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한국 e-CALLISTO 관측소 자동 관측 시스템 개발
박종엽,최성환,봉수찬,권용준,백지혜,장비호,조경석,문용재,PARK, JONGYEOB,CHOI, SEONGHWAN,BONG, SU-CHAN,KWON, YONGJUN,BAEK, JI-HYE,JANG, BI-HO,CHO, KYUNG-SUK,MOON, YONG-JAE,Monstein, Christian 한국천문학회 2015 天文學論叢 Vol.30 No.3
The e-CALLISTO is a network of CALLISTO (Compact Astronomical Low-frequency, Low-cost Instrument for Spectroscopy in Transportable Observatories) spectrometers which detect solar radio bursts 24 hours a day in frequency range 45-870 MHz. The number of channels per spectrum is 200 and the time resolution of whole spectrum is 0.25 second. The Korean e-CALLISTO station was developed by Korea Astronomy and Space Science Institute (KASI) collaborating with Swiss Federal Institute of Technology Zurich (ETH Zurich) since 2007. In this paper, we report replacement of the tracking mount and development of the control program using Visual C++/MFC. The program can make the tracking mount track the Sun and schedule CALLISTO to start and to finish its observation automatically using the Solar Position Algorithm (SPA). Daily tracking errors (RMSE) are 0.0028 degree in azimuthal axis and 0.0019 degree in elevational axis between 2014 January and 2015 July. We expect that the program can save time and labor to make the observations of solar activity for space weather monitoring, and improve CALLISTO data quality due to the stable and precise tracking methods.
Near-real time Kp forecasting methods based on neural network and support vector machine
지은영,문용재,박종엽,이동훈,Ji, Eun-Young,Moon, Yong-Jae,Park, Jongyeob,Lee, Dong-Hun 한국천문학회 2012 天文學會報 Vol.37 No.2
We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.
백지혜,최성환,박종엽,김수진,심채경,양태용,정민섭,조영수,최영준,Baek, Ji-Hye,Choi, Seonghwan,Park, Jongyeob,Kim, Sujin,Sim, Chae Kyung,Yang, Tae-Yong,Jeong, Minsup,Jo, Young-Soo,Choi, Young-Jun 한국우주과학회 2021 우주기술과 응용 Vol.1 No.2
우주관측 자료는 우주 임무를 통해 관측한 별, 은하, 태양, 우주 플라즈마(plasma), 달, 행성 등의 연구 자료로 관측 자료를 가공 및 활용한 것까지 포함한다. 국내외 천문우주 관측시스템이 대형화되고, 우주 임무의 확대 및 자료 용량 증가(빅 데이터)로 인해 우주관측 자료의 체계적이고 효율적인 관리에 대한 필요성이 증대되고 있다. 이에 우리나라도 우주관측 자료의 전략을 세우고, 이를 바탕으로 우주관측 자료 정책을 수립해야 한다. 이를 위한 준비 단계로 우주관측 자료에 대한 광범위한 이해와 다년간의 경험으로부터 발전된 미 항공우주국(National Aeronautics and Space Administration, NASA)의 자료 전략을 분석하였다. NASA의 자료 전략 분석 결과를 바탕으로 우리나라의 우주관측 자료 전략 방향과 앞으로 우주관측 자료 정책을 수립하는 데 기반이 될 우주관측 자료 전략 권고 사항 10가지를 제안한다. Space observation data includes research data such as stars, galaxies, Sun, space plasma, planets, and minor bodies observed through space missions, including processing and utilizing the observation data. Astronomy and space science observation systems are getting larger, and space mission opportunities and data size are increasing. Accordingly, the need for systematic and efficient management of space observation data is growing. Therefore, in Korea, a strategy and policy for space observation data should be established. As a stage of preparation, National Aeronautics and Space Administration (NASA)'s data strategy, which developed from extensive understanding and long-term experience for space observation data, was analyzed. Based on the analysis results, we propose a strategic direction and 10 recommendations for Korean space observation data strategies that will be the basis for establishing space observation data policies in the future.