeol;Moon, Hyun Su;Chun, Pang-Jo;Lim, Yun Mook
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.34 no.6
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    • pp.1655-1665
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    • 2014
  • Dynamic displacements of structures shows general behavior of structures. Generally, It is used to estimate structure condition and trustworthy physical quantity directly. Especially, measuring vertical displacement which is affected by moving load is very important part to find or identify a problem of bridge in advance. However directly measuring vertical displacement of the bridge is difficult because of test conditions and restriction of measuring equipment. In this study, Artificial Neural Network (ANN) is used to suggest estimation method of bridge displacement to overcome constrain conditions, restriction and so on. Horizontal strain and vertical displacement which are measured by appling random moving load on the bridge are applied for learning and verification of ANN. Measured horizontal strain is used to learn ANN to estimate vertical displacement of the bridge. Numerical analysis is used to acquire learning data for axis strain and vertical displacement for applying ANN. Moving load scenario which is made by vehicle type and vehicle distance time using Pearson Type III distribution is applied to analysis modeling to reflect real traffic situation. Estimated vertical displacement in respect of horizontal strain according to learning result using ANN is compared with vertical displacement of experiment and it presents vertical displacement of experiment well.

A Prediction of Shear Behavior of the Weathered Mudstone Soil Using Dynamic Neural Network (동적신경망을 이용한 이암풍화토의 전단거동예측)

  • 김영수;정성관;김기영;김병탁;이상웅;정대웅
    • Journal of the Korean Geotechnical Society
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    • v.18 no.5
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    • pp.123-132
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    • 2002
  • The purpose of this study is to predict the shear behavior of the weathered mudstone soil using dynamic neural network which mimics the biological system of human brain. SNN and RNN, which are kinds of the dynamic neural network realizing continuously a pattern recognition as time goes by, are used to predict a nonlinear behavior of soil. After analysis, parameters which have an effect on learning and predicting of neural network, the teaming rate, momentum constant and the optimum neural network model are decided to be 0.5, 0.7, 8$\times$18$\times$2 in SU model and 0.3, 0.9, 8$\times$24$\times$2 in R model. The results of appling both networks showed that both networks predicted the shear behavior of soil in normally consolidated state well, but RNN model which is effective fir input data of irregular patterns predicted more efficiently than SNN model in case of the prediction in overconsolidated state.

Development of Travel Time Estimation Algorithm for National Highway by using Self-Organizing Neural Networks (자기조직형 신경망 이론을 이용한 국도 통행시간 추정 알고리즘)

  • Do, Myungsik;Bae, Hyunesook
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.28 no.3D
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    • pp.307-315
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    • 2008
  • The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.

Dimensionality Reduced Wave Transmission Function and Neural Networks for Crack Depth Estimation in Concrete Structures (차원 축소된 표면파 투과 함수와 인공신경망을 이용한 콘크리트 구조물의 균열 깊이 평가 기법)

  • Shin, Sung-Woo;Yun, Chung-Bang
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.20 no.3
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    • pp.247-253
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    • 2007
  • Determination of crack depth in filed using the self-calibrating surface wane transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A Principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural networks (NNs) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

Prediction of Retention Time for PAH Molecule in HPLC (고속액체 크로마토그래피에서 PAH분자의 구조에 따른 용리시간 예측)

  • Kim, Young-Gu
    • Journal of the Korean Chemical Society
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    • v.44 no.2
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    • pp.102-108
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    • 2000
  • Relative retention times (RRTs) of RAH molecules in HPLC are trained and predicted intesting sets using a multiple linear regression (NLR) and an artificial neural network (ANN). The maindescriptors in QSRR are molecular connectivity ($^1X_v,\;^2X_v$), the length-to-breadth ratios (L/B), and molecular dipole moment(D). L/B which is related with slot model is a good descripter in ANN, but isn't in MLR. Varainces which show the accuracy of prediction times in testing sets are 0.0099, 0.0114 for ANN and MLR, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

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A Study on Optimized Artificial Neural Network Model for the Prediction of Bearing Capacity of Driven Piles (항타말뚝의 지지력 예측을 위한 최적의 인공신경망모델에 관한 연구)

  • Park Hyun-Il;Seok Jeong-Woo;Hwang Dae-Jin;Cho Chun-Whan
    • Journal of the Korean Geotechnical Society
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    • v.22 no.6
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    • pp.15-26
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    • 2006
  • Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

Optimization of Neural Network Structure for the Efficient Bushing Model (효율적인 신경망 부싱모델을 위한 신경망 구성 최적화)

  • Lee, Seung-Kyu;Kim, Kwang-Suk;Sohn, Jeong-Hyun
    • Transactions of the Korean Society of Automotive Engineers
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    • v.15 no.5
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    • pp.48-55
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    • 2007
  • A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

Application of Artificial Neural Network to Improve Quantitative Precipitation Forecasts of Meso-scale Numerical Weather Prediction (중규모수치예보자료의 정량적 강수추정량 개선을 위한 인공신경망기법)

  • Kang, Boo-Sik;Lee, Bong-Ki
    • Journal of Korea Water Resources Association
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    • v.44 no.2
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    • pp.97-107
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    • 2011
  • For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.

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.