• 천문학논총
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• 제13권1호
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• pp.55-64
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• 1998

We have analyzed 255 data of the daily sunspot observations during the period of January 3 to December 31 in 1997 and present the daily relative sunspot numbers. During the 1997, the preliminary annual average of the relative sunspot numbers found to be 28.2 based on 9.7 distinct spots in a single group for 1.4 spot groups. According to the appearance of 366 spot groups, our analysis shows that the mean life time of spot group is about 4 day and 17.0 hours.

• 천문학논총
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• 제11권1호
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• pp.251-261
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• 1996

We analyze 280 data of the daily sunspot observations during the period of January 1 to December 31 in 1995 and present the daily relative sunspot numbers. During the 1995, the preliminary annual average of the relative sunspot numbers is found to be 20.1 based on 7.9 distinct spots in a single group for 1.3 spot groups. According to the appearance of 366 spot groups, our analysis shows that the mean life time of spot group is about 5 day and 10.1 hours.

• 천문학논총
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• 제17권1호
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• pp.15-22
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• 2002

We have analyzed 209 data of daily sunspot observations made during the period of January 3 to December 31 In 2001 and presented the daily relative sunspot numbers. During the year of 2001, our annual average of relative sunspot numbers is found to be 92.2. This number is obtained from the averaged daily number of 8.4 spot groups, in which there are about 57.5 distinct spots observed. According to the appearance of 370 spot groups, our analysis shows that the mean life time of spot groups is about 4 day and 20.4 hours.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.523-523
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• 2000

We have developed a method to build time series prediction models by Genetic Programming (GP). Our proposed CP includes two new techniques. One is the parameter optimization algorithm, and the other is the new mutation operator. In this paper, the sunspot prediction experiment by our proposed CP was performed. The sunspot prediction is good benchmark, because many researchers have predicted them with various kinds of models. We make three experiments. The first is to compare our proposed method with the conventional methods. The second is to investigate about the relation between a model-building period and prediction precision. In the first and the second experiments, the long-term data of annual sunspots are used. The third is to try the prediction using monthly sunspots. The annual sunspots are a mean of the monthly sunspots. The behaviors of the monthly sunspot cycles in tile annual sunspot data become invisible. In the long-term data of the monthly sunspots, the behavior appears and is complicated. We estimate that the monthly sunspot prediction is more difficult than the annual sunspot prediction. The usefulness of our method in time series prediction is verified by these experiments.

• 천문학회보
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• 제41권1호
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• pp.45.3-46
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• 2016

Solar activity shows a self-similarity as it has many periods of activity cycle in the time series of long-term observation, such as 13.5, 51, 150, 300 days, and 11, 88 years and so on. Since fractal dimension is a quantitative parameter for this kind of an irregular time series, we applied this method to long-term observations including sunspot number, total solar irradiance, and 3.75 GHz solar radio flux to predict the start and maximum times as well as expected maximum sunspot number for the next solar cycle. As a result, we found that the radio flux data tend to have lower fractal dimensions than the sunspot number data, which means that the radio emission from the sun is more regular than the solar activity expressed by sunspot number. Based on the relation between radio flux of 3.75 GHz and sunspot number, we could calculate the expected maximum sunspot number of solar cycle 24 as 156, while the observed value is 146. For the maximum time, estimated mean values from 7 different observations are January 2013 and this is quite different to observed value of February 2014. We speculate this is from extraordinary extended properties of solar cycle 24. As the cycle length of solar cycle 24, 10.1 to 12.8 years are expected, and the mean value is 11.0. This implies that the next solar cycle will be started at December 2019.

• 천문학회보
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• 제40권2호
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• pp.39.1-39.1
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• 2015

Korea has a long history in astronomy, which is proved by many observational records written in Korean chronicles. There are 43 sunspot records in Goryeo dynasty (高麗 918-1392) and 13 records in Joseon dynasty (朝鮮 1392-1910). According to analysis of Korean historical records, it is known that sunspot records in Goryeo dynasty show well in match with the well-known solar activity of 11.3 years. It means that Korean historical sunspot records show real solar phenomena. Korean sunspot records also show that solar activity decrease in Joseon dynasty compared with the previous ~500 years. In order to know the change of solar activity in detail, we examine Korean historical atmospheric records which can indicate climate change. We first analyze historical frost records. Korean chronicles have around 600 frost records during the last millennium. We find that the climate change shows sign of cooling down when check the variation of epoch that the first and last frost events in each year are written. This result is well in accord with that of historical sunspot records. Therefore, we claim that solar activity decrease during the last thousand years.

• 천문학회지
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• 제29권1호
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• pp.9-18
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• 1996

We have analyzed vector magnetograms and $H\beta$ filtergrams of two sunspot groups, one in a growing phase and the other in a decaying phase. In this study, the temporal evolution of their magnetic morphology has been investigated in association with solar activity. The morphological variations of the growing and decaying phase of these sunspots revealed in detail the coalescence of small spots into a large spot and the fragmentation of a large spot into many small spots, respectively. Numerous small flares were detected in the spot group during the decaying phase. This seems to be intimately associated with the shearing motions of many spots with different polarities created by fragmentation of a large sunspot. The magnetic flux and the average shear angle are found to be substantially reduced during the decaying phase, especially in the course of the flarings. This implies that the decaying phase of the sunspot is, to some degree, involved with magnetic field cancellation. The growing spot group has not shown any large activities, but numerous small spots have grown into a typical bipolar sunspot.

• 천문학회지
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• 제47권6호
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• pp.255-258
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• 2014

Using sunspot number data from 270 historical stations for the period 1981-2013, we investigate their personal reduction coefficients (k) statistically. Chang & Oh (2012) perform a simulation showing that the k varies with the solar cycle. We try to verify their results using observational data. For this, a weighted mean and weighted standard deviation of monthly sunspot number are used to estimate the error from observed data. We find that the observed error (noise) is much smaller than that used in the simulation. Thus no distinct k-variation with the solar cycle is observed contrary to the simulation. In addition, the probability distribution of k is determined to be non-Gaussian with a fat-tail on the right side. This result implies that the relative sunspot number after 1981 might be overestimated since the mean value of k is less than that of the Gaussian distribution.

• Journal of Astronomy and Space Sciences
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• 제29권2호
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• pp.97-101
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• 2012

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor $k$ and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis $k$ into account lead to a better agreement with the international relative sunspot number $R_i$, and that yearly $k$ apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor $k$ on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor $k$ on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.

• 천문학회보
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• 제44권1호
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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.