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    ๊ณต๊ฐ„๊ตฌ๋ฌธ๋ก ์„ ํ™œ์šฉํ•œ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€์™€ ์„œ์šธ์‹œ ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„ ์‚ฌ๋ก€๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์ถ•ํ•™๊ณผ, 2020. 8. ์ตœ์žฌํ•„.๊ณต๊ฐœ๊ณต์ง€(Privately Owned Public Spaces)๋ž€ ์‚ฌ์ ์ธ ์˜์—ญ ๋‚ด ๊ณต์ ์ธ ๊ณต๊ฐ„์œผ๋กœ ์‹œ๋ฏผ์˜ ํœด์‹๊ณผ ๋ณดํ–‰์„ ์œ„ํ•ด์„œ ๊ณต๊ณต์— ์ œ๊ณต๋˜๋Š” ๊ณต๊ณต๊ณต๊ฐ„์ด๋‹ค. ์ด์ฒ˜๋Ÿผ ๊ณต๊ฐœ๊ณต์ง€๋Š” ๋„์‹œ ๋‚ด ํ•„์ˆ˜์ ์ธ ๊ณต๊ณต๊ณต๊ฐ„์œผ๋กœ์จ ๋„์‹œ๊ณต๊ฐ„์—์„œ์˜ ์ค‘์š”๋„๋Š” ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ์ ๊ทน์ ์ธ ์„ค์น˜๊ฐ€ ๊ถŒ์žฅ๋˜๊ณ  ์žˆ๋‹ค. ํ•œํŽธ, ํ˜„์žฌ ์„œ์šธ์‹œ ๊ฑด์ถ•์กฐ๋ก€์™€ ์ง€๊ตฌ๋‹จ์œ„๊ณ„ํš ์ˆ˜๋ฆฝ๊ธฐ์ค€์—์„œ ์ œ์‹œํ•˜๊ณ  ์žˆ๋Š” ๊ณต๊ฐœ๊ณต์ง€ ์œ ํ˜•์€ ๋ชจ๋‘ ๊ฑด๋ฌผ ์™ธ๋ถ€์— ๊ตญํ•œ๋˜๋Š” ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ณต๊ฐœ๊ณต์ง€๊ฐ€ ์ œ๋„ํ™”๋œ 1991๋…„ ์ดํ›„ ์˜๋ฌด์กฐ์„ฑ์œผ๋กœ ์ธํ•œ ์–‘์ ์ธ ์ฆ๊ฐ€๋Š” ์žˆ์–ด์™”์ง€๋งŒ ํ˜•์‹์ ์ธ ์„ค์น˜๋กœ ์ธํ•ด ๊ณต๊ฐœ๊ณต์ง€์˜ ์‹ค์ œ ํ™œ์šฉ๋„๋Š” ๋งค์šฐ ์ €ํ•˜๋˜์–ด์™”๋‹ค. ์ด์— ๋”ฐ๋ฅธ ์‹ค์™ธ๊ณต๊ฐœ๊ณต์ง€์˜ ํ•œ๊ณ„๋ฅผ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๊ณต๊ฐœ๊ณต์ง€ ์œ ํ˜• ์ œ์‹œ์™€ ๋Œ€์ฑ… ๋งˆ๋ จ์ด ํ•„์š”ํ•œ ์‹œ์ ์ด๋‹ค. ๋ฐ˜๋ฉด ์™ธ๊ตญ์˜ ๊ฒฝ์šฐ ์ด๋ฏธ ์‹ค๋‚ด ํ™œ๋™์ด ๋†’์€ ๋„์‹œ๋ฏผ์˜ ์ƒํ™œ์„ ๊ณ ๋ คํ•˜์—ฌ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ์ œ๋„๋ฅผ ๋„์ž…ํ•˜๊ณ  ์—…๋ฌด์‹œ์„ค์˜ ์ €์ธต๋ถ€๋ฅผ ์‹œ๋ฏผ์—๊ฒŒ ๋ณดํ–‰๊ณผ ํœด์‹๊ณต๊ฐ„์œผ๋กœ ์ œ๊ณตํ•˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋ž€ ๊ฑดํ๋œ ๊ณต๊ฐœ๊ณต์ง€๋กœ ๋ณดํ–‰์ž๊ฐ€ ํ†ต์ œ๋ฅผ ๋ฐ›์ง€ ์•Š๊ณ  ์ž์œ ๋กญ๊ฒŒ ์ ‘๊ทผํ•˜๊ณ  ์ด์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฐœ๋ฐฉ๋œ ์˜ฅ๋‚ด๊ณต๊ฐ„์„ ์ผ์ปซ๋Š”๋‹ค. ์ด์ฒ˜๋Ÿผ ์—…๋ฌด์‹œ์„ค ์ €์ธต๋ถ€์— ์œ„์น˜ํ•˜๋Š” ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋Š” ๊ฑด์ถ•์ฃผ์˜ ์‚ฌ์ต๊ณผ ๋Œ€์ค‘์„ ์œ„ํ•œ ๊ณต์ต์˜ ๋งค๊ฐœ ๊ณต๊ฐ„์œผ๋กœ์จ ์ƒˆ๋กœ์šด ์œ ํ˜•์˜ ๊ณต๊ณต๊ณต๊ฐ„์„ ์ œ์‹œํ•˜๊ณ  ํ˜„ ์‹ค์™ธ๊ณต๊ฐœ๊ณต์ง€ ํ•œ๊ณ„์ ์— ๋Œ€ํ•œ ํ•ด๊ฒฐ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„์‹œ ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ ํ™•๋ณด๋ฅผ ์œ„ํ•˜์—ฌ ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด์— ๊ณต๊ฐœ๊ณต์ง€ ์ œ๋„๊ฐ€ ํ™œ์„ฑํ™”๋œ ๋‰ด์š•์‹œ์˜ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ณต๊ฐ„๊ตฌ๋ฌธ๋ก ์˜ ์ผ์ข…์ธ ๊ฐ€์‹œ์„ฑ ๊ทธ๋ž˜ํ”„ ๋ถ„์„(Visibility Graph Analysis)์„ ํ™œ์šฉํ•˜์—ฌ ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ์„ ๋ถ„์„ํ•œ ํ›„, ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๋ฐ ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ์ง€ํ‘œ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋‰ด์š•์‹œ ์ด์šฉ๋„๊ฐ€ ๋†’์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€์™€ ์ด์šฉ๋„๊ฐ€ ๋‚ฎ์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋ฅผ ๋ถ„๋ฅ˜ํ•˜์—ฌ ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ์„ ์ ‘๊ทผ์„ฑ๊ณผ ๊ฐœ๋ฐฉ์„ฑ์˜ ํ‹€๋กœ ๋ถ„์„ํ•˜์˜€๊ณ , ์ด์šฉ๋„๊ฐ€ ๋†’์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€์˜ ํŠน์„ฑ์„ ๋„์ถœํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ฐœ๋ฐœ๋œ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ๊ตญ๋‚ด ์ดˆ๋Œ€ํ˜• ์—…๋ฌด์‹œ์„ค์˜ ๊ณต๊ณต๊ณต๊ฐ„์— ์ ์šฉํ•˜์—ฌ ์—…๋ฌด์‹œ์„ค ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋กœ์˜ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๋„์ถœํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฅผ ์ข…ํ•ฉํ•˜์—ฌ ์ •๋ฆฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋Š” 1์ธต ๋‚ด ๊ทธ ์™ธ ๊ณต๊ณต๊ณต๊ฐ„๊ณผ ๋น„๊ต ์‹œ ์ ‘๊ทผ์„ฑ๊ณผ ๊ฐœ๋ฐฉ์„ฑ์ด ๋†’์€ ๊ณณ์— ์„ค์น˜๋˜์–ด ์žˆ์Œ์„ VGA๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ์„ค์น˜๋œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€์˜ ์ ‘๊ทผ์„ฑ๊ณผ ๊ฐœ๋ฐฉ์„ฑ์€ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ์„ค์น˜๊ธฐ์ค€์— ์ƒ์‘ํ•˜์˜€๋‹ค. ๋‘˜์งธ, ์ ‘๊ทผ์„ฑ ๋†’์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณ„ํš ํŠน์„ฑ์œผ๋กœ๋Š” ์ถœ์ž…๊ตฌ์˜ ๊ฐœ์†Œ๊ฐ€ ๋งŽ์•˜์œผ๋ฉฐ, ๊ฐœ๋ฐฉ์„ฑ์ด ๋†’์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€์˜ ๊ฒฝ์šฐ์—๋Š” ๊ณต๊ฐ„ ๋‚ด ์œ ๋ฆฌ๋ฒฝ์˜ ๋น„์œจ์ด ๋†’์•˜๋‹ค. ์…‹์งธ, ์ตœ์ข… ํ‰๊ฐ€์ง€ํ‘œ๊ฐ€ ๋†’์€ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋Š” ์ด์šฉ๋„๊ฐ€ ๋†’์€ ์•„ํŠธ๋ฆฌ์›€ ์œ ํ˜•์ด์—ˆ์œผ๋ฉฐ, ์ตœ์ข… ํ‰๊ฐ€์ง€ํ‘œ๊ฐ€ ๋‚ฎ์€ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋Š” ์ด์šฉ๋„๊ฐ€ ๋‚ฎ์€ ์„ ํ˜• ์œ ํ˜•์˜ ๊ณต๊ฐœ๊ณต์ง€์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋„ท์งธ, ๊ฐœ๋ฐœ๋œ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ์„œ์šธ์‹œ ์—…๋ฌด์‹œ์„ค ๊ณต๊ณต๊ณต๊ฐ„์˜ ์•„ํŠธ๋ฆฌ์›€ ๊ณต๊ฐ„์— ๋Œ€์ž…ํ•˜์—ฌ ๊ณต๊ณต์„ฑ์„ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ, ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€์ง€ํ‘œ์™€ ๊ด€์ฐฐ์กฐ์‚ฌ๋ฅผ ํ†ตํ•œ ํ†ตํ–‰๋Ÿ‰ ์‚ฌ์ด์— ์œ ์˜๋ฏธํ•œ ์ƒ๊ด€์„ฑ์„ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ด์— ๋”ฐ๋ผ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœ๋œ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค์„ฏ์งธ, ํ•ด๋‹น ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ์ ์šฉํ•˜์—ฌ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๊ตญ๋‚ด ์ดˆ๋Œ€ํ˜• ์—…๋ฌด์‹œ์„ค์˜ ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„์€ ํ™œ์„ฑํ™”๋œ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋งŒํผ์˜ ๊ณต๊ณต์„ฑ์„ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋ฉฐ, ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€๋กœ์˜ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค.Privately owned public spaces (POPS) are public spaces in private areas that provide citizens with resting areas and walking environments in the city. The importance of