• 한국전자통신학회논문지
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• 제18권6호
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• pp.1171-1176
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• 2023

본 논문은 입찰사이트 전기넷과 OK EMS에서 입수한 입찰데이터로 DNBP(Deep learning Network to predict Budget Price) 모델을 통해 예정가격을 예측한다. 우리는 DNBP 모델을 활용하여 4개의 추첨예비가격을 예측을 하고, 이를 산술평균 한 뒤 예정가격 사정률을 계산하여, 실제 예정가격 사정률과 비교하여 모델의 성능을 평가한다. DNBP의 15개의 입력노드 중 일부 입력노드를 제거하여 모델을 학습시켰다. 예측 결과 예측 결과 입력노드가 6개(a, g, h, i, j, k) 일 때 DNBP의 RMSE가 0.75788% 로 가장 낮았다.

• Journal of Mechanical Science and Technology
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• 제20권11호
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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.

• 한국자동차공학회논문집
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• 제5권2호
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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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• 제23권1호
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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.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집B
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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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• 제13권1호
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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.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 춘계학술발표회논문집(1)
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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.

• 터널과지하공간
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• 제28권3호
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• pp.277-291
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• 2018

이 논문에서는 재료의 단축하중 하에서의 응력-변형률 곡선을 예측하기 위하여 순환 신경망의 일종인 LSTM(Long Short-Term Memory) 알고리즘을 사용하였다. 석고와 규사를 혼합해 만든 재료에 일축압축시험을 수행하여 얻은 응력-변형률 데이터를 이용하였으며, 낮은 응력 구간의 초반 데이터를 활용해서 파괴 전까지의 거동을 예측하였다. 앞부분의 데이터를 활용하여 단계적으로 뒤쪽 구간의 값을 예측하는 LSTM 순환 신경망의 구조상 큰 응력에 대응하는 변형률을 예측할 경우에는 앞쪽 구간의 오차가 누적되어 실측값과 차이가 늘어났으나 전반적으로 높은 정확도로 응력-변형률 곡선을 예측하였다. 예측에 사용한 초기 데이터의 길이가 늘어나는 경우 정확도는 조금 증가했다. 그러나 접선을 이용한 단순 예측과의 성능 차이는 초기 데이터의 길이가 작은 경우에 두드러졌으며, 적은양의 데이터로도 응력-변형률 곡선 전체 구간의 예측을 가능하게 한다는 점으로부터 신경망 모델의 필요성을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제22권3호
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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.