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    ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€ 3๋‹จ์ง€์˜ ๊ณต๊ฐœ๊ณต์ง€์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ, 2020. 8. ๊น€์„ธํ›ˆ.Nowadays, most land of Seoul is covered with concrete roads and buildings. Seoul became covered with concrete within only about 50 years. Therefore, green area became scarce in Seoul nowadays. Privately owned public space(POPS hereafter) is one solution to offer a green space for public in urban area. Building a POPS will give an incentive to floor area ratio. A building over 5000m2 in Gross floor area must include a certain amount of POPS. There are many POPS built in tall buildings in Seoul. If there was no POPS, it would have been hard to find a green space in Seoul. POPS is a space of relax and recharge in modern city. However, POPS in South Korea nowadays are mostly built as a border space around a building, isolated green space, and a space for sculpture installation. Also many POPS built in Seoul has no connectivity with neighboring urban environment. In foreign countries like USA or Japan, architects suggest many different plans for POPS considering its connectivity with surrounding environment like roads, building limit lines, shapes of buildings, and colors of buildings. This study was done over the Seoul Digital Industrial Complex 3(SDIC 3 hereafter), which is placed in Gasan-dong, Geumcheon-Gu, Seoul and Guro-dong, Guro-gu, Seoul. SDIC 3 is the largest complex in land area among the three complexes; SDIC 1, SDIC 2, and SDIC 3. SDIC 3 takes 57.2% of the whole area in SDIC. SDIC is the center of the industrial development in South Korea. Currently, there are many knowledge- based industrial companies in the apartment factories built in SDIC 3. Most of the apartment factories in SDIC 3 received incentives in Floor Area Ratio(FAR hereafter) by building POPS of over 10% to the building area. Nowadays, one can easily observe many POPS neighboring each other in SDIC 3. Unlike SDIC 1 and 2, SDIC 3 is still in development. So it can be said that SDIC 3 is a precedent of future industrial complex in South Korea. Therefore, it is meaningful to study the urban structure of SDIC 3 for the development of industrial complex. This research studied about the POPS in SDIC 3. It analyzed natural condition of green spaces in the POPS, connectivity of neighboring POPS, and usage of POPS by people.1950๋…„๋Œ€ ์„œ์šธ์˜ ์œ„์„ฑ์‚ฌ์ง„์„ ๋ณด๋ฉด ์„œ์šธ์˜ ๋งŽ์€ ๋ถ€๋ถ„์ด ๋…ผ๊ณผ ๋ฐญ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ง€๋‚œ 50๋…„๊ฐ„์˜ ๊พธ์ค€ํ•œ ๊ฒฝ์ œ์„ฑ์žฅ์œผ๋กœ ์ด์ œ๋Š” ์„œ์šธ์˜ ๋งŽ์€ ๋ฉด์ ์— ๊ฑด๋ฌผ๋“ค์ด ๋“ค์–ด์„œ ์žˆ๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ถˆ๊ณผ 50๋…„์—ฌ ๋งŒ์— ์„œ์šธ์˜ ๋งŽ์€ ๋ถ€๋ถ„์ด ์ฝ˜ํฌ๋ฆฌํŠธ๋กœ ๋’ค๋ฎ์ธ ๊ฒƒ์ด๋‹ค. ์ด์— ๋”ฐ๋ผ ์„œ์šธ์—์„œ๋Š” ์˜คํžˆ๋ ค ๋…น์ง€๊ณต๊ฐ„์ด ๋ถ€์กฑํ•˜๊ฒŒ ๋˜๋Š” ์ƒํ™ฉ์ด ๋ฐœ์ƒํ–ˆ๋‹ค. ๊ณต๊ฐœ๊ณต์ง€๋Š” ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์— ์ œ์‹œ๋œ ๋„์‹ฌ ๋‚ด ์‹œ๋ฏผ๋“ค์„ ์œ„ํ•ด ๊ฑด์„ค๋œ ๊ณต๊ณต๊ณต๊ฐ„์ด๋‹ค. ๊ณต๊ฐœ๊ณต์ง€๋Š” ๊ฑด์ถ•๋ฉด์ ์˜ ์ผ๋ถ€๋ฅผ ๊ณต๊ณต์„ ์œ„ํ•œ ๊ณต๊ณต๊ณต๊ฐ„์œผ๋กœ ์กฐ์„ฑํ•จ์œผ๋กœ์จ ๊ฑด์ถ•์„ค๊ณ„์‹œ ์—ฐ๋ฉด์ ์„ ๋Š˜๋ ค์ฃผ๋Š” ์ œ๋„์ด๋‹ค. ์ด๋Ÿฌํ•œ ๊ณต๊ฐœ๊ณต์ง€๋Š” ๊ฑด์ถ•๋ฉด์  5000m2 ์ด์ƒ์˜ ๊ฑด๋ฌผ์„ค๊ณ„์‹œ ํ•„์ˆ˜์ ์œผ๋กœ ํฌํ•จ๋˜์–ด์•ผ ํ•œ๋‹ค. ๊ทธ๋ ‡๊ธฐ์— ์„œ์šธ์˜ ๋Œ€ํ˜•๋นŒ๋”ฉ๋“ค์—๋Š” ๊ณต๊ฐœ๊ณต์ง€๋“ค์ด ํ•„์ˆ˜์ ์œผ๋กœ ํฌํ•จ๋œ ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋งŒ์•ฝ ๊ณต๊ฐœ๊ณต์ง€๋ผ๋Š” ์ œ๋„๊ฐ€ ์—†์—ˆ๋‹ค๋ฉด ๋นŒ๋”ฉ๋“ค์ด ์ˆฒ์ฒ˜๋Ÿผ ๋ชจ์—ฌ์žˆ๋Š” ์„œ์šธ์„ ๋ช‡๋ช‡ ๋„์‹œ ์ค‘์‹ฌ๋ถ€์—์„œ๋Š” ๋…น์ง€๊ณต๊ฐ„์„ ์ฐพ์•„๋ณด๊ธฐ ํž˜๋“ค์—ˆ์„ ๊ฒƒ์ด๋‹ค. ๊ณต๊ฐœ๊ณต์ง€๋Š” ์ด์ฒ˜๋Ÿผ ๋„์‹ฌ ์†์—์„œ ๋…น์ง€๊ณต๊ฐ„๊ณผ ํœด๊ฒŒ๊ณต๊ฐ„ ๊ทธ๋ฆฌ๊ณ  ์žฌ์ถฉ์ „์˜ ๊ณต๊ฐ„์œผ๋กœ์จ์˜ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์†Œ์ด๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์žฌ ๊ฑด์„ค๋œ ๋Œ€๋ถ€๋ถ„์˜ ๊ณต๊ฐœ๊ณต์ง€๋“ค์€ ๊ฑด๋ฌผ์ฃผ๋ณ€์˜ ์šธํƒ€๋ฆฌ ์—ญํ• ์ด๋‚˜ ์ž‘์€ ์ •์› ๋˜๋Š” ๋นŒ๋”ฉ ์•ž์˜ ๋Œ€ํ˜• ์‹œ์„ค๋ฌผ ์„ค์น˜๊ณต๊ฐ„์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ๋งˆ๋Š” ๊ฒƒ์ด ํ˜„์‹ค์ด๋‹ค. ๋˜ํ•œ ๋นŒ๋”ฉ๋“ค์ด ๋ชจ์—ฌ์žˆ๋Š” ์„œ์šธ ๋„์‹ฌ ์ค‘์‹ฌ๋ถ€์—์„œ๋Š” ๋ช‡๋ช‡ ๊ณต๊ฐœ๊ณต์ง€๋“ค์ด ์„œ๋กœ ๋งž๋‹ฟ์•„ ์žˆ๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด๋ ‡๊ฒŒ ์ธ์ ‘ํ•œ ๊ณต๊ฐœ๊ณต์ง€๋“ค์€ ์„œ๋กœ ๊ฐ„์˜ ์—ฐ๊ฒฐ์„ฑ์ด ์—†๊ณ  ๊ฐ ๊ณต๊ฐœ๊ณต์ง€๊ฐ€ ์†ํ•ด์žˆ๋Š” ๊ฑด๋ฌผ๋“ค์˜ ์šธํƒ€๋ฆฌ ์—ญํ• ์— ๊ทธ์น˜๋ฉฐ ์˜คํžˆ๋ ค ์ธ์ ‘ํ•œ ๊ณต๊ฐœ๊ณต์ง€๋“ค๋ผ๋ฆฌ ์„œ๋กœ๋ฅผ ๊ตฌ๋ถ„ ์ง“๋Š” ํ˜•ํƒœ๋กœ ์„ค๊ณ„๋œ ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ๊ณต๊ฐœ๊ณต์ง€์˜ ์„ค๊ณ„์‹œ ๊ณต๊ฐœ๊ณต์ง€์™€ ์ฃผ๋ณ€๊ณต๊ฐ„๊ณผ์˜ ์—ฐ๊ณ„์„ฑ์— ๋Œ€ํ•ด ๊ณ ๋ คํ•˜์ง€ ์•Š๊ณ  ์„ค๊ณ„๊ฐ€ ์ง„ํ–‰๋œ ๊ฒฐ๊ณผ์ด๋‹ค. ํ•ด์™ธ์—์„œ๋Š” ๊ฑด๋ฌผ์„ค๊ณ„์‹œ ๊ณต๊ฐœ๊ณต์ง€์˜ ์ธ์ ‘๋„๋กœ ์—ฐ๊ฒฐ์„ฑ ๋˜๋Š” ์ง€์—ญํŠน์ƒ‰์— ๋งž์ถ˜ ๊ณต๊ฐœ๊ณต์ง€์˜ ๊ฑด์ถ•์„  ์ง€์ •, ๊ฑด๋ฌผ์˜ ๋ฐฐ์น˜, ํ˜•ํƒœ, ์ƒ‰ํƒœ๋ฅผ ๊ณ ๋ คํ•œ ๊ณต๊ฐœ๊ณต์ง€ ํ™•๋ณด ๋“ฑ ๊ณต๊ฐœ๊ณต์ง€ ์„ค๊ณ„์— ์ด์šฉ์ž๋“ค์„ ๊ณ ๋ คํ•œ ์—ฌ๋Ÿฌ ๋ฐฉํ–ฅ๋“ค์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ๊ณต๊ฐœ๊ณต์ง€๊ฐ€ ๋‹จ์ˆœํžˆ ๊ฑด๋ฌผ์— ํฌํ•จ๋˜์–ด ์žˆ๋Š” ๋ถ€์ˆ˜์ ์ธ ๊ณต๊ฐ„์ด ์•„๋‹Œ ํŒ๋งค์‹œ์„ค, ๋ฏธ์ˆ ๊ด€, ์Œ์‹์ ์ฒ˜๋Ÿผ ์ด์šฉ์ž๋“ค์—๊ฒŒ ํฅ๋ฏธ๋ฅผ ์ฃผ๊ณ  ๋…๋ฆฝ๋œ ๋„์‹ฌ ๊ณต๊ฐ„์œผ๋กœ์จ ์‹œ๋ฏผ๋“ค์„ ๋Œ์–ด๋“ค์ผ ์ˆ˜ ์žˆ๋Š” ์žฅ์†Œ๋กœ ์‚ฌ์šฉ๋˜๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ๋Š” ์„œ์šธ์‹œ ๊ตฌ๋กœ๊ตฌ ๊ตฌ๋กœ๋™๊ณผ ๊ธˆ์ฒœ๊ตฌ ๊ฐ€์‚ฐ๋™ ์ผ๋Œ€์— ๊ฑธ์ณ ์กฐ์„ฑ๋œ ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€ 3๋‹จ์ง€๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. 3๋‹จ์ง€๋Š” ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€ 3๊ฐœ ๋‹จ์ง€์ค‘ ๊ฐ€์žฅ ํฐ ๊ทœ๋ชจ๋กœ ์กฐ์„ฑ๋œ ๋‹จ์ง€์ด๋‹ค. ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€๋Š” ํ•œ๊ตญ์‚ฐ์—…๋ฐœ์ „์˜ ํ•ต์‹ฌ์ง€์—ญ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฌ ์ด๊ณณ์—๋Š” ๋งŽ์€ ์ง€์‹๊ธฐ๋ฐ˜์‚ฐ์—… ๊ธฐ์—…๋“ค์ด ์ž…์ฃผํ•ด ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋Ÿฌํ•œ ์ง€์‹๊ธฐ๋ฐ˜์‚ฐ์—… ๊ธฐ์—…๋“ค์˜ ์ž…์ฃผ๋ฅผ ์œ„ํ•ด ์ด๊ณณ์—๋Š” ๋งŽ์€ ์ˆ˜์˜ ์•„ํŒŒํŠธํ˜• ๊ณต์žฅ๋“ค์ด ๋“ค์–ด์„œ ์žˆ๋‹ค. ์ด๊ณณ์˜ ๋Œ€๋ถ€๋ถ„์˜ ์•„ํŒŒํŠธํ˜• ๊ณต์žฅ๋“ค์€ ๋Œ€์ง€๋ฉด์ ์˜ 10% ์ด์ƒ์„ ๊ณต๊ฐœ๊ณต์ง€๋กœ ๊ฑด์„คํ•˜๋ฉฐ ์ด์— ํ•ด๋‹นํ•˜๋Š” ์šฉ์ ๋ฅ ์˜ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ๋ฐ›์•„์„œ ๊ฑด์„ค๋˜์—ˆ๋‹ค. ๊ทธ๋ ‡๋‹ค๋ณด๋‹ˆ ์—ฌ๋Ÿฌ ์•„ํŒŒํŠธํ˜• ๊ณต์žฅ๋“ค์ด ๋ชฐ๋ ค์žˆ๋Š” ์‚ฌ์ด์— ๊ทธ ์•ž๋งˆ๋‹น์—๋Š” ์—ฌ๋Ÿฌ ๊ณต๊ฐœ๊ณต์ง€๋“ค์ด ์„œ๋กœ ์ธ์ ‘ํ•ด์žˆ๋Š” ๋ชจ์Šต์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€ 3๋‹จ์ง€๋Š” ์ด๋ฏธ ๊ฐœ๋ฐœ์ด ์™„๋ฃŒ๋œ 1๋‹จ์ง€, 2๋‹จ์ง€์™€ ๋‹ค๋ฅด๊ฒŒ ์•„์ง๋„ ๊ฐœ๋ฐœ์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋Š” ์žฅ์†Œ์ด๋‹ค. ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€ 3๋‹จ์ง€์˜ ๊ฐœ๋ฐœ์€ ์•ž์œผ๋กœ์˜ ์‚ฐ์—…๋‹จ์ง€์˜ ๊ฐœ๋ฐœ์— ์ข‹์€ ์˜ˆ์‹œ๊ฐ€ ๋  ๊ฒƒ์ด๋‹ค. ์ด์— ๋”ฐ๋ผ ์ด๋ฒˆ ์—ฐ๊ตฌ์— ์„œ๋Š” ์„œ์šธ๋””์ง€ํ„ธ์‚ฐ์—…๋‹จ์ง€ 3๋‹จ์ง€์˜ ๊ณต๊ฐœ๊ณต์ง€ ์„ค์น˜ ํ˜„ํ™ฉ์„ ์กฐ์‚ฌํ•˜๊ณ  ๊ทธ ์•ˆ์˜ ์ž์—ฐํ™˜๊ฒฝ, ๊ณต๊ฐœ๊ณต์ง€ ๊ฐ„์˜ ์—ฐ๊ฒฐ์„ฑ, ์‹œ๋ฏผ๋“ค์— ์˜ํ•œ ์‚ฌ์šฉ์— ๊ด€ํ•œ ์กฐ์‚ฌ๋ฅผ ํ•˜์˜€๋‹ค.Chapter 1 Introduction 01 1. Research Background and Purpose 01 1. Research Backgroud 01 2. Research Purpose 05 2. Research Area and Method 07 1. Research Area 07 2. Research Method 09 3. Precedent studies 10 Chapter 2 Privately Owned Public Space (POPS) 12 1. Definition of POPS 12 1. Concept of POPS 12 2. Keywords about POPS 14 3. Law about POPS 17 2. Purpose of POPS 20 1. Importance of POPS 20 2. Physical requirement of POPS 25 3. Categories of POPS 26 3. POPS in Foreign Countries 28 1. POPS in USA 28 2. POPS in Japan 30 3. POPS in Europe 32 Chapter 3 Site Analysis 34 1. About SDIC 3 34 1. Description of SDIC 3 34 2. Site background 35 2. Urban infrastructure of SDIC 3 35 1. Industries in SDIC 3 35 2. Roads in SDIC 3 37 3. Public transportation in SDIC 3 38 3. POPS SDIC 3 39 1. Categorization of POPS in SDIC 3 39 Chapter 4 Analysis on POPS in SDIC 3 44 1. Standard for analysis of POPS 44 1. Major issues for the analysis of POPS in SDIC 3 44 2. Standard of analysis for issues 46 2. Analysis of POPS in SDIC 3 according to issues 49 1. Ecology of natural space 49 2. Connection with urban environment 61 3. Public open space for people 71 Chapter 5 Conclusion 78 1. Meaning of Research 78 2. Limit of the Research 79 References 80 ์ดˆ๋ก 82Maste

