25 research outputs found

    ์กฐ์Œ์žฅ์• ๋กœ ์ง„๋‹จ๋œ ๋‡Œ์กธ์ค‘ ํ›„ ํŒŒํ‚จ์Šจ์ฆ์˜ ๋ฆฌ์‹ค๋ฒ„๋งŒ ์Œ์„ฑ ์น˜๋ฃŒ ํšจ๊ณผ: ์ฆ๋ก€๋ณด๊ณ 

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    Post-stroke parkinsonism usually presents with bradykinesia, resting tremor, and movement disabilities and reportedly responds poorly to rehabilitative and pharmacological treatment in contrast to Parkinsonโ€™s disease. However, we encountered a patient with subarachnoid hemorrhage caused by a ruptured anterior communicating artery aneurysm who developed parkinsonism, which manifested with hypokinetic hypophonic dysarthria and masked facies. Brain 18F-fluorodeoxyglucose positron emission tomography/computed tomography revealed decreased glucose metabolism in the bilateral basal ganglia. He underwent 18 sessions of Lee Silverman Voice Treatment (LSVT) for 60 min, once daily, and he gradually increased the previously prescribed doses of levodopa and benserazide to 200 mg and 50 mg, respectively, three times a day. The patientโ€™s dysarthria improved from moderate to mild dysarthria. His masked facies also improved remarkably 6 weeks after admission. Along with levodopa administration, LSVT could be suggested as an effective treatment tool for hypophonic dysarthria due to post-stroke parkinsonism.ope

    Atomoxetine (Strattera)-Induced Pathologic Laughing in a Patient With Pontine Hemorrhage: A Case Report

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    Objectives: Pathologic laughing, characterized by episodes of abrupt and inappropriate laughter occurring irrespective of a person's emotional feelings, has been reported in patients with neurologic deficits. Some pharmacotherapies, including selective serotonin reuptake inhibitors, are effective in alleviating pathologic laughing. However, contrary to previous reports, we report a case of pathologic laughing that developed after a patient with pontine hemorrhage was administered atomoxetine (Strattera). Case presentation: A 55-year-old man was diagnosed with acute intracerebral hemorrhage in the right pons and midbrain. The patient showed mild cognitive impairment, and he was administered 10 mg of atomoxetine once daily as a cognitive enhancer. On the day of atomoxetine administration, he suddenly developed episodes of inappropriate laughter that was uncontrollable. The Pathological Laughter and Crying Scale showed a score of 4 of 54 on the day he started taking atomoxetine, and his score was 18 on the second day. He was administered atomoxetine for 3 consecutive days, but it was stopped on the fourth day. His laughing diminished, and his score improved to 5. His smiling expression and a score of 1 on the scale lasted for 1 week. On day 8 of drug discontinuation, his score was zero and all symptoms disappeared. Conclusions: Previously, no single medication has been reported to cause pathologic laughing. Atomoxetine is a stimulant that increases norepinephrine and dopamine levels in the prefrontal cortex. This report suggests that, unlike what is known thus far, norepinephrine and dopamine might play a crucial role in the development of pathologic laughing.restrictio

