The inscription of Cheonsang Yeolcha Bunyajido (天象列次分野之圖) has the sun’s locations at the equinoxes, which must have been copied from the astronomical treatises in Chinese historical annals, Songshu (宋書) and Jinshu (晉書). According to the treatises, an astronomer Wang Fan (王蕃, 228–266 CE) referred those values from a calendrical system called Qianxiangli (乾象 曆, 223 CE), from which it is confirmed that it adopted the sun’s location at the winter solstice of the (211 4 )th du of the 8th lunar lodge Dou (斗) as the reference direction for equatorial lodge angles. This indicates that the sun’s locations at equinoxes and solstices in the calendrical system are the same as those in Jingchuli (景初曆, 237 CE). Hence, we propose that the sun’s location at the autumnal equinox in Cheonsang Yeolcha Bunyajido should be corrected from ‘wu du shao ruo’ (五度少弱), meaning the (51 6 )th du, to ‘wu du ruo’ (五度弱), meaning the (411 12 )th du, of the first lunar lodge Jiao (角), as seen in Jingchuli. We reconstruct the polar coordinate system used in circular star charts, assuming that the mean motion rule was applied and its reference direction was the sun’s location at the winter solstice. Considering the precession, we determined the observational epoch of the sun’s location at the winter solstice to be 𝑡o = −18.3 ± 43.0 adopting the observational error of the so-called archaic determinatives (古度). It is noteworthy that the sun’s locations at equinoxes inscribed in Cheonsang Yeolcha Bunyajido originated from Houhan Sifenli (後漢四分曆) of the Latter Han dynasty (85 CE), while the coordinate origin in the star chart is related to Taichuli (太初 曆) of the Former Han dynasty (104 BCE).

Shoushili was the official calendrical method promulgated in 1280 CE by the Yuan dynasty. It contains a list of the angular spans in right ascensions for the 28 lunar lodges. They are known to have been measured by Guo Shoujing with his advanced instruments with an unprecedented precision or reading error of 5′. Such precise data are useful to determine their observational epoch with an error range which is narrow enough to pinpoint on which historical occasion they were observed. Using the precise SIMBAD data based on eDR3 of GAIA and carefully identified determinative stars and considering the precession of equinoxes and proper motions, we apply linear regression methods to those data and obtain the observational epoch of 1271 ± 16 CE and the measurement error of 4.1′. We also have polar distances corresponding to declinations written in another manuscript of the Ming dynasty. Since the two data sets have similar significant digits, they were suggested to have the same origin. However, we obtain their observational epoch of 1364±5 CE and the measurement error of 5.7′. They must have been measured with different instruments and on a different occasion from the observations related to Shoushili. We review the history of the calendrical reform during the 13th century in the Yuan dynasty. We conclude that the observational epoch obtained from lodge spans in Shoushili agrees with the period of observations led by Guo Shoujing or 1276–1279 CE, which is also supported by the fact that the ecliptic lodge span values listed in Shoushili were calculated from the equatorial lodge spans.

VR 및 AR은 대중들이 접근하기 어려운 기술이 아닌, 개인용 스마트 폰 하나로 체험 및 활용 할 수 있는 시 대가 되었다. 최근 이런 개인용 스마트 폰의 다양한 센서를 활용한 AR 콘텐츠가 개발되고 서비스 되고 있다. AR 콘텐츠의 수요가 커지면서Software교육의 수요도 커지게 되었다. 하지만, 비전공자들도 배우기 쉬운 Python 언어를 중심으로 SW 교육이 활발해졌음에도, 아직까지 AR 콘텐츠 개발에서는 Python을 적극적으로 사용할 수 없다. AR 콘텐츠는 기술 분야 뿐 아니라 인터렉티브 아트 분야에서도 활발하게 사용되고 있다. 최근 인터 렉티브 아티스트들은 Python을 이용하여 인공지능을 활용한 작품을 개발 및 전시하고 있다. Python을 통한 SW 교육은 SW 분야의 취업에만 필요한 것이 아니라 아트 분야에서도 필요한 교육이 되었다. 본 논문에서는 AR 콘텐츠 개발 교육을 위한 Python과 Unity 3D Engine을 이용한 네트워크 기반 AR 프레임 워크를 제안한다. 제 안한 AR 프레임 워크는 Web 기반 브라우저에서 개인용 스마트 폰의 카메라에 접근하여 카메라 정보를 Main Server에 전송하고 Python에서 Mark를 분석한다. Mark 정보에 맞춰 Unity 3D Engine에서 3D 오브젝트를 렌더 링하고, 카메라 정보화 합성 후, MJPEG 스트리밍으로 개인용 스마트 폰 화면에 렌더링 된다. 본 논문에서 제 안한 AR 프레임 워크는 SW 교육 플랫폼과 비대면 교육 플랫폼의 요구사항을 반영하며, 인터렉티브 아티스트 들의 다양한 도전에 필요한 기술적 제한을 낮춰 줄 것으로 기대한다.

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.