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Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases. [ABSTRACT FROM AUTHOR]

In 1721, the Qing dynasty began to send eclipse documents to Joseon, which influenced the astronomy of Joseon Gwansanggam. The Qing documents contained predictions of solar and lunar eclipses in Seoul. This papers argues that the Qing eclipse documents became a new criterion for evaluating the accuracy of the eclipse calculations carried out by Joseon astronomers, and such changes affected the overall reform and improvement of their solar and lunar eclipse calculation skills. They had to calculate not only calendars but also solar and lunar eclipses based on documents provided by the Qing dynasty. This imperative, this article suggests, urged the Joseon government to dispatch official astronomers to Beijing every year from 1741. Learning how to calculate solar and lunar eclipses from Qing astronomers became the most urgent goal for Joseon astronomers during the 1740s and 1750s. By the early 1750s, they were able to produce the same results as the Qing documents. [ABSTRACT FROM AUTHOR]