• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.6
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• pp.667-673
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• 2010

Recently, structures are becoming longer and higher because of the developments of new materials and construction techniques. However, such modern structures are more susceptible to excessive structural vibrations which cause deterioration in serviceability and structural safety. A modified probabilistic neural network(MPNN) approach is proposed to reduce the structural vibration. In this study, the global probability density function(PDF) of MPNN is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard deviation of each variable. The proposed algorithm is applied for the vibration control of a three-story shear building model under Northridge earthquake. When the control results of the MPNN are compared with those of conventional PNN to verify the control performance, the MPNN controller proves to be more effective than PNN methods in decreasing the structural responses.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.71-76
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• 2002

Defining the relationship between the quality of Injection molded parts and the process condition is very complicate because of lots of factors are involved and each factor has a non-linearity. With the development of CAE(Computer Aided Engineering) technology, the estimation of volumetric shrinkage of injection mold parts is possible by computer simulation in spite of restricted application. In this research, the Taguchi method md Neural Network are applied for finding optimal processing condition. The percent of volumetric shrinkage is compared on each case and shows neural network can be successfully applied.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.354-368
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• 1999

This research aims to compare between regression and neural network in terms of the predictive ability of the overhead costs in hospitals. For this purpose, this research uses the number of out-patients and complex medical treatments as explaining variables. Thirty-one hospitals were used for the empirical test The test result shows that the regression model has a more predictive ability than the neural network.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

• Journal of Construction Engineering and Project Management
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• v.4 no.4
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.645-650
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• 2000

The discrete wavelet transform is utilized as processing of neural network(NN) to identifying aging state of internal partial discharge in transformer. The discrete wavelet transform is used to produce wavelet coefficients which are used for classification. The mean values of the wavelet coefficients are input into an back-propagation neural network. The networks, after training, can decide if the test signals is aging early state or aging last state, or normal state.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.286-293
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• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• Computers and Concrete
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• v.11 no.3
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• pp.237-252
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• 2013

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.