• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.54.2-54.2
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• 2016

Korea Microlensing Telescope Network (KMTNet) is the first optical survey system of its kind in a way that three KMTNet observatories are longitudinally well-separated, and thus have the benefit of 24-hour continuous monitoring of the southern sky. The wide-field and round-the-clock operation capabilities of this network facility are ideal for survey and the physical characterization of small Solar System bodies. We obtain their orbits, absolute magnitudes (H), three dimensional shape models, spin periods and spin states, activity levels based on the time-series broadband photometry. Their approximate surface mineralogy is also identified using colors and band slopes. The automated observation scheduler, the data pipeline, the dedicated computing facility, related research activity and the team members are collectively called 'DEEP-South' (DEep Ecliptic Patrol of Southern sky). DEEP-South observation is being made during the off-season for exoplanet search, yet part of the telescope time is shared in the period between when the Galactic bulge rises early in the morning and sets early in the evening. We present here the observation mode, strategy, software, test runs, early results, and the future plan of DEEP-South.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.42.1-42.1
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• 2018

Deep Ecliptic Patrol of the Southern Sky (DEEP-South) observation is being made during the off-season for exoplanet survey, using Korea Microlensing Telescope Network (KMTNet). An optimal combination of its prime focus optics and the 0.3 billion pixel CCD provides a four square degrees field of view with 0.4 arcsec/pixel plate scale which is also best suited for small body studies. Normal operation of KMTNet started in October 2015, and a significant portion of the allocated telescope time for DEEP-South is dedicated to targeted observation, Opposition Census (OC), of near-Earth asteroids for physical and taxonomic characterization. This is effectively achieved through multiband, time series photometry using Johnson-Cousins BVRI filters. Uninterrupted monitoring of the southern sky with KMTNet is optimized for spin characterization of a broad spectrum of asteroids ranging from the near-Earth space to the main-belt, including binaries, asteroids with satellites, slow/fast- and non-principal axis-rotators, and thus is expected to facilitate the debiasing of previously reported lightcurve observations. Our software subsystem consists of an automated observation scheduler, a pipelined data processing system for differential photometry, and an easy-to-use lightcurve analysis toolkit. Lightcurves, spin periods and provisional determination of class of asteroids to which the lightcurve belongs will be presented, using the dataset from first year operation of KMTNet. Our new taxonomic classification scheme for asteroids will also be summarized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.139-147
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• 2010

A novel turbo coded modulation scheme, called turbo-APPM, for deep space optical communications is constructed. The constructed turbo-APPM is a serial concatenations of turbo codes, an accumulator and a pulse position modulation (PPM), where turbo codes act as an outer code while the accumulator and the PPM act together as an inner code. The generator polynomial and the puncturing rule for generating turbo codes are chosen to show the low bit error rate. At the receiver, the joint decoding is performed by exchanging soft information iteratively between the inner decoder and the outer decoder. In the outer decoder, a local iterative decoding for turbo codes is conducted before transferring soft information to the inner decoder. Poisson distribution is used to model the deep space optical channel. It is shown by simulations that the constructed turbo-APPM provides coding gains over all previously proposed schemes such as LDPC-APPM, RS-PPM and SCPPM.

• 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 Society for Aeronautical & Space Sciences
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• v.48 no.4
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• pp.295-301
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• 2020

When a spacecraft in deep space falls into an abnormal state, an emergency communication channel between ground and the spacecraft is essential in order to perform analysis to the cause of the anomaly, and to remedy the spacecraft from the distressed state. Because the recovery actions generally comprises of long and complicated sequences of commands, the transmission of the recovery commands may require a reliable and a delay tolerant networking technology based on bundle routing. While the delay tolerant networking protocol becomes a prominent method interfacing ground and space into a internet-like Solar system network because it can address the issues of the severe communication problems in deep space, the communication system on the spacecraft which based on space packet protocol cannot use the delay tolerant networking technology directly. So a community of the consultative committee for space data systems starts a discussion of the first-hop last-hop mechanism to establish a feasible concept and standardization. This paper presents an enhanced concept of the first-hop last-hop by applying it a virtual cislunar communication environment, and we believe this contributes to make a way applicable to an interoperable relay concept of the first hop last hop between the delay tolerant networking and space packet protocol standard.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.80.2-80.2
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• 2019

