• Journal of Korean Society for Quality Management
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• v.41 no.1
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• pp.109-118
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• 2013

Purpose: Given the several proper models for given mixture components-process variables experimental data, we propose a strategy to find the optimal condition in which the performance of the responses is well-behaved under those models. Methods: Given the mixture experimental data with process variables, first we choose the reasonable starting models among the class of admissible product models based on the model selection criteria and then, search for the candidate models that are the subset models of the starting model by the sequential variable selection method or all possible regressions procedure. Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. Results: We propose a strategy to find the optimal condition in which the performance of the responses is well-behaved under those good candidate models by adopting the optimization methods developed in multiple responses surface methodology. Conclusion: A strategy is proposed to find the optimal condition in which the performance of the responses is well-behaved under those proper combined models. This strategy to find the optimal condition is illustrated with the example in this paper.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.83-90
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• 2011

The purpose of this study is to develop a series of trend analysis models for industrial water consumption and to propose a blueprint for the integration of the developed models with GIS. For the consumption data acquisition, a real-time sensing technique was adopted. Data were transformed from the field equipments to the management server in every 5 minutes. The data acquired were substituted to a polynomial formula selected. As a result, a series of models were developed for the consumption of each day. A series of validation processes were applied to the developed models and the models were finalized. Then the finalized models were transformed to the average models representing a day's average consumption or an average daily consumption of each month. Demand pattern analyses were fulfilled through the visualization of the finally derived models. It has founded out that the demand patterns show great consistency and, therefore, it is concluded that high probability of demand forecasting for a day or for a season is available. Also proposed is the integration with GIS as an IT tool by which the developed forecasting models are utilized.

• The Research Journal of the Costume Culture
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• v.25 no.3
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• pp.285-300
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• 2017

This study analyzes the images of models in womenswear advertisements targeting women in their fifties. The goals of this study are: 1) to investigate beauty ideals for middle-aged women by analyzing models' look age, chronological age, wrinkles, gray hair, hair length, body type, and race; and 2) to explore how ageing is dealt with in advertisements by analyzing the range of bodies shown in advertisements, the color mode of photographs, and the clarity of models' figures in relation to models' look ages. A total of 155 printed advertisements from January 2012 to January 2017 from the brands Daks Ladies, Lebeige, Luciano Choi, PAT, and Zishen were selected for analysis. Womenswear brands targeting middle-aged women reinforce cultural ideals of female beauty that emphasize youth and slenderness. They do this by using thin and slender models, who most often appear to be in their twenties and thirties, and have hair longer than their shoulders. Brands with higher price ranges show a preference for Caucasian models, which reveals that a Caucasian identity is associated with sophistication. In addition, the bodies of models who appear to be in their forties and fifties were concealed by framing photographs mostly above the knees. Older models' features were also obscured via the use of black and white photography, strong lighting and contrast, and digital editing that blurred the boundaries between figures and their backgrounds. These decisions for how to represent models could result in negative self-esteem and a denial of the symptoms of ageing among middle-aged women.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1367-1375
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• 2015

Consider the linear growth models for longitudinal data analysis. Several kind of linear growth models are selected such as time-effect and random-effect models as well as a dummy variable included model. In this work, simulation data are generated with normality assumption, and both binormal ROC curve and AUC are obtained and compared for various linear growth models. It is found that ROC curves have different shapes and AUC increase slowly, as values of the covariance increase and the time passes for random-effect models. On the other hand, AUC increases very fast as values of covariance decrease. When the covariance has positive value, we explored that the variances of random-effect models increase and the increment of AUC is smaller than that of AUC for time-effect models. And the increment of AUC for time-effect models is larger than the increment for random-effect models.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Journal of electromagnetic engineering and science
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• v.16 no.2
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• pp.87-99
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• 2016