    Prevalence of depressive symptoms in patients with chronic pain with no history of psychiatric diseases

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2017. 2. ์ดํ‰๋ณต.Background Depression is the most common psychiatric disease related to chronic pain. The incidence of depression is higher among patients with chronic pain than in the general population. In Korea, the study of these comorbidities is insufficient. We aimed to investigate the prevalence of unrecognized depressive symptoms in patients with chronic pain with no history of psychiatric diseases. Methods Patients with chronic pain for more than six months with no history of psychiatric disease were selected. The Beck Depression Index (BDI) self-reported questionnaire and Pittsburgh Sleep Quality Index (PSQI) self-reported questionnaire were used to evaluate depressive symptoms and sleep disturbances respectively. Socio-demographic characteristics and pain-related characteristics were recorded. Results Ninety-four patients participated in this study. Based on the BDI score, 35.1% of patients with chronic pain had comorbid depressive symptoms. The prevalence of depressive symptoms was significantly higher than in the general population (P < 0.001, 95% CI: 25.73โ€“45.71). The standardized incidence ratio, adjusted for age and sex, was 2.77 in men and 2.60 in women. Patients with low education levels (OR = 14.022, 95% CI: 2.393โ€“94.303, P = 0.004), who were unmarried (OR = 32.514, 95% CI: 3.076โ€“343.681, P = 0.004), who were not economically active (OR = 5.274, 95% CI: 1.022โ€“27.212, P = 0.047), and who had higher NRS score (OR = 1.564, 95% CI: 1.084โ€“2.256, P = 0.017) were more likely to have moderate to severe depressive symptoms. Conclusions The results indicate that unrecognized depressive symptoms in patients with chronic pain are common. Pain physicians should pay particular attention to the psychological problems of patients with chronic pain.Introduction 1 Methods 3 Subjects 3 Self-reported measure of depression 3 Self-reported measure of sleep disturbances 4 Primary endpoint and power analysis 5 Statistical analysis 5 Results 6 Prevalence of depressive symptoms 6 The association between depressive symptoms and Sociodemographic factors 7 Follow-up results of patients with depressive symptoms 7 Suicidal ideation 8 Discussion 9 References 14 Abstract in Korean 24Maste

    Demagnetization Fault Diagnosis in IPMSM Using Linear Interpolation

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    This paper proposes a demagnetization fault diagnosis method for interior permanent magnet synchronous motors(IPMSMs). In particular, a demagnetization fault is one of the most frequent electrical faults in IPMSMs. This paper proposes an estimation method for permanent magnet flux. The method is based on linear interpolation. The effectiveness of the proposed method for diagnose demagnetization faults is verified through various operating conditions by finite element simulation.11Yscopuskc

    A study on the effect factors of drug reimbursement decision-making under the positive list system

