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문용재 교수

Yong-Jae Moon

경희대학교 우주과학과 · 물리·천문학

연구실 소개

문용재 교수의 연구실은 태양의 자기장 구조와 활동 현상, 특히 플레어와 코로날 질량 분출(CME)의 발생 메커니즘을 중심으로 태양대칭성과 자기 에너지의 축적 및 방출 과정을 고해상도 관측 데이터를 기반으로 분석합니다. 주로 SOHO, SDO 등 위성의 자기장 이미지 및 고해상도 망원경 자료를 활용해 태양대기에서의 자기헤리시티의 유입, 축적 및 급격한 방출 과정을 정량적으로 연구하며, 인공지능 기반 예측 모델 개발도 함께 진행하고 있습니다. 특히 플레어와 CME의 연관성, 동반 플레어, 자기장의 힘균형 상태 등 태양의 극단적 활동 현상의 물리적 기초를 규명하는 데 초점을 맞추고 있습니다.

태양 플레어코로날 질량 분출자기헤리시티인공지능 예측태양 자기장

연구 현황

논문 수
423
총 인용 수
6,554
최근 5년 논문
52
주요 분야
물리·천문학

연구 성과 추이

표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수
52총합
2022
2023
2024
2025
2026
5개년 연도별 피인용 수
173총합
20222023202420252026

주요 논문

15
1
논문|인용수 215·2002
A Statistical Study of Two Classes of Coronal Mass Ejections
Yong‐Jae Moon, G. S. Choe, Haimin Wang, Y. D. Park, N. Gopalswamy, Guo Yang, S. Yashiro
SJR Q1The Astrophysical JournalOA

A comprehensive statistical study is performed to address the question of whether two classes of coronal mass ejections (CMEs) exist. A total of 3217 CME events observed by SOHO/LASCO in 1996-2000 have been analyzed. We have examined the distributions of CMEs according to speed and acceleration, respectively, and investigated the correlation between speed and acceleration of CMEs. This statistical analysis is conducted for two subsets containing those CMEs that show a temporal and spatial associ

Astronomy and AstrophysicsPhysics and Astronomy
2
논문|인용수 107·2019
Solar farside magnetograms from deep learning analysis of STEREO/EUVI data
Tae Young Kim, Eunsu Park, Harim Lee, Yong‐Jae Moon, Sung‐Ho Bae, Daye Lim, Soojeong Jang, Lok-Won Kim, Il‐Hyun Cho, Myungjin Choi, Kyung‐Suk Cho
SJR Q1Nature Astronomy
Astronomy and AstrophysicsPhysics and Astronomy
3
논문|인용수 103·2018
Application of the Deep Convolutional Neural Network to the Forecast of Solar Flare Occurrence Using Full-disk Solar Magnetograms
Eunsu Park, Yong‐Jae Moon, Seulki Shin, Kangwoo Yi, Daye Lim, Harim Lee, Gyungin Shin
SJR Q1The Astrophysical Journal

Abstract In this study, we present the application of the Convolutional Neural Network (CNN) to the forecast of solar flare occurrence. For this, we consider three CNN models (two pretrained models, AlexNet and GoogLeNet, and one newly proposed model). Our inputs are SOHO /Michelson Doppler Imager (from 1996 May to 2010 December) and SDO /Helioseismic and Magnetic Imager (from 2011 January to 2017 June) full-disk magnetograms at 00:00 UT. Model outputs are the “Yes or No” of daily flare occurren

Astronomy and AstrophysicsPhysics and Astronomy
4
논문|인용수 98·2002
Flare Activity and Magnetic Helicity Injection by Photospheric Horizontal Motions
Yong‐Jae Moon, Jongchul Chae, G. S. Choe, Haimin Wang, Y. D. Park, H. S. Yun, Vasyl Yurchyshyn, Philip R. Goode
SJR Q1The Astrophysical Journal

