• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Journal of the Korean Society of Costume
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• v.62 no.6
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• pp.139-153
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• 2012

To prolong the rapid progress of IT, it is necessary to develop contents through IT convergence among the existing goods & service and process areas to create new added-values. In particular, the wedding dress industry has infinite potential in utilizing various contents like virtual fitting by connecting with newly compelling IT areas such as smart phones, Augmented Reality (AR), and application contents. In the meantime, a large scale of the wedding industry has gained global competitiveness due to consulting expertise and the influence of the Korean Wave, whereas most small-sized wedding dress shops in Korea fall short of developing wedding dress designs and receiving relevant information. Accordingly, the purpose of this study was to help brides who have difficulties in choosing a wedding dress by decreasing their time and effort by providing wedding dress designs and information, according their desired image, body type, and circumstances through the utilization of virtual fitting application contents. Not only that, this study aims to diversify and specialize in wedding information and to help users to set a guideline for wedding dresses that are most suitable for them. Moreover, this study has an academic meaning in proposing an interdisciplinary convergence research model through the study of wedding dress design development, AR, and application contents utilization.

• Smart Structures and Systems
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• v.17 no.2
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• pp.231-256
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• 2016

This study concerns the derivation of optimum tuning formulas for a passive Tuned Mass Damper (TMD) device, for the case of benchmark ideal excitations acting on a single-degree-of-freedom (SDOF) damped primary structure. The free TMD parameters are tuned first through a non-linear gradient-based optimisation algorithm, for the case of harmonic or white noise excitations, acting either as force on the SDOF primary structure or as base acceleration. The achieved optimum TMD parameters are successively interpolated according to appropriate analytical fitting proposals, by non-linear least squares, in order to produce simple and effective TMD tuning formulas. In particular, two fitting models are presented. The main proposal is composed of a simple polynomial relationship, refined within the fitting process, and constitutes the optimum choice. A second model refers to proper modifications of literature formulas for the case of an undamped primary structure. The results in terms of final (interpolated) optimum TMD parameters and of device effectiveness in reducing the structural dynamic response are finally displayed and discussed in detail, showing the wide and ready-to-use validity of the proposed optimisation procedure and achieved tuning formulas. Several post-tuning trials have been carried out as well on SDOF and MDOF shear-type frame buildings, by confirming the effective benefit provided by the proposed optimum TMD.

• Transactions of Materials Processing
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• v.28 no.5
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• pp.266-274
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• 2019

Metal sealing is used to connecting the parts between valves and fuel pipes for a FCEV which utilizes hydrogen gas of 700 bar. Instead of general carbon steel, stainless steel is the primary material used to manufacture fuel pipes due to hydrogen embrittlement. The shape of deformation between metals is an important factor on the air-tightness of the metal to metal contact. Since the stainless steel pipe is hardened using the plastic forming during the tip shaping stage, this work hardening could have an effect on the deformed shape and characteristics of contact surfaces in fastening of pipes. In this paper, the deformation history of the pipe model was considered in order to analyze the hydrogen-tightness on the metal sealing part. The contact distance and the forward displacement for fastening were compared using experimental results and the simulation results. The simulation of the effect of material change on the fitting body demonstrated that the hardness or the strength of the formed tip of the pipe was designed to a proper valued level since the characteristics of the contact surface was exhibited better when the strength of the pipe was lower than that of the fitting body.

• Wind and Structures
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• v.34 no.6
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• pp.469-482
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• 2022

The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.

