• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.998-1012
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• 2013

The article is concerned with a mathematical modeling which can improve performances of PDE-based restoration models. Most PDE-based restoration models tend to lose fine structures due to certain degrees of nonphysical dissipation. Sources of such an undesirable dissipation are analyzed for total variation-based restoration models. Based on the analysis, the so-called equalized net diffusion (END) modeling is suggested in order for PDE-based restoration models to significantly reduce nonphysical dissipation. It has been numerically verified that the END-incorporated models can preserve and recover fine structures satisfactorily, outperforming the basic models for both quality and efficiency. Various numerical examples are shown to demonstrate effectiveness of the END modeling.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1517-1522
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• 2012

Anion effects on structures of $Cd^{II}$ complexes containing 2,2'-bipyridine (2,2'-bpy) ligands have been studied, and compared with $Zn^{II}$-(2,2'-bpy) complexes. For each anion, different structures have been obtained in both $Zn^{II}$-(2,2'-bpy) and $Cd^{II}$-(2,2'-bpy). Polymeric structures of $Cd^{II}$-2,2'-bpy complexes can be produced by hydrogen bonding interactions as shown in $Zn^{II}$-2,2'-bpy complexes. In addition, the bigger size of a $Cd^{II}$ ion gives higher coordination numbers forming variety of structures, and it makes that chlorides can act as bridging ligands to form a one-dimensional structure. The compound $\mathbf{5}$ catalyzed efficiently the transesterification of a variety of esters with methanol, while the rest of the compounds have displayed very slow conversions. In addition, the emission bands of complexes $\mathbf{1}$, $\mathbf{2}$, $\mathbf{4}$, and $\mathbf{6}$ are blue-shifted compared to the corresponding ligand 2,2'-bpy, whereas $\mathbf{3}$ and $\mathbf{5}$ showed the similar emission observed for the ligand.

• Publications of The Korean Astronomical Society
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• v.13 no.1 s.14
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• pp.31-37
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• 1998

The size of fine structures in the quiescent prominence that appeared on August 16, 1992 has been estimated using power spectra generated from intensity variations of Ha images of the lower part of the prominence, which were taken with a G1 CCD camera attached to 25cm coronagraph at Norikura Coronal Station in Japan. The lower part of the prominence has shown a distinct intensity variation with optical thickness of $\tau=1\~5$. Our analysis yields a mean size of fine structures ranging from 350 km to 1,000 km, in good agreement with Hirayama(1985) and Zirker & Koutchmy(1989, 1991).

• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.04a
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• pp.119-122
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• 2004

A CPGFRP(Carbon Powder Glass Fiber Reinforced Plastics) sensor was fabricated for fine crack detection of structures. The electrical resistance of the sensor was measured on condition of various composition of carbon powders and thickness of bundle of glass fibers. The resistance was decreased as the increase of the content of carbon powders and the TEX of the glass fibers. In the case of loading on CPGFRP sensor, because inner crack was propagated, the part of percolation structures was disconnected. The sensor is superior to carbon fiber for the detecting ability of fine crack.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.96-96
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• 2016

