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    ๋“€ํ”ผํŠธ๋ Œ ๊ตฌ์ถ•์˜ ๋ณ‘์  ์กฐ์ง์—์„œ ๋น„ํƒ€๋ฏผD์™€ ๋น„ํƒ€๋ฏผD ์ˆ˜์šฉ์ฒด์˜ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2022. 8. ๊ณตํ˜„์‹.Dupuytrenโ€™s disease(DD) is a progressive fibroproliferative condition involving contractures of the fascia of the palm. Up to now, there are no relevant investigations on patients with DD in case of serum vitamin D deficiency. We hypothesized that transforming growth factor-ฮฒ1 (TGF-ฮฒ1) is increased in patients with DD in consequence of vitamin D deficiency, thereby leading to myofibroblast differentiation and subsequent progression of contractures. The studyโ€™s aim was to analyze serum vitamin D levels and explore possible clinical and immunohistochemical correlates with vitamin D concentrations in a group of patients with DD. Vitamin D levels were measured in all DD patients and healthy controls. In the patient group, clinical characteristics were compared between vitamin D deficient and non-deficient subgroups. Diseased palmar fascia samples were obtained from 14 patients undergoing fasciectomy for DD. Correlations between vitamin D levels and vitamin D receptor(VDR), TGF-ฮฒ1 expression levels in collected fascia samples were evaluated. Vitamin D concentrations were significantly lower in patients than in healthy controls. In addition, total extension deficit of involved fingers was higher in vitamin D deficient patients. Moreover, a positive correlation was found between vitamin D levels and expression of VDR in pathologic fascia in patients undergoing fasciectomy for contracture. Serum vitamin D levels were found to be low in DD patients. Expression of VDR was lower in the vitamin D deficient group. The results suggest a potential link between vitamin D status and DD but causation is not yet established. The potential role of vitamin D and its interaction with VDR and the TGF-ฮฒ1 signaling pathway in the pathogenesis of DD needs to be explored further.๋“€ํ”ผํŠธ๋ Œ ์งˆํ™˜์€ ์ˆ˜์žฅ ๋ฐ ์ˆ˜์ง€๊ฑด๋ง‰์˜ ์ง„ํ–‰์„ฑ ์ฆ์‹์„ฑ ์„ฌ์œ  ํ˜•์„ฑ์œผ๋กœ, ์†Œ๊ฒฐ์ ˆ์ด๋‚˜ ์„ฌ์œ ๋Œ€๋ฅผ ํ˜•์„ฑํ•˜์—ฌ ์ค‘์ˆ˜์ง€ ๊ด€์ ˆ๊ณผ ์ง€๊ด€์ ˆ์— ๊ตด๊ณก ๋ณ€ํ˜• ๋ฐ ์ˆ˜์ง€ ๊ธฐ๋Šฅ์˜ ์žฅ์• ๋ฅผ ์ดˆ๋ž˜ํ•˜๋Š” ์งˆํ™˜์ด๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๋น„ํƒ€๋ฏผD ๊ฒฐํ•๊ณผ ๋“€ํ”ผํŠธ๋ Œ ์งˆํ™˜๊ณผ์˜ ์—ฐ๊ด€์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ „๋ฌดํ•˜๋‹ค. ๋น„ํƒ€๋ฏผD ๊ฒฐํ•์‹œ TGF-ฮฒ1์˜ ๋ฐœํ˜„์ด ์ƒํ–ฅ ์กฐ์ ˆ๋˜์–ด ์ด๋ฅผ ๋งค๊ฐœ๋กœ ๊ทผ์„ฌ์œ ์•„์„ธํฌ ์ฆ์‹์ด ์ด‰์ง„๋˜๋ฉฐ ์†์˜ ๊ตฌ์ถ•์ด ์ง„ํ–‰๋œ๋‹ค๋Š” ๊ฐ€์„ค ์•„๋ž˜ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋“€ํ”ผํŠธ๋ Œ ์งˆํ™˜์„ ์•“๊ณ  ์žˆ๋Š” ํ™˜์ž๊ตฐ์—์„œ ํ˜ˆ์ฒญ ๋น„ํƒ€๋ฏผD ๋†๋„๋ฅผ ์ธก์ •ํ•˜๊ณ  ํ˜ˆ์ฒญ๋†๋„์™€ ์ž„์ƒ์–‘์ƒ, ๊ทธ๋ฆฌ๊ณ  ๋ฉด์—ญ์กฐ์งํ™”ํ•™์  ํŠน์„ฑ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํ˜ˆ์ฒญ ๋น„ํƒ€๋ฏผD ๋†๋„๋Š” ํ™˜์ž๊ตฐ๊ณผ ๊ฑด๊ฐ•ํ•œ ๋Œ€์กฐ๊ตฐ ๋ชจ๋‘์—์„œ ์ธก์ •ํ•˜์˜€๋‹ค. ํ™˜์ž๊ตฐ์€ ๋น„ํƒ€๋ฏผD ๊ฒฐํ•๊ตฐ๊ณผ ์ถฉ๋ถ„๊ตฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ž„์ƒ ์–‘์ƒ์„ ๋น„๊ตํ•˜์˜€๋‹ค. ์ง„ํ–‰์„ฑ ๊ตฌ์ถ•์œผ๋กœ ์ˆ˜์žฅ๋ง‰ ์ ˆ์ œ์ˆ ์„ ์‹œํ–‰ ๋ฐ›์€ 14๋ช…์˜ ํ™˜์ž์—์„œ ๋ณ‘์  ๊ฑด๋ง‰์กฐ์ง์„ ์ฑ„์ทจํ•˜์˜€๊ณ  ์ด ๊ฑด๋ง‰์กฐ์ง ๋‚ด ๋น„ํƒ€๋ฏผD ์ˆ˜์šฉ์ฒด์™€ TGF-ฮฒ1 ๋‹จ๋ฐฑ์งˆ์˜ ๋ฐœํ˜„์ •๋„์™€ ํ˜ˆ์ฒญ ๋น„ํƒ€๋ฏผD ๋†๋„ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ํ™˜์ž๊ตฐ์˜ ํ˜ˆ์ฒญ ๋น„ํƒ€๋ฏผD ๋†๋„๋Š” ๋Œ€์กฐ๊ตฐ์— ๋น„ํ•ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ๊ฒŒ ์ธก์ •๋˜์—ˆ์œผ๋ฉฐ, ๋น„ํƒ€๋ฏผD ๊ฒฐํ•๊ตฐ์—์„œ ์†์˜ ๊ตฌ์ถ• ๊ฐ๋„๊ฐ€ ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์•„์šธ๋Ÿฌ ๋ณ‘์  ๊ฑด๋ง‰์กฐ์ง ๋‚ด ๋น„ํƒ€๋ฏผD ์ˆ˜์šฉ์ฒด ๋ฐœํ˜„์ •๋„์™€ ํ˜ˆ์ฒญ ๋น„ํƒ€๋ฏผD ๋†๋„๋Š” ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋ฉฐ, ๋น„ํƒ€๋ฏผD ๊ฒฐํ•๊ตฐ์—์„œ ๋น„ํƒ€๋ฏผD ์ˆ˜์šฉ์ฒด ๋ฐœํ˜„์ด ๋‚ฎ๊ฒŒ ์ธก์ •๋˜์—ˆ๋‹ค. ์œ„ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋น„ํƒ€๋ฏผD๋Š” ๋“€ํ”ผํŠธ๋ Œ ์งˆํ™˜์˜ ๋ฐœ์ƒ์— ์žˆ์–ด ์ž ์žฌ์  ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋˜๋‚˜, ๋น„ํƒ€๋ฏผD ์ˆ˜์šฉ์ฒด์˜ ๋ฐœํ˜„ ๋˜๋Š” TGF-ฮฒ1์„ ๋งค๊ฐœ๋กœ ํ•œ ์‹ ํ˜ธ์ „๋‹ฌ์ฒด๊ณ„์— ์žˆ์–ด ์—ญํ•  ๊ทœ๋ช…์ด ์ถ”๊ฐ€์ ์œผ๋กœ ์—ฐ๊ตฌ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค.I. Introduction 1 1.1 Study Background 1 1.2 Purpose of Research 4 II. Body 5 2.1 Patients and Methods 5 2.1.1 Measurement of serum vitamin D levels 6 2.1.2 Evaluation of clinical features of DD 6 2.1.3 Sample preparation 7 2.1.4 Immunohistochemical analysis 7 2.1.5 Statistical analysis 9 2.2 Results 10 2.2.1 Serum vitamin D levels in patients and controls 10 2.2.2 Correlation between vitamin D levels and clinical features of DD 10 2.2.3 Correlation between vitamin D levels and VDR, TGF-ฮฒ1 expression in pathologic tissue 11 III. Discussion 12 IV. Conclusion 18 V. Bibliography 19 VI. Abstract in Korean 27 VII. List of Tables 29 VIII. List of Figures 33๋ฐ•

