• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.10
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• pp.767-770
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• 2010

In this work, we investigated the static characteristics of 4H-SiC vertical metal-oxidesemiconductor field effect transistors (VMOSFETs) by adjusting the doping level of n-epilayer and the effect of a current spreading layer (CSL), which was inserted below the p-base region with highly doped n+ state ($5{\times}10^{17}cm^{-3}$). The structure of SiC VMOSFET was designed by using a 2-dimensional device simulator (ATLAS, Silvaco Inc.). By varying the n-epilayer doping concentration from $1{\times}10^{16}cm^{-3}$ to $1{\times}10^{17}cm^{-3}$, we investigated the static characteristics of SiC VMOSFETs such as blocking voltages and on-resistances. We found that CSL helps distribute the electron flow more uniformly, minimizing current crowding at the top of the drift region and reducing the drift layer resistance. For that reason, silicon carbide VMOSFET structures of highly intensified blocking voltages with good figures of merit can be achieved by adjusting CSL and doping level of n-epilayer.

• Journal of the Korean Magnetic Resonance Society
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• v.15 no.1
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• pp.25-39
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• 2011

Syndecan-4 is a transmembrane heparan sulfate proteoglycan, which is a coreceptor with integrins in cell adhesion. To get better understand the mechanism and function of Syndecan-4, it is critical to elucidate the three-dimensional structure of a single transmembrane spanning region of them. Unfortunately, it is hard to prepare the peptide because syndecan-4 is membrane-bound protein that transverse the lipid bilayer of the cell membrane. Generally, the preparation of transmembrane peptide sample is seriously difficult and time-consuming. In fact, high yield production of transmembrane peptides has been limited by experimental adversities of insufficient yields and low solubility of peptide. Here, we demonstrate experimental processes and results to optimize expression, purification, and NMR measurement condition of Syndecan-4 transmembrane peptide.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.10
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• pp.865-871
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• 2014

Paradigm shift to 3-D chip stacking in electronic packaging has induced a lot of integration challenges due to the reduction in wafer thickness and pitch size. This study presents a hybrid bonding technology by self-alignment effect in order to improve the flip chip bonding accuracy with ultra-thin wafer. Optimization of Cu pillar bump formation and evaluation of various factors on self-alignment effect was performed. As a result, highly-improved bonding accuracy of thin wafer with a $50{\mu}m$ of thickness was achieved without solder bridging or bump misalignment by applying reflow process after thermo-compression bonding process. Reflow process caused the inherently-misaligned micro-bump to be aligned due to the interface tension between Si die and solder bump. Control of solder bump volume with respect to the chip dimension was the critical factor for self-alignment effect. This study indicated that bump design for 3D packaging could be tuned for the improvement of micro-bonding quality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• Industrial Engineering and Management Systems
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• v.3 no.2
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• pp.129-133
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• 2004

Studies on the optimal location of retail store have been made in case of no obstacle(Minagawa etal. 1999). This paper deals with the location problem of retail store considering obstacles (e.g. rivers, railways, highways, etc.) and obstacle-overcoming points (e.g. bridges, railway crossings, zebra crossings, overpasses, etc.). We assume that (1) commercial goods dealt here are typically convenience goods, (2) the population is granted as potential demand, (3) the apparent demand is a function of the maximum migration length and the distance from the store to customers, (4) the scale of a store is same in every place and (5) there is no competitor. First, we construct the basic model of customers' behavior considering obstacles and obstacle-overcoming points. Analyzing the two dimensional model, the arbitrary force attracting customers is represented as a height of a cone where the retail store is located on the center. Second, we formulate the total demand of customers and determine the optimal location that maximizes the total demand. Finally, the properties of the optimal location are investigated by simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1765-1772
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• 1997

The layered composites have a character to change of structure stiffness with respect to the layup angles. The deformations in the fan-blades to be initially designed by considering efficiency and noise, etc., which arise due to the pressure during the fan operation, can make the fan inefficient. Thus, so as to minimize the deformations of the blades, it is needed to increase the stiffness of the blades. An investigation has been performed to develop the three dimensional layered composite shell element with the drilling degree of freedom and the optimization module for finding optimal layup angles with sensitivity analysis. And then they have been verified. In this study, the analysis model is engine cooling fan of automobile. In order to analyzes the stiffness of the composite fan blades, finite element analysis is performed. In addition, it is linked with optimal design process, and then the optimal angles that can maximize the stiffness of the blades are found. In the optimal design process, the deformations of the blades are considered as multiobjective functions, and this results minimum bending and twisting simultaneously.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.4
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• pp.410-417
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• 2017

Hydro-forming is being employed increasingly to realize lightweight vehicular parts. The bumper beam produced by this process weighs 30% less than the conventional products with equal stiffness. However, hydro-forming involves complex parameters to obtain the target geometry and low residual stress. Parametric studies are conducted using finite element analysis to obtain optimized process conditions. Through these numerical approaches, the internal and holding pressures and feeder forward stroke along the extruded direction are optimized to achieve low residual stress and to minimize springback. The numerical results are verified by experimental observations made by employing a three-dimensional laser scanner. The numerical and experimental results are compared in terms of the springback. Both results show similar tendencies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.8
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• pp.10-20
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• 2002

The present paper investigates methods to control dynamic stall using an optimal approach. An unsteady aerodynamic sensitivity analysis code is developed by a direct differentiation method from a two-dimensional unsteady compressible Navier-Stokes solver including a two-equation turbulence model. Dynamic stall control is conducted by minimizing an objective function defined at an instant instead of integrating for a period of time. Unsteady sensitivity derivatives of the objective function are calculated by the sensitivity code, and optimization is carried out using a linear line search method at every physical time step. Numerous examples of dynamic stall control using control parameters such as nose radius, maximum thickness of airfoil, or suction show satisfactory results.

• International Journal of Contents
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• v.13 no.2
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• pp.21-28
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• 2017

Abnormal data in the manufacturing process makes it difficult to find useful information that can be applied in data management for the manufacturing industry. It causes various problems in the daily process of production. An issue from the abnormal data can be handled by our method that uses big data and visualization. Visualization is a new technology that transforms data representation into a two-dimensional representation. Nowadays, many newly developed technologies provide data analysis, algorithm, optimization, and high efficiency, and they meet user requirements. We propose combined production of the data visualization approach that uses integrative visualization of sources of abnormal pattern analysis results. The perceived idea of the proposed approach can solve the problem as it also works for big data. It can also improve the performance and understanding by using visualization and solving issues that occur in the manufacturing process with a calendar heat map.