POPS in urban space is increasing and encouraged by the government. However, the Seoul Metropolitan Government Building Ordinance and District Unit Plan Standards only suggest POPS types restricted to the exterior of buildings. Since 1991, when POPS were institutionalized, their number has increased quantitatively due to obligations to install them; however, the actual utilization of the spaces has been limited due to careless installation. It is, therefore, necessary to suggest countermeasures to improve these outdoor POPS and introduce new types of POPS. Other countries have already adopted indoor POPS systems, taking into consideration the living conditions of urban citizens with numerous indoor activities. These spaces provide citizens with resting and pedestrian areas on the ground floors of commercial and office buildings. Indoor POPS refers to enclosed, indoor open spaces that pedestrians can freely access and use without restriction. These spaces, installed on the ground floors of commercial and office buildings, represent a new type of public space. Moreover, as an intermediary space for the private interests of building owners and the public, they may provide a solution to the current limitations of outdoor POPS in Korea. The purpose of this study is to develop an evaluation index for indoor POPS to secure the publicness of urban public spaces and to introduce an indoor POPS system in Korea. To do so, highly used indoor POPS in New York City were analyzed using Visibility Graph Analysis, a type of space syntax theory. As a result, a quantitative publicness index for indoor POPS and indoor public space was developed. To develop the index, ten cases of indoor POPS in New York City were analyzed by evaluating their publicness, which is physical accessibility and visual connectivity (openness). The characteristics of frequently used indoor POPS were also considered. The developed evaluation tools were also used to analyze the publicness of indoor public spaces of office buildings in Korea. The results are as follows. First, New York Citys indoor POPS are installed in places with high accessibility and openness compared to other public spaces on the first floor of the buildings. The evaluation of the accessibility and openness of the space corresponds to the standards for installing indoor POPS in New York City. Second, cases with a high accessibility index possessed a greater number of entrances, and cases with a high visual openness index were primarily composed of glass. Third, among the ten cases of New York City indoor POPS, atrium types were highly utilized and their evaluation index was high, whereas linear types were less utilized and their evaluation index was low. Fourth, by applying the developed evaluation index to assess the publicness of the atrium spaces of the six office buildings in Seoul, a significant correlation was derived between the accessibility evaluation and the atrium pedestrian flow rate, which verifies the effectiveness of the developed evaluation index. Fifth, by evaluating the publicness of indoor public space, it was proved that the indoor public space in Korea was as public as the indoor POPS in New York City. This research proves, therefore, that the public spaces of office buildings in Korea can feasibly be used as indoor POPS.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 4 1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 4 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 5 3. ์—ฐ๊ตฌ์˜ ํ๋ฆ„ 6 ์ œ 2 ์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ 8 ์ œ 1 ์ ˆ ๊ตญ๋‚ด ๊ณต๊ฐœ๊ณต์ง€ ์ œ๋„ ๊ณ ์ฐฐ 8 1. ๊ตญ๋‚ด ๊ณต๊ฐœ๊ณต์ง€ ์ œ๋„ 8 2. ๊ตญ๋‚ด ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ํ˜„ํ™ฉ 15 3. ํ˜„ ๊ณต๊ฐœ๊ณต์ง€์˜ ๋ฌธ์ œ์  17 4. ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ด€๋ จ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 19 ์ œ 2 ์ ˆ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ์ œ๋„ 20 1. ๋‰ด์š•์‹œ ๊ณต๊ฐœ๊ณต์ง€ ์ œ๋„ 20 2. ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ํ˜„ํ™ฉ 21 ์ œ 3 ์ ˆ ์—…๋ฌด์‹œ์„ค ๊ณต๊ณต๊ณต๊ฐ„ 24 1. ๊ณต๊ณต๊ณต๊ฐ„์˜ ์ •์˜ 24 2. ์—…๋ฌด์‹œ์„ค์˜ ๊ณต๊ณต๊ณต๊ฐ„ 25 ์ œ 4 ์ ˆ ๊ณต๊ฐ„๋ถ„์„๋„๊ตฌ 30 1. ๊ณต๊ฐ„๊ตฌ๋ฌธ๋ก ์˜ ๊ฐœ๋… 30 2. ๊ฐ€์‹œ์„ฑ ๊ทธ๋ž˜ํ”„ ๋ถ„์„ 32 3. ๊ณต๊ณต์„ฑ ๋ถ„์„์˜ ํ‹€ ์„ค์ • 34 ์ œ 5 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 36 1. ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ด€๋ จ ์—ฐ๊ตฌ 38 2. ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„ ๊ณต๊ณต์„ฑ ๊ด€๋ จ ์—ฐ๊ตฌ 40 3. ์—…๋ฌด์‹œ์„ค ์ €์ธต๋ถ€ ๊ด€๋ จ ์—ฐ๊ตฌ 41 4. ๊ณต๊ณต๊ณต๊ฐ„ ๋Œ€์ƒ VGA๋ถ„์„ ๊ด€๋ จ ์—ฐ๊ตฌ 42 5. ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 43 ์ œ 3 ์žฅ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณต๊ณต์„ฑ ๋ถ„์„ 44 ์ œ 1 ์ ˆ ๋ถ„์„๋Œ€์ƒ ์„ ์ • 44 ์ œ 2 ์ ˆ ๋ถ„์„ ๋ฐฉ๋ฒ• 56 ์ œ 3 ์ ˆ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณต๊ณต์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ 60 1. ์ ‘๊ทผ์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ 60 2. ์ ‘๊ทผ์„ฑ ๋ถ„์„ ์†Œ๊ฒฐ 65 3. ๊ฐœ๋ฐฉ์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ 67 4. ๊ฐœ๋ฐฉ์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ 72 ์ œ 4 ์žฅ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ ๊ฐœ๋ฐœ 74 ์ œ 1 ์ ˆ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณ„ํš ํ‰๊ฐ€ 74 1. ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 76 2. ๊ฐœ๋ฐฉ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 58 ์ œ 2 ์ ˆ 1์ธต ๊ณต๊ณต๊ณต๊ฐ„ ๊ณ„ํš ํ‰๊ฐ€ 78 1. ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 80 2. ๊ฐœ๋ฐฉ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 81 ์ œ 3 ์ ˆ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ ๊ฐœ๋ฐœ 83 1. ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 84 2. ๊ฐœ๋ฐฉ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 86 ์ œ 4 ์ ˆ ํ‰๊ฐ€์ง€ํ‘œ ๊ฒ€์ฆ ๋ฐ ์†Œ๊ฒฐ 88 ์ œ 5 ์žฅ ๊ตญ๋‚ด ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€ 91 ์ œ 1 ์ ˆ ๋ถ„์„๋Œ€์ƒ ์„ ์ • 91 ์ œ 2 ์ ˆ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ ์ ์šฉ 108 1. ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 108 2. ๊ฐœ๋ฐฉ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 115 3. ๊ตญ๋‚ด ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„ ํ‰๊ฐ€์ง€ํ‘œ ์ ์šฉ ์†Œ๊ฒฐ 121 ์ œ 3 ์ ˆ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ ๊ฒ€์ฆ 125 1. ๊ด€์ฐฐ์กฐ์‚ฌ ๋ฐฉ๋ฒ•๊ณผ ๊ฒฐ๊ณผ 125 2. ๊ด€์ฐฐ์กฐ์‚ฌ์™€ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ ๋น„๊ต ๊ฒ€์ฆ 128 ์ œ 4 ์ ˆ ๊ตญ๋‚ด ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€ ์†Œ๊ฒฐ 132 ์ œ 6 ์žฅ ๊ฒฐ๋ก  134 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ์ข…ํ•ฉ 134 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ํ•œ๊ณ„ 135 ์ฐธ๊ณ ๋ฌธํ—Œ 136 Abstract 139Maste

    e-TMS and Super Wi-Fi for Improving Telecommunication Systems Environment on Ships and Offshore Plants

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    Telecom systems for ships and offshore plants used to be a simple tool for distance communication. In contrast to land-based telecom systems technology today that has been rapidly evolving, telecom systems for ships and offshore plants are far from being used as administration tools for safety, control and operation. Unlike other systems, telecom systems for ships and offshore plants have not been systematized by the IMO(International Maritime Organization). Telecom systems developed decades ago are still installed on ships and in offshore plants. However, to meet the increasing demand for marine accident prevention and operation cost saving, a wide range of technologies based on telecom systems have been emerging. Thus, telecom systems will play pivotal roles in controlling and administering all systems for ships and offshore plants. At the end of the 1990s, the NORSOK proposed the TMS for administering the telecom systems for offshore plants. Yet, the TMS for offshore plants fails to meet the technical standard proposed by the NORSOK due to the diversity of telecom systems. The high installation cost thwarts the applicability and scalability of the TMS for offshore plants. This paper proposes a method of redefining and systematizing the telecom systems installed on ships and in offshore plants with intent to address the challenges relevant to the TMS for offshore plants. Systematization of the telecom systems would add to their scalability as well as compatibility with other systems operated on ships and in offshore plants. This paper proposes an improved e-TMS(enhanced-TMS) based on the systematized telecom systems for ships and offshore plants, and experimentally demonstrates the performance of the proposed e-TMS in terms of its data processing time and operation cost. The scales and the structural complexity of ships and offshore plants continue to increase. With the operation and administration of ships and offshore plants drawing increasing attention, they need to process ever larger volumes of data. Next-generation ships and offshore plants including unmanned ships, unmanned offshore plants, remote-controlled ships and smart ships should be fitted with scalable and compatible systems capable of processing large volumes of data in real time, which calls for the installation of a wireless network environment that ensures ubiquity and mobility. The wireless network environment for ships and offshore plants features the real-time administration and control over not only communication but also status information, fault diagnosis, safety control, location tracking and accident prevention. Yet, ships and offshore plants characterized by steel structures have Wi-Fi networks installed in limited onboard areas. Using shared frequency band, a Wi-Fi network provides a short radio propagation range, which is limited to short-range transmissions. Internationally travelling ships and offshore plants require a long-range radio propagation and a method of using shared frequency band as private frequency band. This paper analyzes the factors precluding the full implementation of wireless networks on ships and in offshore plants, experimentally verifies the attributes as well as the strengths and weaknesses of UHF, TETRA and Wi-Fi frequencies widely used for ships and offshore plants, and also analyzes the radio propagation range and radio transmittance of super Wi-Fi using the TV white space. Finally, this paper proposes implementing a super Wi-Fi environment using the UHF system of TMS, and experimentally tests the performance of an implemented wireless network environment using the super Wi-Fi on ships and in offshore plants. |๊ณผ๊ฑฐ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์—์„œ ํ†ต์‹  ์‹œ์Šคํ…œ์€ ์›๊ฑฐ๋ฆฌ ์˜์‚ฌ์†Œํ†ต์„ ์œ„ํ•œ ๋‹จ์ˆœ ํ†ต์‹ ๋„๊ตฌ์˜€๋‹ค. ์ง€๊ธˆ๊นŒ์ง€๋„ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์—์„œ์˜ ํ†ต์‹  ์‹œ์Šคํ…œ์€ ์•ˆ์ „, ๊ด€๋ฆฌ, ์šด์˜๊ณผ๋Š” ๊ฑฐ๋ฆฌ๊ฐ€ ๋ฉ€๋‹ค. ๊ตญ์ œํ•ด์‚ฌ๊ธฐ๊ตฌ๋Š” ํ†ต์‹  ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์ฒด๊ณ„๋ฅผ ํ™•๋ฆฝํ•˜์ง€ ์•Š๊ณ  ์žˆ๋‹ค. ์ˆ˜์‹ญ ๋…„ ์ „์— ๊ฐœ๋ฐœ๋˜์–ด ๊ธฐ์ˆ ์ ์ธ ๋ฐœ์ „์ด ์—†๋Š” ํ†ต์‹  ์‹œ์Šคํ…œ์ด ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์— ์„ค์น˜๋œ๋‹ค. ์˜ค๋Š˜๋‚  ํ•ด์–‘ ์‚ฌ๊ณ ์™€ ๊ฒฝ์ œ์„ฑ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋น„์šฉ ์ ˆ๊ฐ์˜ ์š”๊ตฌ๊ฐ€ ๋†’์•„์ง€๋ฉด์„œ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋‹ค์–‘ํ•œ ์ฐจ์„ธ๋Œ€ ๊ธฐ์ˆ ๋“ค์ด ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ๊ณง ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์˜ ๋ชจ๋“  ์‹œ์Šคํ…œ๋“ค์€ ํ†ต์‹  ์‹œ์Šคํ…œ์— ์˜ํ•ด ๊ด€๋ฆฌ์™€ ํ†ต์ œ๋ฅผ ๋ฐ›๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. NORSOK์€ 1990๋…„๋Œ€ ๋ง ํ†ต์‹  ์‹œ์Šคํ…œ์˜ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด ํ•ด์–‘ํ”Œ๋žœํŠธ์šฉ TMS๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๋‹น์‹œ์˜ ํ•ด์ƒ์šฉ ํ†ต์‹  ์‹œ์Šคํ…œ์˜ ๊ธฐ์ˆ ์ ์ธ ํ•œ๊ณ„์™€ ๋†’์€ ์„ค์น˜ ๋น„์šฉ์œผ๋กœ ์ธํ•ด ๋งŒ์กฑ๋„๊ฐ€ ๋‚ฎ์•„์กŒ๊ณ  ๊ฒฐ๊ตญ ํ™•์žฅ์— ์‹คํŒจํ•œ๋‹ค. ํ•ด์–‘ํ”Œ๋žœํŠธ์šฉ TMS๊ฐ€ ์‹คํŒจํ•œ ์›์ธ์„ ๋ถ„์„ํ•˜๊ณ  ํ†ต์‹  ์‹œ์Šคํ…œ์˜ ์žฌ์ •์˜๋ฅผ ํ†ตํ•ด ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋ฅผ ์œ„ํ•œ ๊ฐœ์„ ๋œ TMS๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋Š” ์œก์ƒ๋ณด๋‹ค ํ›จ์”ฌ ๋†’์€ ์•ˆ์ •์„ฑ๊ณผ ๋‹ค์–‘์„ฑ์ด ํ•„์š”ํ•˜๋‹ค. ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์—์„œ ๊ฒ€์ฆ๋˜์ง€ ์•Š์€ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜๋ฉด ํ•ด์–‘ ์‚ฌ๊ณ ์™€ ๊ฐ™์€ ์‹ฌ๊ฐํ•œ ๋ถ€์ž‘์šฉ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ํ†ต์‹  ์‹œ์Šคํ…œ์€ ๊ธฐ๋Šฅ์„ ์žฌ์ •์˜ํ•˜๊ณ  ์ฒด๊ณ„ํ™”ํ•ด์•ผ ํ•œ๋‹ค. ํ†ต์‹  ์‹œ์Šคํ…œ์˜ ์ฒด๊ณ„ํ™”๋Š” ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์˜ ์‹œ์Šคํ…œ ๊ฐ„ ํ†ต์ผ์„ฑ๊ณผ ํ™•์žฅ์„ฑ์„ ๊ฐ€์ ธ์˜จ๋‹ค. ์ฒด๊ณ„ํ™”๋œ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ๋ฐ”ํƒ•์œผ๋กœ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋ฅผ ์œ„ํ•œ ๊ฐœ์„ ๋œ e-TMS๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” e-TMS๊ฐ€ ์ฒ˜๋ฆฌ ์‹œ๊ฐ„๊ณผ ์‹œ์Šคํ…œ์˜ ์šด์˜ ๋น„์šฉ ๋ฉด์—์„œ ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์‹คํ—˜์„ ํ†ตํ•ด ์ฆ๋ช…ํ•œ๋‹ค. ์ตœ๊ทผ ๋“ค์–ด ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋Š” ๊ทœ๋ชจ๊ฐ€ ์ปค์ง€๊ณ  ๊ตฌ์กฐ๋Š” ๋” ๋ณต์žกํ•ด์ง€๊ณ  ์žˆ๋‹ค. ์ด์™€ ํ•จ๊ป˜ ๋‹ค์–‘ํ•œ ์‹œ์Šคํ…œ๋“ค์ด ์ถ”๊ฐ€๋˜์–ด ๊ณผ๊ฑฐ์— ๋น„ํ•ด ๋งŽ์€ ์–‘์˜ ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋ฌด์ธ ์„ ๋ฐ•, ๋ฌด์ธ ํ•ด์–‘ํ”Œ๋žœํŠธ, ์ธ๊ณต์ง€๋Šฅ ์„ ๋ฐ•๊ณผ ๊ฐ™์€ ์ฐจ์„ธ๋Œ€ ๊ธฐ์ˆ ๋“ค์€ ๋Œ€๊ทœ๋ชจ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•ด์•ผ ํ•œ๋‹ค. ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋„ ์œก์ƒ์—์„œ์ฒ˜๋Ÿผ ์ „ ๊ตฌ์—ญ์— ๋ฌด์„  ๋„คํŠธ์›Œํฌ๋ฅผ ๊ตฌ์ถ•ํ•˜์—ฌ ์žฅ์†Œ์™€ ์‹œ๊ฐ„์— ์ œ์•ฝ ์—†์ด ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์—์„œ์˜ ๋ฌด์„  ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์€ ๋‹จ์ˆœ ํ†ต์‹ ์ˆ˜๋‹จ์„ ๋„˜์–ด ์ƒํƒœ ์ •๋ณด, ๊ณ ์žฅ ์ง„๋‹จ, ์œ„์น˜ ์ถ”์ , ์‚ฌ๊ณ  ์˜ˆ๋ฐฉ, ์•ˆ์ „ ๊ด€๋ฆฌ์— ๋Œ€ํ•œ ์‹ค์‹œ๊ฐ„ ๊ด€๋ฆฌ์™€ ํ†ต์ œ๋ฅผ ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ํ˜„์žฌ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋Š” Wi-Fi๋ฅผ ์„ค์น˜ํ•˜์—ฌ ๋ถ€๋ถ„์ ์œผ๋กœ ๋ฌด์„  ๋„คํŠธ์›Œํฌ๋ฅผ ๊ตฌ์ถ•ํ•œ๋‹ค. ๊ณต์šฉ ์ฃผํŒŒ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜๋Š” Wi-Fi๋Š” ์ „ํŒŒ ์ „๋‹ฌ ๋ฒ”์œ„๊ฐ€ ์ข์•„ ๋ณต์žกํ•œ ์ฒ  ๊ตฌ์กฐ๋ฌผ์ธ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์—๋Š” ์ ํ•ฉํ•˜์ง€ ์•Š๊ณ  ์‚ฌ์šฉ ๋ฒ”์œ„๋„ ๊ทนํžˆ ์ œํ•œ์ ์ด๋‹ค. ์ง€๊ธˆ๊นŒ์ง€ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์˜ ๋ฌด์„  ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ ๊ตฌํ˜„์— ๋Œ€ํ•œ ์‹คํŒจ ์›์ธ๊ณผ UHF, TETRA, Wi-Fi์˜ ์ฃผํŒŒ์ˆ˜ ๋ณ„ ํŠน์„ฑ๊ณผ ์žฅ๋‹จ์ ์„ ์‹คํ—˜์„ ํ†ตํ•ด ํ™•์ธํ•œ๋‹ค. ํ•ด์ƒ์„ ์ด๋™ํ•˜๋Š” ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ๋Š” ์ „ํŒŒ ์ „๋‹ฌ ๋ฒ”์œ„๊ฐ€ ๋„“์–ด์•ผ ํ•˜๊ณ  ๊ณต์šฉ ์ฃผํŒŒ์ˆ˜๋ฅผ ์‚ฌ์„ค ์ฃผํŒŒ์ˆ˜์™€ ๊ฐ™์ด ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. TV ํ™”์ดํŠธ ์ŠคํŽ˜์ด์Šค๋ฅผ ์ด์šฉํ•˜๋ฉฐ Wi-Fi๋ณด๋‹ค ์ „ํŒŒ ์ „๋‹ฌ ๋ฒ”์œ„, ํˆฌ๊ณผ์œจ์ด ๋ชจ๋‘ ์šฐ์ˆ˜ํ•œ ์Šˆํผ Wi-Fi์— ๋Œ€ํ•ด ์‹คํ—˜ํ•˜๊ณ  ๋ถ„์„ํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ UHF ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์Šˆํผ Wi-Fi ํ™˜๊ฒฝ์„ ๊ตฌํ˜„ํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์„ ๋ฐ•๊ณผ ํ•ด์–‘ํ”Œ๋žœํŠธ์— ์Šˆํผ Wi-Fi๋ฅผ ์ด์šฉํ•œ ๋ฌด์„  ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์„ ๊ตฌํ˜„ํ•˜๋Š” ๊ฒƒ์„ ์‹คํ—˜์„ ํ†ตํ•ด ์ฆ๋ช…ํ•œ๋‹ค.List of Tables iv List of Figures vi ์•ฝ์–ด (Abbreviations) x ์ดˆ๋ก xiv Abstract xvii ์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ 2 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 4 ์ œ 2 ์žฅ TMS์™€ ํ†ต์‹  ์‹œ์Šคํ…œ ํ˜„ํ™ฉ 6 2.1 TMS ๊ฐœ์š” 6 2.1.1 TMS ์ •์˜ 6 2.1.2 TMS ๋ฌธ์ œ์  10 2.1.3 TMS ์„ฑ์žฅ ํ•œ๊ณ„ 14 2.1.4 TMS ๊ฐœ์„ ์˜ ํ•„์š”์„ฑ 15 2.2 ํ†ต์‹  ์‹œ์Šคํ…œ ๊ฐœ์š” 21 2.2.1 ํ†ต์‹  ์‹œ์Šคํ…œ ์ •์˜ 21 2.2.2 ํ†ต์‹  ์‹œ์Šคํ…œ ๋ฌธ์ œ์  25 2.2.3 ํ†ต์‹  ์‹œ์Šคํ…œ ์ฒด๊ณ„ํ™” ํ•„์š”์„ฑ 29 ์ œ 3 ์žฅ e-TMS๋ฅผ ์œ„ํ•œ ํ†ต์‹  ์‹œ์Šคํ…œ ์ฒด๊ณ„ํ™” 31 3.1 ํ†ต์‹  ์‹œ์Šคํ…œ ์žฌ์ •์˜ 31 3.2 ํ†ต์‹  ์‹œ์Šคํ…œ ์ฒด๊ณ„ํ™” ๋ฐฉ๋ฒ• 34 3.3 PAGA ์‹œ์Šคํ…œ์˜ ์ฒด๊ณ„ํ™” 35 3.3.1 ๋ฐ์ดํ„ฐ ์š”๊ตฌ ๋ถ„์„ 36 3.3.2 ๋ฐ์ดํ„ฐ ๊ณต๊ธ‰ ๋ถ„์„ 38 3.3.3 ๋ฐ์ดํ„ฐ ์ทจํ•ฉ 40 3.3.4 ๋ฐ์ดํ„ฐ์˜ ๊ตฌ๋ถ„ 40 3.3.5 ๋ฐ์ดํ„ฐ ์ •๊ทœํ™” 40 3.4 e-TMS๋ฅผ ์œ„ํ•œ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์ •์˜ 45 3.4.1 TMS์˜ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋ถ„์„ 46 3.4.2 e-TMS๋ฅผ ์œ„ํ•œ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์žฌ๊ตฌ์„ฑ 47 3.5 TMS ์„ฑ๋Šฅ ๊ฐœ์„  51 3.5.1 e-TMS ๊ตฌ์กฐ 52 3.5.2 ํ†ต์‹  ์‹œ์Šคํ…œ ๊ฐ„ ์ธํ„ฐํŽ˜์ด์Šค ์‹คํ—˜ 53 3.5.3 e-TMS์˜ ์„ฑ๋Šฅ ๊ฐœ์„  ํšจ๊ณผ 59 ์ œ 4 ์žฅ e-TMS ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋ฌด์„  ๋„คํŠธ์›Œํฌ์˜ ํ•„์š”์„ฑ 64 4.1 ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ ๋ถ„์„ 64 4.2 ๋ฌด์„  ๋„คํŠธ์›Œํฌ ๊ตฌ์„ฑ ๋ฐฉ๋ฒ• 66 ์ œ 5 ์žฅ ์Šˆํผ Wi-Fi ํ™˜๊ฒฝ ๊ตฌํ˜„์„ ์œ„ํ•œ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ ๋ถ„์„๊ณผ UHF ์ปค๋ฒ„๋ฆฌ์ง€ ์—ฐ๊ตฌ 69 5.1 ๋ฌด์„  ์ฃผํŒŒ์ˆ˜ ๋ถ„์„ 69 5.2 UHF ์‹œ์Šคํ…œ ๊ฐœ์š” 72 5.2.1 UHF ์‹œ์Šคํ…œ ์ •์˜ 73 5.2.2 UHF ์‹œ์Šคํ…œ ๋ฌธ์ œ์  75 5.2.3 UHF ์ปค๋ฒ„๋ฆฌ์ง€ ์—ฐ๊ตฌ ํ•„์š”์„ฑ 77 5.3 UHF ์ปค๋ฒ„๋ฆฌ์ง€ ์‹คํ—˜ 79 5.3.1 ์‹คํ—˜ ํ™˜๊ฒฝ 79 5.3.2 ์‹คํ—˜ ๋ฐฉ๋ฒ• 81 5.3.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 83 ์ œ 6 ์žฅ ์Šˆํผ Wi-Fi๋ฅผ ํฌํ•จํ•œ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ ๋ณ„ ์ปค๋ฒ„๋ฆฌ์ง€ ์—ฐ๊ตฌ 89 6.1 ์Šˆํผ Wi-Fi ์ •์˜ 89 6.2 ์ปค๋ฒ„๋ฆฌ์ง€ ์—ฐ๊ตฌ ํ™˜๊ฒฝ 92 6.3 ์ปค๋ฒ„๋ฆฌ์ง€ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 94 6.4 ์ปค๋ฒ„๋ฆฌ์ง€ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 95 6.4.1 TETRA 96 6.4.2 UHF 98 6.4.3 Super Wi-Fi 101 6.4.4 Wi-Fi (2.4GHz) 103 6.4.5 Wi-Fi (5GHz) 106 ์ œ 7 ์žฅ ์‹คํ—˜ ๋ฐ ์„ฑ๋Šฅ ํ‰๊ฐ€ 111 7.1 e-TMS ์„ฑ๋Šฅ ํ‰๊ฐ€ 111 7.1.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๋ฐ ๊ตฌ์„ฑ 111 7.1.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 115 7.2 UHF ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜ ์Šˆํผ Wi-Fi ํ™˜๊ฒฝ ํ‰๊ฐ€ 126 7.2.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๋ฐ ๊ตฌ์„ฑ 126 7.2.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 128 ์ œ 8 ์žฅ ๊ฒฐ๋ก  134 ๊ฐ์‚ฌ์˜ ๊ธ€ 136 ์ฐธ๊ณ ๋ฌธํ—Œ 138Docto

    ๋ฌธํ•™์‚ฌ๊ต์œก์—์„œ์˜ ์ง€์‹์˜ ๋ฌธ์ œ -๊ตญ์–ด ์ง€์‹ ๊ต์œก์˜ ์˜์—ญ ๋ฐ ํ™œ๋™๊ณผ ๊ด€๋ จํ•˜์—ฌ

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    ๋ฌธํ•™์‚ฌ๊ต์œก์„ ๋…ผํ•˜๋Š” ์ž๋ฆฌ๋งˆ๋‹ค ๋น ์ง์—†์ด ๊ฐ•์กฐ๋˜์–ด ์˜จ ๊ฒƒ์€ ๋ฌธํ•™์‚ฌ๊ต์œก์„ ํ†ตํ•ด ํ˜•์„ฑ๋œ ๊ทธ ๋ฌด์—‡์ด, ์ž‘ํ’ˆ์˜ ์ดํ•ด์™€ ๊ฐ์ƒ์— ๋ฐฐ๊ฒฝ ์ง€์‹์ด ๋˜์–ด ์ค€๋‹ค๋Š” ์ ์ด์—ˆ๋‹ค.1) ๊ทธ๋Ÿฐ๋ฐ ์—ฌ๊ธฐ์„œ ๋งํ•˜๋Š” ๋ฌธํ•™์‚ฌ๋ž€ ๋ฌด์—‡์„ ์˜๋ฏธํ•˜๋Š”์ง€, ์ž‘ํ’ˆ์˜ ์ดํ•ด์™€ ๊ฐ์ƒ์— ํ•„์š”ํ•œ ๋ฐฐ๊ฒฝ ์ง€์‹์ด๋ž€ ๋ฌด์—‡์ธ์ง€, ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๋ฐฐ๊ฒฝ ์ง€์‹์ด๋ผ๋Š” ๊ฒƒ์ด ๋ฌธํ•™์‚ฌ๋ฅผ ๊ฐ€๋ฅด์นจ์œผ๋กœ์จ ํš๋“๋  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์ธ์ง€ ๋“ฑ์˜ ๋ฌธ์ œ๋Š” ์ œ๋Œ€๋กœ ๊ฒ€์ฆ๋˜์ง€ ์•Š์•˜๋˜ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค

    ์ˆœ์ฐจ์  ์˜คํ† ์ธ์ฝ”๋” ๊ธฐ๋ฐ˜ FDA ์Šน์ธ ์•ฝ๋ฌผ๋“ค์˜ ํ™”ํ•™ ๊ณต๊ฐ„ ์ž„๋ฒ ๋”ฉ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021.8. ๊น€์„ .Drug discovery requires decade of expensive efforts to meet sufficient needs. Computer-Aided drug discovery (CADD) is an emerging field of study that aims to systematically reduce the time and cost of a new durg development by adapting computer science to identify structural and physical properties of chemical compounds used as drugs and derive new drug candidates with similar characteristics. In particular, it is most important to identify the char acteristics of chemical compounds approved by the U.S. Food and Drug Ad ministration (FDA). FDA approved chemical compounds are validated drugs in terms of toxicity, efficacy of drug and side effects. The question arises here i how these chemical compounds are distributed in an embedding space. Tradi tionally, hand-crafted rule is the only way of constructing the chemical space. Traditional chemical compound representations have made it difficult to clas sify FDA approved chemical compounds. With the advent of the era of big data and artificial intelligence technology, deep learning is the leading technol ogy that drives to build an embedding space. However, there is few adaptive methods to identify the embedding space of FDA approved chemical com pounds. In this work, I propose a framework that encodes features of FDA approved chemical compounds by constructing a discriminative embedding space. Var ious encoding methods were used to encode information from FDA approved chemical compounds. The proposed framework consists of three stacked deep autoencoder modules. The proposed framework effectively integrate the in formation of the chemical compounds by cascade modeling. Connected three autoencoder modules in cascade is used to continuously use latent represen tation learned from previous modules. Whether FDA approved chemical com pounds have discriminative regions in the embedding space is well visualized by the proposed framework. And perform machine learning classification tasks to evaluate whether the latent representation effectively characterize the FDA approval information. The proposed framework incorporates complex repre sentation information to understand the embedding of FDA drugs. Ultimately, the framework proposed in this paper can be used as an embedding method for determining whether or not new drug candidates will be approved. Keywords: FDA Approved drug, Cascade Autoencoder, Chemical space em bedding Student Number: 2019-24822์‹ ์•ฝ ๊ฐœ๋ฐœ์‹œ ์—ฌ๋Ÿฌ ์กฐ๊ฑด๋“ค์„ ์ถฉ์กฑํ•˜๋Š” ์•ฝ๋ฌผ์„ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜์‹ญ๋…„์˜ ๋…ธ๋ ฅ์ด ํ•„์š” ํ•˜๋‹ค. ์ปดํ“จํ„ฐ ๋ณด์กฐ ์‹ ์•ฝ ๊ฐœ๋ฐœ(CADD)์€ ์ปดํ“จํ„ฐ ๊ณผํ•™์„ ์ ์šฉ์‹œ์ผœ ์•ฝ๋ฌผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์•ฝ๋ฌผ์˜ ๊ตฌ์กฐ์  ๋ฐ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ์„ ํ™•์ธํ•˜๊ณ  ์œ ์‚ฌํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง„ ์‹ ์•ฝ ํ›„๋ณด๋ฅผ ๋„ ์ถœํ•จ์œผ๋กœ์จ ์‹ ์•ฝ ๊ฐœ๋ฐœ์˜ ์‹œ๊ฐ„๊ณผ ๋น„์šฉ์„ ์ฒด๊ณ„์ ์œผ๋กœ ์ ˆ๊ฐํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์‹ ํฅ ์—ฐ๊ตฌ ๋ถ„์•ผ์ด๋‹ค. ํŠนํžˆ ๋ฏธ๊ตญ ์‹ํ’ˆ์˜์•ฝ๊ตญ(FDA)์ด ์Šน์ธํ•œ ์•ฝ๋ฌผ์˜ ํŠน์„ฑ์„ ํ™• ์ธํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค. FDA์—์„œ ์Šน์ธํ•œ ์•ฝ๋ฌผ๋“ค์€ ๋…์„ฑ, ํšจ๋Šฅ ๋ฐ ๋ถ€์ž‘์šฉ ์ธก๋ฉด์—์„œ ๊ฒ€์ฆ๋œ ์˜์•ฝํ’ˆ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์•ฝ๋ฌผ๋“ค์ด ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„ ์ƒ์—์„œ ์–ด๋–ป๊ฒŒ ๋ถ„ ํฌ๋˜์–ด ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ์˜๋ฌธ์ ์—์„œ ์‹œ์ž‘ํ•œ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ๋Š” ์ „๋ฌธ๊ฐ€์˜ ์ˆ˜์ž‘์—…์œผ๋กœ ๋งŒ๋“  ๊ทœ์น™๋“ค๋กœ ํ™”ํ•ฉ๋ฌผ์˜ ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์„ ๊ตฌ์„ฑํ–ˆ๋‹ค. ์ „ํ†ต์ ์ธ ํ™”ํ•ฉ๋ฌผ ํ‘œํ˜„๋งŒ์œผ๋กœ๋Š” FDA ์Šน์ธ ์•ฝ๋ฌผ๋“ค์„ ๋ถ„๋ฅ˜ํ•˜๋Š” ๊ฒƒ์ด ์–ด๋ ต๋‹ค. ๋น…๋ฐ์ดํ„ฐ์™€ ์ธ๊ณต์ง€๋Šฅ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ ๋กœ ๋”ฅ๋Ÿฌ๋‹์„ ์ด์šฉํ•ด ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์„ ๊ตฌ์ถ•ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์—์„  FDA ์Šน์ธ ์•ฝ๋ฌผ๋“ค์˜ ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์„ ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ์ ์ ˆํ•œ ๋ฐฉ๋ฒ•์ด ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” FDA ์Šน์ธ ์•ฝ๋ฌผ๋“ค์˜ ํŠน์ง•์„ ์ธ์ฝ”๋”ฉํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์‚ฌ์šฉํ•ด ์ฐจ ๋ณ„์ ์ธ ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์„ ๊ตฌ์ถ•ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” 3๊ฐœ์˜ ์ˆœ์ฐจ์  ๋”ฅ ์˜คํ† ์ธ์ฝ”๋” ๋ชจ๋“ˆ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ˆœ์ฐจ์  ๋ชจ๋ธ๋ง์„ ํ†ตํ•ด ์•ฝ๋ฌผ์˜ ์ •๋ณด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ†ตํ•ฉํ•œ๋‹ค. ์ˆœ์ฐจ์ ์œผ๋กœ ์—ฐ๊ฒฐ๋œ 3๊ฐœ์˜ ์˜คํ† ์ธ์ฝ”๋” ๋ชจ๋“ˆ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด์ „ ๋ชจ๋“ˆ์—์„œ ํ•™์Šตํ•œ ์ž ์žฌ ํ‘œํ˜„์„ ์ง€์†์ ์œผ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. FDA ์Šน์ธ ํ™”ํ•™ ํ™”ํ•ฉ๋ฌผ์ด ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์ƒ์—์„œ ์ฐจ๋ณ„์ ์ธ ์˜์—ญ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š”์ง€ ์—ฌ๋ถ€๋Š” ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ์— ์˜ํ•ด ์‹œ๊ฐํ™”๋œ๋‹ค. ๋˜ํ•œ ์ž ์žฌ๋œ ํ‘œํ˜„์ด FDA ์Šน์ธ ์ •๋ณด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํŠน์„ฑํ™”ํ•˜๋Š”์ง€ ์—ฌ๋ถ€๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ๊ณ„ ํ•™์Šต ๋ถ„๋ฅ˜ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•œ ๋‹ค. ๊ถ๊ทน์ ์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์‹ ์•ฝ ํ›„๋ณด์ž์˜ ์Šน์ธ ์—ฌ๋ถ€๋ฅผ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•œ ์ž„๋ฒ ๋”ฉ ๋ฐฉ๋ฒ•์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์ฃผ์š”์–ด: FDA ์Šน์ธ ์•ฝ๋ฌผ, ์ˆœ์ฐจ์  ์˜คํ† ์ธ์ฝ”๋”, ํ™”ํ•ฉ๋ฌผ ๊ณต๊ฐ„ ์ž„๋ฒ ๋”ฉ ํ•™๋ฒˆ: 2019-24822Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Chemical space 1 1.1.2 FDA Approval of chemical drugs 2 1.2 Current Method and Limitation 4 1.3 Problem Statement and Contributions 5 Chapter 2 Related Works 7 2.1 Cascade Autoencoder 7 2.2 Chemical Space Embedding Methods 8 Chapter 3 Methods and Materials 9 3.1 Notation and Problem Definition 9 3.2 Chemical Compound Encoding Process 10 3.2.1 Morgan Fingerprints 11 3.2.2 Mol2vec 12 3.2.3 Junction Tree Variational Autoencoder 12 3.2.4 Continuous and Data-Driven Descriptors Variational Au toencoder 14 3.3 Model Architecture 14 3.3.1 Autoencoder Module 14 3.3.2 Cascade Autoencoder 16 3.4 Loss function, Optimizer 17 3.4.1 Reconstruction Loss 17 3.4.2 Metric Loss 17 3.4.3 Optimizer 18 3.5 Principal Component Analysis 18 3.6 Machine Learning Classifiers 19 3.6.1 Support Vector Machine 19 3.6.2 Naive Bayes 19 3.6.3 Random Forset 20 3.6.4 Adaboost 20 Chapter 4 Experiments 21 4.1 Datasets 21 4.1.1 Datasets for pre-trained model 21 4.1.2 FDA Approved and Discontinued dataset 22 4.2 Model Training Hyper Parameter Settings 22 4.2.1 The dimension of input data 23 4.2.2 Model Training 23 4.2.3 Embedding and Evaluation method 23 4.2.4 Comparison Models 24 Chapter 5 Results 25 5.1 Visualization of Chemical Embedding Space 25 5.2 Performance Comparisons with Traditional Machine Learning Method 26 5.3 Performance of using each input representation 27 5.4 Effect of Cascade Modeling 28 Chapter 6 Conclusion 30 ๊ตญ๋ฌธ์ดˆ๋ก 36 ๊ฐ์‚ฌ์˜ ๊ธ€ 37์„

    The Study on the relationships between government and interest group in the policy making process

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› :ํ–‰์ •ํ•™๊ณผ(์ •์ฑ…ํ•™์ „๊ณต),2007.Maste

    A Study on Verification of Publicness Evaluation Index of Indoor Public Space in Large Office Buildings: focusing on Atrium Spaces