    ๋ ˆ์ด์ € ๋ถ„๊ด‘ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•œ ์‹ค์‹œ๊ฐ„ ๋น„์ ‘์ด‰ ํƒ์‚ฌ์šฉ ์„ฑ๋ถ„ ๋ถ„์„ ๊ธฐ๋ฒ• ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2015. 2. ์—ฌ์žฌ์ต.๋ ˆ์ด์ € ์œ ๋„ ํ”Œ๋ผ์ฆˆ๋งˆ ๋ถ„๊ด‘๋ถ„์„๋ฒ• (LIBS : Laser-Induced Breakdown Spectroscopy) ์€ ์ƒ˜ํ”Œ์— ๋ ˆ์ด์ €๋ฅผ ์กฐ์‚ฌํ–ˆ์„ ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ํ”Œ๋ผ์ฆˆ๋งˆ๋ฅผ ํ™œ์šฉํ•œ ์‹ค์‹œ๊ฐ„ ๋น„์ ‘์ด‰ ์„ฑ๋ถ„ ๋ถ„์„ ๊ธฐ๋ฒ•์ด๋‹ค. ๋ ˆ์ด์ € ์—๋„ˆ์ง€๋ฅผ ์ง‘์ค‘์‹œ์ผœ 109 W/cm2 ์ด์ƒ์˜ ๋†’์€ ์—๋„ˆ์ง€ ๋ฐ€๋„๋กœ ์กฐ์‚ฌํ•˜๋ฉด ์ƒ˜ํ”Œ์ด ๊ณ ์˜จ ๊ณ ์••์˜ ํ”Œ๋ผ์ฆˆ๋งˆ ํ˜•ํƒœ๋กœ ๋ถ•๊ดด๋œ๋‹ค. ํ”Œ๋ผ์ฆˆ๋งˆ๋Š” ์ด์˜จ, ์›์ž, ์ „์ž, ๋ถ„์ž ๋“ฑ์ด ๊ณต์กดํ•˜๋Š” ์ƒํƒœ๋กœ, ์—ฌ๊ธฐ๋œ ๋ฌผ์งˆ์ด ์—๋„ˆ์ง€๋ฅผ ์žƒ๊ณ  ์•ˆ์ •ํ•œ ์ƒํƒœ๋กœ ๋˜๋Œ์•„ ๊ฐ€๋ฉด์„œ ์›์†Œ์˜ ์„ฑ๋ถ„๊ณผ ์—ฌ๊ธฐ ์ƒํƒœ์— ๋”ฐ๋ผ ํŠน์ •ํ•œ ํŒŒ์žฅ์˜ ๋น›์„ ๋ฐฉ์ถœํ•œ๋‹ค. ์ด๋•Œ ๋ฐฉ์ถœ๋œ ๋น›์˜ ํŒŒ์žฅ์„ ๋ถ„์„ํ•˜์—ฌ ๋ฌผ์งˆ์˜ ๊ตฌ์„ฑ ์„ฑ๋ถ„์„ ์ •์„ฑโ€ข์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋‹ค. LIBS๋Š” ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ฌผ์งˆ์˜ ์„ฑ๋ถ„ ๋ถ„์„์ด ๊ฐ€๋Šฅํ•˜๊ณ , ์ƒ˜ํ”Œ์˜ ์ „์ฒ˜๋ฆฌ ๊ณผ์ • ์—†์ด ๋น„์ ‘์ด‰์œผ๋กœ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฒ•์ด๋ผ๋Š” ์ ์—์„œ ์šฐ์ฃผ ํƒ์‚ฌ ์ฐฉ๋ฅ™์„ ์˜ ๋กœ๋ฒ„์— ํƒ‘์žฌ๋˜์–ด ํƒ์‚ฌ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” LIBS ๊ธฐ์ˆ ์˜ ์šฐ์ฃผ ํ™œ์šฉ์„ ์œ„ํ•œ ๊ธฐ์ดˆ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‹ค์ œ ์šฐ์ฃผ ํƒ์‚ฌ์— LIBS ๊ธฐ์ˆ ์„ ์ ์šฉํ•˜๊ณ  ์žˆ๋Š” NASA์˜ ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์šฐ์ฃผ ํƒ์‚ฌ ํƒ‘์žฌ์ฒด์— ์š”๊ตฌ๋˜๋Š” LIBS ๊ทœ๊ฒฉ์„ ๊ทœ๋ช…ํ•˜๊ณ , ์šฐ์ฃผ ํ™˜๊ฒฝ์„ ๋ชจ์‚ฌํ•˜๋Š” ์ง„๊ณต ์ฑ”๋ฒ„๋ฅผ ๊ตฌ์ถ•ํ•จ์œผ๋กœ์จ ์‹ค์ œ ์šด์šฉ ํ™˜๊ฒฝ์„ ๊ณ ๋ คํ•œ ์—ฐ๊ตฌ์˜ ๊ธฐ๋ฐ˜์„ ๋งˆ๋ จํ•˜์˜€๋‹ค. ๋ ˆ์ด์ €์™€ ์‹œํŽธ์˜ ํŠน์„ฑ์— ๋Œ€ํ•œ ์‹ ํ˜ธ ์˜ํ–ฅ์„ ํŒŒ์•…ํ•˜๊ณ , ์ด๋ฅผ ๋‚ฎ์€ ์••๋ ฅ ์กฐ๊ฑด์— ์ ์šฉํ•˜์—ฌ ๋ณ€ํ™”ํ•˜๋Š” ์šด์šฉ ํ™˜๊ฒฝ์—์„œ๋„ ํŠน์„ฑ์˜ ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์œ„์˜ ๊ฒฐ๊ณผ๋Š” ์‹œ์Šคํ…œ ์šด์šฉ ์กฐ๊ฑด๊ณผ ๊ทธ์— ๋Œ€ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ฌผ๋ฆฌํ™”ํ•™์ ์ธ ๊ด€์ ์—์„œ ๋ถ„์„ํ•จ์œผ๋กœ์จ LIBS ์—ฐ๊ตฌ ๋ถ„์•ผ์— ํ˜„์žฌ๊นŒ์ง€ ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š์•˜๋˜ ๊ฒฐ๋ก ์„ ๋„์ถœํ•˜์˜€๋‹ค. ํƒ์‚ฌ ๋Œ€์ƒ ๋ฌผ์งˆ์˜ ๋†๋„ ๋ถ„์„์„ ์œ„ํ•˜์—ฌ ์šฐ์ฃผ ํƒ์‚ฌ์— ์ ํ•ฉํ•œ ํ‘œ์ค€๋ฌผ์งˆ์„ ํ™œ์šฉํ•˜์—ฌ ์••๋ ฅ, ์›์†Œ, ์‹œํŽธ์˜ ํŠน์„ฑ์— ๋Œ€ํ•œ ๊ฒ€๋Ÿ‰์„ ์„ ์ž‘์„ฑํ•˜๊ณ  ํŠน์„ฑ์„ ๊ทœ๋ช…ํ•จ์œผ๋กœ์จ ์ •๋Ÿ‰ ๋ถ„์„ ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€๊ณ  ํŠน์„ฑ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ํŠนํžˆ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์™€ ๋‹ค๋ฅด๊ฒŒ 6๊ฐ€์ง€์˜ ๊ทธ๋ฃน์œผ๋กœ ๋ถ„๋ฅ˜๋œ ๋‹ค์–‘ํ•œ ํ‘œ์ค€ ๋ฌผ์งˆ์„ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ์ •๋Ÿ‰ ๋ถ„์„ ์‹œ ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ๊ฐ€ ๋˜๋Š” ๋งคํŠธ๋ฆญ์Šค ํšจ๊ณผ๋ฅผ ๊ทน๋ณตํ•˜์—ฌ ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ๋˜ํ•œ ๋‹จ๋ณ€๋Ÿ‰/๋‹ค๋ณ€๋Ÿ‰ ๊ธฐ๋ฒ•์— ์˜ํ•œ ์ •๋Ÿ‰ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ๊ฒ€๋Ÿ‰์„ ์˜ ์ƒ๊ด€๊ณ„์ˆ˜๋ฅผ ํ–ฅ์ƒ์‹œํ‚ด์œผ๋กœ์จ ๋งคํŠธ๋ฆญ์Šค ํšจ๊ณผ์— ์˜ํ•œ ์˜ค์ฐจ๋ฅผ ๋ณด์ •ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‹ค์ œ ํƒ์‚ฌ๋ฅผ ์œ„ํ•˜์—ฌ 5~7 m ์›๊ฑฐ๋ฆฌ ๊ฒ€์ถœ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์—ˆ์œผ๋ฉฐ, ์ตœ์ข…์ ์œผ๋กœ ์†Œํ˜•ํ™”๋œ LIBS ์žฅ๋น„๋ฅผ ์ œ์ž‘ํ•˜์—ฌ ์†Œํ˜•ํ™” ์žฅ๋น„์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ณ  ํƒ‘์žฌ์ฒด๋กœ์จ์˜ LIBS ์‹œ์Šคํ…œ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‹ค์ œ ํƒ์‚ฌ ํ™˜๊ฒฝ์— ํ•ด๋‹นํ•˜๋Š” ์ €์•• ์กฐ๊ฑด์—์„œ๋Š” ํ”Œ๋ผ์ฆˆ๋งˆ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ํŒฝ์ฐฝํ•˜์—ฌ ์†Œ๋ฉธํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ณ  ์ €์••์—์„œ์˜ ๊ฒ€์ถœ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ํ”Œ๋ผ์ฆˆ๋งˆ์˜ ํŒฝ์ฐฝ์„ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ๋ง‰๊ณ  ํŒฝ์ฐฝ ๋ฐฉํ–ฅ์„ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•œ confinement ๊ธฐ๋ฒ• ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๊ธฐ์กด์—๋Š” 0.1 torr ์ดํ•˜์˜ ์••๋ ฅ ํ™˜๊ฒฝ์—์„œ๋Š” ICCD ๊ฒ€์ถœ๊ธฐ๋ฅผ ์‚ฌ์šฉํ•ด์•ผ ํ”Œ๋ผ์ฆˆ๋งˆ ๊ฒ€์ถœ์ด ๊ฐ€๋Šฅํ•˜์˜€๋Š”๋ฐ, confinement ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•จ์œผ๋กœ์จ CCD ๊ฒ€์ถœ๊ธฐ๋ฅผ ํ™œ์šฉํ•œ ์ €์•• ๊ฒ€์ถœ์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ๋˜ํ•œ ์‹ ํ˜ธ ์„ธ๊ธฐ๊ฐ€ ํฌ๊ฒŒ ํ–ฅ์ƒ๋˜์—ˆ๊ณ , ๋‚ฎ์€ ์—๋„ˆ์ง€๋กœ๋„ ๊ฒ€์ถœ์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋จ์œผ๋กœ์จ ์ „์ฒด ์‹œ์Šคํ…œ์˜ ์†Œํ˜•ํ™” ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. LIBS์˜ ์‘์šฉ ๋ฒ”์œ„๋กœ๋Š” ๋ฐ”์ด์˜ค ์—ฐ๋ฃŒ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ ์ค‘๊ฐ„ ๊ณต์ • ์žฅ๋น„์˜ ์†์ƒ์„ ์•ผ๊ธฐํ•˜๋Š” ์—ผ์†Œ์™€ ํ™ฉ์˜ ๊ฒ€์ถœ ๋ฐฉ๋ฒ•์ด ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋Š” ๊ฐ€์šด๋ฐ LIBS๋ฅผ ๋ฐ”์ด์˜ค ์—ฐ๋ฃŒ ๊ฐœ๋ฐœ์— ์ ‘๋ชฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋…น์ƒ‰ ์‚ฐ์—…์˜ ์ค‘์š”์„ฑ์ด ๋Œ€๋‘๋˜๋ฉด์„œ ๊ทธ๋™์•ˆ ์•ˆ์ •์„ฑ์„ ์ค‘์š”์‹œํ•˜์—ฌ ๊ทœ์ œํ•˜์ง€ ์•Š์•˜๋˜ ํ•ญ๊ณต ๋งค์—ฐ์˜ ๊ทœ์ œ๊ฐ€ ์ด๋ฃจ์–ด์งˆ ์‹œ๊ธฐ์ด๋ฏ€๋กœ ํ•ญ๊ณต ๋งค์—ฐ์˜ ์‹ค์‹œ๊ฐ„ ๊ฒ€์ถœ์— ์‘์šฉ๋˜์–ด ๋งค์—ฐ ๊ทœ์ œ ๋ฐ ํ™˜๊ฒฝ ์˜ค์—ผ ๋ฐฉ์ง€๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ๋˜ํ•œ ๊ณ ์—๋„ˆ์ง€ ์œ„ํ—˜ ๋ฌผ์งˆ ๊ฒ€์ถœ. ๋…์„ฑ ๋ฌผ์งˆ ๋ถ„์„์„ ํ†ตํ•œ ์ œ์–ดโ€ข๊ด€๋ฆฌ ๋ฐ ๋Œ€๊ธฐ ์ค‘ ์ค‘๊ธˆ์† ์ž…์ž์˜ ์‹ค์‹œ๊ฐ„ ๋ถ„์„, ๊ทธ๋ฆฌ๊ณ  ์น˜์•„, ์„ํšŒํ™” ์กฐ์ง, ์ธ์ฒด ํ”ผ๋ถ€, ์ธ์ฒด ๋ชจ๋ฐœ, ๋ฐ•ํ…Œ๋ฆฌ์•„ ์—ฐ๊ตฌ์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ์ƒ์ฒด ๊ณตํ•™ ์—ฐ๊ตฌ์— LIBS ์„ฑ๋ถ„ ๋ถ„์„๋ฒ•์ด ์‘์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ๋ฒ”์ฃ„์ˆ˜์‚ฌํ•™์ ์ธ ์ธก๋ฉด์—์„œ ์ง€๋ฌธ ๋ถ„์„์—๋„ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ LIBS ์‹œ์Šคํ…œ์„ ํƒ์‚ฌ ํƒ‘์žฌ์ฒด๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๋‹ค๊ฐ๋„๋กœ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋˜ํ•œ ์œ„์—์„œ ์–ธ๊ธ‰ํ•œ ๋‹ค์–‘ํ•œ ํ™œ์šฉ ๋ถ„์•ผ์— ์žˆ์–ด์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋ชฉ์ ์— ์ ํ•ฉํ•œ ์‹œ์Šคํ…œ ์„ค๊ณ„๋ฅผ ์œ„ํ•œ ์ผ๋ จ์˜ ์ฐธ๊ณ  ์ž๋ฃŒ๊ฐ€ ๋  ์ˆ˜ ์žˆ๋‹ค.The