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    ๊ณต๊ฐ„๋น…๋ฐ์ดํ„ฐ์ฒด๊ณ„ ๊ตฌ์ถ•, ํ™œ์šฉ ์ •์ฑ…๋ฐฉํ–ฅ

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    ๋…ธํŠธ : [ํŠน์ง‘ | ๊ณต๊ฐ„๋น…๋ฐ์ดํ„ฐ์™€ ์ƒˆ๋กœ์šด ๊ตญํ† ๊ฐ€์น˜ ์ฐฝ์ถœ 1

    DNA metabarcoding ์„ ์ด์šฉํ•œ ์ˆ˜์ƒํ™˜๊ฒฝ์—์„œ์˜ ์ƒ๋ฌผ์ •๋ณดํ•™์  ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ƒ๋ช…๊ณผํ•™๋ถ€, 2020. 8. ๊น€์›.The development of next-generation sequencing technology has led to the advent of DNA metabarcoding that identify many organisms in the mixture or environmental samples at once. This approach enables the efficient acquisition of large amounts of biological data, and has the ability to evaluate the biodiversity and community structure of ecosystems. With the importance of DNA metabarcoding recognized, many research projects are already actively underway in other countries. However, compared with the research trends of DNA metabarcoding around the world, researches of DNA metabarcoding in Korea are more basic and limited in scope. This dissertation reports three case studies of the aquatic environments that were conducted using DNA metabarcoding to compensate for the drawbacks of domestic research trends in DNA metabarcoding. The final objective of this study is to apply DNA metabarcoding approach to various case studies in aquatic environments. Based on this, it is to understand and explain the biological phenomena of aquatic environments with metadata produced DNA metabarcoding. Each chapter of the dissertation was organized according to the case study. In Chapter 1, DNA metabarcoding was newly applied along with the traditional morphological identification to establish a method for zooplankton community survey in the Marine and Coastal National Park areas of Korea. By comparing the results of these two identification methods, the strengths and limitations of DNA metabarcoding were verified with the zooplankton communities appearing in these areas. The sensitive detection capability of DNA metabarcoding enabled the identification of potential bioindicator taxa associated with external factors in these national parks. I propose the use of metabarcoding for efficient surveys of mesozooplankton communities in the Marine and Coastal National Parks to establish monitoring of bioindicator taxa. It is also necessary to continuously search for taxa with high research value in these national parks using metabarcoding. Establishing an ongoing monitoring system that employs this approach can provide an effective tool for managing marine ecosystems in the Marine and Coastal National Parks. In Chapter 2, the association between family of crabs and feeding behavior on their intestinal microbiomes of Korean crabs was confirmed using DNA metabarcoding. With the metadata of the intestinal microbiome in the crabs, the controversial phylogenetic relationship between the superfamilies Ocypodoidea and Grapsoidea was newly interpreted. It was confirmed that the intestinal microbiome differed according to the family of crabs and specific microbial operational taxonomic units (OTUs) related to the evolution of Malacostraca were indentified. Intestinal microbial biodiversity and community were found to differ according to the feeding behavior. The function and role of intestinal microbiomes associated with the feeding behavior were predicted. These results were inferred to be related to the type of food available to hosts and its nutritional characteristics. In Chapter 3, as a case study, microeukaryotic biodiversity and community structures of car bonnet and pig carcass were investigated to determine the applicability of DNA metabarcoding in drowning case. Pig carcass was used to simulate the decomposing process of drowning bodies. As a control, car bonnet was used to confirm the general process of succession occurring in aquatic environments. Using DNA metabarcoding, I confirmed that the microeukaryotic biodiversity in pig carcass was relevantly lower than that in car bonnet. Also, some taxa were related to the decomposition. The relative abundances of these taxa varied with the decomposition period. It is expected that the change pattern of these taxa may be used as a good indicator for estimating the postmortem submersion interval (PMSI) of drowning cases. This dissertation includes manuscripts that were submitted to peer-reviewed journals during my Ph.D. course.NGS ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ, ํ˜ผํ•ฉ๋œ ์ƒ˜ํ”Œ์ด๋‚˜ ํ™˜๊ฒฝ์ƒ˜ํ”Œ์—์„œ ๋งŽ์€ ์ƒ๋ฌผ์„ ํ•œ๋ฒˆ์— ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” DNA metabarcoding ์ด ๋“ฑ์žฅํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ ๋Œ€๋Ÿ‰์˜ ์ƒ๋ฌผํ•™์  ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ํš๋“ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ƒํƒœ๊ณ„์˜ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ๊ณผ ๊ตฐ์ง‘๊ตฌ์กฐ๋ฅผ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค. DNA metabarcoding ์˜ ์ค‘์š”์„ฑ์„ ์ผ์ฐ์ด ์ธ์ง€ํ•˜๊ณ  ์ด๋ฏธ ๊ตญ์™ธ์˜ ๊ฒฝ์šฐ, ๋งŽ์€ ์—ฐ๊ตฌํ”„๋กœ์ ํŠธ๊ฐ€ ์ด๋ฏธ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ตญ์™ธ์˜ ์—ฐ๊ตฌ๋™ํ–ฅ๊ณผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ, ๊ตญ๋‚ด์˜ DNA meteabarcoding ์—ฐ๊ตฌ๋Š” ๊ธฐ์ดˆ์ ์ด๊ณ  ์—ฐ๊ตฌ๋ฒ”์œ„๊ฐ€ ์ œํ•œ์ ์ด๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ๊ตญ๋‚ด ์—ฐ๊ตฌ๋™ํ–ฅ์˜ ๋‹จ์ ๋“ค์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜์ƒํ™˜๊ฒฝ์—์„œ์˜ ์„ธ๊ฐ€์ง€ ์‚ฌ๋ก€์—ฐ๊ตฌ์— DNA metabarcoding ์„ ์ ์šฉํ•˜์˜€๋‹ค. ์ด ํ•™์œ„๋…ผ๋ฌธ์˜ ์ตœ์ข…๋ชฉํ‘œ๋Š” DNA metabarcoding ์„ ์ด์šฉํ•˜์—ฌ ์ƒ์‚ฐ๋œ DNA ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ๋กœ ์ˆ˜์ƒํ™˜๊ฒฝ์—์„œ์˜ ์ƒ๋ช…ํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๊ณ  ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์˜ ๊ฐ ์žฅ์€ ์‚ฌ๋ก€์—ฐ๊ตฌ ๋ณ„๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ œ 1 ์žฅ์—์„œ๋Š” ํ•œ๊ตญ์˜ ํ•ด์ƒโˆ™ํ•ด์•ˆ๊ตญ๋ฆฝ๊ณต์› ์ง€์—ญ์˜ ๋™๋ฌผ ํ”Œ๋ž‘ํฌํ†ค๊ตฐ์ง‘์˜ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์„ ํ™•๋ฆฝํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์กด์˜ ํ˜•ํƒœํ•™์  ์‹๋ณ„๊ณผ ํ•จ๊ป˜ DNA metabarcoding ์„ ์ƒˆ๋กญ๊ฒŒ ์ ์šฉํ•˜์˜€๋‹ค. ๊ณต์›์ง€์—ญ์—์„œ ์ถœํ˜„ํ•˜๋Š” ๋™๋ฌผํ”Œ๋ž‘ํฌํ†ค ๊ตฐ์ง‘์„ ๋Œ€์ƒ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ์‹๋ณ„๋ฐฉ๋ฒ•์˜ ๊ฒฐ๊ณผ๋“ค์„ ๋น„๊ตํ•˜์—ฌ DNA metabarcoding ์˜ ์žฅ, ๋‹จ์ ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, DNA metabarcoding ์˜ ๋ฏผ๊ฐํ•œ ํƒ์ง€๋Šฅ๋ ฅ์€ ๊ตญ๋ฆฝ๊ณต์›์—์„œ์˜ ์ˆ˜์˜จ, ์—ผ๋„, ์ง€ํ˜•, ์—ฝ๋ก์†Œ ๋†๋„์™€ ๊ฐ™์€ ์™ธ๋ถ€์š”์ธ๊ณผ ์—ฐ๊ด€๋œ ์ž ์žฌ์ ์ธ ์ƒ๋ฌผ์ง€ํ‘œ ๋ถ„๋ฅ˜๊ตฐ์„ ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๊ตญ์˜ ํ•ด์ƒโˆ™ํ•ด์•ˆ๊ตญ๋ฆฝ๊ณต์› ์ง€์—ญ์˜ ๋™๋ฌผ ํ”Œ๋ž‘ํฌํ†ค๊ตฐ์ง‘์„ ํšจ์œจ์ ์œผ๋กœ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” DNA