• Analyses & Alternatives
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• v.5 no.1
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• pp.3-24
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• 2021

This paper introduces a multilevel item-response-theory (IRT) model as a unifying model for hypothesis testing using legislative voting data. This paper shows that a probit or logit model is a special type of multilevel IRT model. In particular, it is demonstrated that, when a probit or logit model is applied to multiple votes, it makes unrealistic assumptions and produces incorrect coefficient estimates. The advantages of a multilevel IRT model over a probit or logit model are illustrated with a Monte Carlo experiment and an example from the U.S. House. Finally, this paper provides a practical guide to fitting this model to legislative voting data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.697-703
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• 2000

As device dimensions are lastly scaled down, impact ionization(I.I.) events are very important to analyze hot carrier transport in high energy region, and the exact model of impact ionization is demanded on device simulation. We calculate full band model by empirical pseudopotential method and the impact ionization rate is derived from modified Keldysh formula. We calculate impact ionization coefficients by full band Monte Carlo simulator to investigate temperature dependent characteristics of impact ionization for GaAs as a function of field. Resultly impact ionization coefficients are in good agreement with experimental values at look. We how energy is increasing along increasing the field, while energy is decreasing along increasing the temperature since the phonon scattering rates for emission mode are very high at high temperature. The logarithmic fitting function of impact ionization coefficients is described as a second orders function of temperature and field. The residuals of the logarithmic fitting function are mostly within 5%. We Dow, therefore, the logarithm of impact ionization coefficients has quadratic dependence on temperature, and we can save time of calculating the temperature dependent impact ionization coefncients as a function of field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.460-464
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• 2000

As device dimensions are lastly scaled down, impact ionization(I.I.) events are very important to analyze hot carrier transport in high energy region, and the exact model of impact ionization is demanded on device simulation. We calculate full band model by empirical pseudopotential method and the impact ionization rate is derived from modified Keldysh formula. We calculate impact ionization coefficients by full band Monte Carlo simulator to investigate temperature-and field-dependent characteristics of impact ionization for GaAs. Resultly impact ionization coefficients are In good agreement with experimental values at 300k. We know energy is increasing along increasing the field. while energy is decreasing along increasing the temperature since the phonon scattering rates for omission mode are very high at high temperature. The logarithmic fitting function of impact ionization coefficients is described as a second orders function for temperature and field. The residuals of the logarithmic fitting function are mostly within 5%. We know, therefore, logarithm of impact ionization coefficients has quadratic dependence on temperature and field, and we can save time of calculating the temperature- and field-dependent impact ionization coefficients.

• Korean Journal of Optics and Photonics
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• v.18 no.3
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• pp.179-184
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• 2007

In order to extract more accurate skin parameters, this study was focused on the improvement of the efficiency of a previous inverse fitting algorithm based on genetic algorithms. The algorithm provides the best fitting result of the diffusion approximation model to a VRS (visual reflectance spectroscopy) curve of skin. Simplex and wavelength selection methods were applied to the previous algorithm. Nine skin parameters were inversely extracted from the modeling studies. The revised inverse fitting algorithm was determined to produce an 83% reduction of computation time and a 0.64% reduction of sum of square error, compared to the previous algorithm. In conclusion, we confirmed that the new algorithm provides faster and more accurate solutions for the diffusion approximation model.

• Fashion & Textile Research Journal
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• v.23 no.5
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• pp.611-623
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• 2021

In the era of COVID-19 and advanced ICT technology, retail technologies such as those that use virtual reality(VR) have been drawing significant attention in the fashion industry. This study investigated the impact of shopping motivation and telepresence on consumer attitude, trust, and behavioral intentions in VR fitting room application environments. An online survey was conducted on female consumers in their 20s and 30s after exploring a VR fitting room application. Overall, 225 responses were used for the analysis. The study demonstrated that usefulness had a significant effect on attitude toward product (ATP) and trust toward app (TTA), while enjoyment had a significant effect on ATP, but did not significantly affect TTA. Telepresence did not significantly affect TTA, but had a significant influence on ATP and behavioral intention. TTA had a significant influence on ATP, and both ATP and TTA had significant effects on behavioral intention. Moreover, the effects of usefulness, enjoyment, and telepresence on ATP, TTA, and behavioral intention were significant, as the self-congruity between consumers and avatars increased. The application of the motivation theory and technology acceptance model offers theoretical perspectives for understanding VR fitting room application users' attitudinal and behavioral responses in mobile shopping environments. In addition, this study provides practical implications to mobile retailers that utilize advanced technologies.