We present observational evidence that the occurrence of homologous flares in an active region is physically related to the injection of magnetic helicity by horizontal photospheric motions. We have analyzed a set of 1 minute cadence magnetograms of NOAA AR 8100 taken over a period of 6.5 hr by the Michelson Doppler Imager on board the Solar and Heliospheric Observatory. During this observing time span, seven homologous flares took place in the active region. We have computed the magnetic helici

Astronomy and AstrophysicsPhysics and Astronomy
5
논문|인용수 88·2002
Statistical Evidence for Sympathetic Flares
Yong‐Jae Moon, G. S. Choe, Y. D. Park, Haimin Wang, P. T. Gallagher, Jongchul Chae, H. S. Yun, Philip R. Goode
SJR Q1The Astrophysical JournalOA

Sympathetic flares are a pair of flares that occur almost simultaneously in different active regions, not by chance, but because of some physical connection. In this paper statistical evidence for the existence of sympathetic flares is presented. From GOES X-ray flare data, we have collected 48 pairs of near simultaneous flares whose positional information and Yohkoh soft X-ray telescope images are available. To select the active regions that probably have sympathetic flares, we have estimated t

Astronomy and AstrophysicsPhysics and Astronomy
6
논문|인용수 87·2012
Solar Flare Occurrence Rate and Probability in Terms of the Sunspot Classification Supplemented with Sunspot Area and Its Changes
Kang-Jin Lee, Yong‐Jae Moon, Jinyi Lee, Kyoung‐Sun Lee, Hyeonock Na
SJR Q2Solar Physics
Astronomy and AstrophysicsPhysics and Astronomy
7
논문|인용수 82·2002
Impulsive Variations of the Magnetic Helicity Change Rate Associated with Eruptive Flares
Yong‐Jae Moon, Jongchul Chae, Haimin Wang, G. S. Choe, Y. D. Park
SJR Q1The Astrophysical JournalOA

In this paper, we investigate impulsive variations of the magnetic helicity change rate associated with eruptive solar flares (three X class flares and one M class flare) accompanying halo coronal mass ejections. By analyzing four sets of 1 minute cadence full-disk magnetograms taken by the Michelson Doppler Imager on board the Solar and Heliospheric Observatory, we have determined the rates of magnetic helicity transport due to horizontal photospheric motions. We have found that magnetic helici

Astronomy and AstrophysicsPhysics and Astronomy
8
논문|인용수 66·2002
Force‐Freeness of Solar Magnetic Fields in the Photosphere
Yong‐Jae Moon, G. S. Choe, H. S. Yun, Y. D. Park, D. L. Mickey
SJR Q1The Astrophysical JournalOA

It is widely believed that solar magnetic fields are force-free in the solar corona but not in the solar photosphere at all. In order to examine the force-freeness of active region magnetic fields at the photospheric level, we have calculated the integrated magnetic forces for 12 vector magnetograms of three flare-productive active regions. The magnetic field vectors are derived from simultaneous Stokes profiles of the Fe I doublet λλ6301.5 and 6302.5 obtained by the Haleakala Stokes Polarimeter

Astronomy and AstrophysicsPhysics and Astronomy
9
논문|인용수 60·2020
One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model
Sujin Lee, Eun‐Young Ji, Yong‐Jae Moon, Eunsu Park
SJR Q2Space WeatherOA

Abstract In this study, we make a global total electron content (TEC) forecasting using a novel deep learning method, which is based on conditional generative adversarial networks. For training, we use the International GNSS Service (IGS) TEC maps from 2003 to 2012 with 2‐h time cadence. Our model has two input images (IGS TEC map and 1‐day difference map between the present day and the previous day) and one output image (1‐day future map). The model is tested with two data sets: solar maximum p

Molecular BiologyBiochemistry, Genetics and Molecular Biology
10
논문|인용수 57·2014
Short-term periodicities in interplanetary, geomagnetic and solar phenomena during solar cycle 24
Partha Chowdhury, Debi Prasad Choudhary, Sanjay Gosain, Yong‐Jae Moon
SJR Q3Astrophysics and Space Science
Astronomy and AstrophysicsPhysics and Astronomy
11
논문|인용수 57·2005
New Geoeffective Parameters of Very Fast Halo Coronal Mass Ejections
Yong‐Jae Moon, K.‐S. Cho, M. Dryer, Yong Ha Kim, Su‐Chan Bong, Jongchul Chae, Y. D. Park
SJR Q1The Astrophysical JournalOA