metabarcoding ์„ ์‚ฌ์šฉํ•œ ์ž ์žฌ์ ์ธ ์ƒ๋ฌผ์ง€ํ‘œ ๋ถ„๋ฅ˜๊ตฐ์„ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ํ•  ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ DNA metabarcoding ์€ ์ด๋Ÿฌํ•œ ๊ตญ๋ฆฝ ๊ณต์› ์ง€์—ญ์—์„œ ์—ฐ๊ตฌ ๊ฐ€์น˜๊ฐ€ ๋†’์€ ๋ถ„๋ฅ˜๊ตฐ์„ ์ง€์†์ ์œผ๋กœ ํƒ์ƒ‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋„๊ตฌ๋กœ ์ด์šฉ ๋  ์ˆ˜ ์žˆ๋‹ค. DNA metabarcoding ์„ ์ด์šฉํ•œ ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ง€์†์ ์ธ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์˜ ๊ตฌ์ถ•์€ ํ•ด์ƒ โˆ™ ํ•ด์•ˆ ๊ตญ๋ฆฝ ๊ณต์›์˜ ํ•ด์–‘ ์ƒํƒœ๊ณ„ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ๋„๊ตฌ๋ฅผ ์ œ๊ณต ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ 2 ์žฅ์—์„œ๋Š” DNA metabarcoding ์„ ์ด์šฉํ•˜์—ฌ ์กฐ๊ฐ„๋Œ€์—์„œ ์„œ์‹ํ•˜๋Š” ๊ฒŒ ์žฅ๋‚ด๋ฏธ์ƒ๋ฌผ ๊ตฐ์ง‘๊ณผ ๊ฒŒ์˜ ๊ณผ, ๋จน์ด์Šต์„ฑ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ๊ฒŒ ์žฅ๋‚ด๋ฏธ์ƒ๋ฌผ์˜ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ธฐ์กด์˜ ๋…ผ๋ž€์ด ์žˆ์—ˆ๋˜ ๋ฐ”์œ„๊ฒŒ์ƒ๊ณผ์™€ ๋‹ฌ๋ž‘๊ฒŒ์ƒ๊ณผ๊ฐ„์˜ ๊ณ„ํ†ตํ•™์  ๊ด€๊ณ„๋ฅผ ์ƒˆ๋กญ๊ฒŒ ํ•ด์„ํ•˜์˜€๋‹ค. ๊ฒŒ์˜ ๊ณผ ์ˆ˜์ค€ ์— ๋”ฐ๋ผ ๊ฒŒ ์žฅ๋‚ด๋ฏธ์ƒ๋ฌผ์˜ ๊ตฐ์ง‘์ด ์„œ๋กœ ๋‹ค๋ฅธ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ๊ทธ ์ค‘ ์ผ๋ถ€ ๊ฒŒ์˜ ๊ณผ์—์„œ ์—ฐ๊ฐ‘๋ฅ˜์˜ ์ง„ํ™”์™€ ์—ฐ๊ด€๋œ ์žฅ๋‚ด๋ฏธ์ƒ๋ฌผ OTUs ๋ฅผ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋จน์ด์Šต์„ฑ์— ๋”ฐ๋ฅธ ๊ฒŒ ์žฅ๋‚ด๋ฏธ์ƒ๋ฌผ์˜ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ๊ณผ ๊ตฐ์ง‘์ด ์„œ๋กœ ๋‹ค๋ฆ„์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด์™€ ๊ด€๋ จ๋œ ์žฅ๋‚ด๋ฏธ์ƒ๋ฌผ์˜ ๊ธฐ๋Šฅ๊ณผ ์—ญํ• ์„ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๊ฒŒ์˜ ์„ญ์ทจํ•  ์ˆ˜ ์žˆ๋Š” ๋จน์ด์˜ ์œ ํ˜•๊ณผ ์˜์–‘์ ์ธ ํŠน์ง•๊ณผ ์—ฐ๊ด€์ด ์žˆ์Œ์ด ์œ ์ถ”๋˜์—ˆ๋‹ค. ์ œ 3 ์žฅ์—์„œ๋Š” ์‚ฌ๋ก€์—ฐ๊ตฌ๋กœ์จ, ์ต์‚ฌ์‚ฌ๊ฑด์—์„œ์˜ DNA metabarcoding ์˜ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜๊ณ ์ž DNA metabarcoding ์„ ์ด์šฉํ•˜์—ฌ ์ž๋™์ฐจ ๋ณด๋‹›๊ณผ ์ต์‚ฌํ•œ ๋ผ์ง€์˜ ๋ฏธ์†Œ์ง„ํ•ต์ƒ๋ฌผ์˜ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ๊ณผ ๊ตฐ์ง‘๊ตฌ์กฐ๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๋ผ์ง€ ์‚ฌ์ฒด๋Š” ์ต์‚ฌ์ฒด์˜ ๋ถ€ํŒจ๊ณผ์ •์„ ๊ฐ€์ •ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋Œ€์กฐ๊ตฐ์œผ๋กœ์จ, ์ž๋™์ฐจ ๋ณด๋‹›์€ ์ˆ˜์ƒํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ผ๋ฐ˜์ ์ธ ์ฒœ์ด๊ณผ์ •์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜์˜€๋‹ค. DNA metabarcoding ์„ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋ผ์ง€์‚ฌ์ฒด์˜ ๋ฏธ์†Œ์ง„ํ•ต์ƒ๋ฌผ์˜ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ์€ ์ž๋™์ฐจ ๋ณด๋‹›์˜ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ๋ณด๋‹ค ๋‚ฎ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ถ€ํŒจ์™€ ์—ฐ๊ด€์ด ์žˆ๋Š” ๋ถ„๋ฅ˜๊ตฐ๋“ค์ด ํŒŒ์•…๋˜์—ˆ์œผ๋ฉฐ, ๋ถ€ํŒจ์‹œ๊ธฐ์— ๋”ฐ๋ผ ์ƒ๋Œ€์ ์ธ ํ’๋ถ€๋„๊ฐ€ ๋ณ€ํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”ํŒจํ„ด์€ ์ต์‚ฌ์‚ฌ๊ฑด์˜ ์‚ฌํ›„์‹œ๊ฐ„์„ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•œ ์ข‹์€ ์ƒ๋ฌผ์ง€ํ‘œ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ ๋‚ด์šฉ์€ ํ•™์œ„ ๊ณผ์ • ์ค‘ ์ €๋„์— ํˆฌ๊ณ ํ•œ ์›๊ณ ๋ฅผ ํฌํ•จํ•˜์˜€๋‹ค.General introduction 3 Biodiversity and community structure of mesozooplankton in the Marine and Coastal National Park areas of Korea 11 1.1 Introduction 11 1.2 Materials and Methods 14 1.3 Results 21 1.4 Discussion 41 Association between host traits and the intestinal microbiome of Korean crabs 51 2.1 Introduction 51 2.2 Materials and Methods 54 2.3 Results 61 2.4 Discussion 86 Preliminary study on microeukaryotic community analysis using DNA metabaracoding to determine postmortem submersion interval (PMSI) in the drowned pig 93 3.1 Introduction 93 3.2 Materials and Methods 96 3.3 Results 100 3.4 Discussion 112 Conclusions 123 References 125 Abstract in Korean 157 Appendix 163Docto

    Characterization of emulsion-filled chitosan-pectin hydrogel prepared by cold-set gelation

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€, 2021.8. ์žฅํŒ์‹.์ˆ˜์ค‘์œ ์ ํ˜• ์—๋ฉ€์ ผ์€ ์นœ์œ ์„ฑ ์ƒ๋ฆฌํ™œ์„ฑ ๋ฌผ์งˆ์˜ ์šด๋ฐ˜์ฒด๋กœ์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด์˜ค๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์—๋ฉ€์ ผ์€ ์†Œํ™”๊ณผ์ • ์ค‘ ๋ถˆ์•ˆ์ •ํ™”๊ฐ€ ๋ฐœ์ƒํ•˜๊ณ  ์ด๋Š” ์ƒ๋ฆฌํ™œ์„ฑ ๋ฌผ์งˆ์˜ ์ƒ์ฒด์ด์šฉ๋ฅ ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋œ๋‹ค. ์ตœ๊ทผ ์šด๋ฐ˜ ์‹œ์Šคํ…œ์„ ํ™œ์šฉํ•˜์—ฌ ์œ„์žฅ๊ด€ ๋‚ด ์—์„œ ์—๋ฉ€์ ผ์˜ ์•ˆ์ •์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ์ƒ๋ฆฌํ™œ์„ฑ๋ฌผ์งˆ์˜ ์ƒ์ฒด์ด์šฉ๋ฅ ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฐ์„ฑ์กฐ๊ฑด์—์„œ ํ™”ํ•™์  ๋ฌผ์งˆ์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  cold-set ๊ฒ”ํ™”๋ฅผ ์ด์šฉํ•œ ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์„ ์ œ์กฐํ•˜์˜€๋‹ค. ํ‚คํ† ์‚ฐ๊ณผ ํŽ™ํ‹ด์„ ํ•˜์ด๋“œ๋กœ๊ฒ” ๊ตฌ์กฐ์ฒด๋กœ์„œ ์‚ฌ์šฉํ•˜๊ณ  ์นด์ œ์ธ ๋‚˜ํŠธ๋ฅจ์„ ์œ ํ™”์ œ๋กœ์„œ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ ํƒ„์„ฑ ์ธก์ •์„ ํ†ตํ•ด ํŽ™ํ‹ด ๋†๋„๊ฐ€ 0.75-1.50% (w/v) ๋ฒ”์œ„์—์„œ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์ €์žฅํƒ„์„ฑ๋ฅ ์ด ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋ฏธ์„ธ๊ตฌ์กฐ ๊ด€์ฐฐ๊ฒฐ๊ณผ, ํŽ™ํ‹ด ๋†๋„ ์ฆ๊ฐ€์— ๋”ฐ๋ผ ํ•˜์ด๋“œ๋กœ๊ฒ” ๋‚ด๋ถ€ ๊ธฐ๊ณต์˜ ํฌ๊ธฐ๊ฐ€ ๊ฐ์†Œํ•˜๊ณ  ๋” ์กฐ๋ฐ€ํ•œ ํ•˜์ด๋“œ๋กœ๊ฒ” ๊ตฌ์กฐ๊ฐ€ ์ƒ์„ฑ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. FT-IR ๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์„ ๊ตฌ์„ฑํ•˜๋Š” ํ‚คํ† ์‚ฐ๊ณผ ํŽ™ํ‹ด๊ฐ„์˜ ๊ฒฐํ•ฉ์„ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ํ‚คํ† ์‚ฐ๊ณผ ํŽ™ํ‹ด๊ฐ„์˜ ์ˆ˜์†Œ๊ฒฐํ•ฉ๊ณผ ์ •์ „๊ธฐ์ ๊ฒฐํ•ฉ์€ ๊ฐ๊ฐ 3,326 cm-1 ๊ณผ 1,500-1,600 cm-1 ์— ์กด์žฌํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ•˜์ด๋“œ๋กœ๊ฒ” ๋‚ด๋ถ€์— ์—๋ฉ€์ ผ์˜ ์กด์žฌ ์œ ๋ฌด๋Š” ํ•˜์ด๋“œ๋กœ๊ฒ”์„ ๊ตฌ์„ฑํ•˜๋Š” ๊ณ ๋ถ„์ž๊ฐ„์˜ ๊ฒฐํ•ฉ์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š์•˜๋‹ค. ํ•˜์ด๋“œ๋กœ๊ฒ” ๊ตฌ์กฐ์ฒด๋กœ๋ถ€ํ„ฐ ์—๋ฉ€์ ผ์€ pH 2.0์—์„œ ๋ฐฉ์ถœ๋˜์ง€ ์•Š์€ ๋ฐ˜๋ฉด pH 7.4์—์„œ๋Š” ํ•˜์ด๋“œ๋กœ๊ฒ”์ด ๋ถ•๊ดด๋˜๋ฉด์„œ ์—๋ฉ€์ ผ์ด ๋ฐฉ์ถœ๋˜๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋˜ํ•œ ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์˜ ๋ฐฉ์ถœํŠน์„ฑ์€ ํŽ™ํ‹ด๋†๋„์— ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์†Œํ™”๋ชจ๋ธ ๋‚ด์—์„œ ์—๋ฉ€์ ผ ๋ฐ ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”๋กœ๋ถ€ํ„ฐ ์ƒ์„ฑ๋˜๋Š” ์ž์œ ์ง€๋ฐฉ์‚ฐ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์—์„œ ๋ฐฉ์ถœ๋œ ์ž์œ ์ง€๋ฐฉ์‚ฐ์€ 0.75, 1.00, 1.25 ๊ทธ๋ฆฌ๊ณ  1.50% ํŽ™ํ‹ด๋†๋„์—์„œ ๊ฐ๊ฐ 58.67, 55.88, 48.87 ๋ฐ 43.76% ์˜€๋‹ค. ์—๋ฉ€์ ผ๊ณผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ, 0.75% ํŽ™ํ‹ด์„ ํ•จ์œ ํ•˜๋Š” ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์—์„œ์˜ ๋ฐฉ์ถœ๋œ ์ž์œ ์ง€๋ฐฉ์‚ฐ์˜ ์–‘์ด ์—๋ฉ€์ ผ๋ณด๋‹ค ๋” ๋†’์•˜๋‹ค. ๊ณต์ดˆ์  ๋ ˆ์ด์ € ์ฃผ์‚ฌํ˜„๋ฏธ๊ฒฝ์„ ์ด์šฉํ•˜์—ฌ ์†Œํ™”๋ชจ๋ธ ๋‚ด ๊ตฌ๊ฐ• ๋‹จ๊ณ„ ๋ฐ ์œ„์žฅ ๋‹จ๊ณ„์—์„œ ์—๋ฉ€์ ผ์ด ํ•˜์ด๋“œ๋กœ๊ฒ” ๊ตฌ์กฐ์ฒด์— ์˜ํ•ด ๋ถˆ์•ˆ์ •ํ•ด ์ง€์ง€ ์•Š์€ ๊ฒƒ์„ ๊ด€์ฐฐ ํ•˜์˜€๋‹ค. ์ปคํ๋ฏผ์„ ์ง€์šฉ์„ฑ ์ƒ๋ฆฌํ™œ์„ฑ๋ฌผ์งˆ์˜ ์ง€ํ‘œ๋ฌผ์งˆ๋กœ์„œ ์‚ฌ์šฉํ•˜์—ฌ ์†Œ์žฅ์†Œํ™” ํ›„ bioaccessibility๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์— ํฌ์ง‘๋œ ์ปคํ๋ฏผ์˜ bioaccessibility๋Š” ํŽ™ํ‹ด ๋†๋„๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ฐ์†Œํ•˜์˜€๊ณ  0.75% ํŽ™ํ‹ด ๋†๋„๋ฅผ ํ•จ์œ ํ•œ ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์—์„œ ์—๋ฉ€์ ผ์— ํ•จ์ž…๋˜์—ˆ์„ ๋•Œ ๋ณด๋‹ค 1.38๋ฐฐ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์—๋ฉ€์ ผ ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์€ ์œ„์žฅ๊ด€ ๋‚ด์—์„œ ์—๋ฉ€์ ผ์˜ ์•ˆ์ •์„ฑ์„ ๊ฐœ์„ ํ•จ์œผ๋กœ์จ ์ƒ๋ฆฌํ™œ์„ฑ๋ฌผ์งˆ์˜ bioaccessibility๋ฅผ ํ–ฅ์ƒ์‹œํ‚จ ๊ฒƒ์„ ์ž…์ฆํ–ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ์—๋ฉ€์ ผ-ํ•จ์ž… ํ•˜์ด๋“œ๋กœ๊ฒ”์€ ์‹ํ’ˆ์‚ฐ์—…์—์„œ ์นœ์œ ์„ฑ ์ƒ๋ฆฌํ™œ์„ฑ๋ฌผ์งˆ๋“ค ์œ„ํ•œ ์šด๋ฐ˜ ์‹œ์Šคํ…œ์œผ๋กœ์„œ ํ™œ์šฉ๋  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Oil-in-water (O/W) emulsions are widely utilized as a carrier for a lipophilic bioactive compound. However, destabilization of emulsion occurs during digestion, affecting on bioaccessibility of the lipophilic compound. Recently, delivery systems have been developed to improve the stability of emulsion within gastrointestinal (GI) tract and enhance the bioaccessibility of bioactive compounds. In this study, emulsion-filled hydrogel (EFH) was prepared through cold-set gelation under acidic conditions without any chemical agents. Chitosan and pectin were used as a hydrogel matrix and sodium caseinate as an emulsifier. The oscillation frequency sweep test showed that the elasticity (G') increased as pectin concentration increased (0.75-1.50%, w/v). Moreover, all EFH exhibited weak gel properties. Scanning electron microscope was used to observe the changes in the structure of EFH with different pectin concentrations. The microstructure of EFH showed that pore size decreased as the pectin concentration increased, indicating that the hydrogel networks of EFH became more compact as pectin concentration increased. FT-IR analysis was used to verify the interaction between chitosan and pectin in EFH. The hydrogen bond and electrostatic interaction between chitosan and pectin are assigned to 3,326 cm-1 and 1,500-1,600 cm-1, respectively. There was no significant difference depending on the presence or absence of the emulsion in the range from 1,500 to 1,800 cm-1, suggesting that the addition of emulsion did not affect the interaction between the polymers, which constitutes the hydrogel matrix. Release profile of emulsion indicated that the hydrogel matrix prevented the release of emulsion at pH 2.0. On the other hand, the emulsion was released at pH 7.4. Moreover, release rate and release amount of emulsion were influenced by the pectin concentration. The release of emulsion reduced as pectin concentration increased. During in vitro digestion, the free fatty acid release in EFH was 58.67, 55.88, 48.87, and 43.76% at pectin concentrations of 0.75, 1.00, 1.25, and 1.50%, respectively. The amount of free fatty acids was decreased as the pectin concentration increased. Compared with emulsion, the free fatty acid release in EFH with 0.75% pectin was significantly higher than that of emulsion (p < 0.05). Confocal laser scanning microscopy images showed that the hydrogel matrix protected the emulsion from destabilization within the oral and gastric stages. The bioaccessibility of curcumin, a model lipophilic compound, decreased as the pectin concentration increased, and the bioaccessibility in EFH with 0.75% pectin (23.95 ยฑ 0.8%) was 1.38 fold higher than that in emulsion (17.25 ยฑ 2.1%). Therefore, this study demonstrated that EFH improves emulsion stability in GI tract and enhances the bioaccessibility of lipophilic compound. Therefore, EFH is a potential carrier system for lipophilic bioactive compounds in the food industry.1. Introduction 1 2. Materials and Methods 4 2.1. Materials 4 2.2. Preparation of emulsion-filled hydrogel 4 2.2.1. Emulsion 4 2.2.2. Emulsion-filled hydrogel 5 2.3. Rheological properties of emulsion-filled hydrogel 5 2.3.1. Oscillation frequency sweep test 5 2.3.2. Texture profile analysis 6 2.4. Scanning electron microscopy 7 2.5. Fourier-transform infrared spectroscopy 7 2.6. Release profile of emulsion from hydrogel 8 2.7. Confocal laser scanning microscopy 8 2.8. In vitro digestion model 9 2.9. Bioaccessibility determination 11 2.10. Statistical analysis 12 3. Results and Discussion 13 3.1. Fabrication of emulsion-filled hydrogel 13 3.2. Rheological properties of emulsion-filled hydrogel 18 3.3. Scanning electron microscopy 25 3.4. FT-IR analysis 27 3.5. Release properties of emulsion from hydrogel matrix 30 3.6. Free fatty acid release from emulsion-filled hydrogel 35 3.7. Bioaccessibility of curcumin 40 4. Conclusion 43 5. Reference 44 ๊ตญ๋ฌธ์ดˆ๋ก 55์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์ดํ˜์žฌ.Approximate computing reduces the cost (energy and/or latency) of computations by relaxing the correctness (i.e., precision) of computations up to the level, which is dependent on types of applications. Moreover, it can be realized in various hierarchies of computing system design from circuit level to application level. This dissertation presents the methodologies applying approximate computing across such hierarchies; compensating aging-induced delay in logic circuit by dynamic computation approximation (Chapter 1), designing energy-efficient neural network by combining low-power and low-latency approximate neuron models (Chapter 2), and co-designing in-memory gradient descent module with neural processing unit so as to address a memory bottleneck incurred by memory I/O for high-precision data (Chapter 3). The first chapter of this dissertation presents a novel design methodology to turn the timing violation caused by aging into computation approximation error without the reliability guardband or increasing the supply voltage. It can be realized by accurately monitoring the critical path delay at run-time. The proposal is evaluated at two levels: RTL component level and system level. The experimental results at the RTL component level show a significant improvement in terms of (normalized) mean squared error caused by the timing violation and, at the system level, show that the proposed approach successfully transforms the aging-induced timing violation errors into much less harmful computation approximation errors, therefore it recovers image quality up to perceptually acceptable levels. It reduces the dynamic and static power consumption by 21.45% and 10.78%, respectively, with 0.8% area overhead