• Progress in Medical Physics
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• v.28 no.1
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• pp.11-15
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• 2017

Relative biological effectiveness (RBE) of particle beam needs to be evaluated at particle beam therapy centers before the clinical application of the particle beam. However, since RBE analysis is implemented manually, it is useful to have a tool that can easily and effectively handle the data of experiments to generate cell survival curve and to analyze RBE simultaneously. In this work, the development of a program for RBE analysis of particle beam therapy was presented. This RBE analysis program was developed to include two parts; fitting the cell survival curves to linear-quadratic model and calculating the RBE values at a certain endpoint using fitting results. This program was also developed to simultaneously compare and analyze the template results that stored experiment data with photon and particle beam irradiations. The results of the cell survival curve obtained by each irradiation can be analyzed by the user on a desired data after reading the template stored in the easy-to-use excel file. The analysis results include the cell survival curves with error range, which are appeared in the screen and the ${\alpha}$ and ${\beta}$ parameters of linear-quadratic model with 95% confidence intervals, RBE values, and $R^2$ values to evaluate goodness-of-fit of survival curves to model, which are stored in a text cvs file. This software can generate cell survival curve, fit to model, and calculate RBE all at once with raw experiment data, so it helps users to save time for data handling and to reduce the possibility of making error on analysis. As a coming plan, we will create a user-friendly graphical user interface to present the results more intuitively.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.141-147
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• 2018

This study analyzed the early retired employees who stayed in company no longer than 3 years based on a certain company's personality evaluation result data. The predicted model was analyzed by dividing into two categories; the manufacture group and the R&D group. Independent variables were selected according to the stepwise method. A logistic regression model was selected as a prediction model among various supervised learning methods, and trained through cross-validation to prevent over-fitting or under-fitting. The accuracy of the two groups were confirmed by the confusion matrix. The most influential factor for early retirement in the manufacture group was revealed as "immersion," and for the R&D group appeared as "antisocial." In the past, people concentrated on collecting data by questionnaire and identifying factors that are highly related to the retirement, but this study suggests a sustainable early retirement prediction model in the future by analyzing the tangible outcome of the recruitment process.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.29-40
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• 2008

Although a good understanding of the relationship between highway traffic accidents and highway geometric features is fundamental in highway design and safety, the relationship is not well understood quantitatively. The overall goal of this paper is to formulate a reliable statistical model fitting to historical highway accident data. The model can be used to estimate the effect of road design elements on safety for the practical purposes of highway design applications. En route to achieving this goal, a number of specific research objectives were accomplished: investigate the major design elements affecting highway safety; review the existing modeling approaches in order to assess the relationship between safety and highway design features; and formulate a statistical model fitting to the accident data in order to estimate the interchange ramp junction accident frequency of rural highways.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.11
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• pp.92-99
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• 2021

Agricultural tractors drive and operate both off-road and on-road. Tire-road interaction significantly affects the tractive performance of a tractor, which is difficult to predict numerically. Many empirical models have been developed to predict the tractive performance of tractors using the cone index, which can be measured through simple tests. However, a magic formula model that can determine the tractive performance without a cone index can be used instead of traditional empirical models as the cone index cannot be measured on asphalt roads. The aim of this study was to predict the tractive performance of a tractor using the magic formula tire model. The traction force of the tires on an asphalt road was measured using an agricultural tractor. The dynamic wheel load was calculated to derive the coefficients of the traction-slip curve using the measured static wheel load and drawbar pull of the tractor. Curve fitting was performed to fit the experimental data using the magic formula. The parameters of the magic formula tire model were well identified, and the model successfully determined the coefficient of traction of the tractor.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.3
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• pp.45-52
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• 1992