We present newly discovered dwarf galaxy candidates in deep wide-field images of NGC 1291 obtained with KMTNet. We initially identify 20 dwarf galaxy candidates through visual inspection. 13 out of 20 appears to be high priority candidates, according to their central surface brightness (${\mu}_{0,R}{\sim}22.5$ to $26.5mag\;arcsec^{-2}$) and effective radii (350 pc to 1 kpc). Structural and photometric properties of dwarf candidates appear to be consistent with those of ordinary dwarf galaxies in nearby groups and clusters. Using imaging simulations, we demonstrate that our imaging data is complete up to $26mag\;arcsec^{-2}$ with > 70% of the completeness rate. In order to find an optimal way to automate detecting dwarf galaxies in our dataset, we test detection methods by varying parameters in SExtractor. We find that the detection efficiency from the automated method is relatively low and the contamination due to the artifacts is non-negligible. Therefore, it can be only applicable for pre-selection. We plan to conduct the same analysis for deep images of other nearby galaxies obtained through KMTNet Nearby Galaxy Survey (KNGS).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2381-2398
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• 2024

The construction of virtual indoor spaces is crucial for the development of metaverses, virtual production, and other 3D content domains. Traditional methods for creating these spaces are often cost-prohibitive and labor-intensive. To address these challenges, we present a pipeline for generating digital twins of real indoor environments from RGB-D camera-scanned data. Our pipeline synergizes space structure estimation, 3D object detection, and the inpainting of missing areas, utilizing deep learning technologies to automate the creation process. Specifically, we apply deep learning models for object recognition and area inpainting, significantly enhancing the accuracy and efficiency of virtual space construction. Our approach minimizes manual labor and reduces costs, paving the way for the creation of metaverse spaces that closely mimic real-world environments. Experimental results demonstrate the effectiveness of our deep learning applications in overcoming traditional obstacles in digital twin creation, offering high-fidelity digital replicas of indoor spaces. This advancement opens for immersive and realistic virtual content creation, showcasing the potential of deep learning in the field of virtual space construction.

• Journal of Astronomy and Space Sciences
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• v.34 no.2
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• pp.139-151
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• 2017

This paper presents an overview of deep space orbit determination software (DSODS), as well as validation and verification results on its event prediction capabilities. DSODS was developed in the MATLAB object-oriented programming environment to support the Korea Pathfinder Lunar Orbiter (KPLO) mission. DSODS has three major capabilities: celestial event prediction for spacecraft, orbit determination with deep space network (DSN) tracking data, and DSN tracking data simulation. To achieve its functionality requirements, DSODS consists of four modules: orbit propagation (OP), event prediction (EP), data simulation (DS), and orbit determination (OD) modules. This paper explains the highest-level data flows between modules in event prediction, orbit determination, and tracking data simulation processes. Furthermore, to address the event prediction capability of DSODS, this paper introduces OP and EP modules. The role of the OP module is to handle time and coordinate system conversions, to propagate spacecraft trajectories, and to handle the ephemerides of spacecraft and celestial bodies. Currently, the OP module utilizes the General Mission Analysis Tool (GMAT) as a third-party software component for high-fidelity deep space propagation, as well as time and coordinate system conversions. The role of the EP module is to predict celestial events, including eclipses, and ground station visibilities, and this paper presents the functionality requirements of the EP module. The validation and verification results show that, for most cases, event prediction errors were less than 10 millisec when compared with flight proven mission analysis tools such as GMAT and Systems Tool Kit (STK). Thus, we conclude that DSODS is capable of predicting events for the KPLO in real mission applications.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.48.3-49
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• 2016

We have five different types of observation modes with regard to the Deep Ecliptic Patrol of the Southern Sky (DEEP-South); Opposition Census (OC) for targeted photometry, Sweet Spot Survey (S1) for discovery and orbit characterization of Atens and Atiras, Ecliptic Survey (S2) for asteroid family studies and comet census, NEOWISE follow-up (NW) for near simultaneous albedo measurements in the visible bands, and Target of Opportunity (TO) observation for follow-up either for unpredictable events or targets of special interests. Different exposures with such different modes result in a wide range of background noise level, the number of background stars and the mover's projected speed in each image. The Moving Object Detection Program (MODP) utilizes multiple mosaic images being taken for the same target fields at different epochs at the three KMTNet sites. MODP employs existing software packages such as SExtractor (Source-Extractor) and SCAMP (Software for Calibrating Astrometry and Photometry); SExtractor generates object catalogs, while SCAMP conducts precision astrometric calibration, then MODP determines if a point source is moving. This package creates animated stamp images for visual inspection with MPC reports, the latter for checking whether an object is known or unknown. We evaluate the astrometric accuracy and efficiency of MODP using the year one dataset obtained from DEEP-South operations.