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ณด๊ฑดํ•™๊ณผ, 2017. 2. ์ดํƒœ์ง„.๊ฑด๊ฐ•๋ณดํ—˜ ์•ฝ์ œ๋น„๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ  ์•ฝ์ œ๋น„ ์ฆ๊ฐ€์œจ์ด ์ด ์ง„๋ฃŒ๋น„ ์ฆ๊ฐ€์œจ์„ ํ›Œ์ฉ ๋›ฐ์–ด๋„˜๋Š” ์ƒํ™ฉ์—์„œ, ์ •๋ถ€๋Š” 2006๋…„ 5์›” ์ด ์ง„๋ฃŒ๋น„ ์ค‘ ์•ฝ์ œ๋น„ ๋น„์ค‘์„ ์ค„์ด๊ณ  ๊ฑด๊ฐ•๋ณดํ—˜ ์žฌ์ •์˜ ์•ˆ์ •ํ™”์— ๊ธฐ์—ฌํ•˜๊ณ ์ž '์•ฝ์ œ๋น„ ์ ์ •ํ™” ๋ฐฉ์•ˆ'์„ ๋ฐœํ‘œํ•˜์˜€๋‹ค. ์ด์— ์˜์•ฝํ’ˆ์˜ ๋ณดํ—˜ ๋“ฑ์žฌ ์ œ๋„๊ฐ€ ๊ธ‰์—ฌ์ œ์™ธ์ œ๋„(negative list system)์—์„œ ์น˜๋ฃŒ์ ยท๊ฒฝ์ œ์  ๊ฐ€์น˜๊ฐ€ ์šฐ์ˆ˜ํ•œ ์•ฝ์ œ๋งŒ ์„ ๋ณ„ํ•˜์—ฌ ๊ธ‰์—ฌํ•˜๋Š” ์„ ๋ณ„๋“ฑ์žฌ์ œ๋„(positive list system)๋กœ ๋ณ€๊ฒฝ๋˜์—ˆ๋‹ค. ์„ ๋ณ„๋“ฑ์žฌ์ œ๋„ ํ•˜์—์„œ ์‹ ์•ฝ์˜ ๋ณดํ—˜ ๋“ฑ์žฌ ๊ณผ์ •์€ ํฌ๊ฒŒ ๊ฑด๊ฐ•๋ณดํ—˜์‹ฌ์‚ฌํ‰๊ฐ€์› ์‚ฐํ•˜ ์•ฝ์ œ๊ธ‰์—ฌํ‰๊ฐ€์œ„์›ํšŒ์—์„œ ์ง„ํ–‰๋˜๋Š” ๊ธ‰์—ฌ ๊ฒฐ์ • ๊ณผ์ •๊ณผ ๊ตญ๋ฏผ๊ฑด๊ฐ•๋ณดํ—˜๊ณต๋‹จ์—์„œ ์ง„ํ–‰๋˜๋Š” ์•ฝ๊ฐ€ ๊ฒฐ์ • ๊ณผ์ •์œผ๋กœ ์ด์›ํ™”๋˜์–ด ์žˆ๋‹ค. ์ž„์ƒ์  ์œ ์šฉ์„ฑ, ๋น„์šฉํšจ๊ณผ์„ฑ, ๊ฑด๊ฐ•๋ณดํ—˜ ์žฌ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ๋“ฑ์˜ ํ•ญ๋ชฉ์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ๊ธ‰์—ฌํ‰๊ฐ€๋ฅผ ์ง„ํ–‰ํ•œ ํ›„, ์ด ์ž๋ฃŒ ๋“ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ตญ๋ฏผ๊ฑด๊ฐ•๋ณดํ—˜๊ณต๋‹จ๊ณผ ์ œ์•ฝ์‚ฌ๊ฐ€ ์•ฝ๊ฐ€๋ฅผ ํ˜‘์ƒํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์˜์•ฝํ’ˆ ์„ ๋ณ„๋“ฑ์žฌ์ œ๋„์˜ ์‹œํ–‰ ์ดํ›„ ๋ณดํ—˜ ๋“ฑ์žฌ ๊ณผ์ • ์ค‘ ๊ธ‰์—ฌ ๊ฒฐ์ • ๊ณผ์ •์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ์•ฝ์ œ๊ธ‰์—ฌํ‰๊ฐ€์œ„์›ํšŒ์˜ ๊ธ‰์—ฌํ‰๊ฐ€ ์˜ํ–ฅ์š”์ธ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. 2007๋…„๋ถ€ํ„ฐ 2016๋…„ 9์›” 30์ผ๊นŒ์ง€ ๊ณต๊ฐœ๋œ ์•ฝ์ œ๊ธ‰์—ฌํ‰๊ฐ€์œ„์›ํšŒ ๊ธ‰์—ฌํ‰๊ฐ€ ๊ฒฐ๊ณผ ์ž๋ฃŒ๋ฅผ ์ •๋ฆฌํ•œ 369๊ฑด์˜ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๊ธ‰์—ฌํ‰๊ฐ€ ๋Œ€์ƒ ์˜์•ฝํ’ˆ์˜ ํŠน์„ฑ๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ๊ธ‰์—ฌํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•˜๊ณ  ๊ธ‰์—ฌํ‰๊ฐ€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์˜์•ฝํ’ˆ ํŠน์„ฑ ๋ฐ ์น˜๋ฃŒ๊ตฐ, ๊ด€๋ จ ์ œ๋„์˜ ์‹œํ–‰์— ๋”ฐ๋ฅธ ๊ธ‰์—ฌํ‰๊ฐ€ ๊ธฐ์ค€๊ณผ ๊ฒฐ๊ณผ๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ธ‰์—ฌํ‰๊ฐ€ ์ž๋ฃŒ 369๊ฑด ์ค‘ 291๊ฑด์ด ๊ธ‰์—ฌ๋กœ ํ‰๊ฐ€๋˜์–ด ์•ฝ 78.9%์˜ ๊ธ‰์—ฌ์œจ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๋˜ํ•œ ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ๊ธ‰์—ฌ์œจ์ด ์ ์ฐจ ๋†’์•„์ง€๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋Š”๋ฐ ์ด๋Š” 4๋Œ€ ์ค‘์ฆ์งˆํ™˜ ๋ณด์žฅ ๊ฐ•ํ™” ์ •์ฑ…, ์œ„ํ—˜๋ถ„๋‹ด์ œ๋„, ๊ฒฝ์ œ์„ฑํ‰๊ฐ€ ํŠน๋ก€ ์ œ๋„ ๋“ฑ ๊ด€๋ จ ์ •์ฑ… ๋ฐ ์ œ๋„์˜ ๋„์ž…๊ณผ๋„ ๋ฌด๊ด€ํ•˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. ๊ธ‰์—ฌํ‰๊ฐ€์˜ ์˜ํ–ฅ์š”์ธ์— ๋Œ€ํ•œ ๋‹ค์ค‘ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด ์ž„์ƒ์  ์œ ์šฉ์„ฑ, ๋น„์šฉํšจ๊ณผ์„ฑ์ด ๊ธ‰์—ฌํ‰๊ฐ€ ๊ฒฐ๊ณผ์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ๋กœ ๊ธ‰์—ฌํ‰๊ฐ€ ๊ณผ์ •์—์„œ๋Š” ๋‹ค๋ฅธ ์š”์†Œ๋“ค๋„ ํ•จ๊ป˜ ๊ณ ๋ ค๋˜๋Š”๋ฐ, ์œ„ํ—˜๋ถ„๋‹ด์ œ๋„ ์ ์šฉ ์˜์•ฝํ’ˆ๊ณผ ๊ฒฝ์ œ์„ฑํ‰๊ฐ€ ํŠน๋ก€ ๋Œ€์ƒ ์˜์•ฝํ’ˆ์ด ๊ทธ ๋Œ€ํ‘œ์ ์ธ ์˜ˆ์ด๋‹ค. ์ƒˆ๋กœ์šด ์ œ๋„๋ฅผ ํ†ตํ•ด ์งˆํ™˜์˜ ์ค‘์ฆ๋„, ์‚ฌํšŒ์  ์˜ํ–ฅ ๋“ฑ์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•จ์œผ๋กœ์จ ํ™˜์ž์˜ ์ ‘๊ทผ์„ฑ์„ ์ œ๊ณ ํ•˜๊ณ ์ž ํ•œ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ œํ•œ์ ์€ ๊ธ‰์—ฌํ‰๊ฐ€ ์ž๋ฃŒ๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ๊ณผ์ •์—์„œ ์ฃผ๋กœ ๋ฐœ์ƒํ•˜์˜€์„ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์•ฝ์ œ๊ธ‰์—ฌํ‰๊ฐ€์œ„์›ํšŒ์˜ ๊ธ‰์—ฌํ‰๊ฐ€ ์ž๋ฃŒ์— ์‚ฌ์šฉ๋œ ์šฉ์–ด๊ฐ€ ์ผ๊ด€๋˜์ง€ ์•Š์•˜๊ณ  ์ผ๋ถ€ ๋‚ด์šฉ์ด ๊ณต๊ฐœ๋˜์ง€ ์•Š์•„ ์ด์— ๋Œ€ํ•œ ์ •๋Ÿ‰์  ๋ถ„์„์ด ๋ถˆ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ๋˜ํ•œ 10์—ฌ ๋…„์— ๊ฑธ์ณ 369๊ฑด์˜ ์ž๋ฃŒ๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€์œผ๋‚˜ ๊ธ‰์—ฌํ‰๊ฐ€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ธฐ์— ์ž๋ฃŒ์˜ ์ˆ˜๊ฐ€ ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๋‹ค๋Š” ๊ฒƒ ์—ญ์‹œ ์ œํ•œ์ ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„, ๋ณธ ์—ฐ๊ตฌ๋Š” ์žฅ๊ธฐ๊ฐ„์— ๊ฑธ์นœ ๊ธ‰์—ฌํ‰๊ฐ€ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์„ ๋ณ„๋“ฑ์žฌ์ œ๋„ ์‹œํ–‰์œผ๋กœ ์ธํ•œ ๊ธ‰์—ฌํ‰๊ฐ€ ํ˜„ํ™ฉ์„ ๊ตฌ์ฒด์ ์œผ๋กœ ํŒŒ์•…ํ•˜์˜€๋‹ค๋Š” ๋ฐ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ํ–ฅํ›„ ๋ณด๋‹ค ๋งŽ์€ ์ˆ˜์˜ ๊ธ‰์—ฌํ‰๊ฐ€ ์ž๋ฃŒ๊ฐ€ ์ถ•์ ๋œ ํ›„ ๋น„๊ณต๊ฐœ ์ž๋ฃŒ๋ฅผ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋‹ค๋ฉด ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์˜ ์ •ํ™•์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  3 โ…ก. ์ œ๋„์  ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธํ—Œ๊ณ ์ฐฐ 4 1. ์ œ๋„์  ๋ฐฐ๊ฒฝ 4 2. ๋ฌธํ—Œ๊ณ ์ฐฐ 11 โ…ข. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 18 1. ์—ฐ๊ตฌ ๊ฐ€์„ค ๋ฐ ๋ชจํ˜• 18 2. ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ์ž๋ฃŒ์› 20 3. ๋ณ€์ˆ˜ ์ •์˜ 22 4. ๋ถ„์„ ๋ฐฉ๋ฒ• 26 โ…ฃ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 27 1. ์—ฐ๊ตฌ ๋Œ€์ƒ์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 27 2. ๊ธ‰์—ฌํ‰๊ฐ€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 37 3. ๊ด€๋ จ ์ œ๋„ ์‹œํ–‰์— ๋”ฐ๋ฅธ ์˜์•ฝํ’ˆ์˜ ๊ธ‰์—ฌํ‰๊ฐ€ ๋ถ„์„ 46 4. ์˜์•ฝํ’ˆ ํŠน์„ฑ ๋ฐ ์น˜๋ฃŒ๊ตฐ์— ๋”ฐ๋ฅธ ๊ธ‰์—ฌํ‰๊ฐ€ ๋ถ„์„ 50 โ…ค. ๊ณ ์ฐฐ ๋ฐ ๊ฒฐ๋ก  53 1. ๊ณ ์ฐฐ 53 2. ์—ฐ๊ตฌ์˜ ์ œํ•œ์  56 3. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 57 ์ฐธ๊ณ ๋ฌธํ—Œ 58 Abstract 62Maste

    Differences of Net Ecosystem Production Change between a Pinus koraiensis Plantation and a Deciduous Broad-leaved Forest over a Four-year Period after Drought

