• Journal of the Korea Society for Simulation
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• v.1 no.1
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• pp.1-16
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• 1992

This research develops two variance reduction methods for estimating the parameters of the experimental simulation model having multiple design points based on an approach focusing on reduction of the variances of the mean responses across multiple design points. The first method extends a combined approach of antithetic variates and control variates for a single design point to the multipopulation context with independent streams across the design points. The second method extends the same strategy in conjunction with the Schruben-Margolin method for improving the first method. We illustrate an example for implementing the second method. We expect these two approaches may improve the estimation of the parameters of interest compared with the control variates method.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.333-351
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• 2022

The skew-t distribution is an attractive family of asymmetrical heavy-tailed densities that includes the normal, skew-normal and Student's-t distributions as special cases. In this work, we propose an EM-type algorithm for computing the maximum likelihood estimates for skew-t linear regression models with censored response. In contrast with previous proposals, this algorithm uses analytical expressions at the E-step, as opposed to Monte Carlo simulations. These expressions rely on formulas for the mean and variance of a truncated skew-t distribution, and can be computed using the R library MomTrunc. The standard errors, the prediction of unobserved values of the response and the log-likelihood function are obtained as a by-product. The proposed methodology is illustrated through the analyses of simulated and a real data application on Letter-Name Fluency test in Peruvian students.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2712-2722
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• 2023

For an accurate and efficient time-dependent Monte Carlo (TDMC) neutron transport analysis, several advanced methods are newly developed and implemented in the Seoul National University Monte Carlo code, McCARD. For an efficient control of the neutron population, a dynamic weight window method is devised to adjust the weight bounds of the implicit capture in the time bin-by-bin TDMC simulations. A moving geometry module is developed to model a continuous insertion or withdrawal of a control rod. Especially, the history-based batch method for the TDMC calculations is developed to predict the unbiased variance of a bin-wise mean estimate. The developed methods are verified for three-dimensional problems in the C5G7-TD benchmark, showing good agreements with results from a deterministic neutron transport analysis code, nTRACER, within the statistical uncertainty bounds. In addition, the TDMC analysis capability implemented in McCARD is demonstrated to search the optimum detector positions for the pulsed-neutron-source experiments in the Kyoto University Critical Assembly and AGN201K.

• East Asian mathematical journal
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• v.40 no.1
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• pp.63-74
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• 2024

Timer options are financial instruments proposed by Société Générale Corporate and Investment Banking in 2007. Unlike vanilla options, where the expiry date is fixed, the expiry date of timer options is determined by the investor's choice, which is in linked to a variance budget. In this study, we derive a pricing formula for hybrid options that combine timer options, digital options, and power options, considering an environment where volatility of an underlying asset follows a fast-mean-reverting process. Additionally, we aim to validate the pricing accuracy of these analytical formulas by comparing them with the results obtained from Monte Carlo simulations. Finally, we conduct numerical studies on these options to analyze the impact of stochastic volatility on option's price with respect to various model parameters.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• Research in Community and Public Health Nursing
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• v.31 no.1
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• pp.96-106
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• 2020

Purpose: This study aimed to examine the level of acculturative stress, career stress, social support and depression, and identify factors affecting depression among Korean international students in China. Methods: Data were collected from 157 Korean students studying in undergraduate, graduate, students exchange programs and language training courses in G university, J university, and S university in G city, Guangdong Province, China, from September 1 to October 27, 2017. The data were analyzed with descriptive statistics, t-test, ANOVA, and multiple linear regression. Results: The mean acculturative stress was 62.24±18.08 out of 165, whereas the mean career stress was 65.47±19.79 out of 125. The mean social support was 95.03±14.64 out of 125, and the mean depression score was 13.83±9.24 out of 60. The factor that had the greatest effect on depression among the participants was acculturative stress (β=.26, p=.001), followed by career stress (β=.24, p=.002), frequency of weekly phone calls with family (β=.19, p=.006), source of tuition payment (β=.18, p=.009), and self-perceived health (β=.15, p=.040). The model explained 33% of the variance. Conclusion: It is necessary to develop depression prevention and management programs as well as a customized health promotion program that account for the factors identified to have an effect on depression, namely, acculturative stress, career stress, frequency of weekly phone calls with family, source of tuition payment, and self-perceived health, and increase awareness of depression among international students.