    ์ƒ๋ณ€ํ™”์„ฑ ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœํ•œ ์Šˆํผ์ปคํŒจ์‹œํ„ฐ์™€ ์—ด์ „ ์†Œ์ž์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2022. 8. ์„ ์ •์œค.With the rapid development of society and industry, the demand for unprecedented energy is increasing. In order to supply energy to meet the demand, various types of energy supply methods have been studied. First, energy storage system (ESS) is a charge / discharge type energy supply device, which is usually applied to a field requiring a large amount of energy. A supercapacitor is a representative ESS. In the ion solution electrolyte in the supercapacitor, an electric double layer is formed by charging and energy is stored. Next, the self-generated energy supply device is applied to a field that has relatively little energy but requires continuous energy supply. An ionic thermocell is a typical self-generated energy supply device. Thermoelectric is generated due to a difference in ion concentration at both electrodes according to a temperature gradient. However, the ion solvent electrolyte applied to these devices has issues in evaporation and chemical stability. Therefore, ionic liquids that have chemical stability and very low vapor pressure are being evaluated as next-generation materials. Here, we demonstrate that ionic liquids are promising materials to be introduced into electrolytes of supercapacitors and thermocells. Particularly, it is possible to dramatically control the ionic conductivity at their melting point by preparing electrolytes through phase-transitional ionic liquids. These electrolytes make it possible to design energy supply devices that show new functions or improved performance. Two energy supply devices with a phase-transitional ionic liquid electrolyte were explored; phase-transitional supercapacitor and thermocell In first part, selective operation supercapacitors were demonstrated by introducing phase-transitional ionogel as electrolyte into supercapacitor. Super-capacitors with excellent physical stability was manufactured through gelation of ionic liquid. The phase transitional characteristic of the ionic liquid made them possible to selectively operate by distinguishing the operating mode and storage mode. Furthermore, they showed long-term energy storage by effectively suppressing the self-discharge of the supercapacitor in the storage mode. The selective operating supercapacitor is expected to expand their used in environments with high temperatures or large temperature variation such as a desert or space industry. In second part, the high seebeck coefficient of the ionic thermocell was demonstrated through the phase transitional ionic liquid. The mechanism of a thermoelectric phenomenon due to the phase difference occurring at both electrode was introduced. It was shown that the seebeck coefficient could change or obtain a high value depending on the phase difference. Furthermore, it was demonstrated that the Seebeck coefficient was significantly improved by the phase difference in all phase transitional ionic liquids. It is expected that the enhanced thermogalvanic cell due to the phase difference has advanced the commercialization of the thermoelectric device by proposing a novel mechanism for the study of thermoelectric effect improvement, which is still in early stage.์‚ฌํšŒ์™€ ์‚ฐ์—…์˜ ๊ธ‰์†ํ•œ ๋ฐœ์ „์œผ๋กœ ์ „๋ก€ ์—†๋Š” ์—๋„ˆ์ง€์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฉด์„œ, ์ˆ˜์š”์— ๋งž๊ฒŒ ์—๋„ˆ์ง€๋ฅผ ๊ณต๊ธ‰ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ์—๋„ˆ์ง€ ๊ณต๊ธ‰ ๋ฐฉ์‹์ด ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ๋จผ์ €, ์—๋„ˆ์ง€ ์ €์žฅ ์‹œ์Šคํ…œ (ESS)์€ ์ฃผ๋กœ ๋‹ค๋Ÿ‰์˜ ์—๋„ˆ์ง€๊ฐ€ ์š”๊ตฌ๋˜๋Š” ๋ถ„์•ผ์— ์ฃผ๋กœ ์ ์šฉ๋˜๋Š” ์ถฉ์ „/๋ฐฉ์ „ ๋ฐฉ์‹์˜ ์—๋„ˆ์ง€ ๊ณต๊ธ‰ ์žฅ์น˜์ด๋‹ค. ๋Œ€ํ‘œ์ ์ธ ESS๋กœ๋Š” ์Šˆํผ์ปคํŒจ์‹œํ„ฐ๊ฐ€ ์žˆ๋‹ค. ์Šˆํผ์ปคํŒจ์‹œํ„ฐ์˜ ์ „ํ•ด์งˆ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์ด์˜จ ์šฉ์•ก์—์„œ, ์ถฉ์ „์‹œ ์ „๊ธฐ ์ด์ค‘์ธต ์ด ํ˜•์„ฑ๋˜์–ด ์—๋„ˆ์ง€๊ฐ€ ์ €์žฅ๋œ๋‹ค. ๋‹ค์Œ์œผ๋กœ๋Š”, ์ƒ๋Œ€์ ์œผ๋กœ ์ ์€ ์—๋„ˆ์ง€๊ฐ€ ํ•„์š”ํ•˜์ง€๋งŒ, ์ง€์†์ ์ธ ์—๋„ˆ์ง€ ๊ณต๊ธ‰์ด ํ•„์š”ํ•œ ๋ถ„์•ผ์— ์ ์šฉ๋˜๋Š” ์ž๊ฐ€ ๋ฐœ์ „์‹ ์—๋„ˆ์ง€ ๊ณต๊ธ‰์žฅ์น˜์ด๋‹ค. ์ด์˜จ ์—ด์ „ ์†Œ์ž๊ฐ€ ์ „ํ˜•์ ์ธ ์ž๊ฐ€ ๋ฐœ์ „์‹ ์—๋„ˆ์ง€ ๊ณต๊ธ‰ ์žฅ์น˜ ์ด๋‹ค. ์—ด์ „์€ ์–‘ ์ „๊ทน์—์„œ ์˜จ๋„ ๊ตฌ๋ฐฐ์— ์˜ํ•œ ์ด์˜จ ๋†๋„ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๋ฉด์„œ ํ˜•์„ฑ๋˜๋Š” ์—๋„ˆ์ง€์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ด๋Ÿฌํ•œ ๋””๋ฐ”์ด์Šค์— ์ ์šฉ๋˜๋Š” ์ด์˜จ ์šฉ์•ก์€ ๋ณดํ†ต ์ฆ๋ฐœ๊ณผ ํ™”ํ•™ ์•ˆ์ •์— ๋Œ€ํ•œ ๋ฌธ์ œ์ ์„ ๊ฐ–๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ, ํ™”ํ•™์ ์œผ๋กœ ์•ˆ์ •ํ•˜๊ณ , ๋งค์šฐ ๋‚ฎ์€ ์ฆ๊ธฐ์••์„ ๊ฐ–๋Š” ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ๊ฐ€ ์ฐจ์„ธ๋Œ€ ๋ฌผ์งˆ๋กœ ์ฃผ๋ชฉ ๋ฐ›๊ณ  ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์šฐ๋ฆฌ๋Š” ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ๋ฅผ ์Šˆํผ์ปคํŒจ์‹œํ„ฐ์™€ ์—ด์ „ ์†Œ์ž์˜ ์ „ํ•ด์งˆ๋กœ ๋„์ž…ํ•˜์—ฌ ์ฐจ์„ธ๋Œ€ ๋ฌผ์งˆ๋กœ์„œ์˜ ํšจ์šฉ์„ ์ฆ๋ช…ํ•œ๋‹ค. ํŠนํžˆ, ์ƒ๋ณ€ํ™”์„ฑ ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœํ•œ ์ „ํ•ด์งˆ์„ ํ†ตํ•ด ๋…น๋Š”์ ์—์„œ ์ด์˜จ ์ „๋„์„ฑ์„ ๊ทน์ ์œผ๋กœ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ „ํ•ด์งˆ์€ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์„ ๊ฐ–๊ฑฐ๋‚˜ ํ–ฅ์ƒ๋œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๋””๋ฐ”์ด์Šค๋ฅผ ์ œ์ž‘ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ์ œ 1๋ถ€์—์„œ๋Š”, ์ƒ ๋ณ€ํ™”์„ฑ ์•„์ด์˜ค๋…ธ์ ค์„ ์ „ํ•ด์งˆ๋กœ ํ•˜๋Š” ์Šˆํผ์ปคํŒจ์‹œํ„ฐ๋ฅผ ์ œ์ž‘ํ•˜์—ฌ ์„ ํƒ์ ์œผ๋กœ ์ž‘๋™๋˜๋Š” ์Šˆํผ์ปคํŒจ์‹œํ„ฐ๋ฅผ ์ œ์ž‘ํ•œ๋‹ค. ์Šˆํผ์ปคํŒจ์‹œํ„ฐ๋Š” ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ๋ฅผ ์ คํ™”๋ฅผ ํ†ตํ•ด ๋งค์šฐ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ์•ˆ์ •ํ•จ์„ ๋ณด์˜€๋‹ค. ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ์˜ ์ƒ ๋ณ€ํ™”์„ฑ ํŠน์ง•์€ ์ž‘๋™ ๋ชจ๋“œ์™€ ๋ณด๊ด€ ๋ชจ๋“œ๋ฅผ ๊ตฌ๋ณ„ํ•˜์—ฌ ์„ ํƒ์ ์œผ๋กœ ์ž‘๋™ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์˜€๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ์Šˆํผ์ปคํŒจ์‹œํ„ฐ์˜ ๋‹จ์ ์œผ๋กœ ์•Œ๋ ค์ง„ ์ž๊ฐ€ ๋ฐฉ์ „์„ ๋ณด๊ด€ ๋ชจ๋“œ๋ฅผ ํ†ตํ•ด ํšจ๊ณผ์ ์œผ๋กœ ์–ต์ œํ•˜์—ฌ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๋ณด๊ด€ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ์„ ํƒ์ ์œผ๋กœ ์ž‘๋™๋˜๋Š” ์Šˆํผ์ปคํŒจ์‹œํ„ฐ๋Š” ์‚ฌ๋ง‰๊ณผ ์šฐ์ฃผ์™€ ๊ฐ™์€ ๋†’์€ ์˜จ๋„์™€ ํฐ ์˜จ๋„์ฐจ์ด๋ฅผ ๋ณด์ด๋Š” ํ™˜๊ฒฝ์—์„œ ํ™œ์šฉ ๊ฐ€๋Šฅ ํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค. ์ œ 2๋ถ€์—์„œ๋Š”, ์ƒ ๋ณ€ํ™”์„ฑ ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ด์˜จ ์—ด์ „ ์†Œ์ž์˜ ์ œ๋ฒก ๊ณ„์ˆ˜๊ฐ€ ์ฆ๊ฐ€๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์–‘ ์ „๊ทน์—์„œ ์ƒ ๋ณ€ํ™”์— ์˜ํ•œ ์—ด์ „ ํ˜„์ƒ์˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์†Œ๊ฐœ๋˜์—ˆ๋‹ค. ๊ธฐ์กด์—๋Š” ๋ฌผ์งˆ์˜ ๊ณ ์œ  ํŠน์„ฑ์œผ๋กœ ์•Œ๋ ค์ง„ ์ œ๋ฒก ๊ณ„์ˆ˜๊ฐ€, ์ƒ ๋ณ€ํ™”๋กœ ์ธํ•ด ๋ฐ”๋€Œ๊ณ  phase์˜ ์ฐจ์ด๋กœ ์ธํ•ด ๋†’์€ ์—ด์ „ ํšจ๊ณผ ๊ฐ’์ด ๋„์ถœ๋˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๋ชจ๋“  ๋‹ค๋ฅธ ์ƒ ๋ณ€ํ™”์„ฑ ์•„์ด์˜ค๋‹‰ ๋ฆฌํ€ด๋“œ์— ๋Œ€ํ•ด์„œ๋„ ์ƒ ์ฐจ์ด๋กœ ์ธํ•ด ์ œ๋ฒก ๊ณ„์ˆ˜๊ฐ€ ๊ฐ•ํ™”๋จ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ์ƒ ๋ณ€ํ™”์— ์˜ํ•ด ๊ฐ•ํ™”๋œ ์—ด์ „ ์†Œ์ž๋Š” ์•„์ง ์ดˆ๊ธฐ๋‹จ๊ณ„์ธ ์—ด์ „ ํšจ๊ณผ ๊ฐ•ํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ์ƒˆ๋กœ์šด ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ œ์‹œํ•จ์œผ๋กœ์จ ์—ด์ „์†Œ์ž์˜ ์ƒ์šฉํ™”๋ฅผ ์•ž๋‹น๊ฒผ๋‹ค๊ณ  ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter 1. Introduction 1 1.1 Study background 1 1.1.1 Supercapacitor 1 1.1.2 Thermocell 2 1.1.3 Ionic liquid 3 1.1.4 Goals and outline of this dissertation 3 Chapter 2. Phase-transitional ionogel-based supercapacitors for a selective temperature operation 7 2.1 Introduction 7 2.2 Experimental section 9 2.2.1 Fabrication of SCs 9 2.2.2 Ionogel analysis 10 2.2.3 Characterization of supercapacitor electrochemical properties 11 2.2.4 Self-discharging test 11 2.3 Results and Discussion 12 2.3.1 Phase-transitional characteristics of the ionogel 12 2.3.2 Selective operation of SCs in operating mode and storage mode 21 2.3.3 The capacitive performance of SCs in operating mode 31 2.3.4 Self-discharge characteristic of SCs in storage mode 36 2.4 Conclusion 45 Chapter 3. The Enhancement of Ionic Thermoelectric Seebeck Coefficient by Phase Difference 46 3.1 Introduction 46 3.2 Experimental section 48 3.2.1 Fabrication of the thermogalvanic cell 48 3.2.2 Ionic liquid analysis 49 3.2.3 Characterization of thermogalvanic cell thermoelectrical properties 49 3.3 Results and Discussion 50 3.3.1 A thermoelectric enhancement mechanism induced by phase difference 50 3.3.2 Properties of the ionic liquid in different phases 54 3.3.3 Thermoelectric performance of [AMIM]+[Cl]- based thermogalvanic cell 61 3.3.4 Thermoelectric performance of other phase transitional ionic liquid 67 3.4 Conclusion 71 Chapter 4. Conclusion 72 References 74 Abstract in Korean 84๋ฐ•

    ์ƒ์ง•์ฒœํ™ฉ์ œ์™€ ๋ฏธ๊ตญ

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    ์ผ๋ณธ ๊ทผํ˜„๋Œ€์‚ฌ์—์„œ ๋ฏธ๊ตญ์€ ๊ฐ๋ณ„ํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง„๋‹ค. ํŽ˜๋ฆฌ๋‚ดํ•ญ์€ ๊ทผ๋Œ€ ์ผ๋ณธ์˜ ์ถœ๋ฐœ์ ์ด ๋˜์—ˆ์œผ๋ฉฐ ์ผ๋ณธ ๊ทผ๋Œ€ํ™” ๊ณผ์ •์—์„œ ๋ฏธ๊ตญ์€ ์ž์œ ์˜ ์„ฑ์ง€์ด์ž ๋ชจ๋”๋‹ˆ์ฆ˜์˜ ํ‘œ์ƒ์œผ๋กœ ์ด์ƒํ™”๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํƒœํ‰์–‘์ „์Ÿ์—์„œ ์ผ๋ณธ์€ ์„ฑ์ „์ด๋ผ๋Š” ๋ฏธ๋ช… ์•„๋ž˜ ๋ฏธ๊ตญ์„ ๊ท€์ถ•๋ฏธ์˜(้ฌผ็•œ็ฑณ่‹ฑ)์ด๋ผ ๋ถ€๋ฅด๋ฉด์„œ ํ˜์˜ค์™€ ๊ฒฝ๋ฉธ์˜ ๋Œ€์ƒ์œผ๋กœ ์ƒ๋Œ€ํ™”ํ•˜๊ณ  ์‚ฌ์ดํŒ์˜ฅ์‡„, ์˜คํ‚ค๋‚˜์™€์ „ํˆฌ, ๊ฐ€๋ฏธ๊ฐ€์ œํŠน๊ณต๋Œ€, ํžˆ๋กœ์‹œ๋งˆยท๋‚˜๊ฐ€์‚ฌํ‚ค ์›ํญ ๋“ฑ์œผ๋กœ ์ƒ์ง•๋˜๋“ฏ์ด ์ผ๋ณธ์—ญ์‚ฌ์ƒ ๊ฐ€์žฅ ์น˜์—ดํ•˜๊ณ  ๋น„๊ทน์ ์ธ ์ „์Ÿ์„ ์น˜๋ €๋‹ค. ๊ทธ๋Ÿผ์—๋„ ํŒจ์ „ ํ›„์—๋Š” ๋ฏธ๊ตญ์˜ ๊ด€๋Œ€ํ•œ ์ ๋ น์ง€๋ฐฐ์™€ ๊ฐ•ํ™”์กฐ์•ฝ์œผ๋กœ ๊ตญ์ œ์‚ฌํšŒ์— ๋ณต๊ท€ํ•˜๊ณ  ์˜ค๋Š˜๋‚ ๊นŒ์ง€ ๊ฐ€์žฅ ์ถฉ์‹คํ•œ ์นœ๋ฏธ๊ตญ๊ฐ€๋กœ์„œ ๊ธด๋ฐ€ํ•œ ๊ด€๊ณ„๋ฅผ ์œ ์ง€ํ•˜๊ณ  ์žˆ๋‹ค. ์š”์‹œ๋ฏธ ์ŠŒ์•ผ(ๅ‰่ฆ‹ไฟŠๅ“‰)๋„ ์ตœ๊ทผ์˜ ์—ฐ๊ตฌ์—์„œ ์ง€์ ํ•˜๊ณ  ์žˆ๋“ฏ์ด ๋ฏธ๊ตญ์˜ ์ด๋ผํฌ ๋ฌด๋ ฅ์นจ๊ณต ์ดํ›„ ์ „์„ธ๊ณ„์ ์œผ๋กœ ๋ฐ˜๋ฏธ์ ์ธ ์ถ”์ด๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ์†์—์„œ๋„ ์ผ๋ณธ์€ ํŠน์ดํ•  ์ •๋„๋กœ ์นœ๋ฏธ์ ์ธ ๊ฒฝํ–ฅ์„ ๋ณด์ด๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ์ผ๋ณธ์ธ์˜ ์นœ๋ฏธ์˜์‹์€ ํŒจ์ „ ํ›„ ์ผ๋ณธ์˜ ์ ๋ น์ง€๋ฐฐ์— ํ—ค๊ฒŒ๋ชจ๋‹ˆ๋ฅผ ์žก์€ ๋ฏธ๊ตญ์˜ ๋Œ€์ผ์ ๋ น์ •์ฑ…์—์„œ ๋น„๋กฏ๋˜์–ด ๋ฐ˜์„ธ๊ธฐ ์ด์ƒ์— ๊ฑธ์ณ ๊ตฌ์กฐํ™”๋˜์–ด ์˜จ ๊ฒƒ์ด๋‹ค.1) ๋ƒ‰์ „์ฒด์ œ ํ•˜์—์„œ ์ค‘๊ตญ์˜ ๊ณต์‚ฐํ™”์™€ ํ•œ๋ฐ˜๋„์˜ ๋‚จ๋ถ๋ถ„๋‹จ ์ดํ›„ ๊ณต์‚ฐ์„ธ๋ ฅ์˜ ํ™•์‚ฐ์„ ์ €์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๋ฏธ๊ตญ์€ ์ผ๋ณธ์˜ ๊ฒฝ์ œ๋ฅผ ๋ถ€ํฅ์‹œํ‚ค๊ณ  ์•„์‹œ์•„์˜ ๊ฐœ๋ฐœ๊ฒฝ์ œ๋ฅผ ์ง€ํƒฑํ•˜๋Š” ์ค‘ํ•ต์œผ๋กœ ์‚ผ์•˜๋‹ค. ๊ทธ๋Ÿฌํ•œ ๊ฒฐ๊ณผ ๋™์•„์‹œ์•„์—์„œ์˜ ๊ตฐ์‚ฌ์  ๊ธฐ์ง€์˜ ์—ญํ• ์€ ํ•œ๊ตญ๊ณผ ๋Œ€๋งŒ, ์˜คํ‚ค๋‚˜์™€๊ฐ€ ๋ถ€๋‹ดํ•˜๊ฒŒ ๋˜๊ณ  ์ผ๋ณธ์€ ๊ฒฝ์ œ๋ฐœ์ „์˜ ์ค‘์ถ”๋กœ์„œ์˜ ์—ญํ• ์„ ๋‹ด๋‹นํ•˜๋ฉด์„œ ๊ณ ๋„์„ฑ์žฅ์„ ๊ณ„์†ํ•ด ์™”๋‹ค. ์ด๋Ÿฌํ•œ ๊ณผ์ •์—์„œ ์ผ๋ณธ์˜ ์ „์Ÿ์ฑ…์ž„๊ณผ ์ „ํ›„์ฒ˜๋ฆฌ, ๊ทธ๋ฆฌ๊ณ  ์ „ํ›„์ฑ…์ž„์— ๊ด€ํ•œ ๋ฌธ์ œ๋Š” ์˜ค๋žซ๋™์•ˆ ๋ฏธ๊ตญ์˜ ์–‘ํ•ด์™€ ๋ฌต์ธ ์•„๋ž˜ ์• ๋งคํ•˜๊ฒŒ ๋ด‰์ธ๋˜๊ณ , ๊ทผ๋ฆฐ์•„์‹œ์•„์— ๋Œ€ํ•œ ์—ญ์‚ฌ์ ์ธ ๋ฌธ์ œ์˜ ์ฒญ์‚ฐ์ด๋ผ๋Š” ๊ณผ์ œ๊ฐ€ ๋ง๊ฐ๋˜๊ฑฐ๋‚˜ ํ˜น์€ ์™œ๊ณก๋˜์–ด ๋ฒ„๋ ธ

