• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.95-104
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• 2006

The Gradient Vector Flow (GVF) snake or active contour model offers the best performance for image segmentation. However, there are problems in classical snake models such as the limited capture range and the slow progress into concavity. This paper presents a new method for enhancing the performance of the GVF snake model by extending the external force fields from the neighboring fields and using a modified smoothing method to regularize them. The results on a simulated U-shaped image showed that the proposed method has larger capture range and makes it possible for the contour to progress into concavity more quickly compared with the conventional GVF snake model.

• Journal of Magnetics
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• v.18 no.3
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• pp.308-310
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• 2013

Translation induced by the field-gradient force is being observed for a single ferromagnetic iron grain and a ferrimagnetic grain of a ferrite sample ($CuFe_2O_4$). From measurements on the translation, precise saturated magnetization of $M_S$ is possible for a single grain. The method is based on the energy conservation rule assumed for the grain during its translation and the grain is translated through a diffuse area under microgravity conditions. The results of the two materials indicate that a field-induced translation of grain bearing spontaneous moment is generally determined by a field-induced potential $-mM_SH(x)$ where m denotes the mass of sample. According to the above translations, the detection of $M_S$ is not interfered by any signals from the sample holder. The $M_S$ measurement does not require m value. By observing translations resulting from fieldinduced volume forces, the magnetization of a single grain is measurable irrespective of its size; the principle is also applicable to measuring susceptibility of diamagnetic and paramagnetic materials.

• Journal of Magnetics
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• v.8 no.4
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• pp.164-168
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• 2003

In high density recording system, the recording head field on a medium should be focused in small bit area and should have a sufficient value to overcome the medium coercivity, which resulted in head saturation. In this paper, an efficient method to access the head field and field gradient considering head saturation is presented. The magnetic vector potential on the head surface is pre-calculated considering head saturation in several cases and accumulated into database. The head field on the recording media is easily produced solving Laplace equation using accessed magnetic vector potential boundaries. The computed head field is compared with a quantified magnetic force microscopy measurement.

• Advances in nano research
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• v.14 no.5
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• pp.475-493
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• 2023

In the context of nonclassical nonlocal strain gradient elasticity, this article studies the free and forced responses of functionally graded material (FGM) porous nanoplates exposed to thermal and magnetic fields under a moving load. The developed mathematical model includes shear deformation, size-scale, miscorstructure influences in the framework of higher order shear deformation theory (HSDT) and nonlocal strain gradient theory (NSGT), respectively. To explore the porosity effect, the study considers four different porosity models across the thickness: uniform, symmetrical, asymmetric bottom, and asymmetric top distributions. The system of quations of motion of the FGM porous nanoplate, including the effects of thermal load, Lorentz force, due to the magnetic field and moving load, are derived using the Hamilton's principle, and then solved analytically by employing the Navier method. For the free and forced responses of the nanoplate, the effects of nonlocal elasticity, strain gradient elasticity, temperature rise, magnetic field intensity, porosity volume fraction, and porosity distribution are analyzed. It is found that the forced vibrations of FGM porous nanoplates under thermal and live loads can be damped by applying a directed magnetic field.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.943-944
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• 2008

• 한국초전도학회:학술대회논문집
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• 2009.07a
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• pp.123-123
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• 2009

• Journal of Sensor Science and Technology
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• v.15 no.3
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• pp.158-163
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• 2006

This paper reports a novel immunoassay method using superparamagnetic nanoparticles and an enhanced magnetic field gradient for the detection of protein in a microfluidic device. We use superparamagnetic nanoparticles as a label and fluorescent polystyrene beads as a solid support. Based on this platform, magnetic force-based microfluidic immunoassay is successfully applied to analyze the concentration of IgG as model analytes. In addition, we present ferromagnetic microstructure connected with a permanent magnet to increase magnetic flux density gradient (dB/dx, ${\sim}10^{4}$ T/m), which makes limit of detection reduced. The detection limit is reduced to about 1 pg/mL.

• Advances in nano research
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• v.6 no.1
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• pp.21-37
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• 2018

In the present article, wave dispersion behavior of a temperature-dependent functionally graded (FG) nanobeam undergoing rotation subjected to thermal loading is investigated according to nonlocal strain gradient theory, in which the stress numerates for both nonlocal stress field and the strain gradient stress field. The small size effects are taken into account by using the nonlocal strain gradient theory which contains two scale parameters. Mori-Tanaka distribution model is considered to express the gradually variation of material properties across the thickness. The governing equations are derived as a function of axial force due to centrifugal stiffening and displacements by applying Hamilton's principle according to Euler-Bernoulli beam theory. By applying an analytical solution, the dispersion relations of rotating FG nanobeam are obtained by solving an eigenvalue problem. Obviously, numerical results indicate that various parameters such as angular velocity, gradient index, temperature change, wave number and nonlocality parameter have significant influences on the wave characteristics of rotating FG nanobeams. Hence, the results of this research can provide useful information for the next generation studies and accurate deigns of nanomachines including nanoscale molecular bearings and nanogears, etc.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.46.3-47
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• 2016

We construct a solar eruption model with a background solar wind by performing three-dimensional zero-beta magnetohydrodynamic (MHD) simulation. The initial configuration of a magnetic field is given by nonlinear force-free field (NLFFF) reconstruction applied to a flux emergence simulation. The background solar wind is driven by upflows imposed at the top boundary. We analyzed the temporal development of the Lorentz force at the flux tube axis. Based on the results, we demonstrate that a solar eruption is caused by the imbalance between magnetic pressure gradient force and magnetic tension force. We conclude that this imbalance is produced by a weak but continuously existing solar wind above an active region.

• Wind and Structures
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• v.36 no.6
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.