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์‚ฐ๋ฆผ๊ณผํ•™๋ถ€(์‚ฐ๋ฆผํ™˜๊ฒฝํ•™์ „๊ณต),2019. 8. ๊น€ํ˜„์„.๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๊ฐ€๋ญ„๊ณผ ๊ฐ™์€ ๊ธฐ์ƒ์ด๋ณ€์˜ ์ง€์—ญ๋ณ„ ๋ณ€์ด๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ , ์‚ฐ๋ฆผ์ƒํƒœ๊ณ„์˜ ํƒ„์†Œ ์ˆœํ™˜์— ์ƒ๋‹นํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐ€๋ญ„์œผ๋กœ ์ธํ•œ ์‚ฐ๋ฆผ์ƒํƒœ๊ณ„์˜ ํƒ„์†Œ ์ˆœํ™˜์˜ ๋ณ€ํ™”์™€ ๊ฐ€๋ญ„ ์ดํ›„ ํšŒ๋ณต์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ์ค‘์š”ํ•˜๋‹ค. ํŠนํžˆ ์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ๋ด„ ๊ฐ€๋ญ„์˜ ๋ฐœ์ƒ ๋นˆ๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋ด„ ๊ฐ€๋ญ„ ๋ฐœ์ƒ ์‹œ ์žŽ์˜ ์กด์žฌ ์—ฌ๋ถ€์— ๋”ฐ๋ผ ๊ฐ€๋ญ„์ด ์‚ฐ๋ฆผ์˜ ํƒ„์†Œ ์ˆœํ™˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ‰๋…„์˜ 53% ์ˆ˜์ค€์œผ๋กœ ์—ฐ๊ฐ•์ˆ˜๋Ÿ‰์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•˜์—ฌ ๊ฐ€๋ญ„์ด ์‹œ์ž‘๋œ 2014๋…„ ์ดํ›„, 2015๋…„๋ถ€ํ„ฐ 2018๋…„๊นŒ์ง€ ์ด 4๋…„๊ฐ„ ์žฃ๋‚˜๋ฌด์กฐ๋ฆผ์ง€์™€ ์ฐธ๋‚˜๋ฌด๊ณผ ์ˆ˜์ข…์ด ์šฐ์ ํ•˜๊ณ  ์žˆ๋Š” ๋‚™์—ฝํ™œ์—ฝ์ˆ˜๋ฆผ์—์„œ ์ˆœ์ƒํƒœ๊ณ„์ƒ์‚ฐ๋Ÿ‰(net ecosystem production, NEP)์ด ๊ฐ€๋ญ„ ๊ฐ•๋„์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๊ณ , ๊ฐ€๋ญ„ ์ข…๋ฃŒ ํ›„ NEP ํšŒ๋ณต์„ ๋‚˜ํƒ€๋‚ด๋Š” ํšŒ๋ณต์ง€์ˆ˜(RC NEP)๊ฐ€ ๋‘ ์ž„๋ถ„์—์„œ ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ž๋Š”์ง€ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ๋‘ ์ž„๋ถ„ ๊ฐ„์— NEP์˜ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์›์ธ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฒฝ๊ธฐ๋„ ํƒœํ™”์‚ฐ ๋‚ด 50๋…„์ƒ ์žฃ๋‚˜๋ฌด์กฐ๋ฆผ์ง€(Taehwa conifer forest, TCK)์™€ ๊ทธ๋กœ๋ถ€ํ„ฐ ์•ฝ 300 m ๋–จ์–ด์ง„ ๋‚™์—ฝํ™œ์—ฝ์ˆ˜๋ฆผ(Taehwa deciduous broad-leaved forest, TBK)์—์„œ ์—๋””๊ณต๋ถ„์‚ฐ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์ง€์—์„œ SPI(standardized precipitation index)๋Š” 2014๋…„ 7์›” ์ดํ›„ ์‹ฌํ•œ ๊ฐ€๋ญ„์œผ๋กœ -2 ์ดํ•˜๋กœ ๊ฐ์†Œํ•˜์˜€์œผ๋ฉฐ, 2015๋…„ 4์›”๋ถ€ํ„ฐ ๊ฐ€๋ญ„์˜ ๊ฐ•๋„๊ฐ€ ์„œ์„œํžˆ ์•ฝํ•ด์ ธ 2016๋…„ 10์›”๊นŒ์ง€ SPI๊ฐ€ ์ ์ฐจ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. 2016๋…„ 11์›”๋ถ€ํ„ฐ SPI๊ฐ€ ๋‹ค์‹œ ๊ฐ์†Œํ•˜๋ฉด์„œ 2017๋…„ 5์›”๊ณผ 6์›”์— ์—ฐ๊ตฌ ๊ธฐ๊ฐ„(2015-2018๋…„) ์ค‘ ๊ฐ€์žฅ ์‹ฌํ•œ ๊ฐ€๋ญ„์œผ๋กœ SPI๊ฐ€ -2.38๊นŒ์ง€ ๊ฐ์†Œํ•˜์˜€๋‹ค. ์ดํ›„ 2017๋…„ 7์›”์— ํ‰๋…„์˜ 137.7% ์ˆ˜์ค€์ธ 532.3 mm์˜ ๋งŽ์€ ๋น„๊ฐ€ ๋‚ด๋ฆฌ๋ฉด์„œ 8์›”๋ถ€ํ„ฐ๋Š” ๊ฐ€๋ญ„์ด ์ข…๋ฃŒ๋˜์–ด SPI๊ฐ€ ์ •์ƒ๋ฒ”์œ„(-0.99โ‰คSPIโ‰ค0.99)๋กœ ๋Œ์•„์™”๋‹ค. TCK์™€ TBK์—์„œ ๋น„์ƒ์žฅ๊ธฐ๊ฐ„์ธ ๊ฒจ์šธ์ฒ ์„ ์ œ์™ธํ•œ ์—ฐ๊ฐ„ NEP๋Š” 2015๋…„๋ถ€ํ„ฐ 2016๋…„ ์‚ฌ์ด ๊ฐ๊ฐ 539.9 g C m-2 ์—์„œ 565.4 g C m-2, 636.1 g C m-2์—์„œ 756.9 g C m-2๋กœ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, SPI์™€ ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์ด ๊ฐ€์žฅ ํฌ๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋˜ 2017๋…„์—๋Š” ๋‘ ์ž„๋ถ„ ๋ชจ๋‘ NEP๊ฐ€ ๊ฐ์†Œํ•˜์˜€๋‹ค. TBK์—์„œ 2017๋…„์˜ NEP๋Š” 737.8 g C m-2๋กœ ์—ฐํ‰๊ท  SPI๊ฐ€ ์—ฐ๊ตฌ ๊ธฐ๊ฐ„(2015-2018๋…„) ์ค‘ ๊ฐ€์žฅ ๋‚ฎ์•˜๋˜ 2015๋…„์˜ NEP ๋ณด๋‹ค 16.0% ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ฐ˜๋ฉด TCK์—์„œ 2017๋…„์˜ NEP๋Š” 2015๋…„์˜ NEP ๋ณด๋‹ค 10.5% ๋‚ฎ์€ 483.3 g C m-2๋ฅผ ๊ธฐ๋กํ•˜์—ฌ ์—ฐ๊ตฌ ๊ธฐ๊ฐ„(2015-2018๋…„) ์ค‘ NEP๊ฐ€ ๊ฐ€์žฅ ์ž‘์•˜๋‹ค. TBK์™€ ๋‹ฌ๋ฆฌ TCK์—์„œ 2017๋…„์˜ NEP๊ฐ€ ์—ฐ๊ตฌ ๊ธฐ๊ฐ„(2015-2018๋…„) ์ค‘ ๊ฐ€์žฅ ์ž‘์•˜๋˜ ์›์ธ ์ค‘ ํ•˜๋‚˜๋Š” 2016๋…„ ๊ฐ€์„๋ถ€ํ„ฐ 2017๋…„ ๋ด„๊นŒ์ง€ ์ด์–ด์ง€๋Š” ๊ฐ€๋ญ„์œผ๋กœ 2017๋…„ 5์›”๊ณผ 6์›”์— TCK์˜ ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์ด ์‚ฌ์–‘ํ† ์—์„œ ์‹๋ฌผ์˜ ์œ„์กฐ์ ์„ ๋‚˜ํƒ€๋‚ด๋Š” 8% ์ดํ•˜๋กœ ๊ฐ์†Œํ•˜์—ฌ ์žฃ๋‚˜๋ฌด ์ž„๋ถ„์—์„œ ๊ธฐ๊ณต์„ ๋‹ซ๊ณ  ๊ด‘ํ•ฉ์„ฑ ํ™œ๋™์„ ํฌ๊ฒŒ ์ค„์˜€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋˜ํ•œ 2016๋…„ 10์›”๋ถ€ํ„ฐ 2017๋…„ 4์›” ์‚ฌ์ด ์—ฝ๋ฉด์ ์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•˜๋Š” TBK์™€ ๋‹ฌ๋ฆฌ ๊ณ„์†ํ•ด์„œ ์žŽ์„ ๋‹ฌ๊ณ  ์žˆ๋Š” TCK์—์„œ๋Š” TBK๋ณด๋‹ค 51.7 mm ๋” ๋งŽ์€ ๋ฌผ์„ ์ฆ๋ฐœ์‚ฐ์œผ๋กœ ์†Œ๋น„ํ•˜์˜€์œผ๋ฉฐ, 2016๋…„ 11์›”๋ถ€ํ„ฐ 2017๋…„ 3์›” ์‚ฌ์ด TCK์˜ ์ˆ˜๊ด€ ์ฐจ๋‹จ์— ์˜ํ•ด 15.2 mm์˜ ๊ฐ•์ˆ˜๊ฐ€ ํ† ์–‘์œผ๋กœ ๊ณต๊ธ‰๋˜์ง€ ์•Š์•„ 2017๋…„ 5์›”๊ณผ 6์›”์— TCK์—์„œ ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์ด ์œ„์กฐ์  ์ดํ•˜๋กœ ๊ฐ์†Œํ•˜๋Š” ๋ฐ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 2017๋…„ 7์›”์— 22์ผ๊ฐ„ ์ง‘์ค‘์ ์ธ ๊ฐ•์šฐ๋กœ ์‚ฐ๋ฆผ์ด ์ด์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋น›์ด ๊ฐ์†Œํ•˜์—ฌ ๊ด‘ํ•ฉ์„ฑ์ด ํฌ๊ฒŒ ์ €ํ•ด๋˜์—ˆ์œผ๋ฉฐ, TCK์—์„œ ๋Œ€์‚ฌ์ž‘์šฉ์— ํ•„์š”ํ•œ ์—๋„ˆ์ง€์›์˜ ๊ณ ๊ฐˆ๋กœ ์ด์–ด์ ธ ๊ฐ€๋ญ„์ด ์ข…๋ฃŒ๋œ 2017๋…„ 8์›” ์ดํ›„์—๋„ NEP๊ฐ€ ํšŒ๋ณต๋˜์ง€ ๋ชปํ•œ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. TCK์™€ TBK์—์„œ ๊ฒจ์šธ์ฒ ์„ ์ œ์™ธํ•œ 2018๋…„์˜ NEP๋Š” ๊ฐ๊ฐ 614.6 g C m-2, 949.3 g C m-2๋กœ 2015๋…„๋ถ€ํ„ฐ 2018๋…„๊นŒ์ง€ ์—ฐ๊ตฌ ๊ธฐ๊ฐ„(2015-2018๋…„) ์ค‘ NEP๊ฐ€ ๊ฐ€์žฅ ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. TBK์—์„œ 2018๋…„์˜ NEP๋Š” ๋‹ค๋ฅธ ํ•ด ๋ณด๋‹ค 239.0ยฑ64.9 g C m-2(34.4ยฑ12.9%) ์ปธ์œผ๋ฉฐ, TCK์—์„œ๋Š” 85.1ยฑ42.0 g C m-2(16.6ยฑ9.5%) ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์— ๋”ฐ๋ผ 2018๋…„์˜ NEP์™€ 2015๋…„๋ถ€ํ„ฐ 2017๋…„ ์‚ฌ์ด ํ‰๊ท  NEP์˜ ๋น„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” RC NEP(NEP recovery index)๋Š” TCK์™€ TBK์—์„œ ๊ฐ๊ฐ 1.16ยฑ0.10๊ณผ 1.34ยฑ0.13์ด์—ˆ์œผ๋ฉฐ, ์ด๋Š” ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์ด ๋†’์€ ๊ณณ์—์„œ RC(recovery index)๊ฐ€ ๋†’๋‹ค๋Š” ์„ ํ–‰ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์™€ ์œ ์‚ฌํ•˜์˜€๋‹ค. TCK์—์„œ TBK ๋ณด๋‹ค RC NEP๊ฐ€ ๋‚ฎ์•˜๋˜ ์›์ธ์€ ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋†’๊ฒŒ ์œ ์ง€๋˜์—ˆ๋˜ TBK์™€ ๋‹ฌ๋ฆฌ TCK์—์„œ๋Š” ๊ทน์‹ฌํ•œ ์ˆ˜๋ถ„ ์ŠคํŠธ๋ ˆ์Šค๋กœ ๋Œ€์‚ฌ์ž‘์šฉ์ด ์ €ํ•˜๋˜์–ด ๊ฐ€๋ญ„ ์ข…๋ฃŒ ํ›„ NEP์˜ ํšŒ๋ณต์ด ์˜ค๋žœ ๊ธฐ๊ฐ„ ๋Šฆ์–ด์ง€๋Š” legacy effect ๋•Œ๋ฌธ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๊ฐ€๋ญ„ ๊ธฐ๊ฐ„ ์ค‘ ์ˆ˜์ข…์  ์ฐจ์ด์— ์˜ํ•ด ์žฃ๋‚˜๋ฌด์กฐ๋ฆผ์ง€์—์„œ ๋‚™์—ฝํ™œ์—ฝ์ˆ˜๋ฆผ ๋ณด๋‹ค ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋กœ ์ธํ•ด ์žฃ๋‚˜๋ฌด์กฐ๋ฆผ์ง€์—์„œ ๊ทน์‹ฌํ•œ ์ˆ˜๋ถ„ ์ŠคํŠธ๋ ˆ์Šค์™€ legacy effect๋กœ ์‚ฐ๋ฆผ์˜ NEP๊ฐ€ ๊ฐ์†Œํ•˜๊ณ , ๊ฐ€๋ญ„ ์ดํ›„ NEP ํšŒ๋ณต์ด ๋Šฆ์–ด์งˆ ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๊ฐ€๋ญ„ ๋ฐœ์ƒ์œผ๋กœ ์‚ฐ๋ฆผ์˜ ํ”ผํ•ด๊ฐ€ ์ฆ๊ฐ€๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜๋Š” ๊ฐ€์šด๋ฐ, ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ง€์†์ ์ธ ์‚ฐ๋ฆผ๊ด€๋ฆฌ ๋ฐฉ์•ˆ์„ ์œ„ํ•œ ๊ธฐ์ดˆ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.As climate change progresses, regional variability in meteorological changes such as drought will increase and is expected to have a significant impact on the carbon cycle of forest ecosystems. Therefore, it is important to understand the change of carbon cycle of forest ecosystem due to drought and recovery after drought. In Republic of Korea, the frequency of spring drought is increasing, and the effect of drought on forest carbon cycle can be changed depending on the presence of leaves during the drought. In this study, we analyzed the differences of net ecosystem production (NEP) change at a Pinus koraiensis plantation and a deciduous broad-leaved forest where Quercus species are dominant over a four-year period from 2015 to 2018 after about 50% annual precipitation decreased in 2014. This study was carried out in 50 year old Taehwa conifer forest (TCK) and nearby Taehwa deciduous broad-leaved forest (TBK). The standardized precipitation index (SPI) at the study site decreased to -2 or less after July 2014, and it gradually increased from March, 2015 to October, 2016. From November, 2016, the SPI declined to the lowest values during May and June 2017. After a heavy rainfall of 523.3 mm (137.7% of the normal level) in July 2017, the SPI has returned to the normal range (-0.99โ‰คSPIโ‰ค0.99). The annual NEP excluding non-growing winter season in TCK and TBK increased from 539.9 g C m-2 to 565.4 g C m-2 and from 636.1 g C m-2 to 756.9 g C m-2 respectively between 2015 and 2016. And the NEP decreased in both stands in 2017 when SPI and soil moisture content decreased the most. In TBK, the NEP in 2017 was 737.8 g C m-2, which is 16.0% larger than the NEP in 2015. On the other hand, in TCK, NEP in 2017 was 483.3 g C m-2, 10.5% lower than NEP in 2015, and it was the smallest during the period of study (2015-2018). In TCK, one of the reasons for large reduction in NEP during 2017 is that photosynthesis activity of forest ecosystem decreased along with canopy conductance reduction because the soil moisture content in TCK decreased by 8%, which indicates the plant wilting point under sandy loam soil condition, during May and June, 2017 due to the severe drought from autumn, 2016 to spring, 2017. Furthermore, TCK consumed 51.7 mm more water by evapotranspiration than TBK from October, 2016 to April, 2017, and 15.2 mm of precipitation was not supplied to the soil due to the canopy interception in TCK from November, 2016 to March, 2017, thus it affected large reduction of soil moister content in TCK. In July of 2017, intense rainfall for 22 days resulted in a decrease in light available for photosynthesis and severe inhibition of photosynthesis. Therefore, it seemed to lead to depletion of the energy source needed for metabolism after drought in TCK. The NEP both in TCK and TBK in 2018, excluding winter, were 614.6 g C m-2 and 949.3 g C m-2 respectively, which was the largest during study period from 2015 to 2018. NEP in 2018 was 16.6ยฑ9.5% (85.1ยฑ42.0 g C m-2) and 34.4ยฑ12.9% (239.0ยฑ64.9 g C m-2) larger than that of the other years in TCK and TBK, respectively. As a result, the NEP recovery index (RC NEP) was 1.16ยฑ0.10 and 1.34ยฑ0.13 for TCK and TBK, respectively. The reason why RC NEP for TCK was lower than that of TBK was thought to be due to the legacy effect that the recovery of NEP after drought was delayed for a long time due to the extreme water stress and the decrease of metabolism in TCK unlike the TBK where the soil moisture content remained relatively high. The results of this study show that the soil moisture content in Pinus koraiensis plantation can be decreased significantly more than that of deciduous broad-leaved forest due to the difference of species characteristics during the drought period. Therefore, the NEP of the forest is decreased by the extreme water stress and legacy effect in the Pinus koraienesis plantation, suggesting that recovery may be delayed. The results of this study can be applied to sustainable forest management plan, as drought caused by climate change is predicted to increase the damage of forests.๋ชฉ ์ฐจ I. ์„œ๋ก  10 II. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 14 1. ์—ฐ๊ตฌ ๋Œ€์ƒ์ง€ 14 2. ํ”Œ๋Ÿญ์Šค ๋ฐ ํ™˜๊ฒฝ์ธ์ž ๊ด€์ธก 17 3. ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ 22 4. ์ˆ˜๊ด€์ „๋„๋„(canopy conductance, gc) 24 5. Standarized precipitation index(SPI) 26 6. ์ˆœ์ƒํƒœ๊ณ„์ƒ์‚ฐ๋Ÿ‰(NEP) 27 III. ๊ฒฐ๊ณผ 29 1. ํ™˜๊ฒฝ์ธ์ž ๋ฐ SPI ๊ฐ€๋ญ„์ง€์ˆ˜ 29 2. ์ˆœ์ƒํƒœ๊ณ„์ƒ์‚ฐ๋Ÿ‰(NEP) ๋ฐ ํšŒ๋ณต์ง€์ˆ˜(Rc NEP) 34 3. ๋‘ ์ž„๋ถ„์˜ NEP ์ฐจ์ด ๋ฐœ์ƒ ์›์ธ 38 IV. ๊ณ ์ฐฐ 42 1. ํ† ์–‘์ˆ˜๋ถ„ํ•จ๋Ÿ‰์˜ ์ฐจ์ด์™€ NEP์˜ ๊ฐ์†Œ 42 2. ์ˆ˜์ข…๋ณ„ ๊ด‘๋ณด์ƒ์ ๊ณผ NEP ๋ณ€ํ™” 46 3. ํšŒ๋ณต์ง€์ˆ˜(Rc NEP)์™€ legacy effect 47 V. ๊ฒฐ๋ก  52 ์ฐธ๊ณ ๋ฌธํ—Œ 53 Abstract 60Maste