    ํ‰๋ถ€ ์ฒ™์ถ”์—์„œ ๊ฒฝ๋ง‰-์ฒ™์ˆ˜ ์‚ฌ์ด ๊ฑฐ๋ฆฌ์— ๊ด€ํ•œ ๊ณ ์ฐฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ ๋งˆ์ทจํ†ต์ฆ์˜ํ•™ ์ „๊ณต, 2013. 2. ํ—ˆ์ง„.Introduction: Neurologic complications related to thoracic epidural analgesia are rare but devastating. It is important to understand the anatomy of the spinal canal to minimize the risk of needle-related neurologic injury. Methods: We retrospectively investigated T2-weighted spine magnetic resonance images of 346 patients. The vertical distance from the dura mater to the spinal cord (DTC) at all thoracic intervertebral levels was examined. The DTC and distance from the skin to the dura mater (STD) were evaluated at three different thoracic intervertebral levels (T1/2, T5/6, and T10/11) using three different pathways: the U, L, and M lines. The U and L lines contacted the upper and lower borders of the interspinous space, respectively. The line M represented a blind approach, passing the midpoint of two spinous process tips and the point bisecting the ligamentum flavum at each interspinous space. Results: The vertical DTC was longest at the T5/6 intervertebral level and shortest at the T11/12 level. The vertical DTC was positively correlated with height (p = 0.013) and negatively correlated with age (p < 0.001). The U line was more horizontal than the L line at the upper and middle thoracic regions, but the relationship was reversed at the lower thoracic level. Among the three lines, the STD and DTC were longest on the L line at the T1/2 and T5/6 intervertebral levels. The distances were the longest on the U line at the T10/11 level. The angle between the U and L lines was largest at the T1/2 level and the difference in DTC between the U and L lines was greatest at T5/6. The STD on the M line was longer in males than in females (p < 0.001) and was positively correlated with height (p = 0.016) and weight (p < 0.001). The DTC on the M line was also longer in males than in females (p = 0.037) and shortened with age (p = 0.001). Conclusions: Differences in the DTC were observed among thoracic intervertebral levels, mainly due to cervical and lumbar enlargement of the spinal cord. Among the three approaching lines, the dimensions implying a safety margin were longest on the L line at T1/2 and T5/6, and longest on the U line at T10/11. The variability of the safety margin according to the angle of needle insertion was largest at T5/6, and the angle between the upper and lower borders of the interspinous space was largest in the upper thoracic region.1. Introduction ---------------------------------- 1 2. Methods -------------------------------------- 3 Patient selectio --------------------- 3 Study protocol and data collection ---- 4 Statistical analysis ----------------- 6 3. Result -------------------------------------- 7 4. Discussion ---------------------------------- 12 5. References ---------------------------------- 16Maste

    ๋‹ค์ค‘ ํด๋ž˜์Šค ์œ ์ „์ž ๋ฐœํ˜„ ๋ฐ์ดํ„ฐ์—์„œ ํ‘œํ˜„ํ˜• ํŠน์ด์  ์„œ๋ธŒ ๋„คํŠธ์›Œํฌ ๋ฐœ๊ตด ๋ฐ ๋žญํ‚น์„ ์œ„ํ•œ ์ •๋ณด ์ด๋ก  ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2017. 2. ๊น€์„ .There have been extensive studies for inferring transcriptional network from omics data. However, how to utilize networks for specific research projects has not been well established. One of the main hurdles is lack of algorithms for mining biological sub-networks. Existing graph mining algorithms do not consider features of the transcriptional network and they are not effective to obtain biologically meaningful results. In this paper, we define the biological sub-network mining problem and present a new graph mining algorithm that mines and ranks phenotype specific sub-networks of transcriptional regulatory networks constructed from multi-class gene expression data. Our contributions in this paper on the computational side are two folds. First, we suggest a complete research paradigm of utilizing omics data to construct networks and then elucidate s ub-networks that distinguish phenotypes or disease states. Second, we developed an information theoretic algorithm for mining phenotype specific sub-networks. Our contribution on the bio/medical side is that our TF-module based analysis determined biological pathways (cell cycle: M-phase, cell adhesion molecules) related to the phenotype (breast tumor grade) by identifying activation/suppression of specific target genes (TGs) by the combination of multiple transcription factors (TFs). Expression levels of TGs clearly shows correlation between activation/suppression of these pathways and tumor grades. When we used all genes, pathway activation or suppression was not obvious, which shows the effectiveness of our algorithm. Our TF-centric pathway activation/suppression analysis technique is applicable to and useful for many other studies.Chapter 1 Introduction 1 Chapter 2 Pheotype specific subnetwork mining problem 5 2.1 Biological network construction methods 5 2.2 Necessity of biological sub-network mining algorithm 6 2.3 Problem formulation 6 2.4 Our information theoretic algorithm 7 Chapter 3 Method 10 3.1 TF-TG network construction 10 3.1.1 Edge set 10 3.1.2 Multi valued attribute vector 11 3.2 Information scores for TF-modules 11 3.2.1 Definition of TF-module 11 3.2.2 Entropy for TF-module 12 3.2.3 Best entropy with dynamic programming 13 3.2.4 Information score for TF-module 13 3.3 TF-module hyper graph 14 3.4 Merging of TF-modules on hyper-graph 15 Chapter 4 Result and Discussion 16 4.1 Raw biological data 16 4.2 HCS mining algorithm 16 4.3 TF-centric sub-network mining algorithm 17 4.4 Cell cycle: M-phase 20 4.5 Cell adhesion molecules 21 Chapter 5 Conclusion 24 Bibliography 27 ์š”์•ฝ 33Maste

    A Study on Scheduling Methodology for Large-Scale R&D Projects using TRL and Critical Chain