We have examined the physical characteristics of very fast coronal mass ejections (CMEs) and their geoeffective parameters. For this we consider SOHO LASCO CMEs whose speeds are larger than 1300 km s-1. By examining all SOHO EIT and SOHO LASCO images of the CMEs, we selected 38 front-side very fast CMEs and then examined their associations with solar activity such as X-ray flares and type II bursts. As a result, we found that among these front-side fast CMEs, 25 are halo (or full halo) CMEs with

Astronomy and AstrophysicsPhysics and Astronomy
12
논문|인용수 56·2003
Sympathetic Coronal Mass Ejections
Yong‐Jae Moon, G. S. Choe, Haimin Wang, Y. D. Park
SJR Q1The Astrophysical JournalOA

Q1 We address the question whether there exist sympathetic coronal mass ejections (CMEs), which take place almost simultaneously in different locations with a certain physical connection. For this study, the following three investigations are performed. First, we have examined the waiting-time distribution of the CMEs that were observed by the SOHO Large Angle and Spectrometric Coronagraph (LASCO) from 1999 February to 2001 December. The observed waiting-time distribution is found to be well app

Astronomy and AstrophysicsPhysics and Astronomy
13
논문|인용수 56·2001
Flaring time interval distribution and spatial correlation of major X‐ray solar flares
Yong‐Jae Moon, G. S. Choe, H. S. Yun, Y. D. Park
SJR Q1Journal of Geophysical Research AtmospheresOA

A statistical study is performed on X‐ray flares stronger than C1 class that erupted during the solar maximum between 1989 and 1991. We have investigated the flaring time interval distribution (waiting‐time distribution) and the spatial correlation of successive flare pairs. The observed waiting‐time distribution for the whole data is found to be well represented by a nonstationary Poisson probability function with time‐varying mean flaring rates. The period most suitable for a constant mean fla

Astronomy and AstrophysicsPhysics and Astronomy
14
논문|인용수 46·2002
A revised shock time of arrival (STOA) model for interplanetary shock propagation: STOA‐2
Yong‐Jae Moon, M. Dryer, Z. Smith, Y. D. Park, K. S. Cho
SJR Q1Geophysical Research LettersOA

We have examined a possibility for improvement of the STOA (Shock Time Of Arrival) model for interplanetary shock propagation. In the STOA model, the shock propagating velocity is given by V s ∼ R − N with N = 0.5, where R is the heliocentric distance. Noting observational and numerical findings that the radial dependence of shock wave velocity depends on initial shock wave velocity, we suggest a simple modified STOA model (STOA‐2) which has a linear relationship between initial coronal shock wa

Atomic and Molecular Physics, and OpticsPhysics and Astronomy
15
논문|인용수 44·2019
Generation of Solar UV and EUV Images from SDO/HMI Magnetograms by Deep Learning
Eunsu Park, Yong‐Jae Moon, Jin‐Yi Lee, Rok-Soon Kim, Harim Lee, Daye Lim, Gyungin Shin, Tae Young Kim
SJR Q1The Astrophysical Journal Letters

Abstract In this Letter, we apply deep-learning methods to the image-to-image translation from solar magnetograms to solar ultraviolet (UV) and extreme UV (EUV) images. For this, We consider two convolutional neural network models with different loss functions, one (Model A) is with L1 loss ( L 1 ), and the other (Model B) is with L 1 and cGAN loss ( L cGAN ). We train the models using pairs of Solar Dynamics Observatory ( SDO )/Atmospheric Imaging Assembly (AIA) nine-passband (94, 131, 171, 193

Astronomy and AstrophysicsPhysics and Astronomy

대표 연구 분야

Astronomy and AstrophysicsArtificial IntelligenceGeophysicsAtomic and Molecular Physics, and OpticsElectrical and Electronic EngineeringAerospace Engineering

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