    Part โ… . ๋ถ„์ž ๋ชจ๋ธ๋ง์„ ์ด์šฉํ•œ ํ•ญ์•” ํ™œ์„ฑ์„ ๊ฐ–๋Š” peptidomimetic ์•ฝ๋ฌผ ์„ค๊ณ„, Part โ…ก. PDEฮด ์ €ํ•ด์ œ์— ๊ด€ํ•œ ์ดˆ๊ธฐ ์—ฐ๊ตฌ

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    The Structural Relationship between Corporate Culture and Corporate Education Performance using Autoregressive Cross-lagged Modeling with a Focus on Innovation Culture

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ค์ฐจํšŒ๊ท€ ์ง€์—ฐ๋ชจํ˜•์„ ํ†ตํ•˜์—ฌ ํ˜์‹ ๋ฌธํ™”์™€ ๊ธฐ์—…๊ต์œก์„ฑ๊ณผ๊ฐ„์˜ ์ƒํ˜ธ๊ต์ฐจ ์ง€์—ฐํšจ๊ณผ๋ฅผ ์ข…๋‹จ์ ์œผ๋กœ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ชฉ์ ์„ ์œ„ํ•ด ํ•œ๊ตญ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ์›์˜ ์ธ์ ์ž๋ณธ๊ธฐ์—…ํŒจ๋„(Human Capital Corporate Panel, ์ดํ•˜ HCCP) 3,4,5์ฐจ๋…„๋„ ์ž๋ฃŒ(๋ณธ์‚ฌ์šฉ, ๊ทผ๋กœ์ž์šฉ ์ž๋ฃŒ)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๊ตฌ์กฐ๋ฐฉ์ •์‹์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, 8๊ฐœ ๊ฒฝ์Ÿ๋ชจ๋ธ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์ตœ์ข…๋ชจ๋ธ์„ ์„ ์ •ํ•˜์˜€์œผ๋ฉฐ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ž๊ธฐํšŒ๊ท€ ๊ต์ฐจ์ง€์—ฐ ๋ชจํ˜•์„ ํ†ตํ•ด ํ˜์‹ ๋ฌธํ™”๋Š” ์‹œ๊ฐ„์— ๊ฑธ์ณ ์•ˆ์ •์ ์œผ๋กœ ๊ณผ๊ฑฐ ํ˜์‹ ๋ฌธํ™”์— ์˜ํ•ด ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์‹œ๊ฐ„์— ์ง€์—ฐ๋˜์–ด ๊ธฐ์—…๊ต์œก์„ฑ๊ณผ์—๋„ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ๋‘˜์งธ๋กœ, ๊ธฐ์—…๊ต์œก์„ฑ๊ณผ๋Š” ์‹œ๊ฐ„์— ๊ฑธ์ณ ์•ˆ์ •์ ์œผ๋กœ ๊ณผ๊ฑฐ ๊ธฐ์—…๊ต์œก์„ฑ๊ณผ์— ์˜ํ•ด ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋”๋ถˆ์–ด ์‹œ๊ฐ„์— ์ง€์—ฐ๋˜์–ด ํ˜์‹ ๋ฌธํ™”์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ์…‹์งธ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ˜์‹ ๋ฌธํ™”์™€ ๊ธฐ์—…๊ต์œก์„ฑ๊ณผ์™€์˜ ๊ด€๊ณ„๊ตฌ์กฐ๋ฅผ ๊ฒ€์ฆํ•ด ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์™€ ๊ฐ™์ด ๊ธฐ์—…๋ฌธํ™”์™€ ๊ธฐ์—…๊ต์œก์„ฑ๊ณผ๊ฐ„์˜ ์‹œ๊ฐ„์— ๊ฑธ์นœ ์ƒํ˜ธ ๊ต์ฐจ ์ง€์—ฐํšจ๊ณผ๋Š” ๊ธฐ์—…์˜ ์ธ์ ์ž์› ๊ฐœ๋ฐœ์ž๋กœ ํ•˜์—ฌ๊ธˆ ๊ธฐ์—…๋ฌธํ™” ๋ณ€ํ™”์™€ ์„ฑ๊ณผ ๊ฐ„์— ์˜ˆ์ธก ๋ฐ ๊ด€๋ฆฌ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•œ ํ•จ์˜๋ฅผ ์ œ๊ณตํ•œ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค.This study was designed to longitudinally explore the cross-lagged effect between innovation culture and corporate education performance by using the autoregressive cross-lagged modeling. For this purpose, the 3rd, 4th, and 5th waves of Human Capital Corporate Panel (HCCP), selectively targeting headquarters and their employees, were statistically analyzed, and structural equations enabled us to fit the final model through competitive model comparison. The findings of this study indicated that the current corporate innovation culture had interacted with its previous innovation culture on a regular basis and that it had also influenced corporate education performance by time-lagged period. Moreover, corporate education performance was found to be correlated with its pervious performance, thus having a time-lagged effect on innovation culture. Consequently, this study contributed to confirming the structural relationship between innovation culture and corporate education performance. In this manner, the cross-lagged effect between innovation culture and corporate education performance provided the practical implications concerning the prediction and management of corporate culture and performance

    Sea Surface Light Scatter Correction Using Image Information Combined Adaptive threshold adjusting Gamma Correction