• Korean Journal of Human Ecology
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• v.17 no.5
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• pp.963-973
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• 2008

Clothing pressure is closely connected with the degree of comfort of an athlete's tight-fitting garments. Therefore, the construction of sports garments is very important to the wearer's athletic performance. In this study, the fundamental relationship between the reduction rates of stretch fabrics and clothing pressure was explored with the aim of improving clothing comfort and obtaining a systematic pattern reduction for women's tight-fitting bodysuits. A women's bodysuit pattern was obtained by the draping method using a dressform. The basic pattern was divided into four parts and changed into reduced pattems according to the amount of fabric stretch determined by ASTM D2594. Clothing pressure was measured using an air-pack-type pressure sensor (model AMI 3037-2) at 20 locations (shoulder, 9 locations; bust, 5; and armhole, 6). Among the 15 garments tested, the mean pressure of the A1 bodysuit was 4.60 $gf/cm^2$, and that of the C5 bodysuit was 22.98 $gf/cm^2$. The mean pressures of the bodysuits with reduction rates of 10% and 20% were below 10 $gf/cm^2$, while those of suits with reduction rates of 30%,40%, and 50% (except C5) were below 20 $gf/cm^2$. The pressure at the shoulder was 9.50$\sim$32.24 $gf/cm^2$, which was higher than that at the bust (3.34$\sim$24.56 $gf/cm^2$) and the armhole (0.95$\sim$12.15 $gf/cm^2$). The mean pressures of the 15 bodysuits were divided into five groups using analysis of variance (ANOVA), and were found to be significantly different (p<0.001). Regression analysis afforded the following expression: mean pressure ($gf/cm^2$) = 1.607 + 0.369[reduction rate (%)].

• Journal of Korea Water Resources Association
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• v.42 no.2
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• pp.149-160
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• 2009

The objective of this study is to suggest parameter regionalization scheme which is integrated two multivariate statistical methods: principal components analysis(PCA) and hierarchical cluster analysis(HCA). This technique is to apply semi-distributed rainfall-runoff model on ungauged catchments. 7 catchment characteristics (area, mean altitude, mean slope, ratio of forest, water content at saturation, field capacity and wilting point) are estimated for 109 mid-sized sub-basins. The first two components from PCA results account for 82.11% of the total variance in the dataset. Component 1 is related to the location of the catchments relevant to the altitude and Component 2 is connected with the area of these. 103 ungauged catchments are clustered using HCA as the following 6 groups: Goesan 23, Andong 6, Imha 5, Hapcheon 21, Yongdam 4, Seomjin 44. SWAT model is used to simulate runoff and the parameters of the model on the 6 gauged basins are estimated. The model parameters were regionalized for Soyang, Chungju and Daecheong dam basins which are assumed as ungauged ones. The model efficiency coefficients of the simulated inflows for these three dams were at least 0.8. These results also mean that goodness of fit is high to the observed inflows. This research will contribute to estimate and analyze hydrologic components on the ungauged catchments.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1330-1333
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• 2004

The methodology for modeling building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. Mostly, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents the application of neural networks (NN) to model landlord energy consumption of commercial buildings in Singapore. Firstly, a brief background information on NN and its application on the building energy research is provided. Secondly, five commercial buildings with various characteristics were selected for case studies. Monthly mean outdoor dry-bulb temperature ($T_0$), Relative Humidity (RH) and Global Solar Radiation (GSR) are used as network inputs and the landlord monthly energy consumption of the same period is the output. Up to three years monthly data are taken as training data. A forecast has been made for another year for all the five buildings. The performance of the NN analysis was evaluated using coefficient of variance (CV). The results show that NNs is powerful at predicting annual landlord energy consumption with high accuracy.