• Water Engineering Research
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• v.4 no.2
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• pp.69-85
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• 2003

Previously developed empirical equations used to calculate the parameters of the transient storage model are analyzed in depth in order to evaluate their behavior in representing solute transport in the natural streams with storage zone. A comparative analysis of the existing theoretical and experimental equations used to predict parameters of the transient storage (TS) model is reported. New simplified equations for predicting 4 key parameters of the TS model using hydraulic data sets that are easily obtained in the natural streams are also developed. The weighted one-step Huber method, which is one of the nonlinear multi-regression methods, is applied to derive new parameters equation. These equations are proven to be superior in explaining mixing characteristics of natural streams with the transient storage zone more precisely than the other existing equations.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.70-73
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• 2004

The approach in this thesis is for prediction of deformed strip profile in hot rolling mill. This approach shows how to make an expression as a mathematical form in predicting strip profile. This approach is based on the velocity field, shear stress and material flow on the strip edge along width direction and lateral displacement and stress along width are analytically calculated. Roll force is calculated in each section and then combined together to show roll force distribution along width. All the assumptions to make equation form for this approach are supported by FEM simulation result and the result of model is verified by FEM result. So, this model will supply very useful tool to the researcher and engineers which takes less time and has similar accuracy in predicting roll force profile comparing to FEM simulation. This model has to be combined with deformed roll profile model, which include thermal crown prediction and wear prediction model to predict deformed strip profile.

• Journal of Mechanical Science and Technology
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• v.18 no.1
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• pp.153-166
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• 2004

This paper presents a comparative study of a fully coupled, upwind, compressible Navier-Stokes code with three two-equation models and the Baldwin-Lomax algebraic model in predicting transonic/supersonic flow. The k-$\varepsilon$ turbulence model of Abe performed well in predicting the pressure distributions and the velocity profiles near the flow separation over the axisymmetric bump, even though there were some discrepancies with the experimental data in the shear-stress distributions. Additionally, it is noted that this model has y$\^$*/ in damping functions instead of y$\^$＋/. The turbulence model of Abe and Wilcox showed better agreements in skin friction coefficient distribution with the experimental data than the other models did for a supersonic compression ramp problem. Wilcox's model seems to be more reliable than the other models in terms of numerical stability. The two-equation models revealed that the redevelopment of the boundary layer was somewhat slow downstream of the reattachment portion.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.4 no.1
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• pp.23-32
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• 1997

The change in moisture content of moisture sensitive products in moisture-semipermeable packages was investigated for the purpose of predicting the shelf life of a product-package combination. A mathematical model, and a computer program based on the physiochemical properties of the product and the moisture permeability of the package was developed. The moisture content for products in moisture-semipermeable packages was determined under various environmental conditions and the results were compared with the predicted values by means of the simulation model. These experimental studies demonstrated that the prediction of the change in moisture content of packaged products over time by the simulation model is accurate, within a practical range of temperature and relative humidity values. The developed semi-empirical model is considered to have applications in industry, since it provides product shelf life information for a range of temperature and relative humidity conditions, with a limited number of experimentally obtained data points.

• Journal of the Korean School Mathematics Society
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• v.10 no.1
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• pp.113-127
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• 2007

This study explored the possibility of building a statistical model predicting difficulty of mathematics test items through the analysis of nation-wide scholastic ability test results for the past 5 years. Multiple linear regression analysis was conducted in predicting difficulty of mathematics test items. We adopted three major areas for independent variables: the content area, the behavior area, and the test item format area, each of which was categorized into more detailed sub-areas. For the dependent variable, the proportion of correct answer was used to represent the item difficulty. Statistically significant independent variables were included in the regression model based on the stepwise selection method. Several important factors affecting difficulty of mathematics test items for each area were identified. R-squares for the final regression model were fairly high, implying that the regression equation can be used to predict difficulty of test items at an acceptable level. Lastly, the regression model was cross-validated using independently collected data. We believe that this study will provide basic but very critical information for predicting the proportion of correct answer by showing the factors that should be considered for developing mathematics test items for the college entrance examination or high school classroom test.

• Quality Improvement in Health Care
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• v.9 no.1
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• pp.18-32
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• 2002

Background : We developed a model for predicting premature discharge and identifying related factors. Methods : Prediction model was developed by data mining techniques. Basic data were collected from the total discharge data base of a university hospital in Chungnam Province during the period from July 1, 1999 to June 30, 2000. Results : 1. Among 22,873 patients, the number of patients discharged with usual discharge orders were 21,695 or 94.8%. The number of the prematurely discharged patients were 1,178 or 5.2%. 2. The primary reason for unusual discharge was transfer to other hospital. Move to a local hospital closer to their home and burdensome medical expenses were main reasons. 3. Predictability of each model was tested using the top 10 percent of patients with the highest probabilities of premature discharge. The neural network model was chosen as the most appropriate model for predicting prematurely discharged patients. 4. Ten percent of the total number of patients had been selected randomly to test the effectiveness of the neural network model. We have chosen the threshold of the neural network model as 0.7. The number of patients who were expected to discharge prematurely was 312. Among them, 241 had been discharged prematurely (77.2%). Conclusion : Of the several data mining techniques used, the neural network model was the most effective, It can be used to identify and manage the patients who are expected to discharge prematurely.

• Advances in concrete construction
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• v.14 no.1
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• pp.71-77
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• 2022

Creep phenomenon affects the stability and integrity of concrete structures. An inaccurate prediction of these strains may lead to the appearance of cracks and excessive deflections which may cause in some cases the demolition of structures. In fact, the measured values of these uncontrolled strains appear often to be clearly different and larger than the expected ones. Therefore, an accurate prediction of concrete deformations is a necessity. As a matter of fact, the codified descriptions of this phenomenon are unreliable and don't consider the effect of admixtures. The physical nature of creep is not well understood and almost all creep models are mainly of empirical nature. To overcome this issue, a study of the correlation between different parameters affecting concrete creep is performed and a new model for predicting creep of concrete is elaborated. This new model considers the effect of admixtures, specifically the silica fume, in predicting concrete creep and allows an accurate prediction of this phenomenon. The proposed model is based on the observation of physical behavior of creep phenomenon. It targets at expressing creep compliance in terms of structural and environmental parameters. In fact, the experimental observations show that creep curves follow two kinetic regimes leading to a model called Phenomenological Creep Model. By adequate regressions and substitutions, and according to this model, we can express creep compliance in terms of structural, environmental parameters and admixture types and percentage. The proposed new Phenomenological Creep Model Silica Fume (PCM19SF) calculates accurately creep of concrete by considering the effect of silica fume.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• Computers and Concrete
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• v.12 no.5
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• pp.697-715
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• 2013

Numerous studies have been conducted to understand the shear behavior of reinforced concrete (RC) beams since it is a complex phenomenon. The diagonal cracking strength of a RC beam is critical since it is essential for determining the minimum amount of stirrups and the contribution of concrete to the shear strength of the beam. Most of the existing equations predicting the diagonal cracking strength of RC beams are based on experimental data. A powerful computational tool for analyzing experimental data is an artificial neural network (ANN). Its advantage over conventional methods for empirical modeling is that it does not require any functional form and it can be easily updated whenever additional data is available. An ANN model was developed for predicting the diagonal cracking strength of RC slender beams without stirrups. It is shown that the performance of the ANN model over the experimental data considered in this study is better than the performances of six design code equations and twelve equations proposed by various researchers. In addition, a parametric study was conducted to study the effects of various parameters on the diagonal cracking strength of RC slender beams without stirrups upon verifying the model.

• Computers and Concrete
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• v.22 no.4
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.