compared to the conventional approach. The second chapter of this dissertation presents an energy-efficient neural network consisting of alternative neuron models; Stochastic-Computing (SC) and Spiking (SP) neuron models. SC has been adopted in various fields to improve the power efficiency of systems by performing arithmetic computations stochastically, which approximates binary computation in conventional computing systems. Moreover, a recent work showed that deep neural network (DNN) can be implemented in the manner of stochastic computing and it greatly reduces power consumption. However, Stochastic DNN (SC-DNN) suffers from problem of high latency as it processes only a bit per cycle. To address such problem, it is proposed to adopt Spiking DNN (SP-DNN) as an input interface for SC-DNN since SP effectively processes more bits per cycle than SC-DNN. Moreover, this chapter resolves the encoding mismatch problem, between two different neuron models, without hardware cost by compensating the encoding mismatch with synapse weight calibration. A resultant hybrid DNN (SPSC-DNN) consists of SP-DNN as bottom layers and SC-DNN as top layers. Exploiting the reduced latency from SP-DNN and low-power consumption from SC-DNN, the proposed SPSC-DNN achieves improved energy-efficiency with lower error-rate compared to SC-DNN and SP-DNN in same network configuration. The third chapter of this dissertation proposes GradPim architecture, which accelerates the parameter updates by in-memory processing which is codesigned with 8-bit floating-point training in Neural Processing Unit (NPU) for deep neural networks. By keeping the high precision processing algorithms in memory, such as the parameter update incorporating high-precision weights in its computation, the GradPim architecture can achieve high computational efficiency using 8-bit floating point in NPU and also gain power efficiency by eliminating massive high-precision data transfers between NPU and off-chip memory. A simple extension of DDR4 SDRAM utilizing bank-group parallelism makes the operation designs in processing-in-memory (PIM) module efficient in terms of hardware cost and performance. The experimental results show that the proposed architecture can improve the performance of the parameter update phase in the training by up to 40% and greatly reduce the memory bandwidth requirement while posing only a minimal amount of overhead to the protocol and the DRAM area.๊ทผ์‚ฌ ์ปดํ“จํŒ…์€ ์—ฐ์‚ฐ์˜ ์ •ํ™•๋„์˜ ์†์‹ค์„ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ๋ณ„ ์ ์ ˆํ•œ ์ˆ˜์ค€๊นŒ์ง€ ํ—ˆ์šฉํ•จ์œผ๋กœ์จ ์—ฐ์‚ฐ์— ํ•„์š”ํ•œ ๋น„์šฉ (์—๋„ˆ์ง€๋‚˜ ์ง€์—ฐ์‹œ๊ฐ„)์„ ์ค„์ธ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€, ๊ทผ์‚ฌ ์ปดํ“จํŒ…์€ ์ปดํ“จํŒ… ์‹œ์Šคํ…œ ์„ค๊ณ„์˜ ํšŒ๋กœ ๊ณ„์ธต๋ถ€ํ„ฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ๊ณ„์ธต๊นŒ์ง€ ๋‹ค์–‘ํ•œ ๊ณ„์ธต์— ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ทผ์‚ฌ ์ปดํ“จํŒ… ๋ฐฉ๋ฒ•๋ก ์„ ๋‹ค์–‘ํ•œ ์‹œ์Šคํ…œ ์„ค๊ณ„์˜ ๊ณ„์ธต์— ์ ์šฉํ•˜์—ฌ ์ „๋ ฅ๊ณผ ์—๋„ˆ์ง€ ์ธก๋ฉด์—์„œ ์ด๋“์„ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋Š”, ์—ฐ์‚ฐ ๊ทผ์‚ฌํ™” (computation Approximation)๋ฅผ ํ†ตํ•ด ํšŒ๋กœ์˜ ๋…ธํ™”๋กœ ์ธํ•ด ์ฆ๊ฐ€๋œ ์ง€์—ฐ์‹œ๊ฐ„์„ ์ถ”๊ฐ€์ ์ธ ์ „๋ ฅ์†Œ๋ชจ ์—†์ด ๋ณด์ƒํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ (์ฑ•ํ„ฐ 1), ๊ทผ์‚ฌ ๋‰ด๋Ÿฐ๋ชจ๋ธ (approximate neuron model)์„ ์ด์šฉํ•ด ์—๋„ˆ์ง€ ํšจ์œจ์ด ๋†’์€ ์‹ ๊ฒฝ๋ง์„ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ• (์ฑ•ํ„ฐ 2), ๊ทธ๋ฆฌ๊ณ  ๋ฉ”๋ชจ๋ฆฌ ๋Œ€์—ญํญ์œผ๋กœ ์ธํ•œ ๋ณ‘๋ชฉํ˜„์ƒ ๋ฌธ์ œ๋ฅผ ๋†’์€ ์ •ํ™•๋„ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ์—ฐ์‚ฐ์„ ๋ฉ”๋ชจ๋ฆฌ ๋‚ด์—์„œ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ์™„ํ™”์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ (์ฑ•ํ„ฐ3) ์ œ์•ˆํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ฑ•ํ„ฐ๋Š” ํšŒ๋กœ์˜ ๋…ธํ™”๋กœ ์ธํ•œ ์ง€์—ฐ์‹œ๊ฐ„์œ„๋ฐ˜์„ (timing violation) ์„ค๊ณ„๋งˆ์ง„์ด๋‚˜ (reliability guardband) ๊ณต๊ธ‰์ „๋ ฅ์˜ ์ฆ๊ฐ€ ์—†์ด ์—ฐ์‚ฐ์˜ค์ฐจ (computation approximation error)๋ฅผ ํ†ตํ•ด ๋ณด์ƒํ•˜๋Š” ์„ค๊ณ„๋ฐฉ๋ฒ•๋ก  (design methodology)๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ฃผ์š”๊ฒฝ๋กœ์˜ (critical path) ์ง€์—ฐ์‹œ๊ฐ„์„ ๋™์ž‘์‹œ๊ฐ„์— ์ •ํ™•ํ•˜๊ฒŒ ์ธก์ •ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์—ฌ๊ธฐ์„œ ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์€ RTL component์™€ system ๋‹จ๊ณ„์—์„œ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. RTL component ๋‹จ๊ณ„์˜ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•œ ๋ฐฉ์‹์ด ํ‘œ์ค€ํ™”๋œ ํ‰๊ท ์ œ๊ณฑ์˜ค์ฐจ๋ฅผ (normalized mean squared error) ์ƒ๋‹นํžˆ ์ค„์˜€์Œ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  system ๋‹จ๊ณ„์—์„œ๋Š” ์ด๋ฏธ์ง€์ฒ˜๋ฆฌ ์‹œ์Šคํ…œ์—์„œ ์ด๋ฏธ์ง€์˜ ํ’ˆ์งˆ์ด ์ธ์ง€์ ์œผ๋กœ ์ถฉ๋ถ„ํžˆ ํšŒ๋ณต๋˜๋Š” ๊ฒƒ์„ ๋ณด์ž„์œผ๋กœ์จ ํšŒ๋กœ๋…ธํ™”๋กœ ์ธํ•ด ๋ฐœ์ƒํ•œ ์ง€์—ฐ์‹œ๊ฐ„์œ„๋ฐ˜ ์˜ค์ฐจ๊ฐ€ ์—๋Ÿฌ์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ์—ฐ์‚ฐ์˜ค์ฐจ๋กœ ๋ณ€๊ฒฝ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋ก ์„ ๋”ฐ๋ž์„ ๋•Œ 0.8%์˜ ๊ณต๊ฐ„์„ (area) ๋” ์‚ฌ์šฉํ•˜๋Š” ๋น„์šฉ์„ ์ง€๋ถˆํ•˜๊ณ  21.45%์˜ ๋™์ ์ „๋ ฅ์†Œ๋ชจ์™€ (dynamic power consumption) 10.78%์˜ ์ •์ ์ „๋ ฅ์†Œ๋ชจ์˜ (static power consumption) ๊ฐ์†Œ๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ฑ•ํ„ฐ๋Š” ๊ทผ์‚ฌ ๋‰ด๋Ÿฐ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜๋Š” ๊ณ -์—๋„ˆ์ง€ํšจ์œจ์˜ ์‹ ๊ฒฝ๋ง์„ (neural network) ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์‚ฌ์šฉํ•œ ๋‘ ๊ฐ€์ง€์˜ ๊ทผ์‚ฌ ๋‰ด๋Ÿฐ๋ชจ๋ธ์€ ํ™•๋ฅ ์ปดํ“จํŒ…๊ณผ (stochastic computing) ์ŠคํŒŒ์ดํ‚น๋‰ด๋Ÿฐ (spiking neuron) ์ด๋ก ๋“ค์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ชจ๋ธ๋ง๋˜์—ˆ๋‹ค. ํ™•๋ฅ ์ปดํ“จํŒ…์€ ์‚ฐ์ˆ ์—ฐ์‚ฐ๋“ค์„ ํ™•๋ฅ ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ์ด์ง„์—ฐ์‚ฐ์„ ๋‚ฎ์€ ์ „๋ ฅ์†Œ๋ชจ๋กœ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ตœ๊ทผ์— ํ™•๋ฅ ์ปดํ“จํŒ… ๋‰ด๋Ÿฐ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง (deep neural network)๋ฅผ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํ™•๋ฅ ์ปดํ“จํŒ…์„ ๋‰ด๋Ÿฐ๋ชจ๋ธ๋ง์— ํ™œ์šฉํ•  ๊ฒฝ์šฐ ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์ด ๋งค ํด๋ฝ์‚ฌ์ดํด๋งˆ๋‹ค (clock cycle) ํ•˜๋‚˜์˜ ๋น„ํŠธ๋งŒ์„ (bit) ์ฒ˜๋ฆฌํ•˜๋ฏ€๋กœ, ์ง€์—ฐ์‹œ๊ฐ„ ์ธก๋ฉด์—์„œ ๋งค์šฐ ๋‚˜์  ์ˆ˜ ๋ฐ–์— ์—†๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ŠคํŒŒ์ดํ‚น ๋‰ด๋Ÿฐ๋ชจ๋ธ๋กœ ๊ตฌ์„ฑ๋œ ์ŠคํŒŒ์ดํ‚น ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ํ™•๋ฅ ์ปดํ“จํŒ…์„ ํ™œ์šฉํ•œ ์‹ฌ์ธต์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ์™€ ๊ฒฐํ•ฉํ•˜์˜€๋‹ค. ์ŠคํŒŒ์ดํ‚น ๋‰ด๋Ÿฐ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ ๋งค ํด๋ฝ์‚ฌ์ดํด๋งˆ๋‹ค ์—ฌ๋Ÿฌ ๋น„ํŠธ๋ฅผ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์˜ ์ž…๋ ฅ ์ธํ„ฐํŽ˜์ด์Šค๋กœ ์‚ฌ์šฉ๋  ๊ฒฝ์šฐ ์ง€์—ฐ์‹œ๊ฐ„์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ํ™•๋ฅ ์ปดํ“จํŒ… ๋‰ด๋Ÿฐ๋ชจ๋ธ๊ณผ ์ŠคํŒŒ์ดํ‚น ๋‰ด๋Ÿฐ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ ๋ถ€ํ˜ธํ™” (encoding) ๋ฐฉ์‹์ด ๋‹ค๋ฅธ ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•ด๋‹น ๋ถ€ํ˜ธํ™” ๋ถˆ์ผ์น˜ ๋ฌธ์ œ๋ฅผ ๋ชจ๋ธ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํ•™์Šตํ•  ๋•Œ ๊ณ ๋ คํ•จ์œผ๋กœ์จ, ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์˜ ๊ฐ’์ด ๋ถ€ํ˜ธํ™” ๋ถˆ์ผ์น˜๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์กฐ์ ˆ (calibration) ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„์˜ ๊ฒฐ๊ณผ๋กœ, ์•ž ์ชฝ์—๋Š” ์ŠคํŒŒ์ดํ‚น ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ๋ฐฐ์น˜ํ•˜๊ณ  ๋’ท ์ชฝ์• ๋Š” ํ™•๋ฅ ์ปดํ“จํŒ… ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ๋ฐฐ์น˜ํ•˜๋Š” ํ˜ผ์„ฑ์‹ ๊ฒฝ๋ง์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ํ˜ผ์„ฑ์‹ ๊ฒฝ๋ง์€ ์ŠคํŒŒ์ดํ‚น ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ํ†ตํ•ด ๋งค ํด๋ฝ์‚ฌ์ดํด๋งˆ๋‹ค ์ฒ˜๋ฆฌ๋˜๋Š” ๋น„ํŠธ ์–‘์˜ ์ฆ๊ฐ€๋กœ ์ธํ•œ ์ง€์—ฐ์‹œ๊ฐ„ ๊ฐ์†Œ ํšจ๊ณผ์™€ ํ™•๋ฅ ์ปดํ“จํŒ… ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์˜ ์ €์ „๋ ฅ ์†Œ๋ชจ ํŠน์„ฑ์„ ๋ชจ๋‘ ํ™œ์šฉํ•จ์œผ๋กœ์จ ๊ฐ ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ๋”ฐ๋กœ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฝ์šฐ ๋Œ€๋น„ ์šฐ์ˆ˜ํ•œ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ๋” ๋‚˜์€ ์ •ํ™•๋„ ๊ฒฐ๊ณผ๋ฅผ ๋‚ด๋ฉด์„œ ๋‹ฌ์„ฑํ•œ๋‹ค. ์„ธ ๋ฒˆ์งธ ์ฑ•ํ„ฐ๋Š” ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ 8๋น„ํŠธ ๋ถ€๋™์†Œ์ˆซ์  ์—ฐ์‚ฐ์œผ๋กœ ํ•™์Šตํ•˜๋Š” ์‹ ๊ฒฝ๋ง์ฒ˜๋ฆฌ์œ ๋‹›์˜ (neural processing unit) ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐฑ์‹ ์„ (parameter update) ๋ฉ”๋ชจ๋ฆฌ-๋‚ด-์—ฐ์‚ฐ์œผ๋กœ (in-memory processing) ๊ฐ€์†ํ•˜๋Š” GradPIM ์•„ํ‚คํ…์ณ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. GradPIM์€ 8๋น„ํŠธ์˜ ๋‚ฎ์€ ์ •ํ™•๋„ ์—ฐ์‚ฐ์€ ์‹ ๊ฒฝ๋ง์ฒ˜๋ฆฌ์œ ๋‹›์— ๋‚จ๊ธฐ๊ณ , ๋†’์€ ์ •ํ™•๋„๋ฅผ ๊ฐ€์ง€๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜๋Š” ์—ฐ์‚ฐ์€ (ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐฑ์‹ ) ๋ฉ”๋ชจ๋ฆฌ ๋‚ด๋ถ€์— ๋‘ ์œผ๋กœ์จ ์‹ ๊ฒฝ๋ง์ฒ˜๋ฆฌ์œ ๋‹›๊ณผ ๋ฉ”๋ชจ๋ฆฌ๊ฐ„์˜ ๋ฐ์ดํ„ฐํ†ต์‹ ์˜ ์–‘์„ ์ค„์—ฌ, ๋†’์€ ์—ฐ์‚ฐํšจ์œจ๊ณผ ์ „๋ ฅํšจ์œจ์„ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค. ๋˜ํ•œ, GradPIM์€ bank-group ์ˆ˜์ค€์˜ ๋ณ‘๋ ฌํ™”๋ฅผ ์ด๋ฃจ์–ด ๋‚ด ๋†’์€ ๋‚ด๋ถ€ ๋Œ€์—ญํญ์„ ํ™œ์šฉํ•จ์œผ๋กœ์จ ๋ฉ”๋ชจ๋ฆฌ ๋Œ€์—ญํญ์„ ํฌ๊ฒŒ ํ™•์žฅ์‹œํ‚ฌ ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ๋ฉ”๋ชจ๋ฆฌ ๊ตฌ์กฐ์˜ ๋ณ€๊ฒฝ์ด ์ตœ์†Œํ™”๋˜์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ์ถ”๊ฐ€์ ์ธ ํ•˜๋“œ์›จ์–ด ๋น„์šฉ๋„ ์ตœ์†Œํ™”๋˜์—ˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด GradPIM์ด ์ตœ์†Œํ•œ์˜ DRAM ํ”„๋กœํ† ์ฝœ ๋ณ€ํ™”์™€ DRAM์นฉ ๋‚ด์˜ ๊ณต๊ฐ„์‚ฌ์šฉ์„ ํ†ตํ•ด ์‹ฌ์ธต์‹ ๊ฒฝ๋ง ํ•™์Šต๊ณผ์ • ์ค‘ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐฑ์‹ ์— ํ•„์š”ํ•œ ์‹œ๊ฐ„์„ 40%๋งŒํผ ํ–ฅ์ƒ์‹œ์ผฐ์Œ์„ ๋ณด์˜€๋‹ค.Chapter I: Dynamic Computation Approximation for Aging Compensation 1 1.1 Introduction 1 1.1.1 Chip Reliability 1 1.1.2 Reliability Guardband 2 1.1.3 Approximate Computing in Logic Circuits 2 1.1.4 Computation approximation for Aging Compensation 3 1.1.5 Motivational Case Study 4 1.2 Previous Work 5 1.2.1 Aging-induced Delay 5 1.2.2 Delay-Configurable Circuits 6 1.3 Proposed System 8 1.3.1 Overview of the Proposed System 8 1.3.2 Proposed Adder 9 1.3.3 Proposed Multiplier 11 1.3.4 Proposed Monitoring Circuit 16 1.3.5 Aging Compensation Scheme 19 1.4 Design Methodology 20 1.5 Evaluation 24 1.5.1 Experimental setup 24 1.5.2 RTL component level Adder/Multiplier 27 1.5.3 RTL component level Monitoring circuit 30 1.5.4 System level 31 1.6 Summary 38 Chapter II: Energy-Efficient Neural Network by Combining Approximate Neuron Models 40 2.1 Introduction 40 2.1.1 Deep Neural Network (DNN) 40 2.1.2 Low-power designs for DNN 41 2.1.3 Stochastic-Computing Deep Neural Network 41 2.1.4 Spiking Deep Neural Network 43 2.2 Hybrid of Stochastic and Spiking DNNs 44 2.2.1 Stochastic-Computing vs Spiking Deep Neural Network 44 2.2.2 Combining Spiking Layers and Stochastic Layers 46 2.2.3 Encoding Mismatch 47 2.3 Evaluation 49 2.3.1 Latency and Test Error 49 2.3.2 Energy Efficiency 51 2.4 Summary 54 Chapter III: GradPIM: In-memory Gradient Descent in Mixed-Precision DNN Training 55 3.1 Introduction 55 3.1.1 Neural Processing Unit 55 3.1.2 Mixed-precision Training 56 3.1.3 Mixed-precision Training with In-memory Gradient Descent 57 3.1.4 DNN Parameter Update Algorithms 59 3.1.5 Modern DRAM Architecture 61 3.1.6 Motivation 63 3.2 Previous Work 65 3.2.1 Processing-In-Memory 65 3.2.2 Co-design Neural Processing Unit and Processing-In-Memory 66 3.2.3 Low-precision Computation in NPU 67 3.3 GradPIM 68 3.3.1 GradPIM Architecture 68 3.3.2 GradPIM Operations 69 3.3.3 Timing Considerations 70 3.3.4 Update Phase Procedure 73 3.3.5 Commanding GradPIM 75 3.4 NPU Co-design with GradPIM 76 3.4.1 NPU Architecture 76 3.4.2 Data Placement 79 3.5 Evaluation 82 3.5.1 Evaluation Methodology 82 3.5.2 Experimental Results 83 3.5.3 Sensitivity Analysis 88 3.5.4 Layer Characterizations 90 3.5.5 Distributed Data Parallelism 90 3.6 Summary 92 3.6.1 Discussion 92 Bibliography 113 ์š”์•ฝ 114Docto

    Mass Incidents and Government Responses in China

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ, 2017. 8. ์กฐ์˜๋‚จ.1. ๅบ่ฎบ 1 1.1 ็ ”็ฉถ้—ฎ้ข˜ๅŠ็ ”็ฉถๆ„ไน‰ 1 1.2 ๆ–‡็Œฎ็ปผ่ฟฐ 3 1.3 ็ ”็ฉถๆ–นๆณ• 11 1.3.1 ็ ”็ฉถๆ–นๆณ• 11 1.3.2 ็†่ฎบๅŠๅˆ†ๆžๆก†ๆžถ 11 1.3.3 ่ต„ๆ–™ 15 1.4 ่ฎบๆ–‡็ป“ๆž„ 16 2. ็พคไฝ“ๆ€งไบ‹ไปถ็š„ๅŸบๆœฌ็ฑปๅž‹ไธŽๅ‘ๅฑ•่ถ‹ๅŠฟ 17 2.1 ็พคไฝ“ๆ€งไบ‹ไปถ็š„ๆฆ‚ๅฟตๆผ”ๅ˜ 18 2.2 ็พคไฝ“ๆ€งไบ‹ไปถ็š„ๅŸบๆœฌ็ฑปๅž‹ไธŽๅ‘ๅฑ•่ถ‹ๅŠฟ 21 2.2.1 ๆœ‰ๅ…ณๅ†œๆ‘้—ฎ้ข˜็š„็พคไฝ“ๆ€งไบ‹ไปถๅŸบๆœฌ็ฑปๅž‹ 27 2.2.2 ๆœ‰ๅ…ณๅŸŽๅธ‚้—ฎ้ข˜็š„็พคไฝ“ๆ€งไบ‹ไปถๅŸบๆœฌ็ฑปๅž‹ 31 2.3 ็พคไฝ“ๆ€งไบ‹ไปถ็š„็ฉบ้—ดไธŽๆ—ถ้—ดๅˆ†ๅธƒ 34 3. ๅ†œๆ‘ๅœฐๅŒบ็š„็พคไฝ“ๆ€งไบ‹ไปถ 37 3.1 ่ต„ๆบไบ‰ๅคบ 38 3.1.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 38 3.1.2 ไธชๆกˆๅˆ†ๆž๏ผš2004ๅนด็พŠๆ‘8.19ๆ‘ๅบ„ๆขฐๆ–— 38 3.2 ๅ†œๆฐ‘่ดŸๆ‹… 43 3.2.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 43 3.2.2 ไธชๆกˆๅˆ†ๆž๏ผš1993ๅนดไธไฝœๆ˜Žๆƒจๆกˆ 48 3.3 ๅพๅœฐ่กฅๅฟ 52 3.3.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 52 3.3.2 ไธชๆกˆๅˆ†ๆž๏ผš2005ๅนดๅฎšๅทžไบ‹ไปถ 58 3.4 ๅฐ็ป“ 63 4. ๅŸŽๅธ‚ๅœฐๅŒบ็š„็พคไฝ“ๆ€งไบ‹ไปถ 65 4.1 ๅ›ฝไผๆ”นๅˆถ 65 4.1.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 65 4.1.2 ไธชๆกˆๅˆ†ๆž๏ผš2005ๅนด้‡ๅบ†็‰น้’ขๅŽ‚ไบ‹ไปถ 68 4.2 ๅŠณ่ต„็บ ็บท 77 4.2.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 77 4.2.2 ไธชๆกˆๅˆ†ๆž๏ผš2006ๅนดๆพณๅˆฉๅจ๏ผˆ็ƒŸๅฐ๏ผ‰ๅทฅไบบ็ฝขๅทฅไบ‹ไปถ 81 4.3 ๅผบๅˆถๆ‹†่ฟ 87 4.3.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 87 4.3.2 ไธชๆกˆๅˆ†ๆž๏ผš2010ๅนด่‹ๅทž้€šๅฎ‰ไบ‹ไปถ 90 4.4 ็Žฏๅขƒ้—ฎ้ข˜ 97 4.4.1 ้—ฎ้ข˜่ƒŒๆ™ฏ 97 4.4.2 ไธคไธชไธชๆกˆ๏ผš2007ๅนดๅŽฆ้—จๅPXๆธธ่กŒใ€2012ๅนดๅฏไธœไบ‹ไปถ 99 4.4.3 ๆกˆไพ‹ๅˆ†ๆž 104 4.5 ๅฐ็ป“ 107 5. ๆ”ฟๅบœๅบ”ๅฏน๏ผšๅ˜ไธŽไธๅ˜ 109 5.1 ๆ”ฟๅบœๅบ”ๅฏนไน‹ๅ˜๏ผšไปŽ้•‡ๅŽ‹ไธบไธปๅˆฐๅฏน่ฏ่งฃๅ†ณ 109 5.1.1 ้•‡ๅŽ‹ไธบไธป 109 5.1.2 ๅฏน่ฏ่งฃๅ†ณ๏ผšๆฐ‘ไผ—ใ€ๅŸบๅฑ‚ๆ”ฟๅบœใ€ไธญๅคฎๆ”ฟๅบœ้—ด็š„ไธ‰่ง’ๅšๅผˆ 113 5.2 ๆ”ฟๅบœๅบ”ๅฏนไน‹ไธๅ˜๏ผšไพ‹ๅค–็ฑปๅž‹ 121 5.2.1 ๅฎ—ๆ•™ 122 5.2.2 ๆฐ‘ๆ— 126 5.3 ๅฐ็ป“ 128 6. ็ป“่ฎบ 130 ๅ‚่€ƒๆ–‡็Œฎ 132Maste

