Reconstruction of the Origin of the Gudle (구들의 기원지(起源地) 재고(再考))
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- Korean Journal of Heritage: History & Science
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- v.54 no.1
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- pp.100-119
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- 2021
This paper has been written to verify the existing theory that districts occurred independently in various parts of the world, including the Korean Peninsula. Song Giho (2006) claims that the origin of the Gudle, is an example of polygenism that occurred in various areas in the world, including the Korean peninsula. This argument has been corroborated by a large number of researchers. However, it is difficult to understand the lineage of Gudle and its process of development, if a theory of polygenism is continued to be taken into account. The place which is targetet by this theory is the North-West area of the Korean peninsula, south of Primorsky Krai, and in the northern area of Zabaikal-Mongolia. This means that these areas developed independently because they were far from each other and had no direct cultural relationship. However, the structure of Gudle, shape, and assemblages of earthenware it cannot be explained by polygenism, as they are the same in every place. Furthermore, it is also questionable as to the timing and region of emergence of the culture in East Asia over a limited time frame of 3-2 BC. Gudle are formed by furnaces with roofs and walls, Gorae, which saves heat, and it has smoke ventilation that draws smoke out. Therefore, the Gudle is not a structure that anyone can make without advanced technical understanding. So far, the only facility with furnaces and smoke ventilation that appear before the Gudle is Buttumak. Thus, the Gudle is likely to have been invented in the place where Buttumak were used. The area as known for the origin of Gudle was observed to verify the existence of the Buttumak's structure, but none of these facilities were found. The Gudle suddenly appeared within a new culture that had never existed before. That means that none of the three places had the conditions under which the Gudle could be invented, so it must have been introduced from outside. In conclusion, the three places that I mentioned above are not the origin of Gudle. So, the origin of Gudle has to be found elsewhere.
Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.
Due to the system of sending selected hyanggi(local entertaining woman) to the government office in Seoul after the abolition of the system of gyeonggi(entertaining woman in capital area) during the reign of King Injo(1595~1649), the kyobang-jeongjae(local dance performed for the provincial government office) had gotten into the court to be performed at the royal banquet as gungjung-jeongjae(court dance), one of which was seonyurak(dance of boating). It used to be performed for finale of the royal banquet in the late Joseon Dynasty and appeared in several uigwes(record for royal banquet) since its first appearance in the wonhaeng-eulmyo-jeongri-uigwe, documented in 1795, the 19th year of the reign of King Jeongjo. Considering that the yeoggi(female entertainer) responsible for the court dance, seonyurrak was the seonsanggi(selected entertaining woman from provinces) from the northwestern provincial villages such as Euiju, Ahnju, and Seongcheon etc., we can assume that the baettaragi, one of kyobang-jeongjaes whould have been getting into the court to become the seonyurrak as court dance. The baettaragi, kyobang-jeongjae of northwestern province that affected the development of the court dance, seonyurak was created as performance executed by entertaining women of kyobang(local supervisory office for entertaining women) on the basis of the fact that the envoy of Joseon dynasty to the Ming dynasty could not help but taking a sea route when Amaga Aisin Gurun had a grip on the northeastern area of China during the shift of power from Ming to Qing. There had been a lot of banquets for envoys in the northwestern province because of its geographical feature as gateway to trip to China and the baettaragi used to be performed by entertaining women belonged to local provincial office to consolate the sadness of separation with those who destined to depart to China and to hope for their safe return. The kyobang-jeongjae, baettaragi of northwestern province is recorded as performance with sorrowful song to put the pain of parting into work, according to many related documents. It puts together painted boats as props, the march of a couple of dancer dressed up as soldier with marching music called gochiak, the song and musical accompaniment before getting on boat, the dramatic expression of sailing, and the farewell song praying for safe return etc. It turns the situation of dispatching envoys for China by sea into performance with combination of music, song and dance. Created in this way, the kyobang-jeongjae, baettaragi had been performed at the various banquets for envoys departing for China and it affected the formation of court dance or gungjung-jeongjae called seonyurak through the activities of selected local entertaining women. It also exerted influence on other similar performance in provincial area because of the returning home of the selected local entertainers who finished their performance in Seoul and it had been performed with different variation at local banquet including locality in it.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used