• Journal of Astronomy and Space Sciences
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• 제40권2호
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• pp.79-88
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• 2023

On Aug. 4, 2022, at 23:08:48 (UTC), the Korea Pathfinder Lunar Orbiter (KPLO), also known as Danuri, was launched using a SpaceX Falcon 9 launch vehicle. Currently, KPLO is successfully conducting its science mission around the Moon. The National Aeronautics and Space Administration (NASA)'s Deep Space Network (DSN) was utilized for the successful flight operation of KPLO. A great deal of joint effort was made between the Korea Aerospace Research Institute (KARI) and NASA DSN team since the beginning of KPLO ground system design for the success of the mission. The efficient utilization and management of NASA DSN in deep space exploration are critical not only for the spacecraft's telemetry and command but also for tracking the flight dynamics (FD) operation. In this work, the top-level DSN interface architecture, detailed workflows, DSN support levels, and practical lessons learned from the joint team's efforts are presented for KPLO's successful FD operation. Due to the significant joint team's efforts, KPLO is currently performing its mission smoothly in the lunar mission orbit. Through KPLO cooperative operation experience with DSN, a more reliable and efficient partnership is expected not only for Korea's own deep space exploration mission but also for the KARI-NASA DSN joint support on other deep space missions in the future.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 1999년도 한국우주과학회보 제8권2호
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• pp.108-108
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• 1999

No Abstract, See Full Text

• 천문학회보
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• 제44권2호
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• pp.78.1-78.1
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• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.670-683
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• 2016

Network coding, as a new technique to improve the throughput, is studied combined with multi-relay model in this paper to address the challenges of long distance and power limit in deep space communication. First, an amplify-and-forward relaying approach based on analog network coding (AFNC) is proposed in multi-relay network to improve the capacity for deep space communication system, where multiple relays are introduced to overcome the long distance link loss. The design of amplification coefficients is mathematically formulated as the optimization problem of maximizing SNR under sum-power constraint over relays. Then for a dual-hop relay network with a single source, the optimal amplification coefficients are derived when the multiple relays introduce non-coherent noise. Through theoretic analysis and simulation, it is shown that our approach can achieve the maximum transmission rate and perform better over single link transmission for deep space communication.

• 천문학회지
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• 제49권5호
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• pp.225-232
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• 2016

We present an optical imaging survey of AKARI Deep Field South (ADF-S) using the Korea Microlensing Telescope Network (KMTNet), to find optical counterparts of dusty star-forming galaxies. The ADF-S is a deep far-infrared imaging survey region with AKARI covering around 12 deg², where the deep optical imaging data are not yet available. By utilizing the wide-field capability of the KMTNet telescopes (~4 deg²), we obtain optical images in B, R and I bands for three regions. The target depth of images in B, R and I bands is ~24 mag (AB) at 5σ, which enables us to detect most dusty star-forming galaxies discovered by AKARI in the ADF-S. Those optical datasets will be helpful to constrain optical spectral energy distributions as well as to identify rare types of dusty star-forming galaxies such as dust-obscured galaxy, sub-millimeter galaxy at high redshift.

• 천문학회보
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• 제40권1호
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• pp.57.1-57.1
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• 2015

We will obtain deep wide-field images of the 150-200 nearby bright galaxies in the southern hemisphere, in order to explore the origin of faint extended features in the outer regions of target galaxies. Using KMTNet telescopes, we will take very deep images, spending ~ 4.5 hr for the B and R filters for each object. With this dataset, we will look for diffuse, low-surface brightness structures including outer disks, truncated disks, tidal features/stellar streams, and faint companions.

• 항공우주기술
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• 제13권1호
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• pp.120-128
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• 2014

우주 탐사를 향한 인류의 도전은 이제 태양계 바깥으로 확대되고 있다. 2010년대 전후의 우주 탐사 무대는 달에서 벗어나 주로 화성, 금성, 그리고 소행성(Asteroids)이 되고 있다. 우주선이 지구에서 멀어 질수록 여러 가지 기술적인 도전을 받고 있는데 통신 프로토콜이 대표적인 하나이다. 본 논문에서는 심우주 탐사선과 통신을 위해서 국제적으로 사용되고 있는 통신 프로토콜 기술을 소프트웨어적인 측면에서 분석한 결과를 기술하였으며 이는 2017년에 한국으로는 최초로 발사되는 시험용 달 궤도선 개발에 중요한 참고 연구가 될 것으로 판단된다.

• 천문학회보
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• 제43권1호
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• pp.70.1-70.1
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• 2018

The DEEP-South photometric census of small Solar System bodies is producing massive time-series data of variable, transient or moving objects as a by-product. To fully investigate unexplored variable phenomena, we present an application of multi-aperture photometry and FastBit indexing techniques to a portion of the DEEP-South year-one data. Our new pipeline is designed to do automated point source detection, robust high-precision photometry and calibration of non-crowded fields overlapped with area previously surveyed. We also adopt an efficient data indexing algorithm for faster access to the DEEP-South database. In this paper, we show some application examples of catalog-based variability searches to find new variable stars and to recover targeted asteroids. We discovered 21 new periodic variables including two eclipsing binary systems and one white dwarf/M dwarf pair candidate. We also successfully recovered astrometry and photometry of two near-earth asteroids, 2006 DZ169 and 1996 SK, along with the updated properties of their rotational signals (e.g., period and amplitude).

• 천문학회보
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• 제44권1호
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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.

• 천문학회보
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• 제41권2호
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• pp.48.2-48.2
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• 2016

The wide-field and the round-the clock operation capabilities of the KMTNet enables the discovery, astrometry and follow-up physical characterization of asteroids and comets in a most efficient way. We collectively refer to the team members, partner organizations, the dedicated software subsystem, the computing facility and research activities as Deep Ecliptic Patrol of the Southern Sky (DEEP-South). Most of the telescope time for DEEP-South is devoted to targeted photometry of Near Earth Asteroids (NEAs) to push up the number of the population with known physical properties from several percent to several dozens of percent, in the long run. We primarily adopt Johnson R-band for lightcurve study, while we employ BVI filters for taxonomic classification and detection of any possible color variations of an object at the same time. In this presentation, the progress and new findings since the last KAS meeting will be outlined. We report DEEP-South preliminary lightcurves of several dozens of NEAs obtained at three KMTNet stations during the first year runs. We also present a physical model of asteroid (5247) Krylov, the very first Non principal Axis (NPA) rotator that has been confirmed in the main belt (MB). A new asteroid taxonomic classification scheme will be introduced with an emphasis on its utility in the LSST era. The progress on the current version of automated mover detection software will also be summarized.