    Inhibitory effects of Stichopus japonicus extract on melanogenesis of mouse cells via ERK phosphorylation

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    Stichopus japonicus has been used as a folk medicine and as an ingredient in traditional food in East Asian countries. In recent years, the bioactive compounds found in S. japonicus have been reported to possess efficacy in wound healing and may be of potential use in the cosmeceutical, pharmaceutical and biomedical industries. Although the components and their functions require further investigation, S. japonicus extracts exhibit antiโ€‘inflammatory properties, and may be used for cancer prevention and treatment. Although several reports have examined different aspects of S. japo-nicus, the effects of S. japonicus extract on melanogenesis in the skin has not been reported to date. Therefore the present study aimed to investigate the effects of S. japonicus extract on melanogenesis. Treatment with a mixture of S. japonicus extracts (MSCE) reduced melanin synthesis and tyrosinase (TYR) activity in mouse melanocyte cells lines, B16F10 and Melanโ€‘A. In addition, MSCE treatment reduced the protein expression levels of TYR, tyrosinaseโ€‘related proteinโ€‘1 and tyrosinaseโ€‘related proteinโ€‘2. The reduced protein levels may be the result of decreased microphthalmiaโ€‘associated transcription factor (MITF) expression, which is an important regulator of melanogenesis. The reduced expression level of MITF was associated with delayed phosphorylation of extracellular signalโ€‘regulated kinase (ERK) induced by MSCE treatment. A specific MEK inhibitor, PD98059, significantly blocked MSCEโ€‘mediated inhibition of melanin synthesis. In conclusion, these results indicate that MSCE may be useful as a potential skinโ€‘whitening compound in the skin medical industry.ope

    A Study on the Maintenance of Citizen-Participated Show Gardens in Garden Competition

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ, 2022.2. ์„ฑ์ข…์ƒ.Recently after over the years of the preserved gardens, which have been managed as part of a simple park without any special maintenance alternatives have been pointed out for insufficient follow-up management.(2019, Park) In many cases, the design of the garden does not meet the original intention of the designer, facilities have been aging, replaced or the list of plants has been simplified. The form of a garden for preservation through a garden contest requires a garden as an experimental and structured design model that is open to the public. However, in order for the garden to be maintained, it requires gardening, which is different from the management behavior of parks or landscaping. Since then, the importance of the maintenance entity has increased, and after 2017, the Gyeonggi Garden Culture Fair has decided to participate Citizen Gardeners in the maintenance of the Preserved garden, and LH has proposed a plan to recruit volunteers to participate in the maintenance after training. It is time to investigate the unique status of the garden with existing concepts and designs and the participation and activities of Citizens. This study investigated the current status of citizen-participating maintenance by focusing on the maintenance behavior and utilization plan of the existing garden after the garden fair. Target sites that have been conducting citizen participatory maintenance for more than one year were selected. Analysis of management subjects and participating groups and activities were studied. It is a target site for some of the Gyeonggi Garden Culture Fair, Seoul Garden Fair, LH Garden Show, and Garden Dream Project. After that, field surveys, interviews, and surveys were conducted. As a result of the survey, the achievements and tasks of citizen-participating maintenance were summarized. First, in terms of performance, first, there were the effects of the use and education of professional manpower, social participation, and regional contribution. It was found that the development potential of citizen-participating maintenance was also bright. On the other hand, as a task to be solved by citizen-participating maintenance, the maintenance system implemented and citizen participation are passive due to institutional limitations in the process of the project. Second, it is necessary to establish a professional identity as a 'Gardener'. Finally, it is necessary to establish a garden maintenance system different from the park. As a work of art, it is necessary to consider respect and sustainability of the garden, but it should be a space for active activities of civic participation groups.์ตœ๊ทผ ์ •์›๋ฐ•๋žŒํšŒ ์ดํ›„ ํŠน๋ณ„ํ•œ ์œ ์ง€๊ด€๋ฆฌ ๋Œ€์•ˆ ์—†์ด ๋‹จ์ˆœ ๊ณต์›์˜ ์ผ๋ถ€๋กœ ๊ด€๋ฆฌ๋˜์–ด ์˜จ ์กด์น˜์ •์›๋“ค์ด ๋ช‡ ๋…„์ด ์ง€๋‚˜๋ฉด์„œ, ๋ฏธํกํ•œ ์‚ฌํ›„๊ด€๋ฆฌ์— ๋Œ€ํ•œ ์ง€์ ์ด ์žˆ์—ˆ๋‹ค. (2019,๋ฐ•๋‹ค์—ฐ) ์„ค๊ณ„ํ•œ ์ •์› ์ž‘๊ฐ€์˜ ์ดˆ๊ธฐ ์˜๋„์— ๋งž์ง€ ์•Š๊ณ , ์‹œ์„ค๋ฌผ์€ ๋…ธํ›„ํ™” ๋˜๊ณ  ๋Œ€์ฒด๋˜๊ฑฐ๋‚˜, ์‹์žฌ๋œ ์‹๋ฌผ๋ฆฌ์ŠคํŠธ๋Š” ๋‹จ์ˆœํ™” ๋˜๋Š” ์‚ฌ๋ก€๊ฐ€ ๋นˆ๋ฒˆํ•œ ์‹ค์ •์ด๋‹ค. ์ •์›๊ณต๋ชจ์ „์„ ํ†ตํ•œ ์กด์น˜์ •์›์˜ ํ˜•ํƒœ๋Š” ๋Œ€์ค‘๋“ค์—๊ฒŒ ๊ณต๊ฐœ๋˜๋Š” ๊ณต๊ณต์ ์ธ ์„ฑ๊ฒฉ์„ ๋„๊ณ  ์žˆ์œผ๋ฉด์„œ๋„ ์‹คํ—˜์ ์ด๊ณ  ์งœ์ž„์ƒˆ ์žˆ๋Š” ๋””์ž์ธ์ ์ธ ๋ชจ๋ธ๋กœ์„œ์˜ ์ •์›์„ ์š”๊ตฌํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ •์›์ด ์œ ์ง€๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ณต์› ํ˜น์€ ์กฐ๊ฒฝ์˜ ๊ด€๋ฆฌํ–‰ํƒœ์™€๋Š” ๋˜ ๋‹ค๋ฅธ ์ •์›๊ฐ€๊พธ๊ธฐํ™œ๋™(Gardening)์ด ํ•„์š”ํ•˜๋‹ค. ์ดํ›„ ์œ ์ง€๊ด€๋ฆฌ ์ฃผ์ฒด์— ๋Œ€ํ•œ ์ค‘์š”์„ฑ์ด ์ฆ๋Œ€๋˜์–ด, 2017๋…„ ์ดํ›„ ๊ฒฝ๊ธฐ์ •์›๋ฌธํ™”๋ฐ•๋žŒํšŒ๋Š” ์กด์น˜์ •์› ์œ ์ง€๊ด€๋ฆฌ์— ์‹œ๋ฏผ์ •์›์‚ฌ๋ฅผ ์ฐธ์—ฌ์‹œํ‚ค๊ธฐ๋กœ ํ•˜์˜€๊ณ , LH์—์„œ๋Š” ์กด์น˜์ •์› ์กฐ์„ฑ ํ›„ ์ž์›๋ด‰์‚ฌ๋‹จ์„ ๋ชจ์ง‘ํ•˜์—ฌ ๊ต์œก ํ›„ ์œ ์ง€๊ด€๋ฆฌ์— ์ฐธ์—ฌ์‹œํ‚ค๋Š” ๋ฐฉ์•ˆ์„ ์ง„ํ–‰ํ•˜๋Š” ๋“ฑ ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ์•ˆ์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด์˜ ์ปจ์…‰๊ณผ ์„ค๊ณ„์•ˆ์ด ์žˆ๋‹ค๋Š” ์ •์›์˜ ๋…ํŠนํ•œ ํ˜„ํ™ฉ, ์ฃผ๋ฏผ์˜ ์ฐธ์—ฌ๋„ ๋ฐ ํ™œ๋™ ๋“ฑ์— ๋Œ€ํ•œ ์กฐ์‚ฌ๊ฐ€ ํ•„์š”ํ•œ ์‹œ์ ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •์›๋ฐ•๋žŒํšŒ ์ดํ›„์˜ ์กด์น˜์ •์› ์œ ์ง€๊ด€๋ฆฌ ํ–‰ํƒœ์™€ ํ™œ์šฉ์•ˆ์— ์ดˆ์ ์„ ๋‘์–ด ํ˜„์žฌ ์ง„ํ–‰์ค‘์ธ ์กด์น˜์ •์›์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ ํ˜„ํ™ฉ์— ๋Œ€ํ•˜์—ฌ ์กฐ์‚ฌํ–ˆ๋‹ค. 2021๋…„ ๊ธฐ์ค€ 1๋…„ ์ด์ƒ ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ๋ฅผ ์ง„ํ–‰ํ•˜๊ณ  ์žˆ๋Š” ๋Œ€์ƒ์ง€๋ฅผ ์ถ”๋ ค ๊ด€๋ฆฌ์ฃผ์ฒด ๋ฐ ์ฐธ์—ฌ ์ง‘๋‹จ์— ๋Œ€ํ•œ ๋ถ„์„๊ณผ ํ™œ๋™๋‚ด์šฉ์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ฒฝ๊ธฐ์ •์›๋ฌธํ™”๋ฐ•๋žŒํšŒ, ์„œ์šธ์ •์›๋ฐ•๋žŒํšŒ, LH๊ฐ€๋“ ์‡ผ, ์ •์›๋“œ๋ฆผ ํ”„๋กœ์ ํŠธ ์ค‘ ์กฐ์‚ฌ๋Œ€์ƒ์„ ์„ ์ •ํ•ด ํ˜„์žฅ๋‹ต์‚ฌ ๋ฐ ์ธํ„ฐ๋ทฐ์กฐ์‚ฌ, ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ์กฐ์‚ฌ์˜ ๊ฒฐ๊ณผ๋กœ ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ์˜ ์„ฑ๊ณผ์™€ ๊ณผ์ œ๋ฅผ ์ •๋ฆฌํ•˜์˜€๋‹ค. ๋จผ์ € ์„ฑ๊ณผ์ ์ธ ์ธก๋ฉด์—์„œ๋Š”, ์ „๋ฌธ์ธ๋ ฅ์˜ ํ™œ์šฉ ๋ฐ ๊ต์œก์˜ ํšจ๊ณผ์™€ ์‚ฌํšŒ์ฐธ์—ฌ์™€ ์ง€์—ญ๊ธฐ์—ฌ์˜ ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ์˜ ๋ฐœ์ „๊ฐ€๋Šฅ์„ฑ ๋˜ํ•œ ์ „๋ง์ด ๋ฐ๋‹ค๊ณ  ํŒŒ์•…๋˜์—ˆ๋‹ค. ๋ฐ˜๋ฉด ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ๊ฐ€ ํ•ด๊ฒฐํ•ด์•ผํ•  ๊ณผ์ œ๋กœ๋Š”, ์ฒซ ๋ฒˆ์งธ๋กœ ์‚ฌ์—…์ „๊ฐœ๊ณผ์ •์—์„œ์˜ ์‹œํ–‰ํ•˜๋Š” ์œ ์ง€๊ด€๋ฆฌ์ฒด๊ณ„ ๋ฐ ์‹œ๋ฏผ์ฐธ์—ฌ๊ฐ€ ์†Œ๊ทน์ ์ด๋ผ๋Š” ๊ฒƒ. ๋‘๋ฒˆ์งธ๋กœ ์ •์›์‚ฌ๋ผ๋Š” ์ง์—…์  ์ •์ฒด์„ฑ์˜ ํ™•๋ฆฝ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋ฉฐ, ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ณต์›๊ณผ ๋‹ค๋ฅธ ์ •์› ์œ ์ง€๊ด€๋ฆฌ ์ฒด๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•ด ๋‚˜์•„๊ฐ€์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ž‘ํ’ˆ์œผ๋กœ์„œ ์ •์›์— ๋Œ€ํ•œ ์กด์ค‘๊ณผ ์ง€์†์„ฑ์— ๋Œ€ํ•œ ๊ณ ๋ฏผ์ด ํ•„์š”ํ•˜๋ฉด์„œ๋„ ์‹œ๋ฏผ์ฐธ์—ฌ์ง‘๋‹จ์˜ ์ ๊ทน์ ์ธ ํ™œ๋™์˜ ๊ณต๊ฐ„์ด ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์•ผํ•œ๋‹ค.๋ชฉ ์ฐจ ์ œ1์žฅ ์„œ๋ก  1์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  01 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 01 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  02 2์ ˆ. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ 03 1. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ 03 2. ์„ ํ–‰์—ฐ๊ตฌ ์กฐ์‚ฌ 03 3. ์—ฐ๊ตฌ ์งˆ๋ฌธ 07 3์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 08 1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 08 2. ์—ฐ๊ตฌ์˜ ํ๋ฆ„ 08 ์ œ2์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ ๋ฐ ๋ถ„์„์˜ ํ‹€ 1์ ˆ. ์ •์› ๋ฐ ์‹œ๋ฏผ์ฐธ์—ฌ ์œ ์ง€๊ด€๋ฆฌ์˜ ์ดํ•ด 10 1. ์ •์›์˜ ํšจ์šฉ ๋ฐ ํ•„์š”์„ฑ 10 2. ์ •์›๊ณต๋ชจ์ „ ๋‚ด์šฉ ๋ฐ ์˜์˜ 11 3. ์‹œ๋ฏผ์ฐธ์—ฌ ์œ ์ง€๊ด€๋ฆฌ์˜ ๋„์ž… 12 2์ ˆ. ์ •์›๊ณต๋ชจ์ „์˜ ์ดํ•ด 13 1. ์ •์›๋ฐ•๋žŒํšŒ ๊ฐœ์š” 13 2. ์ •์›๊ณต๋ชจ์ „ ํ”„๋กœ๊ทธ๋žจ์˜ ๋‚ด์šฉ ๋ฐ ์˜์˜ 15 3. ์ •์›๊ณต๋ชจ์ „ ์ดํ•ด๊ด€๊ณ„ 16 3์ ˆ. ๋ถ„์„์˜ ํ‹€ 19 1. ํ˜„์žฅ๋‹ต์‚ฌ ๋ฐ ์ธํ„ฐ๋ทฐ 19 2. ์„ค๋ฌธ์กฐ์‚ฌ 23 3. ๋Œ€์ƒ์„ ์ •๊ธฐ์ค€ 24 ์ œ3์žฅ ์ •์›๊ณต๋ชจ์ „ ์œ ์ง€๊ด€๋ฆฌ ํ˜„ํ™ฉ๋ถ„์„ 1์ ˆ. ์กฐ์‚ฌ๋Œ€์ƒ ์„ ์ • 25 1. ๊ตญ๋‚ด ์ •์›๊ณต๋ชจ์ „ ๋ฐ ์กด์น˜์ •์› 25 2. ์กฐ์‚ฌ๋Œ€์ƒ ์„ ์ • 31 3. ๋Œ€์ƒ ์ •์›๊ณต๋ชจ์ „ ๊ฐœ์š” 33 2์ ˆ. ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ ๋Œ€์ƒ์ง€ 39 1. ๊ฒฝ๊ธฐ์ •์›๋ฌธํ™”๋ฐ•๋žŒํšŒ(์„ฑ๋‚จ์‹œ์ฒญ๊ณต์›, ๋ถ€์ฒœ์ค‘์•™๊ณต์›) 39 2. ์„œ์šธ์ •์›๋ฐ•๋žŒํšŒ(์›”๋“œ์ปต๊ณต์›, ์—ฌ์˜๋„๊ณต์›) 42 3. LH๊ฐ€๋“ ์‡ผ(ํ‰ํƒ๋™๋ง๊ทผ๋ฆฐ๊ณต์›) 44 4. ์ •์›๋“œ๋ฆผ ํ”„๋กœ์ ํŠธ(์ˆœ์ฒœ๊ถŒ์—ญ, ์šธ์‚ฐ๊ถŒ์—ญ) 45 3์ ˆ. ๋Œ€์ƒ์ง€ ์œ ์ง€๊ด€๋ฆฌ ํ˜„ํ™ฉ 47 1. ์œ ์ง€๊ด€๋ฆฌ ์ฒด๊ณ„ 47 2. ํ™œ๋™๋‚ด์šฉ 50 ์ œ4์žฅ ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ ์„ฑ๊ณผ ๋ฐ ๊ณผ์ œ 1์ ˆ. ์ฐธ์—ฌ์ง‘๋‹จ ์ธ์‹ ๋ฐ ๋งŒ์กฑ๋„ 52 1. ์ผ๋ฐ˜์‚ฌํ•ญ 52 2. ํ™œ๋™๋™๊ธฐ ๋ฐ ์ดํ•ด๋„ 56 3. ๊ต์œก์ˆ˜์ค€ 58 4. ์„ฑ๊ณผ ๋ฐ ์ „๋ง 60 5. ์†Œ๊ฒฐ 63 2์ ˆ. ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ ์„ฑ๊ณผ 65 1. ์ „๋ฌธ์ธ๋ ฅ ํ™œ์šฉ ๋ฐ ๊ต์œก 65 2. ์‚ฌํšŒ์ฐธ์—ฌ์™€ ์ง€์—ญ๊ธฐ์—ฌ 68 3. ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ ๋ฐœ์ „๊ฐ€๋Šฅ์„ฑ 69 3์ ˆ. ์‹œ๋ฏผ์ฐธ์—ฌํ˜• ์œ ์ง€๊ด€๋ฆฌ ๊ณผ์ œ 70 1. ์‚ฌ์—…์ „๊ฐœ๊ณผ์ •์—์„œ์˜ ์œ ์ง€๊ด€๋ฆฌ์™€ ์‹œ๋ฏผ์ฐธ์—ฌ 70 2. ์ง์—… ์ •์ฒด์„ฑ์˜ ํ™•๋ฆฝ 72 3. ์ •์›์˜ ์ง€์†์„ฑ 74 ์ œ5์žฅ ๊ฒฐ๋ก  1. ์—ฐ๊ตฌ ๊ฒฐ๋ก  76 2. ์‹œ์‚ฌ์  ๋ฐ ํ•œ๊ณ„ 78 [์ฐธ๊ณ ๋ฌธํ—Œ] 80 [Abstract] 83 [๋ถ€๋ก] 85์„

    Do audit fees and audit hours influence credit ratings?: A comparative analysis of Big4 vs Non-Big4

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    We examine the relationship between credit ratings / changes and audit fees (hours) for Big4 and Non-Big4 firms. Audit fee (hours) may be considered as a default risk metric for credit ratings agencies. However, firms audited by Big4 are larger, better performing and operate with lower leverage compared to firms followed by Non-Big4. Therefore, the association between audit fee (hours) may be different for firms followed by Big4 and Non-Big4 audit firms. We find that there is a negative association between audit fees and credit ratings for firms followed by Big4 audit firms. However, we find an insignificant relation for firms followed by Non-Big4. We conjecture the different association due to the Big4 firms having more robust accounting procedures; Big4 firms must offer competitive audit fees because they are engaged in fierce competition with other Big4 firms. Moreover, Big4 and Non-Big4 firms have different relationships with their clients because Non-Big4 firms are more income dependent on their clients. Using a sample of 1,717 firmโ€“year observations between 2002 and 2013, we establish a relation between audit fees in period t and credit ratings in period t+1, for firms followed by Big4 auditors. We do not find a significant relation for firms followed by Non-Nig4 firms, suggesting that credit ratings agencies perceive audit fee differently for Big4 and Non-Big4 firms. Client firms followed by Big4 auditors that experience a credit rating change in period t+1 pay lower audit fees in period t compared to firms that do not experience a credit rating change. Our additional analysis suggests a different association between firms audit fees and firm performance for firms that experience a credit rating increase and decrease. Firms that experience a credit ratings increase in period t+1 have strong performance and lower audit fees in period t. On the other hand, firms that experience a credit rating decrease have weak financial performance and negative audit fees compared to firms that do not experience a credit ratings change. Our results suggest that audit fees combined with financial performance influence a credit ratings agency' perception of default risk

