• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.145-152
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• 1996

In this study, the technique to evaluate the extreme design wave height of certain return period is developed from the given measured or hindcasted sea state data of concerned area for limited period. By using the order statistics and Monte Carlo Simulation method, the best fit probability distribution function with proper parameters describing the given wave height data is chosen, from which extreme design wave height can be predicted by extrapolation to the desired return period. The fitness and the confidence limit of the chosen probability function are also discussed. Application calculation is carried out for the wave height data given by applying the Wilson wave model theory to major 50 typhoon wind data affecting Korean South coast during the year from 1938 to 1987.

• East Asian Journal of Business Economics (EAJBE)
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• v.3 no.3
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• pp.14-26
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• 2015

A wind speed forecast is a crucial and sophisticated task in a wind farm for planning turbines and corresponds to an estimate of the expected production of one or more wind turbines in the near future. By production is often meant available power for wind farm considered (with units KW or MW depending on both the wind speed and direction. Such forecasts can also be expressed in terms of energy, by integrating power production over each time interval. In this study, we technically focused on mathematical modeling of wind speed and direction forecast based on locally data set gathered from Aghdasiyeh station in Tehran. The methodology is set on using most common techniques derived from literature review. Hence we applied the most sophisticated forecasting methods to embed seasonality, trend, and irregular pattern for wind speed as an angular variables. Through this research, we carried out the most common techniques such as the Box and Jenkins family, VARMA, the component method, the Weibull function and the Fourier series. Finally, the best fit for each forecasting method validated statistically based on white noise properties and the final comparisons using residual standard errors and mean absolute deviation from real data.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.64.1-64.1
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• 2013

The point spread function (PSF) of an optical system is in general defined as a two-dimensional intensity distribution which results from a single point source at infinity. It is an important key for the evaluation of the optical performance of an astronomical telescope. The PSFs of the soft X-ray telescope (SXT) aboard Yohkoh were measured in a wide range of the field-of-view under the in-flight configuration at White Sands Missile Range prior to launching the satellite. It has been known that the SXT PSF has a sharp peak at the core and the intensity drops very fast as it goes distant from the center. Due to the combination of this sharp peak at the PSF core and the effect of undersampling by a large pixel size, a carefully designed method is requested in the examination of the PSF data. The pattern of the SXT PSF is determined by the fitting of a mathematical functional form to the pre-launch experimental data. The elliptical Moffat function has been adopted for the evaluation of the SXT PSF. It is revealed from our study that the SXT PSF shows a peculiar characteristics, and thus a careful consideration on the undersampling effect and also a proper choice of statistics are necessary for the determination of the best fit function of the PSF. Details on the on- and off-axis SXT PSF in the field-of-view will be introduced and discussed in our presentation.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.3
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• pp.284-291
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• 2014

Finely-striate buccinum Buccinum striatissimum, a species of whelks, is caught mainly by pot in the eastern coastal waters of Korea. In order to determine the size selectivity of pot for the species, comparative fishing experiments were conducted near Yeongil Bay from June to September in 2003 using the dome-shaped pots with different five mesh sizes (17.1, 24.8, 35.3, 39.8, and 48.3 mm). The parameters of logistic equation were estimated by the SELECT (Share Each Length's Catch Total) method based on a multinomial distribution. The model with the estimated split parameter was found to fit the catch data best. The master selection curve was estimated to be s (R)=exp (13.044R-16.438)/[1 + exp (13.044R-16.438)], where R is the ratio of shell height to mesh size. The relative shell height of 50% retention was 1.260, and the selection range was 0.168. Enlargement in mesh size of the pot allows more small-sized whelks to escape.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.91-102
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• 2018

The purpose of this study is to develop an excellent bodice prototype that is adapted to the body shape of middle-aged women using 3D measurement data. In the evaluation of appearance, S pattern 4.00, B pattern 2.80, E pattern 2.40, L pattern 1.40 were shown in order, and the best fit of S pattern was evaluated as excellent. As a result of looking at the color distribution chart to find out the amount, E pattern and S pattern were not space in the front bust, armhole, and the back waist line. The B pattern and the L pattern were marked in blue because of insufficiency space in the back neck. As a result of evaluation the amount of air gap in the clothing, the air gap of the bust was 0.12, which is the largest pattern of B. Next, the L pattern appears as a tight circle with smallest air gap in the order of the S pattern 0.096, the E pattern 0.08, and the L pattern 0.003. The S pattern was evaluated to be the most appropriate for the body shape of middle-aged women. But the waist and back were slightly tight. Middle-aged women have larger shoulder-related items and larger waist circumference. Therefore, when you set the perimeter item, you should add 1-2cm of space amount and give extra space to the circumference area.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.645-656
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• 2020

In this study (Part II), the empirical formulation of corrosion model of a ship's ballast tank was developed to predict nonlinear time-dependent corrosion wastage based on the advanced data processing technique proposed by Part I. The detail on how to propose generalised mathematical formulation of corrosion model was precisely documented in the previous paper (Part I). The statistical scatter of corrosion data at any exposure time was investigated by the refined method and formulated based on a 2-parameter Weibull distribution which selected the best fit PDF. Throughout the nine (9) steps, empirical formulation of the ship's seawater ballast tank was successfully proposed and four (4) key step results were also obtained. The proposed method in Part I was verified and confirmed by this application of seawater ballast tank, thus making it possible to predict accurate behaviours of nonlinear timedependent corrosion. Developed procedures and obtained corrosion damage model for ship's seawater ballast tank can be used for development of engineering software.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.657-666
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• 2020

A reliable and cost-effective technique for the development of corrosion damage model is introduced to predict nonlinear time-dependent corrosion wastage of steel structures. A detailed explanation on how to propose a generalised mathematical formulation of the corrosion model is investigated in this paper (Part I), and verification and application of the developed method are covered in the following paper (Part II) by adopting corrosion data of a ship's ballast tank structure. In this study, probabilistic approaches including statistical analysis were applied to select the best fit probability density function (PDF) for the measured corrosion data. The sub-parameters of selected PDF, e.g., the largest extreme value distribution consisting of scale, and shape parameters, can be formulated as a function of time using curve fitting method. The proposed technique to formulate the refined time-dependent corrosion wastage model (TDCWM) will be useful for engineers as it provides an easy and accurate prediction of the 1) starting time of corrosion, 2) remaining life of the structure, and 3) nonlinear corrosion damage amount over time. In addition, the obtained outcome can be utilised for the development of simplified engineering software shown in Appendix B.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.69-81
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• 2021

The aim of this paper is to identify the key determinants in the Gulf Cooperation Council (GCC) countries for Foreign Direct Investment (FDI) inflows by using a balanced data panel for the period from 1995 to 2018. This study covers GCC countries in their entirety. The study uses ten explanatory variables, namely, trade ratio, gross domestic product, external balance, fuel exports, gross savings, international tourism, military expenditure, net foreign assets, services value added, and total natural resources. The authors have tried to find the best fit model from the differences methods considered such as OLS, GLS regression with the help of Hausman test, and country by country regressions as additional analysis. The study revealed a significantly positive association between inflation, trade ratio, gross domestic product, gross savings, and net foreign assets with FDI. On the contrary, international tourism was revealed to have a negative association with FDI. The sample of all GCC countries chosen for this study has not been considered widely by any earlier study. Moreover, this study covered many determinants of FDI that add to the previous literature. It is a significant contribution to the current research body and stresses the originality of this paper.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.