• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1519-1528
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• 2018

The objective of the present study is the development of a comprehensive air chemical kinetic model that includes 11 species and 54 chemical reactions for the numerical investigation of air nonequilibrium inductively coupled plasmas. The two-dimensional, compressible Navier-Stokes equations coupled with the electromagnetic-field equations were employed to describe the fundamental characteristics of an inductive plasma. Dunn-Kangs 32 chemical-reaction model of air was reconstructed and used as a comparative model. The effects of the different chemical kinetic models on the flow field were analyzed and discussed at identical/different working pressures. The results theoretically indicate that no matter the working pressure is low or high, the use of the 54 chemical kinetic model presented in this study is a better choice for the numerical simulation of a nonequilibrium air ICP.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.633-645
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• 2018

Gaussian error distributions are a common choice in traditional regression models for the maximum likelihood (ML) method. However, this distributional assumption is often suspicious especially when the error distribution is skewed or has heavy tails. In both cases, the ML method under normality could break down or lose efficiency. In this paper, we consider the log-concave and Gaussian scale mixture distributions for error distributions. For the log-concave errors, we propose to use a smoothed maximum likelihood estimator for stable and faster computation. Based on this, we perform comparative simulation studies to see the performance of coefficient estimates under normal, Gaussian scale mixture, and log-concave errors. In addition, we also consider real data analysis using Stack loss plant data and Korean labor and income panel data.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.139-141
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• 2019

Capacitive power transfer has an advantage in the simplicity of the energy link structure. So, the conventional phase -shift full bridge sometime is not always the best choice because of its complexity and high cost. On the other hand, the link capacitance is usually very low and requires high-frequency operation, but, the series resonant converter loses zero-voltage switching feature in the light load condition, which makes the switching loss high especially in CPT system. The paper proposes a new low-cost topology based on asymmetric half-bridge to achieve simplicity as well as wide zero voltage switching range. The design procedure is presented, and circuit operations are analyzed and verified by simulation.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.12
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• pp.13-20
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• 2019

The purpose of this study was to investigate the effects of visual properties on the human movement behavior experimentally and empirically using spatial analysis technique based on ecological perception theory. For the survey of choosing behaviors of heading direction in built environments, the experiment was conducted in which the subjects were made to choose moving directions in some spaces using the virtual environment simulation tool, and then comparative analysis was conducted on the interrelation between the experiment results and various visual properties in existing spatial analysis techniques based on ecological perception theory. As a result, the occlusivity of the isovist theory was found to be the most significant index in the human choice of heading direction, and the longest radial also showed somewhat significant effect on it.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• Journal of the Korean Statistical Society
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• v.3 no.1
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• pp.3-12
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• 1974

The two-stage sequential test, suggested by Baker [2] for testing hypotheses $H_0:\mu=\mu_0$ and $H_1:\mu=\mu_1$ of $N(\mu,\sigma^2)$ with the unknown $\sigma^2$ would not be amenable for applications unles some cluses on the choice of the first-stage sample size are available. The study in this paper is intended to shed some light on the size of the first-stage sample. An approximate method is used to estimate an optimal initial sample size that minimizes the average sample number. In brief, the optimal size is a strictly monotone decreasing function of the quantity $(\mu_1-\mu_0)/\sigma$. Empirical and simulation results are used to ascertain the negligible effect of possible errors due to approximations and assumptions used.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.531-550
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• 2023

The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries.

• Fire Science and Engineering
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• v.29 no.5
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• pp.42-50
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• 2015

Three simulation approaches for turbulence were applied for the computation of propane dispersion in a simplified real-scale urban area with one building:, Large Eddy Simulation (LES), Detached Eddy Simulation (DES), and Unsteady Reynolds Averaged Navier-Stokes (RANS). The computations were performed using FLUENT 14, and the grid system was made with ICEM-CFD. The propane distribution depended on the prediction performance of the three simulation approaches for the eddy structure around the building. LES and DES showed relatively similar results for the eddy structure and propane distribution, while the RANS prediction of the propane distribution was unrealistic. RANS was found to be inappropriate for computation of the gas dispersion process due to poor prediction performance for the unsteady turbulence. Considering the computational results and cost, DES is believed to be the optimal choice for computation of the gas dispersion in a real-scale space.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.62-74
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• 2020

Although the government and local governments devote efforts to activate bicycles, they only access to the supply infrastructure such as bike lanes and the public bicycle rental service centers without considering the measures to overcome the geographical constraints of slope. Therefore, this study constructs bicyclist's energy consumption estimation model through experimental methods of slope gradient and heart rate measurement and suggest the bicycle route choice model which could minimize the energy by the slope gradient. After calculating the RMSE of the estimated energy consumption by applying this model to the simulation section, it is confirmed to be 41% better than the model which does not reflect slope gradient. The results of this study are expected to be applied to the bicycle infrastructure planning that considers both longitude and transverse of bike lanes and the algorithm of bicycle route guidance system in the future.