    ์ „๋‹จ๋ถ€ ํ˜น์„ ๊ฐ€์ง„ ๋‚ ๊ฐœ ์ฃผ๋ณ€์˜ ์œ ๋™ ํŠน์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€(๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ๊ธฐ๊ณ„์„ค๊ณ„์ „๊ณต), 2019. 2. ์ตœํ•ด์ฒœ.Leading-edge tubercles on a humpback whale flipper are known to enhance its hydrodynamic performance at post-stall angles of attack (Miklosovic et al 2004). We investigate vortical structures above a three-dimensional wing with tubercles using surface-oil-flow visualization and particle image velocimetry measurement. Two wing models with and without tubercles, previously studied by Miklosovic et al (2004), are considered at the Reynolds number of 180,000 based on the free-stream velocity and mean chord length. At this Reynolds number, tubercles delay the stall angle by 7ยฐ and increase the maximum lift coefficient by about 22%. At a low angle of attack, flow separation first occurs near the tip region for both wing models. While flow separation rapidly progresses inboard (toward the wing root) for the model without tubercles with increasing angle of attack, tubercles produce two types of vortical motions and block the inboard progression of flow separation, resulting in delayed stall from ฮฑ = 8ยฐ to 15ยฐ. One of these two vortical structures is pairs of counter-rotating streamwise vortices evolving from hemi-spherical separation bubbles near the leading edge troughs at pre-, near-, and post-stall angles of attack, and the other is asymmetric pairs of streamwise vortices evolving from separated flow regions after the mid-chord region at near-stall angle of attack. At a post-stall angle of attack (ฮฑ = 16ยฐ), strong clockwise and counter-clockwise streamwise vortices are generated from foci at the root and tip near the trailing edge, respectively, and delay flow separation in the mid-span, resulting in a higher lift coefficient than that without tubercles. Leading-edge tubercles are applied to the quadrotor blade(Phantom4, DJI) to improve performance in forward flight condition. The new blade has ten tubercles with amplitude of 6% and wavelength of 50% of the mean chord length of the blade without tubercles. The rotating speed is varied from 3,500 RPM to 5,100 RPM corresponding to Reynolds number range of 62,000 โ€“ 90,000. The forward flight speed is varied from 4 m/s to 16 m/s corresponding to advance ratio range of 0.048 โ€“ 0.279. The angle of attack considered in this study is 40ยฐ. At low advance ratio (ฮผ โ‰ค 0.1), power and thrust coefficients of the both models are similar to each other within the experimental uncertainty range. As advance ratio increases, however, tubercles increase the thrust coefficient more than the power coefficient, indicating the enhancement of the blade performance. Based on velocity field measurement, counter-rotating streamwise vortex pairs are observed in the wake of the blade with tubercles, resulting in reduction of back-flow region behind the peak on the advancing side.ํ˜น๋“ฑ๊ณ ๋ž˜ ๊ฐ€์Šด์ง€๋Š๋Ÿฌ๋ฏธ์˜ ์ „๋‹จ๋ถ€ ํ˜น์€ ์‹ค์† ์ดํ›„ ๋ฐ›์Œ๊ฐ ์˜์—ญ์—์„œ ๊ทธ๋“ค์˜ ์œ ์ฒด์—ญํ•™์  ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ํ‘œ๋ฉด์˜ค์ผ์œ ๋™ ๊ฐ€์‹œํ™”์™€ ์ž…์ž์˜์ƒ์œ ์†๊ณ„๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ „๋‹จ๋ถ€ ํ˜น์„ ๊ฐ€์ง€๋Š” 3์ฐจ์› ๋‚ ๊ฐœ ์œ„์˜ ์™€๋ฅ˜ ๊ตฌ์กฐ์— ๋Œ€ํ•ด ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ž์œ ๋ฅ˜ ์†๋„์™€ ํ‰๊ท  ์‹œ์œ„ ๊ธธ์ด๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํ•˜๋Š” ๋ ˆ์ด๋†€์ฆˆ ์ˆ˜ 180,000์—์„œ ํ˜น์˜ ์œ ๋ฌด์— ๋”ฐ๋ผ ๋‘ ๊ฐ€์ง€ ๋‚ ๊ฐœ ๋ชจ๋ธ์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ด ๋ ˆ์ด๋†€์ฆˆ ์ˆ˜์—์„œ, ์ „๋‹จ๋ถ€ ํ˜น์€ ์‹ค์†๊ฐ์„ 7ยฐ ์ง€์—ฐ์‹œํ‚ค๊ณ , ์ตœ๋Œ€์–‘๋ ฅ๊ณ„์ˆ˜๋ฅผ ์•ฝ 22% ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ๋‚ฎ์€ ๋ฐ›์Œ๊ฐ์—์„œ, ๋‘ ๋ชจ๋ธ ๋ชจ๋‘ ์ต๋‹จ ๊ทผ์ฒ˜์—์„œ ์œ ๋™ ๋ฐ•๋ฆฌ๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋‹ค. ๋ฐ›์Œ๊ฐ์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ, ์ „๋‹จ๋ถ€ ํ˜น์ด ์—†๋Š” ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ ์œ ๋™ ๋ฐ•๋ฆฌ๊ฐ€ ๋‚ ๊ฐœ ๋ชจ๋ธ ์•ˆ์ชฝ์œผ๋กœ (๋ฃจํŠธ ์ง€์—ญ์œผ๋กœ) ๋ฐœ๋‹ฌํ•˜๋Š” ๋ฐ˜๋ฉด, ์ „๋‹จ๋ถ€ ํ˜น์ด ์žˆ๋Š” ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ ๋‘ ๊ฐ€์ง€ ์œ ํ˜•์˜ ์™€๋ฅ˜ ๊ตฌ์กฐ๊ฐ€ ๋ฐœ์ƒํ•˜์—ฌ ์œ ๋™ ๋ฐ•๋ฆฌ๊ฐ€ ๋ชจ๋ธ ์•ˆ์ชฝ์œผ๋กœ ์ง„ํ–‰ํ•˜๋Š” ๊ฒƒ์„ ๋ง‰๊ณ  ์‹ค์†๊ฐ์„ 8ยฐ์—์„œ 15ยฐ๋กœ ์ง€์—ฐ์‹œ์ผฐ๋‹ค. ๋‘ ๊ฐ€์ง€ ์œ ํ˜•์˜ ์™€๋ฅ˜ ๊ตฌ์กฐ ์ค‘ ํ•˜๋‚˜๋Š” ์„œ๋กœ ๋ฐ˜๋Œ€ ๋ฐฉํ–ฅ์œผ๋กœ ํšŒ์ „ํ•˜๋Š” ์ฃผ์œ ๋™๋ฐฉํ–ฅ ์™€๋ฅ˜ ์Œ๋“ค๋กœ, ์‹ค์† ์ „, ์‹ค์† ๊ทผ์ฒ˜, ๊ทธ๋ฆฌ๊ณ  ์‹ค์† ํ›„ ๋ฐ›์Œ๊ฐ ์˜์—ญ์—์„œ ์ „๋‹จ๋ถ€ ๊ณจ ๊ทผ์ฒ˜ ๋ฐ˜๊ตฌํ˜• ๋ฐ•๋ฆฌ๊ฑฐํ’ˆ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐœ๋‹ฌํ•œ๋‹ค. ๋‹ค๋ฅธ ํ•˜๋‚˜์˜ ์™€๋ฅ˜ ๊ตฌ์กฐ๋Š” ๋น„๋Œ€์นญ ์ฃผ์œ ๋™๋ฐฉํ–ฅ ์™€๋ฅ˜ ์Œ๋“ค๋กœ, ์‹ค์† ๊ทผ์ฒ˜ ๋ฐ›์Œ๊ฐ ์˜์—ญ์—์„œ ์ค‘๊ฐ„์‹œ์œ„ ์ง€์—ญ ๋’ค์˜ ๋ฐ•๋ฆฌ ์œ ๋™ ์ง€์—ญ์—์„œ ๋ฐœ๋‹ฌํ•œ๋‹ค. ์‹ค์† ์ดํ›„ ๋ฐ›์Œ๊ฐ์—์„œ๋Š” (ฮฑ = 16ยฐ), ์‹œ๊ณ„๋ฐฉํ–ฅ๊ณผ ์‹œ๊ณ„ ๋ฐ˜๋Œ€ ๋ฐฉํ–ฅ์œผ๋กœ ํšŒ์ „ํ•˜๋Š” ๊ฐ•ํ•œ ์ฃผ์œ ๋™๋ฐฉํ–ฅ ์™€๋ฅ˜๋“ค์ด ๋ฃจํŠธ์™€ ์ต๋‹จ ์ง€์—ญ์˜ ํ›„๋‹จ๋ถ€ ๊ทผ์ฒ˜์—์„œ ๊ฐ๊ฐ ๋ฐœ์ƒํ•˜๋ฉฐ, ์ค‘๊ฐ„์ŠคํŒฌ ์ง€์—ญ์˜ ์œ ๋™ ๋ฐ•๋ฆฌ๋ฅผ ์ง€์—ฐ์‹œ์ผœ ์ „๋‹จ๋ถ€ ํ˜น์ด ์—†๋Š” ๋ชจ๋ธ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ์ „๋‹จ๋ถ€ ํ˜น์ด ์žˆ๋Š” ๋ชจ๋ธ์ด ๋” ๋†’์€ ์–‘๋ ฅ๊ณ„์ˆ˜๋ฅผ ๊ฐ€์ง€๊ฒŒ ํ•ด์ค€๋‹ค. ์ „์ง„ ๋น„ํ–‰ ์กฐ๊ฑด์—์„œ ์ฟผ๋“œ๋กœํ„ฐ ๋ธ”๋ ˆ์ด๋“œ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์ „๋‹จ๋ถ€ ํ˜น์„ ์ ์šฉํ•˜์˜€๋‹ค. ์ƒˆ๋กœ์šด ๋ธ”๋ ˆ์ด๋“œ๋Š” ์ „๋‹จ๋ถ€์— 10๊ฐœ์˜ ํ˜น์„ ๊ฐ€์กŒ์œผ๋ฉฐ, ํ˜น์˜ ํฌ๊ธฐ์™€ ๊ฐ„๊ฒฉ์€ ๊ฐ๊ฐ ํ˜น์ด ์—†๋Š” ๋ธ”๋ ˆ์ด๋“œ ํ‰๊ท ์‹œ์œ„ ๊ธธ์ด์˜ 6%์™€ 50%์— ํ•ด๋‹นํ•œ๋‹ค. ํšŒ์ „์†๋„๋Š” 3,500 RPM โ€“ 5,100 RPM๊นŒ์ง€ ๊ณ ๋ คํ•˜์˜€์œผ๋ฉฐ, ์ด๋•Œ ๋ ˆ์ด๋†€์ฆˆ ์ˆ˜ ๋ฒ”์œ„๋Š” 62,000 โ€“ 90,000์ด๋‹ค. ์ „์ง„ ๋น„ํ–‰ ์†๋„๋Š” 4 m/s โ€“ 16 m/s๊นŒ์ง€ ๊ณ ๋ คํ•˜์˜€์œผ๋ฉฐ, ์ด๋•Œ ์ง„ํ–‰๋น„ ๋ฒ”์œ„๋Š” 0.048 โ€“ 0.279์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฐ›์Œ๊ฐ 40ยฐ๋ฅผ ๊ณ ๋ คํ•˜์˜€๋‹ค. ๋‚ฎ์€ ์ง„ํ–‰๋น„ (ฮผ โ‰ค 0.1)์—์„œ๋Š”, ๋‘ ๋ชจ๋ธ์˜ ๋™๋ ฅ ๊ณ„์ˆ˜์™€ ์ถ”๋ ฅ ๊ณ„์ˆ˜๊ฐ€ ์‹คํ—˜ ์˜ค์ฐจ ๋ฒ”์œ„ ๋‚ด์—์„œ ๋น„์Šทํ•œ ๊ฐ’์„ ๋ณด์˜€๋‹ค. ํ•˜์ง€๋งŒ ์ง„ํ–‰๋น„๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ, ์ „๋‹จ๋ถ€ ํ˜น์ด ๋™๋ ฅ ๊ณ„์ˆ˜๋ณด๋‹ค ์ถ”๋ ฅ ๊ณ„์ˆ˜๋ฅผ ๋” ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ค๋ฉฐ, ์ด๋Š” ๋ธ”๋ ˆ์ด๋“œ์˜ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋˜์—ˆ์Œ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์†๋„์žฅ ์ธก์ • ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ, ์„œ๋กœ ๋ฐ˜๋Œ€ ๋ฐฉํ–ฅ์œผ๋กœ ํšŒ์ „ํ•˜๋Š” ์ฃผ์œ ๋™๋ฐฉํ–ฅ ์™€๋ฅ˜ ์Œ๋“ค์ด ์ „๋‹จ๋ถ€ ํ˜น์„ ๊ฐ€์ง„ ๋ธ”๋ ˆ์ด๋“œ์˜ ํ›„๋ฅ˜์—์„œ ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋“ค์ด ๋ธ”๋ ˆ์ด๋“œ๊ฐ€ ์ „์ง„ํ•˜๋Š” ์˜์—ญ์—์„œ ์ „๋‹จ๋ถ€ ํ˜น ํ”ผํฌ ๋’ค์˜ ์—ญ๋ฅ˜์ง€์—ญ์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Previous studies 2 1.2 Objectives 6 2 Experimental Set-up 8 2.1 Wing model 8 2.2 Force measurement 9 2.3 Particle image velocimetry 10 2.4 Surface pressure measurement 11 2.5 Surface-oil-flow visualization 11 3 Results and Discussion 15 3.1 Aerodynamic forces 15 3.2 Flow pattern on the suction surface 16 3.3 Chorwise pressure distribution 19 3.4 Flow field around the wing model 20 4 Further Discussions 55 4.1 Comparison with 2D airfoil models 55 4.2 Effect of smooth leading edge in the root region 56 5 Application to a Quadrotor Blade 63 5.1 Introduction 63 5.2 Experimental set-up 64 5.3 Results and discussion 66 6 Summary and Concluding Remarks 86 References 89Docto
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