• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1171-1176
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• 2023

In this paper, we predicts the estimated price using the DNBP (Deep learning Network to predict Budget Price) model with bidding data obtained from the bidding websites, ElecNet and OK EMS. We use the DNBP model to predict four lottery preliminary price, calculate their arithmetic mean, and then estimate the expected budget price ratio. We evaluate the model's performance by comparing it with the actual expected budget price ratio. We train the DNBP model by removing some of the 15 input nodes. The prediction results showed the lowest RMSE of 0.75788% when the model had 6 input nodes (a, g, h, i, j, k).

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1891-1897
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• 2006

In this study, a new model to predict the effective elastic constants of composites with spherical fillers is proposed. The original Eshelby model is extended to a finite filler volume fraction without using Mori-Tanaka's mean field approach. When single filler is embedded in the matrix, the effective elastic constants of the composite are computed. The composite is in turn considered as a new matrix, where new single filler is again embedded in the matrix. The predicted results by the present model with a series of embedding procedures are compared with those by Mori-Tanaka, self-consistent, and generalized self-consistent models. It is revealed through parametric studies such as stiffness ratio of the filler to the matrix and filler volume fraction that the present model gives more accurate predictions than Mori-Tanaka model without using the complicated numerical scheme used in self-consistent and generalized self-consistent models.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.2
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• pp.94-110
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• 1997

In the present study, a mean torque predictive model has been proposed and experimentally validated. It includes induction air mass model, fuel delivery model and mean production mode. Air induction and fuel delivery model considering dynamic behaviors of air induction and fuel delivery were proposed to predict the air-fuel ratio excursions under transient condition. Torque function model reflects thermal efficiency, volumetric efficiency, friction and effect of spark timing. In the spark timing model, knock limit and acceleration retard are included. Experiments were carried out to validate the simulation model for the step changes of throttle at constant engine speed. The results show reasonable agreements between simulation and experiment at fully warmed condition. Using this model, fueling strategies are varied with fast throttle open and it can predict air-fuel ratio excursion and IMEP.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.107-114
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• 2003

There are many problems in the prediction of dynamic behaviors of saturated soils because undrained excess pore water pressure builds up and then the strain softening behavior is occurred simultaneously. A few analytical constitutive models based on the effective stress concept have been proposed but most models hardly predict the excess pore water pressure and strain softening behaviors correctly In this study, the disturbed state concept (DSC) model proposed by Dr, Desai was modified to predict the saturated soil behaviors under the dynamic loads. Also, back-prediction program was developed for verification of modified DSC model. Cyclic triaxial tests were carried out to determine DSC parameters and test result was compared with the result of back-prediction. Through this research, it is proved that the proposed model based on the modified disturbed state concept can predict the realistic soil dynamic characteristics such as stress degradation and strain softening behavior according to dynamic process of excess pore water pressure.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.416-421
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• 2000

We have developed a simple model for describing the non-spherical particle growth phenomena using modified 1-dimensional sectional method. In this model, we solve simultaneously particle volume and surface area conservation sectional equations which consider particles' irregularities. From the correlation between two conserved properties of sections, we can predict the evolution of the aggregates' morphology. We compared this model with a simple monodisperse-assumed model and more rigorous two dimensional sectional model. For the comparison, we simulated silica and titania particle formation and growth in a constant temperature reactor environment. This new model shows a good agreement with the detailed two dimensional sectional model in total number concentration, primary particle size. The present model can also successfully predict particle size distribution and morphology without costing very heavy computation load and memory needed for the analysis of two dimensional aerosol dynamics.

• Korea-Australia Rheology Journal
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• v.13 no.1
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• pp.47-54
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• 2001

Prediction of the maximum packing volume fraction with non-spherical particles has been one of the important problems in powder technology. The sphericity of fly ash particles depending on the particle diameter was measured by means of a CCD image processing instrument. An algorithm to predict the maximum packing volume fraction with non-spherical particles is proposed. The maximum packing volume fraction is used to predict the slurry viscosity under well dispersed conditions. For this purpose, Simha's cell model is applied for concentrated slurry with wide particle size distribution. Also, Usui's model developed for aggregative slurries is applied to predict the non-Newtonian viscosity of dense fly ash - water slurry. It is certified that the maximum packing volume fraction for non-spherical particles can be successfully used to predict slurry viscosity. The pressure drop in a pipe flow is predicted by using the non-Newtonian viscosity of dense fly ash-water slurry obtained by the present model. The predicted relationship between pressure drop and flow rate results in a good agreement with the experimented data obtained for a test rig with 50 mm inner diameter tube. Base on the design procedure proposed in this study, a feasibility study of fly ash hydraulic transportation system from a coal-fired power station to a controlled deposit site is carried out to give a future prospect of inexpensive fly ash transportation technology.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.425-432
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• 1996

Integrated performance test of Chemical and Volume Control System (CVCS) was successfully performed in 1994. However, an extensive effort to correct hardware and software problems in the letdown line was required mainly due to the lack of adequate simulation code to predict the test accurately. Although the LTC computer code was used during the YGN 3'||'&'||'4 NSSS design process, the code can not satisfactorily predict the test due to its insufficient letdown line modeling. This study developed a numerical model to simulate the letdown test by modifying the current LTC code, and then verified the model by comparing with the test data. The comparison shows that the modified LTC computer code can predict the transient behavior of letdown system tests very well. Especially, the model was verified to be able to predict the "Stiction" phenomena which caused instantaneous fluctuations in the letdown backpressure and flowrate. Therefore, it is concluded that the modified LTC computer code with the ability of calculating the "Stiction" phenomena wi11 be very useful for future plant desist and test predictions.predictions.

• Tunnel and Underground Space
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• v.28 no.3
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• pp.277-291
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• 2018

LSTM (Long Short-Term Memory) algorithm which is a kind of recurrent neural network was used to establish a model to predict the stress-strain curve of an material under uniaxial compression. The model was established from the stress-strain data from uniaxial compression tests of silica-gypsum specimens. After training the model, it can predict the behavior of the material up to the failure state by using an early stage of stress-strain curve whose stress is very low. Because the LSTM neural network predict a value by using the previous state of data and proceed forward step by step, a higher error was found at the prediction of higher stress state due to the accumulation of error. However, this model generally predict the stress-strain curve with high accuracy. The accuracy of both LSTM and tangential prediction models increased with increased length of input data, while a difference in performance between them decreased as the amount of input data increased. LSTM model showed relatively superior performance to the tangential prediction when only few input data was given, which enhanced the necessity for application of the model.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.