• Progress in Superconductivity
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• v.11 no.2
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• pp.128-134
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• 2010

The magnetic flux profiles in SmBCO and YBCO coated conductors(CC) in the presence of the external field were comparatively investigated by magneto-optic image and scanning hall probe measurements. The current distributions calculated by using the inversion method from measured field profiles show that the decrease of current densities near the edges of SmBCO CC is more significant than those of YBCO CC. Through the comparison of the numerical analysis based on Kim's critical state model and the Brandt and Indenbom's solution, we found that this feature is related to their different field dependant properties of the critical current densities.

• Journal of the Optical Society of Korea
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• v.6 no.3
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• pp.76-82
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• 2002

For dielectric gratings on a flat metal surface, photonic band gaps created by Brags scattering of surface plasmon polaritons are observed. The observation result that directly images this gap is compared with that predicted by a numerical model based on a plane wave expansion. Consistency between the experimental and numerical results is also confirmed by comparison with the well-known calculation method of diffraction, the rigorous coupled wave analysis method.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.79.3-79.3
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• 2019

GMACS is one of the first light instruments for the Giant Magellan Telescope (GMT). The development of GMACS control software follows Agile software development process, and the design of the software is based on the Unified Model Language (UML). In this poster, we present the architecture of the GMACS software and the development processes. As an example of the software development, we show the software of the Slit Mask Exchange Mechanism Prototype (SMEM-P) which is part of the GMACS Device Control Package (DCP).

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.431-434
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• 2008

The physical OLED analog behavioral model for SPICE simulation has been described using Verilog-A language. The model is based on the carrier-balance between the hole and electron injected through Schottky barrier at anode and cathode. The accuracy of this model was examined by comparing with the results from device simulation.

• Corrosion Science and Technology
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• v.23 no.4
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• pp.324-333
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• 2024

Atmospheric corrosion poses a significant threat to durability of metallic materials and safety of structures, making precise prediction of corrosion rates crucial in industrial and engineering settings. Understanding the exact rate of corrosion is essential. However, accurate inclusion of various environmental factors that can influence atmospheric corrosion in the calculation of corrosion rate is a complex challenge. This study introduces a physics-based model that incorporates electrochemical methods and considers active surface area affected by surface contaminants to estimate atmospheric corrosion rate of carbon steel. The model can evaluate corrosion levels using key factors such as chloride deposition rate, relative humidity, and the presence of surface particles. By integrating these considerations, this model moves beyond empirical estimations, providing a more stable prediction of corrosion rate that is less susceptible to environmental variations. This model provides a robust tool for defense applications, offering precise insights into the dynamics of atmospheric corrosion that could enhance the maintenance and safety of weapon systems.

• Journal of Soil and Groundwater Environment
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• v.22 no.6
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• pp.66-73
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• 2017

Empirical erosion models like Universal Soil Loss Equation (USLE) models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well utilizing big data related to climate, geography, geology, land use, etc within study domains, they do not adequately describe the physical process of soil erosion on the ground surface caused by rainfall or overland flow. In other words, such models are still powerful tools to distinguish the erosion-prone areas at large scale, but physics-based models are necessary to better analyze soil erosion and deposition as well as the eroded particle transport. In this study a physics-based soil erosion modeling system was developed to produce both runoff and sediment yield time series at watershed scale and reflect them in the erosion and deposition maps. The developed modeling system consists of 3 sub-systems: rainfall pre-processor, geography pre-processor, and main modeling processor. For modeling system validation, we applied the system for various erosion cases, in particular, rainfall-runoff-sediment yield simulation and estimation of probable maximum sediment (PMS) correlated with probable maximum rainfall (PMP). The system provided acceptable performances of both applications.

• Progress in Medical Physics
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• v.32 no.4
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• pp.172-178
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• 2021

Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility. Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics. Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant. Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.121-122
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• 2002

The main principle of radiation therapy is to deliver optimum dose to tumor to increase tumor cure probability while minimizing dose to critical normal structure to reduce complications. RTP system is required for proper dose plan in radiation therapy treatment. The main goal of this research is to develop dose model for photon, electron, and brachytherapy, and to display dose distribution on patient images with optimum process. The main items developed in this research includes: (l) user requirements and quality control; analysis of user requirement in RTP, networking between RTP and relevant equipment, quality control using phantom for clinical application (2) dose model in RTP; photon, electron, brachytherapy, modifying dose model (3) image processing and 3D visualization; 2D image processing, auto contouring, image reconstruction, 3D visualization (4) object modeling and graphic user interface; development of total software structure, step-by-step planning procedure, window design and user-interface. Our final product show strong capability for routine and advance RTP planning.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.36.2-36.2
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• 2020

Intensive Monitoring Survey of Nearby Galaxies (IMSNG) has been managed over 6 years. It aimed to constrain the progenitor system and explosion mechanism of SNe by detection of very early signal from shock heated emission. We have conducted monitoring observation of nearby bright galaxies those were carefully selected using global network of 1-m class telescopes. More than 20 SNe have occured in our target fields. As One of result of the survey, we present light curve analysis of type Ia SN 2018kp, which was discovered in NGC 3367. Based on photometric analysis, we calculated explosion parameters and set constraints of physical conditions of this supernova. We compared the results with theoretical model progenitor systems to find out which scenario is the most fitted to SN 2018kp case. Moreover, we estimate the distance to the galaxy and look into the relation between SNe and galactic physical parameters.