• Title/Summary/Keyword: Artificial Neural Network

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Prediction of Field Permeability Using by Artificial Neural Network (인공신경망을 이용한 현장투수계수 예측)

  • Kim, Young-Su;Jung, Sung-Gwan;Kim, Dae-Man
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.3C
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    • pp.97-104
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    • 2009
  • In this study, artificial neural network was performed using the data of soils characteristic value, standard penetration test, and field permeability test of the 12 embankment that are located in the near Nak-dong and Kum-ho river to estimate the coefficient of field permeability of river embankment. The 89 data of total 108, 82% was used in learning step, and the other 19 data was used in estimation step. Also the results of generally used empirical equations were compared with those of artificial neural network for evaluation of application. As results, all of the coefficient of field permeability by empirical equation showed below 0.4 in terms of the coefficient of correlation with the measured values, but the coefficient of correlation of the predicted results by artificial neural network was up 0.8 in the all case. Therefore artificial neural network could predict more the precise field permeability well than the empirical equations.

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An attempt to reduce the number of training in the artificial neural network

  • Omae, Akihiro;Ishijima, Shintaro
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10b
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    • pp.1256-1258
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    • 1990
  • A large number of trainings are requested for the artificial neural network using the backpropagation algorithm. It is shown that one dimensional search technique is effective to reduce the number of trainings through some numerical simulations.

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Development of Improvement Effect Prediction System of C.G.S Method based on Artificial Neural Network (인공신경망을 기반으로 한 C.G.S 공법의 개량효과 예측시스템 개발)

  • Kim, Jeonghoon;Hong, Jongouk;Byun, Yoseph;Jung, Euiyoup;Seo, Seokhyun;Chun, Byungsik
    • Journal of the Korean GEO-environmental Society
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    • v.14 no.9
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    • pp.31-37
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    • 2013
  • In this study installation diameter, interval, area replacement ratio and ground hardness of applicable ground in C.G.S method should be mastered through surrounding ground by conducting modeling. Optimum artificial neural network was selected through the study of the parameter of artificial neural network and prediction model was developed by the relationship with numerical analysis and artificial neural network. As this result, C.G.S pile settlement and ground settlement were found to be equal in terms of diameter, interval, area replacement ratio and ground hardness, presented in a single curve, which means that the behavior pattern of applied ground in C.G.S method was presented as some form, and based on such a result, learning the artificial neural network for 3D behavior was found to be possible. As the study results of artificial neural network internal factor, when using the number of neural in hidden layer 10, momentum constant 0.2 and learning rate 0.2, relationship between input and output was expressed properly. As a result of evaluating the ground behavior of C.G.S method which was applied to using such optimum structure of artificial neural network model, is that determination coefficient in case of C.G.S pile settlement was 0.8737, in case of ground settlement was 0.7339 and in case of ground heaving was 0.7212, sufficient reliability was known.

A Comparative Analysis of the Forecasting Performance of Coal and Iron Ore in Gwangyang Port Using Stepwise Regression and Artificial Neural Network Model (단계적 회귀분석과 인공신경망 모형을 이용한 광양항 석탄·철광석 물동량 예측력 비교 분석)

  • Cho, Sang-Ho;Nam, Hyung-Sik;Ryu, Ki-Jin;Ryoo, Dong-Keun
    • Journal of Navigation and Port Research
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    • v.44 no.3
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    • pp.187-194
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    • 2020
  • It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

Artificial neural network calculations for a receding contact problem

  • Yaylaci, Ecren Uzun;Yaylaci, Murat;Olmez, Hasan;Birinci, Ahmet
    • Computers and Concrete
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    • v.25 no.6
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    • pp.551-563
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    • 2020
  • This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three-layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

  • Shahrabi, Jamal;Khameneh, Sara Mottaghi
    • Industrial Engineering and Management Systems
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    • v.15 no.4
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    • pp.324-334
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    • 2016
  • Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

Application of Artificial Neural Network method for deformation analysis of shallow NATM tunnel due to excavation

  • Lee, Jae-Ho;Akutagawa, Shnichi;Moon, Hong-Duk;Han, Heui-Soo;Yoo, Ji-Hyeung;Kim, Kwang-Yeun
    • Proceedings of the Korean Society for Rock Mechanics Conference
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    • 2008.10a
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    • pp.43-51
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    • 2008
  • Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

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Artificial Neural Network based Motion Classification Algorithm using Surface Electromyogram (표면 근전도를 이용한 Artificial Neural Network 기반의 동작 분류 알고리즘)

  • Jeong, E.C.;Kim, S.J.;Song, Y.R.;Lee, S.M.
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.6 no.1
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    • pp.67-73
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    • 2012
  • In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

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The Effect of Seasonal Input on Predicting Groundwater Level Using Artificial Neural Network (인공신경망을 이용한 지하수위 예측과 계절효과 반영을 위한 입력치의 영향)

  • Kim, Incheol;Lee, Junhwan
    • Ecology and Resilient Infrastructure
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    • v.5 no.3
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    • pp.125-133
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    • 2018
  • Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

Prediction of Turbidity in Treated Water and the Estimation of the Optimum Feed Concentration of Coagulants in Rapid Mixing Process using an Artificial Neural Network Model (인공신경망 모형을 이용한 급속혼화공정에서 적정 응집제 주입농도 결정 및 응집처리후 탁도의 예측)

  • Jeong, Dong-Hwan;Park, Kyoohong
    • Journal of Korean Society on Water Environment
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    • v.21 no.1
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    • pp.21-28
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    • 2005
  • The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.