• Advances in materials Research
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• v.1 no.4
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• pp.285-297
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• 2012

The present study addresses the problem of quantitative prediction of effective complex relative permittivity of Barium Titanate/Polyvenylidene Fluoride (PVDF) and $(Bi_{0.5}Na_{0.5})_{0.94}Ba_{0.06}TiO_3$/Poly(VDF-TrFE) biphasic ceramic-polymer composites. Theoretical results for effective relative permittivity derived from several dielectric mixture equations were fitted to the experimental data taken from the works of Prasad et al. (2010), Wang et al. (2004), Takenaka et al. (1991) and Yamada et al. (1982). The study revealed that out of the different test equations, only a few equations like modified Rother-Lichtenecker equation, Dias-Dasgupta equation or Rao equation for the real part and Bruggeman equation for the imaginary part of complex permittivity well fitted the corresponding experimental results. In the present study, some of the equations were used in their original forms, while some others were modified by choosing suitable shape-dependent parameters in order to get reasonably good agreement with experimental results. Besides, the experimental results have been proposed in the form of a mathematical model using first order exponential growth, which provided excellent fits.

• International Journal of Automotive Technology
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• v.4 no.3
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• pp.125-133
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• 2003

Several models for the evaluation of Gross Heat Release from the internel combustion engine (ICE) are often used in literature. One of these is the First Law - Single Zone Model (FL-SZM), derived from the First Law of Thermodynamic. This model present a twice advantage: first it describes with accuracy the physic of the phenomenon (charge heat release during the combustion stroke and heat exchange between gas and cylinder wall); second it hat a great simplicity in the mathematical formulation. The evaluation of Heat Release with the FL-SZM is based on pressure experimental measurements inside the cylinder, and ell the assumption of several parameters as the specific heat ratio, wall temperature, polytropic exponent for the motored cycle evaluation, and many others. In this paper the influence of gases thermodynamic properties on Cross Heat Release has been esteemed. In particular the influence of an appropriate equation for k=k(T) (specific heat ratio vs. temperature) which describes the variations of gases thermodynamic properties with the mean temperature inside the cylinder has been evaluated. This equation has been calculated by new V order Logarithmic Polynomials (VoLP), fitting experimental gases properties through the least square methods.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4350-4356
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• 2023

Radiation detection systems working at high count rates suffer from the overlapping of their output electric pulses, known as pulse pile-up phenomenon, resulting in spectrum distortion and degradation of the energy resolution. Pulse tail extrapolation is a pile-up correction method which tries to restore the shifted baseline of a piled-up pulse by extrapolating the overlapped part of its preceding pulse. This needs a mathematical model which is almost always nonlinear, fitted usually by a nonlinear least squares (NLS) technique. NLS is an iterative, potentially time-consuming method. The main idea of the present study is to replace the NLS technique by an integration-based non-iterative method (NIM) for pulse tail extrapolation by an exponential model. The idea of linear extrapolation, as another non-iterative method, is also investigated. Analysis of experimental data of a NaI(Tl) radiation detector shows that the proposed non-iterative method is able to provide a corrected spectrum quite similar with the NLS method, with a dramatically reduced computation time and complexity of the algorithm. The linear extrapolation approach suffers from a poor energy resolution and throughput rate in comparison with NIM and NLS techniques, but provides the shortest computation time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.4
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• pp.457-465
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• 2010

In this study, a Taylor series (T-S) model based on the Arrhenius, McVetty, and Monkman-Grant equations was developed using a mathematical analysis. In order to reduce fitting errors, the McVetty equation was transformed by considering the first three terms of the Taylor series equation. The model parameters were accurately determined by a statistical technique of maximum likelihood estimation, and this model was applied to the creep data of alloy 617. The T-S model results showed better agreement with the experimental data than other models such as the Eno, exponential, and L-M models. In particular, the T-S model was converted into an isothermal Taylor series (IT-S) model that can predict the creep strength at a given temperature. It was identified that the estimations obtained using the converted ITS model was better than that obtained using the T-S model for predicting the long-term creep life of alloy 617.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.1
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• pp.38-44
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• 2007

Using the mathematical model suggested by Bennet et al.(1992), the reflection and transmission coefficients by a circular pile breakwater has been investigated in the framework of potential theory. Flow separation due to sudden contraction and expansion is generated and is the main cause of significant energy loss. Therefore, evaluation of exact energy loss coefficient is critical to enhance the reliability of mathematical model. To obtain the energy loss coefficient, 2-dimensional turbulent flow is analyzed using the FLUENT commercial code. The energy loss coefficient can be obtained from the pressure difference between upstream and downstream. Energy loss coefficient is the function of porosity and the relation equation between them is suggested throughout the curve fitting processing. To validated the suggested relation, comparison between the analytical results and the experimental results is made for four different porosities with good agreement.

• Journal of Forest and Environmental Science
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• v.28 no.4
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.75-98
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• 2021

This paper compares three statistical models that examine the relationship between national and provincespecific fertility rates. The three models are two of the regression models and a cointegration model. The regression model is by substituting Gompit transformation for the cumulative fertility rate by the average for ten years, and this model applies the raw data without transformation of the fertility data. A cointegration model can be considered when fitting the unstable time series of fertility rate in probability process. This paper proposes the following when it is intended to derive the relation of non-stationary fertility rate between the national and provinces. The cointegrated relationship between national and regional fertility rates is first derived. Furthermore, if this relationship is not significant, it is proposed to look at the national and regional fertility rate relationships with a regression model approach using raw data without transformation. Also, the regression model method of substituting Gompit transformation data resulted in an overestimation of fertility rates compared to other methods. Finally, Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon and Gyeonggi province are expected to show a total fertility rate of 1.0 or less from 2025 to 2030, so an urgent and efficient policy to raise this level is needed.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Journal of Ocean Engineering and Technology
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• v.21 no.4
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• pp.1-7
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• 2007

Based on the eigenfunction expansion method, the wave-absorbing performance of a square or circular pile breakwater was investigated. Flow separation resulting from sudden contraction and expansion is generated and is the main cause of significant energy loss. Therefore, evaluation of an exact energy loss coefficient is critical to enhancing the reliability of the mathematical model. To obtain the energy loss coefficient, 2-dimensional turbulent flow is analyzed using the FLUENT commercial code, and the energy loss coefficient can be obtained from the pressure difference between upstream and downstream. It was found that energy loss coefficient of circular pile is 20% that of a square pile. To validate the fitting equation for the energy loss coefficient, comparison between the analytical results and the experimental results (Kakuno and Liu, 1993) was made for square and circular piles with good agreement. The array of square piles also provides better wave-absorbing efficiency than the circular piles, and the optimal porosity value is near P=0.1.