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2018. 2. ๋‚˜์šฉ์ˆ˜.๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…์€ ๋†’์€ ๊ฐœ๋ฐœ ๋ถˆํ™•์‹ค์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ์žฆ์€ ์ผ์ •์ง€์—ฐ์ด ๋ถˆ๊ฐ€ํ”ผํ•œ ๊ฐ€์šด๋ฐ, ์ด๋กœ ์ธํ•˜์—ฌ ์‚ฌ์—…์‹ ๋ขฐ๋„๊ฐ€ ๋‚ฎ์•„์ง€๊ณ  ํˆฌ์ž… ์š”๊ตฌ์˜ˆ์‚ฐ์ด ๊ฐ์†Œ๋˜์–ด ์„ ํ–‰๊ณผ์ œ์˜ ์ ๊ธฐ ์ฐฉ์ˆ˜๊ฐ€ ์ง€์—ฐ๋˜๊ณ  ์ด๋Š” ๋‹ค์‹œ ์ „์ฒด ์‚ฌ์—…์ง€์—ฐ์„ ์•ผ๊ธฐํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ๊ฒช๊ณ  ์žˆ๋‹ค. PERT/CPM ๋“ฑ ์ „ํ†ต์ ์ธ ์Šค์ผ€์ค„๋ง ๋ฐฉ๋ฒ•๋“ค์—์„œ๋Š” ๋ถˆํ™•์‹ค์„ฑ์ด ํฐ ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—… ์ผ์ •์˜ˆ์ธก์˜ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์„ ํ–‰์ž‘์—…์˜ ์ง€์—ฐ์ด ํ›„์†์ž‘์—…, ํŠนํžˆ ์‹œ๊ฐ„์  ์—ฌ์œ ์‹œ๊ฐ„์ด ์—†๋Š” ์ฃผ๊ฒฝ๋กœ์˜ ์ง€์—ฐ์œผ๋กœ ์ด์–ด์ง€๋Š” ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…์˜ ๊ฐœ๋ฐœ๊ธฐ๊ฐ„์ด ๊ธฐ์ˆ ์„ฑ์ˆ™๋„์™€ ์—ฐ๊ด€์ด ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค๊ณผ ๋†’์€ ์ผ์ •์œ„ํ—˜์„ ๊ฐ€์ง€๋Š” ํ™œ๋™์— ๋ฒ„ํผ๋ฅผ ๋‘์–ด ์ผ์ •์ง€์—ฐ์„ ์˜ˆ๋ฐฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์— ์ฐฉ์•ˆํ•˜์—ฌ, ๊ธฐ์ˆ ์„ฑ์ˆ™๋„์™€ ์ผ์ •์œ„ํ—˜์ˆ˜์ค€์„ ๊ณ ๋ คํ•˜์—ฌ ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—… ์ผ์ •๋ถ„ํฌ๋ฅผ ์˜ˆ์ธกํ•˜๊ณ  ์ง€์—ฐ์„ ์ตœ์†Œํ™”ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋ฒ„ํผ๋ฅผ ๊ณ ๋ คํ•œ ์Šค์ผ€์ค„๋ง ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•๋ก ์—์„œ๋Š” ๋Œ€์ƒ ์‹œ์Šคํ…œ์˜ ์š”์†Œ๊ธฐ์ˆ ๋“ค์„ ์ฐพ์•„ ์ „๋ฌธ๊ฐ€ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด CPM ๋ฐฉ์‹์˜ ์Šค์ผ€์ค„์„ ์–ป๊ณ , ๊ธฐ์ˆ ๋ณ„ ๊ฐœ๋ฐœ์‹œ๊ฐ„๊ณผ ๊ธฐ์ˆ ์„ฑ์ˆ™๋„๋ฅผ ํ‰๊ฐ€ํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ผ์ • ๋‚ด ์—ฌ์œ ์‹œ๊ฐ„์„ ์ œ๊ฑฐํ•˜๊ณ , ํ™œ๋™๋ณ„ ์ผ์ •์œ„ํ—˜์ˆ˜์ค€์„ ํ‰๊ฐ€ํ•˜์—ฌ ๋ชฌํ…Œ์นด๋ฅผ๋กœ ๋ฐฉ๋ฒ•์œผ๋กœ ์ผ์ •์ง€์—ฐ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ง€์—ฐ์ผ์ •์˜ ํ‰๊ท ๊ฐ’์„ ๊ธฐ๋ณธ์ผ์ •์œผ๋กœ ์ •ํ•˜๊ณ  ์—ฌ์œ ์‹œ๊ฐ„์„ ์ œ๊ฑฐํ•œ CPM ์Šค์ผ€์ค„๊ณผ์˜ ์ฐจ์ด๋ฅผ ๋น„๊ตํ•˜์—ฌ ๊ทธ ์ฐจ์ด๋ฅผ ๋ฒ„ํผ๋กœ ๋‘”๋‹ค. ์• ๋กœ์‚ฌ์Šฌ์ผ ๊ฒฝ์šฐ์—๋Š” ํ”„๋กœ์ ํŠธ ๋ฒ„ํผ๋กœ ๋‘๋ฉฐ, ๋น„์• ๋กœ์‚ฌ์Šฌ์ธ ๊ฒฝ์šฐ์—๋Š” FB๋กœ ๋‘”๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…์˜ ์ฃผ์š” ์ œ์•ฝ์š”์ธ์ธ R&D ์žฅ์น˜ ํ˜„ํ™ฉ ๋ฐ ๊ณ„ํš ๋“ฑ์„ ์ฐธ๊ณ ํ•˜์—ฌ ๊ณ„์‚ฐ๋œ ์ผ์ •์„ ์ˆ˜์ • ๋ณด์™„ํ•œ๋‹ค. ๊ฐœ๋ฐœํ•œ ๋ฐฉ๋ฒ•๋ก ์˜ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•˜์—ฌ ํ•ต์œตํ•ฉ์—๋„ˆ์ง€๊ฐœ๋ฐœ์‚ฌ์—…์ธ KSTAR ๊ฑด์„ค์‚ฌ์—… ์ผ์ • ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋จผ์ € KSTAR ๊ฑด์„ค์‚ฌ์—…์˜ ์ตœ์ดˆ ๊ณ„ํš์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์ˆ ์„ฑ์ˆ™๋„์™€ ์ผ์ •์œ„ํ—˜์„ ํ‰๊ฐ€ํ•˜๊ณ  ๋ชฌํ…Œ์นด๋ฅผ๋กœ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ KSTAR ๊ฑด์„ค์‚ฌ์—…์˜ ํ™•๋ฅ ์  ์ผ์ •๋ถ„ํฌ๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ KSTAR์˜ ์ตœ์ดˆ ๊ณ„ํš์€ ์ผ์ •์œ„ํ—˜์ด ๋งค์šฐ ๋†’์€ ๊ณ„ํš์ด์—ˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๊ณ  1, 2์ฐจ ์ˆ˜์ •๋œ ๊ณ„ํš์—์„œ๋Š” ์ผ์ •์œ„ํ—˜์ด ๋‚ฎ์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์ด์œ ๋กœ๋Š” ๊ธฐ๋ฐ˜๊ธฐ์ˆ  R&D์˜ ์กฐ๊ธฐ ์ฐฉ์ˆ˜์™€ ๋‹น์‹œ ๋†’์€ ๊ตญ๋‚ด ์‚ฐ์—…์—ญ๋Ÿ‰์— ์˜ํ•ด ์ผ์ •์œ„ํ—˜์ด ๋‚ฎ์•„์ง„ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋œ๋‹ค. ๋‹ค์Œ์œผ๋กœ DEMO ์Šค์ผ€์ค„๋ง์„ ์œ„ํ•˜์—ฌ ๋ฐฉ๋ฒ•๋ก ์„ ์ ์šฉํ•˜์˜€๋‹ค. DEMO ๊ธฐ์ˆ ์ฒด๊ณ„์™€ ๊ฐœ๋ฐœ์‹œ๊ฐ„์„ ๊ตฌํ•˜๊ณ , ๊ธฐ์ˆ ์„ฑ์ˆ™๋„, ์ผ์ •์œ„ํ—˜์„ ๊ณ ๋ คํ•˜์—ฌ ํ™•๋ฅ ์  ์ผ์ •๋ถ„ํฌ๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. R&D ์ œ์•ฝ์š”์ธ์— ๋”ฐ๋ผ ์ผ์ •์œ„ํ—˜์„ ํ‰๊ฐ€ํ•˜์˜€๊ณ , ์ผ์ •์œ„ํ—˜๊ฐ’์ด ์ปค์ง์— ๋”ฐ๋ผ DEMO ๊ฐœ๋ฐœ์ง€์—ฐ์— ์˜ํ–ฅ์„ ์ค€๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. KSTAR ๊ฑด์„ค์‚ฌ๋ก€๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์กฐ๊ธฐ์— DEMO ๊ธฐ๋ฐ˜๊ธฐ์ˆ  R&D ๋“ฑ ์„ ํ–‰๊ณผ์ œ๋ฅผ ์ ๊ธฐ์— ์ˆ˜ํ–‰ํ•˜์—ฌ ์ผ์ •์œ„ํ—˜์„ ๋‚ฎ์ถœ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ํ•œํŽธ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœํ•œ ๋ฐฉ๋ฒ•๋ก ์€ ํ•ต์œตํ•ฉ์—๋„ˆ์ง€๊ฐœ๋ฐœ์‚ฌ์—… ์™ธ ๋‹ค๋ฅธ ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—… ์Šค์ผ€์ค„๋ง์—๋„ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•  ๊ฒƒ์ด๋ฉฐ, ๋น„์šฉ, ์ธ๋ ฅ ๋“ฑ ์š”์ธ์„ ์ถ”๊ฐ€๋กœ ๋ฐ˜์˜ํ•˜๋ฉด ์˜ˆ์‚ฐ๊ณ„ํš, ์กฐ๋‹ฌ๊ณ„ํš, ์ธ๋ ฅ๊ณ„ํš ๋“ฑ ์‚ฌ์—…์ถ”์ง„์„ ์œ„ํ•œ ์‹ค์งˆ์ ์ธ ์˜์‚ฌ๊ฒฐ์ •๋„ ๊ฐ€๋Šฅํ•  ๊ฒƒ์ด๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ๋‚ด์šฉ 2 ์ œ 2 ์žฅ ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…๊ณผ ์Šค์ผ€์ค„๋ง ๋ฐฉ๋ฒ•๋ก  3 2.1 ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…๊ณผ ์ผ์ •์œ„ํ—˜ 3 2.1.1 ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…์˜ ์ •์˜ ๋ฐ ํŠน์„ฑ 3 2.1.2 ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—… ์ผ์ •์œ„ํ—˜๊ณผ ๊ธฐ์ˆ ์„ฑ์ˆ™๋„ 4 2.2 ํ”„๋กœ์ ํŠธ ์Šค์ผ€์ค„๋ง ๋ฐฉ๋ฒ•๋ก  6 2.2.1 PERT/CPM 6 2.2.2 CCPM 6 2.3 ์„ ํ–‰ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ์‹œ์‚ฌ์  9 ์ œ 3 ์žฅ ๊ธฐ์ˆ ์„ฑ์ˆ™๋„์™€ ์• ๋กœ์‚ฌ์Šฌ์„ ๊ณ ๋ คํ•œ ๋Œ€ํ˜•์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—… ์Šค์ผ€์ค„๋ง ๋ฐฉ๋ฒ•๋ก  ๊ฐœ๋ฐœ 10 3.1 ๊ฐœ๋ฐœ๋ฐฉ๋ฒ•๋ก  ๊ฐœ์š” ๋ฐ ๊ฐ€์ • 10 3.1.1 ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฐœ์š” 10 3.1.2 ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฐ€์ • 11 3.2 ๊ธฐ์ˆ ์„ฑ์ˆ™๋„์™€ ์ผ์ •์œ„ํ—˜์„ ๊ณ ๋ คํ•œ ํ™•๋ฅ ์  ์ผ์ • ๊ณ„์‚ฐ 14 3.2.1 TRL ์ •์˜ ๋ฐ ํ‰๊ฐ€ 14 3.2.2 TRL ํ™•๋ฅ ์  ๊ฐœ๋ฐœ์‹œ๊ฐ„ ๋ถ„ํฌ 15 3.2.3 TRL ํ™œ๋™๊ณผ ์ผ์ •์œ„ํ—˜ 15 3.2.4 ๋ชฌํ…Œ์นด๋ฅผ๋กœ๋ฅผ ์ด์šฉํ•œ ํ™•๋ฅ ์  ์ผ์ • ๊ณ„์‚ฐ 16 3.3 ์• ๋กœ์‚ฌ์Šฌ ํ‰๊ฐ€์™€ ๋ฒ„ํผ๋ฅผ ๊ณ ๋ คํ•œ ์Šค์ผ€์ค„๋ง 17 3.3.1 ํ‰๊ท ๊ฐœ๋ฐœ์‹œ๊ฐ„๊ณผ ์‹œ์Šคํ…œ๊ฐœ๋ฐœ ์ผ์ •์œ„ํ—˜ 17 3.3.2 ์• ๋กœ์‚ฌ์Šฌ์˜ ๊ฒฐ์ • 17 3.3.3 ๋ฒ„ํผ์˜ ๊ฒฐ์ •๊ณผ ์Šค์ผ€์ค„๋ง 18 ์ œ 4 ์žฅ ํ•ต์œตํ•ฉ์—๋„ˆ์ง€๊ฐœ๋ฐœ์‚ฌ์—… ์‚ฌ๋ก€๋ฅผ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•๋ก  ๊ฒ€์ฆ ๋ฐ ์Šค์ผ€์ค„๋ง 19 4.1 ํ•ต์œตํ•ฉ์—๋„ˆ์ง€๊ฐœ๋ฐœ์‚ฌ์—… ์‚ฌ๋ก€ ์„ ์ • ๋ฐฐ๊ฒฝ๊ณผ ์Šค์ผ€์ค„๋ง ๊ฐœ์š” 19 4.1.1 ์‚ฌ๋ก€ ์„ ์ • ๋ฐฐ๊ฒฝ 19 4.1.2 ๊ฒ€์ฆ ๋ฐ ์ ์šฉ ๊ฐœ์š” 20 4.2 KSTAR ๊ฑด์„ค์‚ฌ์—… ์‚ฌ๋ก€๋ฅผ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฒ€์ฆ 21 4.2.1 ๊ฑด์„ค์‚ฌ์—… ๊ฐœ์š” 21 4.2.2 ์ž…๋ ฅ์ž๋ฃŒ์˜ ์ƒ์„ฑ 23 4.2.3 ๊ณ„์‚ฐ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 23 4.3 DEMO ๊ฐœ๋ฐœ์‚ฌ์—… ์Šค์ผ€์ค„๋ง 25 4.3.1 DEMO ์‚ฌ์—… ๊ฐœ์š” 25 4.3.2 ์ž…๋ ฅ์ž๋ฃŒ์˜ ์ƒ์„ฑ 28 4.3.3 ๊ณ„์‚ฐ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 33 ์ œ 5 ์žฅ ๊ฒฐ๋ก  35 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 37 Abstract 39Docto