Nanotechnology mostly employs nano-materials and nano-structures with distinctive properties based on their size, structure, and composition. It is quite difficult to produce nano-materials and nano-structures with identical sizes, structures, and compositions in large quantities, because of spatiotemporal fluctuation of production processes. In other words, fluctuation is the bottleneck in nanotechnology. We propose three strategies to suppress such fluctuations: employing 1) difference between linear and nonlinear phenomena, 2) difference in time constants, and 3) nucleation as a bottleneck phenomenon. We are also developing nano- and micro-scale guided assembly using plasmas as a plasma nanofabrication.1-5) We manipulate nano- and micro-objects using electrostatic, electromagnetic, ion drag, neutral drag, and optical forces. The accuracy of positioning the objects depends on fluctuation of position and energy of an object in plasmas. Here we evaluate such fluctuations and discuss the mechanism behind them. We conducted in-situ evaluation of local plasma potential fluctuation using tracking analysis of fine particles (=objects) in plasmas. Experiments were carried out with a radio frequency low-pressure plasma reactor, where we set two quartz windows at the top and bottom of the reactor. Ar plasmas were generated at 200 Pa by applying 13.56MHz, 450V peak-to-peak voltage. The injected fine particles were monodisperse methyl methacrylate-polymer spheres of $10{\mu}m$ in diameter. Fine particles were injected into the reactor and were suspended around the plasma/sheath boundary near the powered electrode. We observed binary collision of fine particles with a high-speed camera. The frame rate was 1000-10000 fps. Time evolution of their distance from the center of mass was measured by tracking analysis of the two particles. Kinetic energy during the collision was obtained from the result. Potential energy formed between the two particles was deduced by assuming the potential energy plus the kinetic energy is constant. The interaction potential is fluctuated during the collision. Maximum amplitude of the fluctuation is 25eV, and the average is 8eV. The fluctuation can be caused by neutral molecule collisions, ion collisions, and fluctuation of electrostatic force. Among theses possible causes, fluctuation of electrostatic force may be main one, because the fine particle has a large negative charge of -17000e and the corresponding electrostatic force is large compared to other forces.

• The magazine of the Korean Society for Advanced Composite Structures
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• v.6 no.1
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• pp.37-42
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• 2015

• The Korean Journal of Cytopathology
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• v.8 no.1
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• pp.52-56
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• 1997

Oxyphilic (Hurthle) cells have abundant eosinophilic granular cytoplasms and occur in both benign and malignant neoplasms of the thyroid gland. Most published studies described mainly oxyphilic tumors of follicular type, and reports on oxyphilic papillary thyroid carcinomas are rare. The oxyphilic variant of papillary carcinoma differs from the classic papillary carcinoma in its more aggressive biological behavior. We report a case of oxyphilic papillary carcinoma exhibiting exclusively oxyphilic cells in a follicular pattern without papillary structures. The pattern of papillary carcinoma was confirmed by fine needle aspiration material and lymph nodal metastasis, both of which revealed typicaled papillary structures with characteristic nuclear features, psammoma bodies, and oxyphilic cytoplasm.

• Journal of Welding and Joining
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• v.16 no.1
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• pp.98-105
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• 1998

The accuracy of the finite element method depends upon the mesh that is used in the analysis. The temperature around the arc is higher than the melting point of the materials, and it drops sharply in the regions just away from the arc. This requires an extremely fine mesh in the confined high temperature region to predict the temperature accurately in that region. But the computational time increases with the fineness of mesh. Since fine mesh is required only around the arc source, adaptivity of the input mesh according to the position of the arc source is efficient. The remeshing technique gives a fine mesh in the high temperature region around the arc and a coarse mesh in other region at any time step. With this it is possible to achieve desired accuracy with less computation time. In this study a transient adaptive mesh, remeshing technique, is developed and calculated temperature for a sample problem.

• Computers and Concrete
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• v.10 no.2
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• pp.197-217
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• 2012

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.961-971
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• 2012

This article is concerned with effective numerical techniques for partial differential equation (PDE)-based image restoration. Numerical realizations of most PDE-based denoising models show a common drawback: loss of fine structures. In order to overcome the drawback, the article introduces a new time-stepping procedure, called the method of nonflat time evolution (MONTE), in which the timestep size is determined based on local image characteristics such as the curvature or the diffusion magnitude. The MONTE provides PDE-based restoration models with an effective mechanism for the equalization of the net diffusion over a wide range of image frequency components. It can be easily applied to diverse evolutionary PDE-based restoration models and their spatial and temporal discretizations. It has been numerically verified that the MONTE results in a significant reduction in numerical dissipation and preserves fine structures such as edges and textures satisfactorily, while it removes the noise with an improved efficiency. Various numerical results are shown to confirm the claim.