• Journal of the Daesoon Academy of Sciences
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• v.40
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• pp.243-272
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• 2022

Jeongeup, Jeollabuk-do, is an area that requires attention from those who study the history of Korean thought. In addition, Jeongeup is an area wherein many works were recorded for the first time in literary history. This is the case with Jeongeupsa as a style of Baekje songs and the lyrics of the noble families of the Joseon Dynasty, Sangchungok. Jeongeup is likewise the location where Choi Chi-won (857~?) was selected to serve as a local taesu (viceroy) and where a unique tradition of music and style were passed down. In this paper, the relationship between Choi Chi-won's role in the process of establishing a silent Confucian academy in Jeongeup and the emergence of scholar culture was examined. When Choi Chi-won left after his term in office, a birth shrine called Taesansa Temple was built to repay the selection of the villagers, and it became the source that led to the opening of the Confucian academy Museongseowon in the future. Jeongeup will be shown to be the location where Choi Chi-won realized his aspirations and honed his capabilities. In particular, Choi Chi-won's played a crucial role in the mid-Joseon Dynasty by supporting the construction and securing the name of Museongseowon. That is why Choi Chi-won was able to be revived as a symbolic figure in the region. In addition, it can be seen that the shape of Choi Chi-won was more sedentary- in the form of a Confucian scholar- and Confucian scholars emphasized the transfer of portraits at Museongseowon. Through the poetry written by Choi Chi-won, readers can learn about the worries and perceptions of scholars during those times. Although his value in the field of poetry is diverse, he can especially be recognized as a Confucian intellectual. In a large number of his works, he expresses his anxiety, agony, and critical inner consciousness all of which came from his encounter with the realities of his time. In fact, Choi Chi-won showed his qualities as a prominent literary figure of his time who had extraordinary aspirations and an admirable work ethic. However, he failed to overcome his regional and mental alienation as a poet in neighboring countries. Therefore, he internalized a sort of fierceness in terms of his perception of the world. However, it seems that it was rather a factor that made his work exhibit a strong lyrical style. In addition, Choi Chi-won's collection of writings includes a number of works that strongly criticized various forms of pathological phenomena caused by terminal phenomena of the time. He also highlighted the wrong in society by realistically depicting the lives poor and needy people and their eventual sacrifice via distorted relationships. This can be read encapsulating the agony of intellectuals of that time. The dictionary definition of a 'Confucian scholar' is "a Confucian term referring to a person or class that embodies Confucian ideology," and in its contemporary meaning it suggests " ⋯ an example of a personality, but not an identity, and the conscience of one's time period as a source of human morality inwardly and social order outwardly." In this respect, it could even be said that Choi Chi-won could be considered the originator of scholar culture.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.