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    20๋…„ ๋งŒ์— ์ „๋ฉด ๊ฐœ์ •๋œ ์„œ์šธํŠน๋ณ„์‹œ ์ง€๊ตฌ๋‹จ์œ„๊ณ„ํš ์ˆ˜๋ฆฝ๊ธฐ์ค€์—์„œ๋Š” ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์— ๋Œ€์‘ํ•˜์—ฌ ๊ทธ๋™์•ˆ ์™ธ๋ถ€์—๋งŒ ์„ค์น˜๋˜์—ˆ๋˜ ๊ณต๊ฐœ๊ณต์ง€๋ฅผ ๊ฑด๋ฌผ ๋‚ด๋ถ€์— ์กฐ์„ฑํ•  ์ˆ˜ ์žˆ๋„๋ก '์‹ค๋‚ดํ˜• ๊ณต๊ฐœ๊ณต์ง€'๋ฅผ ์ƒˆ๋กญ๊ฒŒ ๋„์ž…ํ•˜์˜€๋‹ค. ์ด์ฒ˜๋Ÿผ ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„ ๊ณ„ํš์˜ ์ค‘์š”์„ฑ์ด ๋Œ€๋‘๋œ ํ˜„์žฌ์˜ ์‹œ์ ์—์„œ ํšจ๊ณผ์ ์ธ ์‹ค๋‚ดํ˜• ๊ณต๊ฐœ๊ณต์ง€ ์กฐ์„ฑ์„ ์œ„ํ•œ ๊ตฌ์ฒด์ ์ธ ์ง€์นจ๊ณผ ๊ณ„ํš ๋„๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„์‹ฌ์ง€ ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ ์ฆ์ง„๊ณผ ํšจ๊ณผ์ ์ธ ์‹ค๋‚ดํ˜• ๊ณต๊ฐœ๊ณต์ง€๊ณ„ํš์„ ์œ„ํ•˜์—ฌ ๋„์‹ฌ์ง€ ์ดˆ๋Œ€ํ˜• ์—…๋ฌด์‹œ์„ค ์‹ค๋‚ด ๊ณต๊ณต๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ์„ ์ ‘๊ทผ์„ฑ๊ณผ ๊ฐœ๋ฐฉ์„ฑ์˜ ํ•ญ๋ชฉ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. VGA๋ถ„์„๊ณผ ์„ ํ–‰์—ฐ๊ตฌ์˜ ๋‰ด์š•์‹œ ์‹ค๋‚ด๊ณต๊ฐœ๊ณต์ง€ ๊ณต๊ณต์„ฑ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ๊ตญ๋‚ด ์‚ฌ๋ก€์— ์ ์šฉํ•˜์—ฌ ์ดˆ๋Œ€ํ˜• ์—…๋ฌด์‹œ์„ค ๋‚ด ์•„ํŠธ๋ฆฌ์›€ ๊ณต๊ฐ„์˜ ๊ณต๊ณต์„ฑ์„ ๋ถ„์„ ๋ฐ ํ‰๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ์ด์šฉ๋„ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•˜์—ฌ ํ•ด๋‹น ํ‰๊ฐ€์ง€ํ‘œ์˜ ๊ตญ๋‚ด์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€์ง€ํ‘œ๋Š” ์•„ํŠธ๋ฆฌ์›€ ๊ณต๊ฐ„์˜ ์ฃผ์ค‘ ํ†ตํ–‰๊ณ„์ˆ˜์™€ ์œ ์˜๋ฏธํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด์–ด ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœ๋œ ํ‰๊ฐ€์ง€ํ‘œ์˜ ๊ตญ๋‚ด ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. The purpose of this study is to evaluate the publicness of indoor public spaces of large office buildings in Seoul. Six cases of indoor publicspace(atrium space) in large office buildings were analyzed using Visibility Graph Analysis by evaluating its accessibility and visual openness. To evaluate the publicness of the atrium space, the Indoor privately owned public spaces evaluation index from former research wasapplied. As result, a significant correlation was derived between the accessibility evaluation and the atrium pedestrian flow rate, whichverifies the effectiveness of the developed evaluation index. Also, it was proved that the indoor public space in Korea was as public as theindoor privately owned public spaces in New York City.N

    Effects of nursing performance at a cardiovascular hospitalโ€™s referral center

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    ๊ฐ„ํ˜ธ๊ด€๋ฆฌ์™€ ๊ต์œก ์ „๊ณต/์„์‚ฌ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ์‹œ ์†Œ์žฌ ์ผ๊ฐœ ์ƒ๊ธ‰์ข…ํ•ฉ๋ณ‘์›์˜ ๋‹จ์œ„๊ธฐ๊ด€์ธ ์‹ฌ์žฅํ˜ˆ๊ด€๋ณ‘์› ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์—…๋ฌด๋ฅผ ํ™•์ธํ•˜๊ณ , ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ๊ณผ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ ๊ฐ„ ์™ธ๋ž˜์ง„๋ฃŒ ๋ฐ ์ž…์›์น˜๋ฃŒ์— ์†Œ์š”๋˜๋Š” ๊ธฐ๊ฐ„๊ณผ ํ™˜์ž๋งŒ์กฑ๋„์˜ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜์—ฌ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ ์—ญํ• ์˜ ํšจ๊ณผ์— ๋Œ€ํ•œ ๊ธฐ์ดˆ์ ์ธ ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ์„œ์ˆ ์  ์กฐ์‚ฌ์—ฐ๊ตฌ์ด๋‹ค. ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ ์—…๋ฌด์˜ ๋‚ด์šฉ๊ณผ ๋นˆ๋„๋Š” ์‹ฌ์žฅํ˜ˆ๊ด€๋ณ‘์› ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ 2016๋…„ 10์›” 24์ผ๋ถ€ํ„ฐ 10์›” 28์ผ๊นŒ์ง€ ์ฒดํฌ๋ฆฌ์ŠคํŠธ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ž๋ฃŒ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์ž๋Š” 2016๋…„ 4์›” 4์ผ๋ถ€ํ„ฐ 9์›” 30์ผ๊นŒ์ง€ ์‹ฌ์žฅ๋‚ด๊ณผ ์™ธ๋ž˜์— ๋‚ด์›ํ•œ ์ดˆ์ง„ํ™˜์ž ์ค‘ ์„ ์ •๊ธฐ์ค€์— ๋ถ€ํ•ฉ๋˜๋Š” ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ 64๋ช…, ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ 64๋ช…์œผ๋กœ ์ด 128๋ช…์„ ์„ ์ •ํ•˜์˜€๋‹ค. ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ๊ณผ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ์˜ ํ™˜์ž๋งŒ์กฑ๋„์™€ ์ผ๋ฐ˜์ ์ธ ํŠน์„ฑ์€ ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋กœ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๊ณ , ์˜๋ขฐ๊ด€๋ จ ํŠน์„ฑ๊ณผ ์ง„๋‹จ ๋ฐ ์น˜๋ฃŒ์— ์†Œ์š”๋˜๋Š” ๊ธฐ๊ฐ„์€ ์˜๋ฃŒ์ •๋ณด์‹œ์Šคํ…œ๊ณผ ์ „์ž์˜๋ฌด๊ธฐ๋ก ์กฐํšŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ™•์ธํ•˜์˜€๋‹ค. ์ž๋ฃŒ ๋ถ„์„์€ SPSS/WIN 20.0 ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์ˆ  ํ†ต๊ณ„, independent t-test๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฃผ์š”๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1. ์‹ฌ์žฅํ˜ˆ๊ด€๋ณ‘์› ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์—…๋ฌด๋Š” ๋…๋ฆฝ์ ์ธ ์—ญํ•  38.3%, ์˜์กด์ ์ธ ์—ญํ•  37.0%, ์ƒํ˜ธ์˜์กด์ ์ธ ์—ญํ• ์ด 24.7%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 2. ์ง„๋ฃŒ์˜ˆ์•ฝ ์˜๋ขฐ์ผ๋ถ€ํ„ฐ ์™ธ๋ž˜ ์ดˆ์ง„์ผ๊นŒ์ง€ ์†Œ์š”๊ธฐ๊ฐ„์€ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์ด 2.41(ยฑ2. 46)์ผ๋กœ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ์˜ 6.88(ยฑ6.10)์ผ ๋ณด๋‹ค ์งง์•˜์œผ๋ฉฐ, ํ†ต๊ณ„์ ์œผ๋กœ ๋งค์šฐ ์œ ์˜ํ•œ ์ฐจ์ด ๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ(t=-5.079, p<.001), ์™ธ๋ž˜ ์ดˆ์ง„์ผ๋ถ€ํ„ฐ 2๋ฒˆ์งธ ์™ธ๋ž˜ ๋‚ด์›์ผ๊นŒ์ง€ ์†Œ์š”๊ธฐ๊ฐ„์€ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์ด 2.82(ยฑ2.32)์ผ๋กœ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ 13.57(ยฑ5.17)์ผ๋ณด๋‹ค ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ์งง์•˜๋‹ค(t=-14.197, p<.001). 3. ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ๊ณผ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ ๊ฐ„ ์™ธ๋ž˜ ์ดˆ์ง„์ผ๋ถ€ํ„ฐ ์ˆ˜์ˆ ์ผ๊นŒ์ง€ ์†Œ์š”๊ธฐ๊ฐ„์€ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์ด 2.29(ยฑ1.38)์ผ๋กœ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ์˜ 11.88(ยฑ9.95)์ผ๋ณด๋‹ค ์งง์•˜์œผ๋ฉฐ, ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค(Z=-2.693, p=.007). 4. ํ™˜์ž๋งŒ์กฑ๋„๋Š” 80์  ๋งŒ์ ์— ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์ด 72.28(ยฑ7.22)์ ์œผ๋กœ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ 68.39(ยฑ8.22)์ ๋ณด๋‹ค ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค(t=2.764, p=.007). ์˜ˆ์•ฝ ๋ฐ ์ ‘์ˆ˜ ์ ˆ์ฐจ (t=2.078, p=.040), ๊ฐ„ํ˜ธ์‚ฌ ์ง„๋ฃŒ์„œ๋น„์Šค(t=2.070, p=.041), ๋ณ‘์›ํ™˜๊ฒฝ ๋ฐ ๊ธฐํƒ€ (t=2.657, p=.009) ์˜์—ญ์˜ ํ™˜์ž๋งŒ์กฑ๋„์—์„œ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์ด ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ ๋ณด๋‹ค ํ†ต๊ณ„์  ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 5. ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์˜ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์—…๋ฌด์— ๋Œ€ํ•œ ํ™˜์ž๋งŒ์กฑ๋„๋Š” 4์  ๋งŒ์  ์ค‘ ์™ธ๋ž˜ ์ง„๋ฃŒ ์ถ”์ฒœ์˜ํ–ฅ 3.91์ , ๊ฒ€์‚ฌ์ง„ํ–‰ ๋ฐ ๊ฒฐ๊ณผ ํ™•์ธ์ผ๊นŒ์ง€ ์†Œ์š”๊ธฐ๊ฐ„ 3.89์ , ์ง„๋ฃŒ ๋ถ„์•ผ ๊ฒฐ ์ • 3.88์ , ์—ฐ์†์ ์ธ ์˜๋ฃŒ์„œ๋น„์Šค ์ œ๊ณต 3.86์ , ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ถฉ๋ถ„ํ•œ ์„ค๋ช…๊ณผ ์•ˆ๋‚ด 3.82์ , ์ง„๋ฃŒ์˜ˆ์•ฝ ๋Œ€๊ธฐ์ผ 3.82์ , ์ž…์›์ผ๊นŒ์ง€ ๋Œ€๊ธฐ์ผ 3.81์ , ์˜ˆ์•ฝ ์ ˆ์ฐจ 3.78์ ์˜ ์ˆœ์„œ๋กœ ๋ชจ ๋“  ๋ฌธํ•ญ์—์„œ ํ™˜์ž๋งŒ์กฑ๋„๊ฐ€ 3.7์  ์ด์ƒ์œผ๋กœ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 6. ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ์ด ๊ฐ€์žฅ ํ•„์š”ํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋Š” ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์—…๋ฌด๋Š” ์™ธ๋ž˜์ง„๋ฃŒ ๋ฐ ๊ฒ€์‚ฌ์ง„ํ–‰์˜ ์‹ ์†์„ฑ์ด 40.6%, ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ถฉ๋ถ„ํ•œ ์„ค๋ช…๊ณผ ์ƒ๋‹ด 32.8%, ์™ธ๋ž˜์ง„๋ฃŒ๋ถ€ํ„ฐ ์ž…์›, ์ˆ˜์ˆ  ์ง„ํ–‰์˜ ์‹ ์†์„ฑ 14.1%, ์ง„๋ฃŒ ๋ถ„์•ผ ์ƒ๋‹ด ๋ฐ ๊ฒฐ์ •์ด 12.5% ์ˆœ์„œ๋กœ ๋‚˜ํƒ€๋‚ฌ ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜๋ฃŒ์„œ๋น„์Šค ์ „๋‹ฌ ๊ณผ์ •์—์„œ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์—ญํ• ์„ ๋…๋ฆฝ์ , ์˜์กด์ , ์ƒํ˜ธ์˜์กด์ ์ธ ์—ญํ• ์˜ ์—…๋ฌด๋กœ ํ™•์ธํ•˜๊ณ , ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ์˜๋ขฐ๊ตฐ๊ณผ ์ผ๋ฐ˜ ์˜๋ขฐ๊ตฐ ๊ฐ„ ์™ธ๋ž˜์ง„๋ฃŒ์™€ ์ž…์›์น˜๋ฃŒ ์†Œ์š”๊ธฐ๊ฐ„๊ณผ ํ™˜์ž๋งŒ์กฑ๋„์˜ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜์—ฌ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ ์—ญํ• ์˜ ํšจ๊ณผ๋ฅผ ํ™•์ธํ•˜๋Š” ์‹œ๋„๋กœ์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ–ฅํ›„ ์—ฐ๊ตฌ๋Œ€์ƒ์ž์™€ ๋ณ€์ˆ˜๋ฅผ ํ™•๋Œ€์‹œ์ผœ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์—ญํ• ๊ณผ ํ™˜์ž๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ์ƒ๊ด€๊ด€๊ณ„์™€ ์˜ํ–ฅ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ ๋ฐ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์—ญํ• ์ด ๊ฐ„ํ˜ธ์˜ ์ „๋ฌธ๋ถ„์•ผ์˜ ํ•˜๋‚˜๋กœ ์ž๋ฆฌ๋งค๊น€ํ•˜๊ธฐ ์œ„ํ•œ ์ง„๋ฃŒํ˜‘๋ ฅ์„ผํ„ฐ ๊ฐ„ํ˜ธ์‚ฌ์˜ ํ‘œ์ค€ํ™”๋œ ์‹ค๋ฌด์™€ ๊ฐ„ํ˜ธํ™œ๋™์— ๋Œ€ํ•œ ์ˆ˜๊ฐ€๋ฅผ ๋งˆ๋ จํ•  ์ˆ˜ ์žˆ๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์–ธํ•œ๋‹ค. The purpose of this study was to identify nursing performance at the referral center of a cardiovascular hospital, a unit of a tertiary general hospital in Seoul, South Korea, and to analyze the differences in the length of time spent for outpatient treatment and hospitalization and patient satisfaction between the group of patients who came in through the referral center (the referral group) and the group of patients who came in through different routes (the general group). The contents and frequency of tasks of nurses at the referral center were obtained from October 24, 2016 to October 28, 2016 by the researcher of this study, who is a nurse at the referral center, using a checklist. A total of 128 patients were selected among the new patients who visited the cardiology outpatient clinic from April 4, 2016 to September 30, 2016, as study participants. The satisfaction and general characteristics of the patients were analyzed by using a structured questionnaire. The referral-related characteristics and duration for the diagnosis and treatment were investigated by using the medical information system and reviewing the electronic medical records. The collected data were analyzed by using the SPSS/WIN 20.0 program for descriptive statistics and independent t-tests. The results of this study are summarized as follows: 1. The tasks of nurses at the referral center were identified as 38.3% of independent role, 37.0% of dependent role, and 24.7% of interdependent roles. 2. The duration between the date of an appointment was made and the date of first visit to the outpatient clinic was calculated as 2.41(ยฑ2.46) days in the referral group, statistically shorter than 6.88(ยฑ6.10) days in the general group (t=-5.079, p<.001). The period from the date of first visit to the date of second visit to the outpatient clinic was also shown as 2.82(ยฑ2.32) days in the referral group, significant...ope

    ๊ทนํ•œ ํ™˜๊ฒฝ์—์„œ ์„œ์‹ํ•˜๋Š” ๋ฏธ์ƒ๋ฌผ๋กœ๋ถ€ํ„ฐ ๋ถ„๋ฆฌํ•œ polyketide์—์„œ ์œ ๋ž˜๋œ ์ƒˆ๋กœ์šด ์ด์ฐจ๋Œ€์‚ฌ๋ฌผ์งˆ์˜ ๊ตฌ์กฐ ๊ทœ๋ช…

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2017. 2. ์˜ค๋™์ฐฌ.Bacteria inhabiting extreme environments have been considered as attracting resources for bioactive compounds. As part of continuing efforts to explore the chemical diversity of bacteria from extreme habitats for drug discovery, in this study, bacterial strains inhabiting saltern and Alaskan permafrost have been investigated. Actinobacterial strains were isolated from these extreme habitats, cultivated, and chemically analyzed by LC/MS. Based on the bacterial chemical profiles acquired by LC/MS analysis, four new polyketide-derived compounds including three new macrolides and an oxazole-bearing compound were discovered. The structures of the new natural products were elucidated by the analysis of spectroscopic data such as 1D and 2D NMR, mass, UV, and IR spectra.Introduction 1 โ… . Borrelidins C-E, new macrolide compounds from a halophilic Nocardiopsis sp.actinobacterium 3 โ…ก. TD3-468, new 26-membered oxazole-polyene compound from a psychrophilic Streptomyces sp. 23 References 31 Appendix: NMR spectroscopic data 32 Abstract in Korean 51Maste
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