• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.58-74
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• 2018

This study aims to propose baseline data for designing restoration of Gaobyulup, researching space formation and characteristics of gardens of Gaobyulup, which located in the foot of Cheonmasan Mountain in Namyangju. Gaobyulup is a remain in retirement of Gyulsan Yu-Won Lee, a representative politician, administrator, and tea drinker in late Joseon Dynasty. The results of the research about the shape of Gaobyulup deducted through reference review, poetry and prose analysis, an on-the-spot survey and residents' interview are below: Lee, who used pseudonym as 'Gyulsan,' which menas Jongnamsan Mountain, yearned Mangcheonbyeoreop(輞川別業) by Yu Wang and retirement with a country house operation by Seogye Sedang Park. In the persuit of this ideal, he created and operated a country house in Gaogok of Yangju, which a family burial ground was located. Gaobyulup, which located in Gaogok in the lower part of Cheonmasan Mountain, was largely composed outer and inner gardens, and the area of house operation was started from a stone post of Gaobokji The inner garden of Gaobyulup was including major garden components like buildings, such as Sasihyanggwan, Obaekganjung, Imharyoe and Toesadam, and Chaewon near Haengrangchae, and Gwawon in an backyard. In addition, Younggwijung pavilion, which located 850m away from Gaobyulup, was the another country house inside the Byulup, thus Gaobyulup shows a duplex space formation. In the inner garden of Gaobyulup, there are Sasihyanggwan, which had functions of Sarangchae as library and depository of old paintings and calligraphic works, and Obaekganjung, a small Sarangchae which connected with Sasihyanggwan in the form of a transept. Yusanggoksuger located near Obaekganjung. Additionally, Imharyeo, a library with a tablet of Byeokryowon(??園), which located in the highest point in Byulup, has the functions of a reading room and a tea house. Many Taihu stones were located not only in Toesadam, a square-formed pond with lotus but also many places in the inner gardens. And rare garden plants were planted. These were closely related to the trend of horticulture for pleasure, wealth, and collecting old paintings and calligraphic works for pleasure of Lee. Meanwhile, the area of Younggwijung pavilion, located in Gaocheon stream fall from Byulup to Manhoiam, looks like Wooampok, a enjoying place of other personages, who use their pseudonym as "Oksan" or "Wooam" Lee identifies Wooampok as "Jesampok" and carved 'Gyulsan' s he declared this place is his operating area. Lee built Younggwijung pavilion and planted many peach trees for recreation of utopia. The stone letters of Byukpadongcheon, located in front of a bridge in the foreside of Younggwijung pavilion, seems another enchanted land created in Gaobokji inside. Lee carved Jeilsan in huge rock on the falls rear Manhoiam temple, which Lee did great role of foundation of the temple, so he identifies that this place was the end of the outer garden of Gaobyulup. This study tries to estimate traces of the country house in Gaogok through reference review and on-th-spot survey, and the results from this study are presumed based on site remains only conformed today. It needs to discover second scenary or stone carved letters between Jeilsan and Jesampok. Additionally, exact formation characteristics of Gaobyulup should be identified through excavation survey later. To do so, an interest and a major role of Namyangju-si must be equipped for future restoration of Gaobyulup.

• Tuberculosis and Respiratory Diseases
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• v.39 no.6
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• pp.453-473
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• 1992

Background: National survey was performed to estimate the incidence of sarcoidosis in Korea. The clinical data of confirmed cases were analysed for the practice of primary care physicians and pulmonary specialists. Methods: The period of study was from January 1991 to December 1992. Data were retrospectively collected by correspondence with physicians in departments of internal medicine, dermatology, ophthalmology and neurology of the hospitals having more than 100 beds using returning postcards. In confirmed and suspicious cases of sardoidosis, case record chart for clinical and laboratory findings were obtained in detail. Results: 1) Postcards were sent to 523 departments in 213 hospitals. Internal medicine composed 41%, dermatology 20%, ophthalmology 20% and neurology 19%. 2) Postcards were returned from 241 departments (replying rates was 48%). 3) There were 113 confirmed cases from 50 departments and 10 cases. The cases were composed from internal medicine (81%), dermatology (13%), ophthalmology (3%) and neurology (3%). 78 confirmed cases were analysed, which were composed from department of internal medicine (92%), dermatology (5%), and neurology (3%). 4) The time span for analysed cases was 1980 to 1992. one case was analysed in 1980 and the number gradually increased to 18 cases in 1991. 5) The majority of patients (84.4%) were in the age group of 20 to 49 years. 6) The ratio of male to female was 1 : 1.5. 7) The most common chief complains were respiratory symptoms, dermatologic symptoms, generalized discomforts, visual changes, arthralgia, abdominal pains, and swallowing difficulties in order. 16% of the patients were asymptomatic. 8) Mean duration between symptom onset and diagnosis was 2 months. 9) The most common symptoms were respiratory, general, dermatologic, ophthalmologic, neurologic and cardiac origin in order. 10) Hemoglobin, hematocrits and platelet were in normal range. 58% of the patients had lymphopenia measuring less than 30% of white cell count. The ratio of CD4 to CD8 lymphocytes was $1.73{\pm}1.16$ with range of 0.43 to 4.62. ESR was elevated in 43% of the cases. 11) Blood chemistry was normal in most cases. Serum angiotensin converting enzyme (S-ACE) was $66.8{\pm}58.6\;U/L$ with the range of 8.79 to 265 U /L. Proteinuria of more than 150 mg was found in 42. 9% of the patients. 12) Serum IgG was elevated in 43.5%, IgA in 45.5%, IgM in 59.1% and IgE in 46.7%. The levels of complement C3 and C4 were in the normal range. Anti-nuclear antibody was detected in 11% of the cases. Kweim test was performed in 3 cases, and in all cases the result was positive. 13) FVC was decreased in 17.3%, FEV1 in 11.5%, FEV1/FVC in 10%, TLC in 15.2%, and DLco in 64.7%. 14) PaO2 was decreased below 90 mmHg in 48.6% and PaCO2 was increased above 45 mmHg in 5.7%. 15) The percentage of macrophages in BAL fluid was $51.4{\pm}19.2%$, lymphocytes $44.4{\pm}21.1%$, and the ratio of CD4 to CD8 lymphocytes was $3.41{\pm}2.07$. 16) There was no difference in laboratory findings between male and female. 17) Hilar enlargement on chest PA was present in 87.9% (bilaterally in 78.8% and unilaterally in 9.1%). 18) According to Siltzbach's classification, stage 0 was 5%, stage 158.3%, stage 228.3%, and stage 38.3%. 19) Hilart enlargement on chest CT was present in 92.6% (bilaterally 76.4% and unilaterally in 16.2%). 20) HRCT was done in 16 cases. The most common findings were nodules, interlobular thickening, focal patchy infiltrations in order. Two cases was normal finding. 21) Other radiologic examinations showed bone change in one case and splenomegaly in two cases. 22) Gallium scan was done in 12 cases. Radioactivity was increased in hilar and mediastinal lymph nodes in 8 cases and in parenchyme in 2 cases. 23) The pathologic diagnosis was commonly performed by transbrochial lung biopsy (TBLB, 47.3%), skin and mediastinal lymph nodes biopsy (34.5%), peripheral lymph nodes biopsy (23.6%), open lung biopsy (18.2%) and bronchial biopsy in order. 24) The most common findings in pathology were non·caseating granuloma (100%), multi-nucleated giant cell (47.3%), hyalinized acellular scar (34.5%), reticulin fibrin network (20%), inclusion body (10.9%), necrosis (9.1%), and lymphangitic distribution of granuloma (1.8%) in order. Conclusion: Clinical, laboratory, radiologic and pathologic findings were summarized. This collected data will assist in finding a test for detection and staging of sarcoidosis in Korea in near future.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.