The Mathematical Model, which can describe the maneuvering motion of a ship in low speed, is highly required these days because it is directly related to the safety of ship in confused harbour. Kose has presented a new model for the low speed maneuvering motion, but the usefulness of it is not confirmed widely. Lets of difficulties are revealed in the case of low speed maneuver, The first is the fact that a ship moves the stirred water region for the longer time than in the case of high speed. So, the hydrodynamic forces, exerted on the hull need to be treated strictly, not by the ordinary differential equation with constant coefficients. Another difficulty is arised from the fact the lateral motion is relatively large comparing to the longitudinal motion in low speed. And, by the result the effect of cross-flow drag or vortex sheding effects are dominant. Besides, the captive model tests of low speed motion has lots of problems. For example, the hydrodynamic forces do not converge to a certain values for the long time. And the absolute values of measured forces are very small, so we must expend lots of efforts to raise up the S/N ratio of the experiments. In this paper, a new mathematical model for the maneuvering motion in low speed, is built up, and the usefulness is discussed, comparing with other models, for example, Kose's model or M.M.G. model or Cross-Flow model, The CMT data for a PCC model of 3.00 M length, released from the RR-742 of Japan, are used for the validation of each models.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• Journal of Life Science
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• v.14 no.1
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• pp.131-140
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• 2004

T7 bacteriophage gp4 is the replicative DNA helicase that unwinds double-stranded DNA by utilizing dTTP hydrolysis energy. The quaternary structure of the active form of T7 helicase is a hexameric ring with a central channel. Single-stranded DNA passes through the central channel of the hexameric ring as the helicase translocates $5'\rightarrow3'$ along the single-stranded DNA. The DNA unwinding was measured by rapid kinetic methods and showed a lag before the single-stranded DNA started to accumulate exponentially. This behavior was analyzed by a kinetic stepping model for the unwinding process. The observed lag phase increased as predicted by the model with increasing double-stranded DNA length. Trap DNA added in the reaction had no effect on the amplitudes of double-stranded DNA unwound, indicating that the $\tau7$ helicase is a highly processive helicase. Global fitting of the kinetic data to the stepping model provided a kinetic step size of 10-11 bp/step with a rate of $3.7 s^{-1}$ per step. Both the mechanism of DNA unwinding and dTTP hydrolysis and the coupling between the two are unaffected by temperature from $4∼37^{\circ}C$. Thus, the kinetic stepping for dsDNA unwinding is an inherent property of tile replicative DNA helicase.

• Journal of applied mathematics & informatics
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• v.30 no.3_4
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• pp.489-498
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• 2012

Most of the software reliability models are based on black box approach and these models consider the entire software system as a single unit. Present day software development process has changed a lot. In present scenario these models may not give better results. To overcome this problem an improved additive model has been proposed in this paper, to estimate the reliability of software with modular structure. Also the concept of imperfect debugging has been also considered. A maximum likelihood estimation technique has been used for estimating the model parameters. Comparison has been made with an existing model. ${\chi}^2$ goodness of fit has been used for model fitting. The proposed model has been validated using real data.

• Korean Management Science Review
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• v.19 no.2
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• pp.125-137
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• 2002

The telecommunications market is expanding rapidly and becoming more substitutive. In this environment, demand forecasting is very difficult, yet important for both practitioners and researchers. in this paper, we adopt the modeling approach proposed dy Jun and Park ［6］. The basic premise is that demand patterns result from choice behavior, where customers choose a product to maximize their utility. We apply a choice-based substitutive diffusion model to the Korean mobile telecommunication service market where digital service has completely replaced analog service. In comparison with Bass-type multigeneration models. our model provides superior fitting and forecasting performance. The choice-based model is useful in that it enables the description of such complicated environments and provides the flexibility to include marketing mix variables such as price and advertising in the regression analysis.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.459-470
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• 2012

This paper summarizes a study for the modal analysis and model updating conducted using the monitoring data obtained from the Canton Tower of 610 m tall, which was established as an international benchmark problem by the Hong Kong Polytechnic University. Modal properties of the tower were successfully identified using frequency domain decomposition and stochastic subspace identification methods. Finite element model updating using the measurement data was further performed to reduce the modal property differences between the measurements and those of the finite element model. Over-fitting during the model updating was avoided by using an optimization scheme with a regularization term.