    ์‚ฌ๋žŒ ๋ฐฐ์•„ ์ค„๊ธฐ์„ธํฌ๋กœ๋ถ€ํ„ฐ ์–ป์–ด๋‚ธ ์‹ ๊ฒฝ ์ „๊ตฌ์„ธํฌ์˜ ์ƒ์กด, ์ฆ์‹, ์•„ํฌํ† ์‹œ์Šค, ๋ถ„ํ™”์— ๋Œ€ํ•œ ์„ธ๋ณดํ”Œ๋ฃจ๋ž€ ๋‹จ๊ธฐ ๋…ธ์ถœ์˜ ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2018. 2. ๊น€์ง„ํฌ.Purpose: Data from animal experiments suggest that exposure to general anesthetics in early life inhibits neurogenesis and causes long-term memory deficit. Regarding a short operating time and popularity of sevoflurane in pediatric anesthesia, it is important to verify effects of short period exposure to sevoflurane on developing brain. Methods: We measured the effects of short-term exposure (2 h) to 3%, 6% or 8% sevoflurane, the most commonly used anesthetic, on neural precursor cells derived from human embryonic stem cells, SNUhES32. On days 1, 3, 5 and 7 post-treatment, cell survival, proliferation, apoptosis and differentiation were analyzed. Results: Treatment with 6% sevoflurane increased cell viability (P = 0.046) and decreased apoptosis (P = 0.014) on day 5, but didnt last on day 7. Survival and apoptosis were not affected by 3% and 8% sevofluranethere was no effect of proliferation at any of the tested concentrations. The differentiation of cells exposed to 6% or 8% sevoflurane decreased on day 1 (P = 0.033 and 0.036 for 6% and 8% sevoflurane, respectively) but was again normalized on days 3โ€“7. Conclusion: The clinically relevant treatment with sevoflurane for 2 h induces no significant changes of the survival, proliferation, apoptosis and differentiation of human neural precursor cells, although supra-clinical doses of sevoflurane alter human neurogenesis transiently.1. Introduction 1 2. Materials and Methods 3 hESC line 3 Derivation of NPCs from hESCs 3 Sevoflurane treatment 4 Determination of the medium concentration of sevoflurane by gas chromatography 5 Cell viability analysis 5 Proliferation analysis 5 Apoptosis analysis 6 Differentiation analysis 6 Statistical analysis 7 3. Result 8 Concentration of sevoflurane in the medium 8 Effects of sevoflurane on the survival of human NPCs 8 Effects of sevoflurane on the proliferation of human NPCs 9 Effects of sevoflurane on apoptosis by human NPCs 10 Effects of sevoflurane on human NPC differentiation 11 4. Discussion 12 5. References 17Docto

    Dominant Feature Pooling for Multi Camera Object Detection and Optimization of Retinex Algorithm

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021.8. ์ดํ˜์žฌ.๋ณธ ๋…ผ๋ฌธ์€ ๋ฉ€ํ‹ฐ ์นด๋ฉ”๋ผ object detection CNN์„ ์œ„ํ•œ detection ๋‹จ๊ณ„์—์„œ ํ™œ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด dominant feature pooling ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ฉ€ํ‹ฐ ์นด๋ฉ”๋ผ ์‹œ์Šคํ…œ์€ ๋‹ค์–‘ํ•œ ๊ด€์ ์—์„œ ๋ฌผ์ฒด์˜ ์ด๋ฏธ์ง€๋ฅผ ์บก์ฒ˜ํ•˜๊ณ , ๋ฌผ์ฒด์˜ ๋” ๋งŽ์€ ์ฃผ์š” feature๋ฅผ detection์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์—ฌ๋Ÿฌ ์นด๋ฉ”๋ผ์—์„œ feature๋ฅผ poolingํ•˜๋ฉด detection ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๊ฐ์ฒด์˜ ๋‹ค์–‘ํ•œ ๋ทฐํฌ์ธํŠธ์—์„œ ์–ป์€ feature vector ์ค‘์—์„œ ๋” ๋งŽ์€ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ฃผ์š” feature์„ ์„ ํƒํ•˜๊ณ  ์„ ํƒํ•œ feature vector๋ฅผ poolingํ•˜์—ฌ ์ƒˆ๋กœ์šด feature map์„ ๊ตฌ์„ฑํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋‹จ์ผ ์นด๋ฉ”๋ผ์— ๋Œ€ํ•œ YOLOv3 ๋„คํŠธ์›Œํฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ๋ฉ€ํ‹ฐ ์นด๋ฉ”๋ผ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์ถ”๊ฐ€ ํ•™์Šต ๊ณผ์ •์ด ํ•„์š”ํ•˜์ง€ ์•Š๋‹ค. Dominant feature pooling์˜ ํšจ๊ณผ๋ฅผ ์ฃผ์žฅํ•˜๊ธฐ ์œ„ํ•ด, ์ด ์—ฐ๊ตฌ์—์„œ๋Š” feature vector๋ฅผ ์‹œ๊ฐํ™”ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋„ ์ œ์•ˆ๋œ๋‹ค. ๋˜ํ•œ object detection CNN์€ ์ €์กฐ๋„ ํ™˜๊ฒฝ์— ๋Œ€์‘์ด ์ทจ์•ฝํ•˜๋ฏ€๋กœ ์ด๋ฅผ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋Š” Retinex ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ™œ์šฉ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ €์กฐ๋„ ์˜์ƒ์„ ๊ทธ๋Œ€๋กœ ํ•™์Šตํ•˜์—ฌ ๊ฐœ์„ ์„ ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ์‹ค ์‚ฌ์šฉ ํ™˜๊ฒฝ์—์„œ ์กฐ๋„ ์ •๋„๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— Retinex ๊ฐœ์„ ์ด ํ•„์ˆ˜์ ์ž„์„ ์‹คํ—˜์„ ํ†ตํ•ด ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๋˜ํ•œ ๊ฐœ์„  ํšจ๊ณผ๊ฐ€ ๋šœ๋ ทํ•˜์ง€๋งŒ ๋ณต์žก๋„๊ฐ€ ๋†’์€ Retinex ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ HW ์„ค๊ณ„๋ฅผ ํ†ตํ•ด ์ตœ์ ํ™” ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. Retinex ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์—ฐ์‚ฐ์— ํ•„์ˆ˜์ ์ธ exponentiation๊ณผ Gaussian filtering์„ ํšจ์œจ์ ์œผ๋กœ ๊ตฌํ˜„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์—ฌ ๋†’์€ ํ•ด์ƒ๋„์—์„œ๋„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋™์ž‘์ด ๊ฐ€๋Šฅํ•œ HW๋ฅผ ๊ตฌํ˜„ํ•˜์˜€๋‹ค.This paper proposes a novel dominant feature pooling method utilized in the detection phase for multi-camera object detection CNNs. Multi-camera systems can capture images of objects from various perspectives and utilize more of the important features of objects for detection. Thus, the detection accuracy can be improved by pooling the features of the multiple cameras. The proposed method constructs a new feature patch by selecting and pooling the dominant features that provides more information among the feature vectors obtained