This paper describes an implementation method and the results of numerical mobile phone models representing real phone models that have been released on the Korean market since 2002. The aim is to estimate the electromagnetic absorption in the human brain for case-control studies to investigate health risks related to mobile phone use. Specific absorption rate (SAR) compliance test reports about commercial phone models were collected and classified in terms of elements such as the external body shape, the antenna, and the frequency band. The design criteria of a numerical phone model representing each type of phone group are as follows. The outer dimensions of the phone body are equal to the average dimensions of all commercial models with the same shape. The distance and direction of the maximum SAR from the earpiece and the area above -3 dB of the maximum SAR are fitted to achieve the average obtained by measuring the SAR distributions of the corresponding commercial models in a flat phantom. Spatial peak 1-g SAR values in the cheek and tilt positions against the specific anthropomorphic mannequin phantom agree with average data on all of the same type of commercial models. Second criterion was applied to only a few types of models because not many commercial models were available. The results show that, with the exception of one model, the implemented numerical phone models meet criteria within 30%.

• Imaging Science in Dentistry
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• v.51 no.4
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• pp.429-438
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• 2021

Purpose: This study investigated the accuracy of laser-scanned models and 3-dimensional(3D) rendered cone-beam computed tomography (CBCT) compared to the gold standard (plaster casts) for linear measurements on dental arches. Materials and Methods: CBCT scans and plaster models from 30 patients were retrieved. Plaster models were scanned by an Emerald laser scanner (Planmeca, Helsinki, Finland). Sixteen different measurements, encompassing the mesiodistal width of teeth and both arches' length and width, were calculated using various landmarks. Linear measurements were made on laser-scanned models using Autodesk Meshmixer software v. 3.0 (Autodesk, Mill Valley, CA, USA), on 3D-rendered CBCT models using OnDemand 3D v. 1.0 (Cybermed, Seoul, Korea) and on plaster casts by a digital caliper. Descriptive statistics, the paired t-test, and intra- and inter-class correlation coefficients were used to analyze the data. Results: There were statistically significant differences between some measurements on plaster casts and laser-scanned or 3D-rendered CBCT models (P<0.05). Molar mesiodistal width and mandibular anterior arch width deviated significantly different from the gold standard in both methods. The largest mean differences of laser-scanned and 3D-rendered CBCT models compared to the gold standard were 0.12±0.23 mm and 0.42±0.53 mm, respectively. Most of the mean differences were not clinically significant. The intra- and inter-class correlation results were acceptable for all measurements(>0.830) and between observers(>0.801). Conclusion: The 3D-rendered CBCT images and laser-scanned models were useful and accurate alternatives to conventional plaster models. They could be used for clinical purposes in orthodontics and prostheses.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Geomechanics and Engineering
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• v.35 no.4
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• pp.449-464
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• 2023

Advanced nonlinear effective stress constitutive models are started to be frequently used in one-dimensional (1D) and two-dimensional (2D) site response analysis for assessment of porewater generation and liquefaction potential in soft soil deposits. The emphasis of this research is on the assessment of the implementation of this category of models at the element stage. Initially, the performance of a coupled porewater pressure (PWP) and constitutive models were evaluated employing a catalogue of 40 unidirectional cyclic simple shear tests with a variety of relative densities between 35% and 80% and effective vertical stresses between 40 and 80 kPa. The authors evaluated three coupled constitutive models (PDMY02, PM4SAND and PDMY03) using cyclic direct simple shear tests and for decide input parameters used in the model, procedures are recommended. The ability of the coupled model to capture dilation as strength is valuable because the studied models reasonably capture the cyclic performance noted in the experiments and should be utilized to conduct effective stress-based 1D and 2D site response analysis. Sandy soils may become softer and liquefy during earthquakes as a result of pore-water pressure (PWP) development, which may have an impact on seismic design and site response. The tested constitutive models are mathematically coupled with a cyclic strain-based PWP generation model and can capture small-strain stiffness and large-strain shear strength. Results show that there are minor discrepancies between measured and computed excess PWP ratios, indicating that the tested constitutive models provide reasonable estimations of PWP increase during cyclic shear (r_u) and the banana shape is reproduced in a proper way indicating that dilation and shear- strain behavior is well captured by the models.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.