• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.609-614
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• 1998

A new small signal modeling of an average current mode control is proposed. In order to analyze the characteristics of the control scheme, the discrete and continuous time small signal models are derived. The derivation are mainly come from the analysis of the sampling effect presented in the current control loop. By the mathematical interpretation of practical sampler representing the sampling effect of a current control loop, the small signal models of an average current mode control can be easily derived. The instability of the current control loop, which gives rise to the subharmonic oscillation, can be identified by the proposed models. To show the usefulness of the proposed models, the simulation and experiment are carried out. The results show that the predicted results by the proposed model are much better agreed with the measured ones than that of the conventional model, even though the high gain of the compensation network of a current control loop is employed.

• BMB Reports
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• v.56 no.4
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• pp.225-233
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• 2023

Hepatocellular carcinoma (HCC) is a very common form of cancer worldwide and is often fatal. Although the histopathology of HCC is characterized by metabolic pathophysiology, fibrosis, and cirrhosis, the focus of treatment has been on eliminating HCC. Recently, three-dimensional (3D) multicellular hepatic spheroid (MCHS) models have provided a) new therapeutic strategies for progressive fibrotic liver diseases, such as antifibrotic and anti-inflammatory drugs, b) molecular targets, and c) treatments for metabolic dysregulation. MCHS models provide a potent anti-cancer tool because they can mimic a) tumor complexity and heterogeneity, b) the 3D context of tumor cells, and c) the gradients of physiological parameters that are characteristic of tumors in vivo. However, the information provided by an multicelluar tumor spheroid (MCTS) model must always be considered in the context of tumors in vivo. This mini-review summarizes what is known about tumor HCC heterogeneity and complexity and the advances provided by MCHS models for innovations in drug development to combat liver diseases.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.1-7
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• 2016

Recently, with the development of advanced thin film devices comes the need for constant high quality vacuum as the deposition pressure is more demanding. It is for this reason our research seeks to understand how the variable design factors are employed in such vacuum systems. In this study, the effects of design factor applications on the vacuum characteristics were simulated to obtain the optimum design modeling of variable models on an ultra high vacuum system. The commercial vacuum system simulator, $VacSim^{(multi)}$, was used in our investigation. The reliability of the employed simulator was verified by the simulation of the commercially available models of ultra high vacuum system. Simulated vacuum characteristics of the proposed modeling aligned with the observed experimental behavior of real systems. Simulated behaviors showed the optimum design models for the ideal conditions to achieve optimal pressure, pumping speed, and compression ratio in these systems.

• Journal of the Korean Statistical Society
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• v.15 no.1
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• pp.46-61
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• 1986

Many procedures for deriving the Moore-Penrose invese $X^+$ have been developed, but the explicit forms of Moore-Penerose inverses for design matrices in analysis of variance models are not known heretofore. The purpose of this paper is to find explicit forms of $X^+$ for the one-way and the two-way analysis of variance models. Consequently, the Moore-Penerose inverse $X^+$ and the shortest solutions of them can be easily obtained to the level of pocket calculator by way of our results.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2960-2967
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• 2021

In this work, a new hardening model is proposed for the depth-dependent hardness of ion-irradiated polycrystals with obvious grain size effect. Dominant hardening mechanisms are addressed in the model, including the contribution of dislocations, irradiation-induced defects and grain boundaries. Two versions of the hardening model are compared, including the linear and square superposition models. A succinct parameter calibration method is modified to parametrize the models based on experimentally obtained hardness vs. indentation depth curves. It is noticed that both models can well characterize the experimental data of unirradiated polycrystals; whereas, the square superposition model performs better for ion-irradiated materials, therefore, the square superposition model is recommended. In addition, the new model separates the grain size effect from the dislocation hardening contribution, which makes the physical meaning of fitted parameters more rational when compared with existing hardness analysis models.

• Journal of the Korean Society for Heat Treatment
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• v.37 no.3
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• pp.103-113
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• 2024

Martensite volume fraction significantly affects the mechanical properties of alloy steels. Martensite start temperature (M_s), transformation temperature for martensite 50 vol.% (M₅₀), and transformation temperature for martensite 90 vol.% (M₉₀) are important transformation temperatures to control the martensite phase fraction. Several researchers proposed empirical equations and machine learning models to predict the Ms temperature. These numerical approaches can easily predict the Ms temperature without additional experiment and cost. However, to control martensite phase fraction more precisely, we need to reduce prediction error of the Ms model and propose prediction models for other martensite transformation temperatures (M₅₀, M₉₀). In the present study, machine learning model was applied to suggest the predictive model for the Ms, M50, M90 temperatures. To explain prediction mechanisms and suggest feature importance on martensite transformation temperature of machine learning models, the explainable artificial intelligence (XAI) is employed. Random forest regression (RFR) showed the best performance for predicting the Ms, M50, M90 temperatures using different machine learning models. The feature importance was proposed and the prediction mechanisms were discussed by XAI.

• Advances in concrete construction
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• v.9 no.2
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• pp.149-159
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• 2020

The present work proposes new engineering models for determining corrosion initiation time in concrete reinforcing steels in marine environment. The models are based on Fick's second law that is commonly used for chloride diffusion. The latter is based on deterministic analyses involving the most influencing parameters such as distance of the concrete structure from the seaside, depth of steel concrete cover, ambient temperature, relative humidity and the water-cement ratio. However, a realistic corrosion initiation time cannot be estimated because of the uncertainties associated to the different parameters of the models. Therefore a reliability approach using FORM/SORM method has been applied to develop the proposed engineering models integrating a limit state function and a reliability index β. As a result, the corrosion initiation time is expressed by new exponential engineering models where the uncertainties are associated to the model parameters. The main emerging result is a realistic decision tool for corrosion planning inspection.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.867-889
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• 2014

Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.

• International Journal of Vascular Biomedical Engineering
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• v.4 no.1
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• pp.9-16
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• 2006

In this article we introduce computer models that have been developed in the past to determine the concentration of metabolic gases, the oxygen and carbon dioxide, along the pulmonary circulation. The terminal concentration of these gases in the arterial blood is related with the total change of the partial pressure of the same gases in the alveoli for the time beginning with inspiration and ending with expiration. It is affected not only by the ventilation-perfusion ratio and the gas diffusion capacity of the lung membrane but also by the pulmonary defect such as shunt, dead space, diffusion impairment and ventilation-perfusion mismatch. Some pathological pulmonary symptoms such as ARDS and CDPD can be understood through the mathematical models of these pulmonary dysfunctions. Quantitative study on the blood oxygenation process using various computer models is therefore of foremost importance in order to monitor not only the pulmonary health but also the cardiac output and cell metabolism. Reviewed in this paper include the basic and advanced methods that enable numerical study on the gas exchange and on the arterial oxygenation process, which might depend on the various heart and lung physiological conditions listed above.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.1
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• pp.23-48
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• 2022

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.