from various viewpoints of objects. The proposed method is based on the YOLOv3 network for a single camera, and does not require additional learning processes for multi-camera systems. To show the effectiveness of dominant feature pooling, a novel method of visualizing feature vectors is also proposed in this work. Furthermore, a method of utilizing Retinex algorithms that can improve response to low-light environments for object detection CNN is proposed. Although improvements can be made by learning low-light images as they are, experimental results show that Retinex improvements are essential because the degree of illumination cannot be predicted accurately to create new datasets in practical environments. This work proposes a method to optimize Retinex algorithms through HW designs. An efficient implementation of the exponentiation operation and the Gaussian filtering, which are essential for Retinex algorithm operations is proposed to implement HW that can operate in real time at high resolution.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ 2 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 4 ์ œ 2 ์žฅ ๋ฐฐ๊ฒฝ ์ด๋ก  ๋ฐ ๊ด€๋ จ ์—ฐ๊ตฌ 5 2.1 Object Detection CNN 5 2.2 Multi View CNN 6 2.3 Retinex ์•Œ๊ณ ๋ฆฌ์ฆ˜ 7 2.3.1 Retinex Algorithm using Gaussian Filter 8 2.3.2 Multiscale Retinex Algorithm 9 2.3.3 Efficient Naturalness Restoration 10 ์ œ 3 ์žฅ ๋ฌด์ธ ํŒ๋งค๋Œ€ ์‹œ์Šคํ…œ 12 3.1 ๋ฌด์ธ ํŒ๋งค๋Œ€ ์‹œ์Šคํ…œ ๊ฐœ์š” 12 3.2 Object Detection CNN์„ ํ™œ์šฉํ•œ ์ƒํ’ˆ ์ธ์‹ 16 3.3 Multi-Object Tracking์„ ํ™œ์šฉํ•œ ์ƒํ’ˆ ๊ตฌ๋งค ํŒ๋‹จ 18 3.4 ๋ฌด์ธ ํŒ๋งค๋Œ€์˜ ์‹ค์‹œ๊ฐ„ ๋™์ž‘์„ ์œ„ํ•œ ์ตœ์ ํ™” ๋ฐฉ์•ˆ 20 3.4.1 ์นด๋ฉ”๋ผ ์„ ํƒ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 20 3.4.2 Multithreading 24 3.4.3 Pruning 25 3.5 ๋ฌด์ธ ํŒ๋งค๋Œ€ ์‹œ์Šคํ…œ ์„ฑ๋Šฅ ํ‰๊ฐ€ 27 3.5.1 Object Detection ์„ฑ๋Šฅ ํ‰๊ฐ€ 27 3.5.2 ๋ฌด์ธ ํŒ๋งค๋Œ€ ์‹œ์Šคํ…œ ์ „์ฒด ๊ฒฐ๊ณผ 29 ์ œ 4 ์žฅ ๋ฉ€ํ‹ฐ ์นด๋ฉ”๋ผ Dominant Feature Pooling 32 4.1 Object Detection CNN๊ณผ ๋ฉ€ํ‹ฐ ์นด๋ฉ”๋ผ Object Clustering 33 4.1.1 Object Detection CNN 33 4.1.2 ๋ฉ€ํ‹ฐ ์นด๋ฉ”๋ผ Object Clustring 35 4.2 Dominant Feature Pooling ๋ฐฉ๋ฒ• 37 4.2.1 Dominant Feature Scoring 40 4.2.2 Dominant Feature Pooling 47 4.2.3 YOLOv3์˜ Detection Layer ์žฌ์‚ฌ์šฉ 50 4.3 Feature ์‹œ๊ฐํ™”๋ฅผ ํ†ตํ•œ ์ œ์•ˆ ๋ฐฉ๋ฒ• ๋ถ„์„ 52 4.3.1 ์ œ์•ˆํ•˜๋Š” Feature ์‹œ๊ฐํ™” ๋ฐฉ๋ฒ• 52 4.3.2 ๊ธฐ์กด ๋‹จ์ผ ์นด๋ฉ”๋ผ YOLOv3์˜ Feature ์‹œ๊ฐํ™” 55 4.3.3 ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์˜ ๋ฉ€ํ‹ฐ์นด๋ฉ”๋ผ Feature ์‹œ๊ฐํ™” 57 4.4 Dominant Feature Pooling ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 59 4.4.1 COCO Dataset์—์„œ์˜ ๊ฒฐ๊ณผ 60 4.4.2 Custom Dataset์—์„œ์˜ ๊ฒฐ๊ณผ 62 4.4.3 Scoring Method ๋ณ„ ๊ฒฐ๊ณผ 63 4.4.3 Dominant Feature Pooling์˜ ์ˆ˜ํ–‰์‹œ๊ฐ„ ๊ฒฐ๊ณผ 64 ์ œ 5 ์žฅ Retinex Applied Object Detection ๋ฐ ํ•˜๋“œ์›จ์„œ ๊ฐ€์†์‹œ์Šคํ…œ 65 5.1 ๊ธฐ์กด Retinex ์ ์šฉ ์—ฐ๊ตฌ 66 5.2 Retinex Applied Object Detection 68 5.2.1 Retinex Applied Object Detection ํ•™์Šต 68 5.2.2 Retinex Applied Object Detection ๊ฒฐ๊ณผ 72 5.3 Object Detection์„ ์œ„ํ•œ Retinex ์ตœ์ ํ™” 76 5.3.1 Gaussian Filter ํฌ๊ธฐ์— ๋”ฐ๋ฅธ Retinex ํšจ๊ณผ ๋ถ„์„ 76 5.3.2 Gaussain Filter ํฌ๊ธฐ์— ๋”ฐ๋ฅธ Object Detection ๊ฒฐ๊ณผ 80 5.4 Retinex ํ•˜๋“œ์›จ์–ด ์‹œ์Šคํ…œ์˜ ํ•„์š”์„ฑ ๋ฐ ๊ธฐ์กด ์—ฐ๊ตฌ 82 5.5 ์ œ์•ˆ ํ•˜๋“œ์›จ์–ด ์‹œ์Šคํ…œ ๊ตฌํ˜„ ๊ฐœ์š” 85 5.6 ์ œ์•ˆ ํ•˜๋“œ์›จ์–ด ์‹œ์Šคํ…œ ๊ตฌํ˜„ ํŠน์žฅ์  89 5.6.1 Gaussian filter์˜ ๊ตฌํ˜„ 89 5.6.2 Exponentiation์˜ ๊ตฌํ˜„ 96 5.6.3 HDMI/DVI ์ง€์› ๋ฐ ์˜์ƒ latency ์ตœ์†Œํ™” 103 5.7 ์ œ์•ˆ ํ•˜๋“œ์›จ์–ด ์‹œ์Šคํ…œ ๊ตฌํ˜„ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 106 5.7.1 ์‹ค์‹œ๊ฐ„ ๋™์ž‘ ๋ฐ ๋‚ฎ์€ latency์— ๋Œ€ํ•œ ๋ถ„์„ 106 5.7.2 ์ œ์•ˆํ•œ ์‹œ์Šคํ…œ์˜ ์˜์ƒ ์ฒ˜๋ฆฌ ์„ฑ๋Šฅ ๊ฒฐ๊ณผ ๋ถ„์„ 109 5.7.3 ์ œ์•ˆํ•œ ์‹œ์Šคํ…œ์˜ FPGA Resource Utilization 112 5.7.4 ๋‹ค๋ฅธ ์‹œ์Šคํ…œ๊ณผ์˜ Resource Utilization ๋น„๊ต 114 5.7.5 ์ œ์•ˆํ•œ ์‹œ์Šคํ…œ์˜ ์˜์ƒ ์ฒ˜๋ฆฌ ์„ฑ๋Šฅ ๊ฒฐ๊ณผ ๋ถ„์„ 119 ์ œ 6 ์žฅ ๊ฒฐ๋ก  120 ์ฐธ๊ณ ๋ฌธํ—Œ 121 Abstract 131๋ฐ•

    A Study on Development and Application of Fatigue Assessment System for Steel Bridges

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    ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •๋Ÿ‰์ ์ด๊ณ  ๊ฐ๊ด€์ ์ธ ์œ ์ง€๊ด€๋ฆฌ ๊ธฐ๋ฒ• ๊ตฌ์ถ• ๋ฐ ํšจ์œจ์ ์ธ ์œ ์ง€๊ด€๋ฆฌ ์ฒด๊ณ„๊ตฌ์ถ•์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ์š”์†Œ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ๊ธฐ์ˆ ์˜ ์ผํ™˜์œผ๋กœ ๊ฐ•๊ต์˜ ํ”ผ๋กœํ‰๊ฐ€๋ฅผ ์‹ค์‹œํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์„ ํ†ตํ•˜์—ฌ ๋Œ€์ƒ ๊ตฌ์กฐ๋ฌผ์˜ ์ž”์กด์ˆ˜๋ช… ๋ฐ ํ”ผ๋กœ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๊ฒƒ์„ ๊ธฐ์ดˆ๋กœ ๋Œ€์ƒ๊ตฌ์กฐ๋ฌผ์˜ ์ž”์กด์ˆ˜๋ช…์— ๊ธฐ์ดˆํ•œ ์œ ์ง€๊ด€๋ฆฌ ์šฐ์„ ์ˆœ์œ„ ๊ฒฐ์ •์—์˜ ๊ธฐ๋ณธ์  ์ž๋ฃŒ๋กœ ํ™œ์šฉํ•˜๋„๋ก ํ•˜์—ฌ ๊ฐ•๊ต์˜ ์•ˆ์ „์„ฑ ๋ฐ ๋‚ด๊ตฌ์„ฑ, ์œ ์ง€๊ด€๋ฆฌ์ธก๋ฉด์—์„œ์˜ ํšจ์œจ์„ฑ ๋ฐ ๊ฒฝ์ œ์„ฑ์„ ํ–ฅ์ƒํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ตญ๋‚ด ๊ฐ•์ฒ ๋„๊ต ํ”ผ๋กœํŠน์„ฑ์„ ์กฐ์‚ฌํ•œ ๊ฒฐ๊ณผ ๊ณต์šฉ๋…„์ˆ˜๊ฐ€ ์˜ค๋ž˜๋œ ๊ต๋Ÿ‰์€ ์ง€๊ฐ„์ด ๋Œ€๋ถ€๋ถ„ ์งง์•„ ํ”ผ๋กœ์— ์ทจ์•ฝํ•จ์„ ์•Œ ์ˆ˜ ์žˆ์–ด ํ•ฉ๋ฆฌ์ ์ธ ๋ณด์ˆ˜๋ณด๊ฐ• ๋Œ€์ฑ… ์ˆ˜๋ฆฝ์ด ํ•„์š”ํ•˜๋‹ค๊ณ  ํŒ๋‹จ๋˜์–ด, ๊ฐ•์ฒ ๋„๊ต์— ๋Œ€ํ•œ ๊ตญ๋‚ด์ž๋ฃŒ๋ฅผ ์ข…ํ•ฉ ๋ถ„์„ํ•˜๊ณ  ๋Œ€ํ‘œ์ ์ธ ๊ต๋Ÿ‰์— ๋Œ€ํ•ด ํ˜„์žฅ๊ณ„์ธก์„ ์‹ค์‹œํ•˜์—ฌ ์—„๋ฐ€ํ•œ ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ต๋Ÿ‰์˜ ์‹ค์ธก๋ฐ์ดํ„ฐ์™€ ๋ณธ ์‹œ์Šคํ…œ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๋น„๊ต ๋ถ„์„ํ•˜์—ฌ ์‹œ์Šคํ…œ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€ํ† ํ•˜๊ณ  ๊ฐ ๊ต๋Ÿ‰์˜ ์˜ํ–ฅ๋ฉด์„ ์ด์šฉํ•˜์—ฌ ์‹ค์ œ ์—ด์ฐจํ•˜์ค‘์ด ๊ฐ•์ฒ ๋„ ๊ต๋Ÿ‰์˜ ํ”ผ๋กœ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‹ค์ œ ๊ณต์šฉ์ค‘์ธ ๊ต๋Ÿ‰์˜ ํ”ผ๋กœ์•ˆ์ „์„ฑ ํ‰๊ฐ€๋ฅผ ๋ณธ ์‹œ์Šคํ…œ์œผ๋กœ ์‹ค์‹œํ•˜๊ณ  ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์ผ๋ณธ ๋ฒ•์ •๋Œ€ํ•™๊ต์—์„œ ๊ฐœ๋ฐœ๋œ ํ”ผ๋กœ์•ˆ์ „์„ฑํ‰๊ฐ€ ํ”„๋กœ๊ทธ๋žจ(Fatigue Safety Assessment Program, FSAP)๊ณผ์˜ ๊ฒฐ๊ณผ์™€ ๋น„๊ต ๋ถ„์„ํ•˜์—ฌ ๋ณธ ์‹œ์Šคํ…œ์˜ ํ™œ์šฉ์„ฑ๊ณผ ํƒ€๋‹น์„ฑ์„ ํ•จ๊ป˜ ๊ฒ€ํ† ํ•˜์˜€๋‹ค.Achieve survival life of target construction and fatigue investigation through system development that can execute fatigue estimation of steel bridge by link of technology for development of element technology that can achieve quantitative and objective preservation administration techniques construction and efficient preservation administration system construction in this research. Wish to utilize this to basic data to preservation administration precedence decision based on survival life of target construction to foundation and enhance safety and durability, preservation administration's efficiency and economic performance of steel bridge. Official business year's number analyzes domestic data for steel bridge synthesis because old bridge is judged that reasonable repair reinforcement countermeasure establishment need because can know that span effective span is weak on announcement because is short mostly as result that investigate domestic steel bridge fatigue special quality and executed strict data analysis because enforcing spot measure about representative bridge. Enforce by system that conduct fatigue safety estimation of bridge that is using in common actuality and the result analyzing result and comparison with fatigue safety Assessment program(FSAP) that is developed in Japan court university and validity of these system together examine .ABSTRACT = i ๋ชฉ์ฐจ = iii ํ‘œ ๋ชฉ์ฐจ = v ๊ทธ๋ฆผ ๋ชฉ์ฐจ = vi 1์žฅ ์„œ๋ก  = 1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ = 1 1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ ๋ฐ ๋ฒ”์œ„ = 2 2์žฅ ๊ธฐ์ดˆ ๊ฐœ๋… = 3 2.1 ์‘๋ ฅ๋ณ€๋™ํ•ด์„ = 3 2.1.1 ํ”ผ๋กœํ•˜์ค‘ = 3 2.1.2 ์˜ํ–ฅ๋ฉด = 5 2.1.3 ํ•˜์ค‘์˜ ์ด๋™๊ฒฝ๋กœ ์„ค์ • = 6 2.1.4 ์‘๋ ฅ๋ณ€๋™ ๊ณ„์‚ฐ๋ฐฉ๋ฒ• = 6 2.1.5 ๋ถ„ํ•  ํญ์˜ ์„ค์ • = 7 2.1.6 ๊ฒฐ๊ณผ์˜ ์ถœ๋ ฅ = 8 2.2 ํ”ผ๋กœ์กฐ์‚ฌ = 8 2.2.1 ํ”ผ๋กœ๊ฐ•๋„ = 8 2.2.2 ํ”ผ๋กœ์กฐ์‚ฌ์— ์‚ฌ์šฉ๋˜๋Š” ์‘๋ ฅ = 16 2.2.3 ํ”ผ๋กœ์กฐ์‚ฌ๋ฐฉ๋ฒ• = 18 2.3 ํ”ผ๋กœ๊ท ์—ด์ง„์ „ํ•ด์„ = 20 2.3.1 ๊ธฐ๋ณธ์ ์ธ ๊ณ ๋ ค์‚ฌํ•ญ = 20 2.3.2 ์‘๋ ฅํ™•๋Œ€๊ณ„์ˆ˜์˜ ๊ณ„์‚ฐ = 21 2.3.3 ๊ท ์—ด์˜ ํ•ฉ์ฒด์กฐ๊ฑด = 30 2.3.4 ํ”ผ๋กœ๊ท ์—ด์ง„์ „์†๋„ ํ‘œ์‹œ์‹ = 30 2.3.5 ์ผ์ •์ง„ํญ์‘๋ ฅํ•˜์—์„œ์˜ ํ”ผ๋กœ๊ท ์—ด์ง„์ „ํ•ด์„ = 31 2.3.6 ๋ณ€๋™์ง„ํญ์‘๋ ฅํ•˜์—์„œ์˜ ํ”ผ๋กœ๊ท ์—ด์ง„์ „ํ•ด์„ = 33 3์žฅ ํ”„๋กœ๊ทธ๋žจ์˜ ๊ธฐ๋Šฅ = 40 3.1 ์‘๋ ฅ๋ณ€๋™ํ•ด์„ = 40 3.1.1 ์ž…๋ ฅ = 41 3.1.2 ์‹คํ–‰ = 50 3.1.3 ์ถœ๋ ฅ = 52 3.2 ํ”ผ๋กœ์กฐ์‚ฌ = 53 3.1.1 ๊ธฐ๋ณธ๋ฐ์ดํ„ฐ = 53 3.2.2 ์‹คํ–‰ = 58 3.2.3 ์ถœ๋ ฅ = 58 4์žฅ ํ”„๋กœ๊ทธ๋žจ์˜ ์ ์šฉ = 59 4.1 ๋ด‰๊ฐ•์ฒœ๊ต = 59 4.1.1 ๊ต๋Ÿ‰ํ˜„ํ™ฉ = 59 4.1.2 ์ธก์ • ํ•˜์ค‘ = 59 4.1.3 ๋นˆ๋„๊ทธ๋ž˜ํ”„ = 60 4.1.4 ํ”ผ๋กœ์„ค๊ณ„๊ณก์„  = 61 4.1.5 ํ”ผ๋กœ์กฐ์‚ฌ = 61 4.2 ์ฃฝ๊ณ„์ฒœ๊ต(์ƒํ–‰) = 62 4.2.1 ๊ต๋Ÿ‰ํ˜„ํ™ฉ = 62 4.2.2 ์ธก์ •ํ•˜์ค‘ = 62 4.2.3 ๋นˆ๋„๊ทธ๋ž˜ํ”„ = 62 4.2.4 ํ”ผ๋กœ์„ค๊ณ„๊ณก์„  = 63 4.2.5 ํ”ผ๋กœ์กฐ์‚ฌ = 64 5์žฅ ํ”„๋กœ๊ทธ๋žจ์˜ ํ™œ์šฉ ๋ฐ ๊ฒ€์ฆ = 65 5.1 ๊ฒ€ํ†  ๋Œ€์ƒ = 65 5.2 ํ•ด์„ ๋ชจํ˜• ๋ฐ ์˜ํ–ฅ๋ฉด = 65 5.3 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•˜์ค‘ = 66 5.4 ๊ตํ†ต๋Ÿ‰ ์˜ˆ์ธก๊ฒฐ๊ณผ = 67 5.5 ์‘๋ ฅ๋ณ€๋™ํ•ด์„๊ฒฐ๊ณผ = 67 5.6 ํ”ผ๋กœ๋“ฑ๊ธ‰๋ถ„๋ฅ˜ ๋ฐ ํ—ˆ์šฉํ”ผ๋กœ์‘๋ ฅ ๋ฒ”์œ„ = 68 5.7 ํ”ผ๋กœ์•ˆ์ „์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ = 69 6์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉํ–ฅ = 71 ์ฐธ๊ณ ๋ฌธํ—Œ = 7

    ์•ผ์Šค๋งˆ๋ฃจ ์‚ฌ์ƒ์‚ฌ ๋‹ค์‹œ ์ฝ๊ธฐ

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    [์„œํ‰] 1. ใ€Ž์•ผ์Šค๋งˆ๋ฃจ ์‚ฌ์ƒ์‚ฌ์™€์˜ ๋Œ€๋ก : ๋ฌธ๋ช…ํ™”ยท๋ฏผ์ค‘ยท์–‘์˜์„ฑใ€(ๅฎ‰ไธธๆ€ๆƒณๅฒใธใฎๅฏพ่ซ–โ”€ๆ–‡ๆ˜ŽๅŒ–ยทๆฐ‘่ก†ยทไธก็พฉๆ€ง, ๅฎ‰ไธธ่‰ฏๅคซยท็ฃฏๅ‰้ †ไธ€ ็ทจ, ใƒšใƒชใ‚ซใƒณ็คพ, 2010). 2. ใ€Ž์ „ํ›„์ง€์˜ ๊ฐ€๋Šฅ์„ฑ: ์—ญ์‚ฌยท์ข…๊ตยท๋ฏผ์ค‘ใ€(ๆˆฆๅพŒ็Ÿฅใฎๅฏ่ƒฝๆ€งโ”€ๆญดๅฒยทๅฎ—ๆ•™ยทๆฐ‘่ก†โ”€, ๅฎ‰ไธธ่‰ฏๅคซยทๅ–œๅฎ‰ๆœ— ็ทจ, ๅฑฑๅทๅ‡บ็‰ˆ่€…, 2010).๋ƒ‰์ „์ฒด์ œ์˜ ๋ถ•๊ดด๋กœ ์‚ฌํšŒ์ฃผ์˜ ํ˜๋ช…์ด ์‹คํŒจ๋กœ ๋๋‚˜๊ณ  ํฌ์ŠคํŠธ๋ชจ๋”๋‹ˆ์ฆ˜๊ณผ ํฌ์ŠคํŠธ์ฝœ๋กœ๋‹ˆ์–ผ๋ฆฌ์ฆ˜, ๋ฌธํ™”์—ฐ๊ตฌ(Cultural Studies), ์„œ๋ฒŒํ„ด์—ฐ๊ตฌ(Subaltern Studies), ๊ตญ๋ฏผ๊ตญ๊ฐ€๋ก  ๋“ฑ๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ์—ฐ๊ตฌ์กฐ๋ฅ˜์˜ ํ™์ˆ˜ ์†์—์„œ ์ •ํ†ต ๋งˆ๋ฅดํฌ์Šค์ฃผ์˜ ์—ญ์‚ฌํ•™์€ ์ด๋ฏธ ์„ค๋“๋ ฅ์„ ์ƒ์‹คํ•˜๊ณ  ์ „ํ›„ ์—ญ์‚ฌํ•™์€ ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์„ ์•”์ค‘๋ชจ์ƒ‰ํ•˜๊ณ  ์žˆ๋‹ค. 21์„ธ๊ธฐ์— ๋“ค์–ด์™€ ์ „ํ›„ ์—ญ์‚ฌํ•™์— ๋Œ€ํ•œ ์žฌ๊ฒ€ํ†  ๋…ผ์˜๊ฐ€ ์ „์— ์—†์ด ํ™œ๋ฐœํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋Š” ๊ฒƒ๋„ ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ๋ชจ์ƒ‰ ์†์—์„œ ์ „ํ›„ ์—ญ์‚ฌํ•™์˜ ์ž๊ธฐ์ ๊ฒ€์ด ์ ˆ์‹คํ•˜๊ฒŒ ์š”๊ตฌ๋˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์ผ ๊ฒƒ์ด๋‹ค. ์ด์™€ ๊ฐ™์ด ์ „ํ›„ ์—ญ์‚ฌํ•™์ด ์žฌ๊ณ ๋˜๋Š” ์ƒํ™ฉ ์†์—์„œ ์•ผ์Šค๋งˆ๋ฃจ ์š”์‹œ์˜ค์˜ ์‚ฌ์ƒ์‚ฌ ์—ฐ๊ตฌ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ด๋ฅผ ์žฌ๊ฒ€ํ† ํ•˜๋ฉด์„œ ์ƒˆ๋กœ์šด ์—ญ์‚ฌ์™€ ์‚ฌ์ƒ ์—ฐ๊ตฌ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํƒ๊ตฌํ•œ ๋ฌต์งํ•œ ๋…ผ๋ฌธ์ง‘ ๋‘ ๊ถŒ์ด ์ถœ๊ฐ„๋˜์—ˆ๋‹ค. ๊ทธ ํ•˜๋‚˜๊ฐ€ ใ€Ž์•ผ์Šค๋งˆ๋ฃจ ์‚ฌ์ƒ์‚ฌ์™€์˜ ๋Œ€๋ก : ๋ฌธ๋ช…ํ™”ยท๋ฏผ์ค‘ยท์–‘์˜์„ฑใ€(ๅฎ‰ไธธๆ€ๆƒณๅฒใธใฎๅฏพ่ซ–โ”€ๆ–‡ๆ˜ŽๅŒ–ยทๆฐ‘่ก†\ยทไธก็พฉๆ€ง, ๅฎ‰ไธธ่‰ฏๅคซยท็ฃฏๅ‰้ †ไธ€ ็ทจ, ใƒšใƒชใ‚ซใƒณ็คพ, 2010)์ด๋ฉฐ, ๋˜ ํ•˜๋‚˜๊ฐ€ ใ€Ž์ „ํ›„์ง€์˜ ๊ฐ€๋Šฅ์„ฑ: ์—ญ์‚ฌยท์ข…๊ตยท๋ฏผ์ค‘ใ€(ๆˆฆๅพŒ็Ÿฅใฎๅฏ่ƒฝๆ€งโ”€ๆญดๅฒยทๅฎ—ๆ•™ยทๆฐ‘่ก†\โ”€, ๅฎ‰ไธธ่‰ฏๅคซยทๅ–œๅฎ‰ๆœ— ็ทจ, ๅฑฑๅทๅ‡บ็‰ˆ่€…, 2010)์ด๋‹ค
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