• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.1-52.1
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• 2019

The Helioseismic and Magnetic Imager (HMI) is the instrument of Solar Dynamics Observatory (SDO) to study the magnetic field and oscillation at the solar surface. The HMI image is not enough to analyze very small magnetic features on solar surface since it has a spatial resolution of one arcsec. Super resolution is a technique that enhances the resolution of a low resolution image. In this study, we use a method for enhancing the solar image resolution using a Deep-learning model which generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained a model based on a very deep residual channel attention networks (RCAN) with HMI images in 2014 and test it with HMI images in 2015. We find that the model achieves high quality results in view of both visual and measures: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is much better than the conventional bi-cubic interpolation. We will apply this model to full-resolution SDO/HMI and GST magnetograms.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.81.2-81.2
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• 2019

We develop an image-to-image translation model, which is a popular deep learning method based on conditional Generative Adversarial Networks (cGANs), to generate solar magnetograms and EUV images from sunspot drawings. For this, we train the model using pairs of sunspot drawings from Mount Wilson Observatory (MWO) and their corresponding SDO/HMI magnetograms and SDO/AIA EUV images (512 by 512) from January 2012 to September 2014. We test the model by comparing pairs of actual SDO images (magnetogram and EUV images) and the corresponding AI-generated ones from October to December in 2014. Our results show that bipolar structures and coronal loop structures of AI-generated images are consistent with those of the original ones. We find that their unsigned magnetic fluxes well correlate with those of the original ones with a good correlation coefficient of 0.86. We also obtain pixel-to-pixel correlations EUV images and AI-generated ones. The average correlations of 92 test samples for several SDO lines are very good: 0.88 for AIA 211, 0.87 for AIA 1600 and 0.93 for AIA 1700. These facts imply that AI-generated EUV images quite similar to AIA ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. This application will be used to generate solar images using historical sunspot drawings.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.159-165
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• 2024

The Global Navigation Satellite System (GNSS) has been used as a tool to accurately extract the Total Electron Content (TEC) in the ionosphere. The multi-GNSS (GPS, GLONASS, BeiDou, Galileo, and QZSS) constellations bring new opportunities for ionospheric research. In this study, we develop a regional ionospheric TEC model using GPS, Galileo, and QZSS measurements. To develop an ionospheric model covering the Asia-Oceania region, we select 13 International GNSS Service (IGS) stations. The ionospheric model applies the spherical harmonic expansion method and has a spatial resolution of 2.5°×2.5° and a temporal resolution of one hour. GPS TEC, Galileo TEC, and QZSS TEC are investigated from January 1 to January 31, 2024. Different TEC values are in good agreement with each other. In addition, we compare the QZSS(J07) TEC and the Center for Orbit Determination in Europe (CODE) Global Ionosphere Map (GIM) TEC. The results show that the QZSS TEC estimated in the study coincides closely with the CODE GIM TEC.

• Korean Institute of Interior Design Journal
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• no.34
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• pp.149-156
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• 2002

This study intends to acquire objective models for basic quantitative analysis of pattern and space through image-recognition technique, and verify the effectiveness of such acquired models. Many experiments showed that the recognized result can be varied depending on the different viewpoints and the analysis based on the single-viewpoint images does not provide objectivity. The experiment using multi-viewpoint image models, which was attempted as an alternative for the disadvantages, showed the recognition similar to that of the actual model. Especially, images generated at laboratory using miniature model may be useful in comparing and understanding plural number of patterns. The models that have been acquired using such images may be hard to use in acquiring images for analyzing actual building patterns or indoor space, although they may be useful in pattern analysis using miniature model. The disadvantage, however, can be supplemented with panorama VR and C. G. simulation technique. Steady researches are required on the application of visual information to the image recognition principle and the model for quantitative analysis of pattern and space in addition to the research on the construction of the model that can be used in comparing and analyzing not only form and space but also miniature models.

• Journal of Astronomy and Space Sciences
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• v.31 no.4
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• pp.303-309
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• 2014

The Earth's outer radiation belt often suffers from drastic changes in the electron fluxes. Since the electrons can be a potential threat to satellites, efforts have long been made to model and predict electron flux variations. In this paper, we describe a prediction model for the outer belt electrons that we have recently developed at Chungbuk National University. The model is based on a one-dimensional radial diffusion equation with observationally determined specifications of a few major ingredients in the following way. First, the boundary condition of the outer edge of the outer belt is specified by empirical functions that we determine using the THEMIS satellite observations of energetic electrons near the boundary. Second, the plasmapause locations are specified by empirical functions that we determine using the electron density data of THEMIS. Third, the model incorporates the local acceleration effect by chorus waves into the one-dimensional radial diffusion equation. We determine this chorus acceleration effect by first obtaining an empirical formula of chorus intensity as a function of drift shell parameter $L^*$, incorporating it as a source term in the one-dimensional diffusion equation, and lastly calibrating the term to best agree with observations of a certain interval. We present a comparison of the model run results with and without the chorus acceleration effect, demonstrating that the chorus effect has been incorporated into the model to a reasonable degree.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.47-68
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• 2008

The aim of this research is to develop a new business model using mobile RFID in exhibition spaces such as museums and art galleries. Using mobile RFID, the exhibition space is expected to be evolved from a simple media space only for the exhibition to an extended space integrating media, commerce and entertainment. This paper proposes a u-Exhibition business model and its scenario in the u-Exhibition space. We discuss the real-world issues for the implementation and show the ways of investigating working conditions for the business model through revenue simulation and analyzing the expected value of tag installed on the exhibition space by so-called 'tag evaluation model.'

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.72.2-72.2
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• 2014

We have made a comparison of three cone models (an asymmetric cone model, an ice-cream cone model, and an elliptical cone model) in terms of space weather application. We found that CME angular widths obtained by three cone models are quite different one another even though their radial velocities are comparable with one another. In this study, we investigate which cone model is proper for halo CME morphology and whether cone model parameters are similar to observations. For this, we look for CMEs which are identified as halo CMEs by one spacecraft and as limb CMEs by the other ones. For this we use SOHO/LASCO and STEREO/SECCHI data during the period from 2010 December to 2011 June when two spacecraft were separated by $90{\pm}10$ degrees. From geometrical parameters of these CMEs such as their front curvature, we classify them into two groups: shallow cone (5 events) and near full-cone (28 events). Noting that the previous cone models are based on flat cone or shallow cone shapes, our results imply that a cone model based on full cone shape should be developed. For further analysis, we are estimating the angular widths of these CMEs near the limb to compare them with those from the cone models. This result shows that the angular widths of the ice-cream cone model are well correlated (CC = 0.81) with those of observations.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.157-164
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• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of the Korean Mathematical Society
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• v.42 no.5
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• pp.1017-1030
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• 2005

In this paper, we study averaging properties in Banach spaces using the Brunel-Sucheston's spreading model. We show that the Schlumprecht space S does not have the Banach­Saks property and II is finitely representable in the Schlumprecht space S using the spreading model properties.