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ์„œ์ข…๋ชจ.๊ณต์ค‘์—์„œ ์ˆ˜๋ฉด ์œ„์˜ ๋ฌผ์ฒด๋ฅผ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ฌผ์ฒด๋ฅผ ์ž˜ ์‹๋ณ„ํ•˜๋Š” ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ์ˆ˜๋ฉด ๋น› ๋ฐ˜์‚ฌ์™€ ๊ฐ™์€ ๊ต๋ž€ ์‹ ํ˜ธ๋ฅผ ์–ต์ œ ํ˜น์€ ์ œ๊ฑฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ˆ˜๋ฉด ๋น› ๋ฐ˜์‚ฌ๋ฅผ ์˜์ƒ์ •๋ณด ๊ฒฐํ•ฉํ˜• ์ ์‘ ์ž„๊ณ„ ๊ฐ’ ๊ฐ๋งˆ ๋ณด์ •ํ•„ํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์–ต์ œํ•จ์œผ๋กœ์จ ๊ณต์ค‘์—์„œ ํ•ด์ƒ์˜ ๋ฌผ์ฒด๋ฅผ ๊ด€์ธกํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ฃผ๋Š” ๋ฐฉ๋ฒ•์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋น› ๋ฐ˜์‚ฌ๋กœ ์ธํ•ด ์˜์ƒ์˜ ๊ฐ ํ™”์†Œ ๊ฐ’์ด ํฌํ™”๋˜์–ด ์™„์ „ํžˆ ์†Œ์‹ค๋œ ์ง€์ ์˜ ์˜์ƒ์ •๋ณด๋ฅผ ๋น› ๋ฐ˜์‚ฌ๊ฐ€ ์—†๋Š” ์ฃผ๋ณ€ ์˜์—ญ์˜ ์˜์ƒ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ€์ƒ์˜ ์ •๋ณด๋ฅผ ๋งŒ๋“ค์–ด ๋‚ด์–ด ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ณ€ํ˜• ์—†์ด๋„ ๋ฌผ์ฒด๋ฅผ ์ง€์†์ ์œผ๋กœ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค.Observing an object on the surface of the water in the air requires not only techniques for identifying objects well, but also techniques for suppressing or eliminating disturbance signals such as reflections from the surface of the water. In this paper, we studied a method that helps to observe a object on the surface of the water in the air by suppressing the light reflection using the adaptive gamma correction filter. In addition, virtual information is generated based on the extra image data of the point where the pixel value is saturated due to the reflection of light and completely lost, so that the object can be continuously observed without modification of the tracking algorithm.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ชฉ์  1 1,2 ์ ˆ ์„ ํ–‰ ์—ฐ๊ตฌ 8 1.3 ์ ˆ ์—๋น„ ์—ฐ๊ตฌ 30 ์ œ 2 ์žฅ ๋ณธ๋ก  37 2.1 ๋น„ํ–‰์ฒด ๊ถค์  ๋ฐ ๋น› ๋ฐ˜์‚ฌ ๋ถ„์„ 37 2.1.1 ๋น„ํ–‰์ฒด ๊ถค์  ๋ถ„์„ 37 2.1.2 ๋น› ๋ฐ˜์‚ฌ ์–‘์ƒ ๋ถ„์„ 45 2.2 ์‹คํ—˜์šฉ ๋ฐ์ดํ„ฐ ์ทจ๋“ ๋ฐ ๋ณด์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ค๊ณ„ 47 2.2.1 ์‹คํ—˜์šฉ ๋ฐ์ดํ„ฐ ์ทจ๋“ 47 2.2.2 ๋ฐ์ดํ„ฐ ๋ถ„์„ 48 2.2.3 ๊ด€์ธก ๋ฐ์ดํ„ฐ ์˜์ƒ ์ „์ฒ˜๋ฆฌ 49 2.2.4 ์ ์‘ํ˜• ์ž„๊ณ„๊ฐ’ ๊ฐ๋งˆ ๋ณด์ • 51 2.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 54 2.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ตฌ๋™ 54 2.3.2 ๋ณ€์ˆ˜ ์˜ํ–ฅ ๋ถ„์„ 56 2.3.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ตœ์  ๋ณ€์ˆ˜ ์„ค์ • 58 ์ œ 3 ์žฅ ๊ฒฐ๋ก  77 ์ฐธ๊ณ ๋ฌธํ—Œ 78 Abstract 81Maste

    ๋ฐ˜์ž‘์šฉ ํœ  ๊ตฌ๋™๊ธฐ๋ฅผ ์ด์šฉํ•œ ์œ„์„ฑ ์ž์„ธ์ œ์–ด์‹œ์Šคํ…œ์˜ ๊ณ ์žฅํ—ˆ์šฉ ์ œ์–ด๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2011.2. ๊น€์œ ๋‹จ.Docto

    ์ง€์—ญ ๊ณต๋™์ฒด ๊ธฐ๋ฐ˜์˜ ์ฐจ๋Ÿ‰๊ฐ„ ์• ๋“œํ˜น ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 8. ์ตœ์–‘ํฌ.As wireless communication technologies have become commodities, it is expected that more and more vehicles will be equipped with wireless communication interfaces. The wireless communication module in vehicles will be used not only for in-vehicle communications, but also for (inter-)vehicular communications. We focus on the latter, which can form a vehicular ad hoc network. Vehicular ad hoc networks (VANETs) open an opportunity for new applications. For instance, the message dissemination of traffic congestion, accident notification across vehicles may be needed and helpful for safe and efficient driving. VANETs with some fixed roadside infrastructure (e.g. static WiFi access points) also allows the geography-based advertisement like local events, free parking spots, and so on. In this dissertation, we focus on routing schemes in VANETs, where i) vehicles move within a specific area, and ii) belong to a specific community. We call this type of networks community-based VANETs. The representative examples are buses and taxis belonging to local transportation service companies. In community-based VANETs, we first propose a single-copy forwarding scheme enhanced by exploiting mobility information, and then extend the scheme to a multicopy version. To do so, we maintains the mobility information in a distributed manner. Second, we present the analysis of the expected delivery delay for single-copy schemes, using a simple model: the expected time to absorption in a continuous-time Markov chain. Third, we propose a novel geographic routing protocol in high vehicle density environments. The proposed scheme models a road topology by a graph in which the intersections of roads and road segments between adjacent intersections correspond to the sets of vertices and edges, respectively. Then, routing is performed vertex by vertex at the macro level and node by node at the micro level.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Vehicular Ad Hoc Networks (VANETs) . . . . . . . . . . . . . . . 5 2.1.1 Overview of VANETs . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Community-Based VANETs . . . . . . . . . . . . . . . . . 7 2.2 Delay Tolerant Networks (DTNs) . . . . . . . . . . . . . . . . . . . 8 2.2.1 Overview of DTNs . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Carry-and-Forward Schemes . . . . . . . . . . . . . . . . . 9 III. Practical Routing in Community-Based VANETs . . . . . . . . . . . 13 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Maintaining Mobility Information . . . . . . . . . . . . . . . . . . 15 3.3.1 Direct Contact Information . . . . . . . . . . . . . . . . . . 16 3.3.2 Mobility Information Matrix . . . . . . . . . . . . . . . . . 16 3.3.3 Mobility Information Reduction . . . . . . . . . . . . . . . 18 3.4 Forwarding Decision . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4.2 Indirect Delivery . . . . . . . . . . . . . . . . . . . . . . . 21 3.4.3 Candidate Set . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 24 3.5.1 Environment Setup . . . . . . . . . . . . . . . . . . . . . . 24 3.5.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . 25 3.6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 IV. Optimal Forwarding in Community-Based VANETs: A Stochastic Control Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.3 Expected Delivery Delay . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 Optimal Forwarding Problem . . . . . . . . . . . . . . . . . . . . . 42 4.4.1 Markov Decision Process . . . . . . . . . . . . . . . . . . 42 4.4.2 MDP Formulation . . . . . . . . . . . . . . . . . . . . . . 43 4.4.3 Converting a Binary Rule into a Subset Rule . . . . . . . . 48 4.5 Greedy Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.5.1 Local Algorithm . . . . . . . . . . . . . . . . . . . . . . . 52 4.5.2 Global Algorithm . . . . . . . . . . . . . . . . . . . . . . . 58 4.5.3 Analysis of Time Complexity . . . . . . . . . . . . . . . . 62 4.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 63 4.6.1 Environment Setup . . . . . . . . . . . . . . . . . . . . . . 63 4.6.2 Numerical Results . . . . . . . . . . . . . . . . . . . . . . 64 4.7 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 iv 4.8 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . 68 V. Geographic Overlay Routing for Road-Based Vehicular Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.2 Preliminaries and Motivations . . . . . . . . . . . . . . . . . . . . 71 5.2.1 Greedy Forwarding and Routing Hole . . . . . . . . . . . . 71 5.2.2 Effect of Node Placement on Routing Holes . . . . . . . . . 72 5.3 Geographic Overlay Routing Framework . . . . . . . . . . . . . . . 75 5.3.1 Models and Assumptions . . . . . . . . . . . . . . . . . . . 75 5.3.2 GORF Overview . . . . . . . . . . . . . . . . . . . . . . . 76 5.3.3 Operations without Disconnectivity . . . . . . . . . . . . . 76 5.3.4 Operations with Disconnectivity . . . . . . . . . . . . . . . 80 5.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 86 5.4.1 Simulation Environment . . . . . . . . . . . . . . . . . . . 87 5.4.2 Performance Comparison in New York City . . . . . . . . . 89 5.4.3 Performance Comparison in Anderson County . . . . . . . 91 5.4.4 Performance Comparison between GORF Variants . . . . . 93 5.5 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 VI. Summary and Future Work . . . . . . . . . . . . . . . . . . . . . . . 100 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Docto
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