Laser-Induced Breakdown Spectroscopy (LIBS) has great advantages as an analytical technique, namely real-time rapid analysis without sample preparation and stand-off detection capability, ideal for mobile chemical sensor for space exploration. In this research, we investigated the LIBS specification required to build the payload for space exploration and prepared the basis for real circumstances analysis by building a vacuum chamber for space environment. We also built a database to understand the effect of LIBS signals with respect to the properties of laser and samples and to predict the plasma signal under the working circumstances that changed through the application of low pressure condition. Moreover, we built a database of quantitative analysis by drawing the calibration curves with the effects of pressure, elements and samples utilizing the certified reference materials (CRMs) which are appropriate for the space exploration and investigated the characteristics of the samples. Researches of stand-off detection were carried out for further understanding of space science by considering the environmental condition. The confinement method was devised to enhance the signal for optimum plasma detection at space environment. The results obtained through this research will allow more precise mapping compared to widely used conventional elemental analyzing method in space exploration and provide a guideline for design of various LIBS experiments.LIST ABSTRACTโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..i LISTโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆ...iii LIST OF FIGURESโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ............vii LIST OF TABLES..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.............xi PREFACEโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆxii CHAPTER 1 INTRODUCTIONโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.1 1.1 Motivation and purposeโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆ.......1 1.2 Laser-material interaction.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..5 1.3 Laser-Induced Breakdown Spectroscopy (LIBS) for space exploration.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.8 CHAPTER 2 EXPERIMENTAL APPARATUSโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆ.14 2.1 Laserโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ15 2.2 Spectrometer and detectorโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆ..17 2.3 Other equipmentsโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆ...19 2.4 Summaryโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ21 CHAPTER 3 FUNDAMENTAL STUDY ON EMISSION SIGNAL CONSIDERING EXPERIMENTAL PARAMETERโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..22 3.1 Background and objectiveโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...22 3.2 Experimental conditionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ23 3.3 Collinear double pulse testโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆ24 3.4 Effect of sample and detector angleโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.28 3.4.1 Sample angle variationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..28 3.4.2 Detector angle variationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ............31 3.5 Optimizing the wavelength of laserโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆ.โ€ฆโ€ฆ34 CHAPTER 4 QUANTITATIVE ANALYSISโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ................37 4.1 Background and objectiveโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...37 4.2 Experimental conditionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ40 4.3 LIBS spectraโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆ...43 4.4 Principal component analysis (PCA)....โ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ44 4.5 Univariate analysisโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆ...47 4.6 Multivariate analysisโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ57 CHAPTER 5 PLASMA CHARACTERISTICS BY ENVIRONMENTAL CONDITION...63 5.1 Background and objectiveโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...63 5.2 Experimental conditionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ64 5.3 Effect of ambient pressure from the elemental lifetime perspective.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆ...65 5.3.1 Characteristics of lifetime with pressure changeโ€ฆ...67 5.3.2 Analysis of elemental boiling point and electronegativityโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆ69 CHAPTER 6 DESIGN OF STAND-OFF DEVICE FOR SPACE EXPLORATIONโ€ฆโ€ฆโ€ฆ74 6.1 Background and objectiveโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...74 6.2 Experimental conditionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...75 6. 3 Stand-off detectionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.76 6.4 Design of portable stand-off device. โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..78 CHAPTER 7 SIGNAL ENHANCEMENT AT LOW PRESSUREโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...80 7.1 Background and objectiveโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...80 7.2 Experimental conditionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ83 7.3 Effective energy delivery at low pressureโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...85 7.4 Low pressure effectโ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆ...88 7.5 Hard-to-detect element โ€“ Sulfur detectionโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ94 7.6 Effect of a confining window materialโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆ...96 CHAPTER 8 CONCLUSIONโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...99 REFERENCES...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...101 Abstract in Koreanโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.111Docto

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†๊ฒฝ์ œ์‚ฌํšŒํ•™๋ถ€ ๋†์—…์ž์›๊ฒฝ์ œํ•™์ „๊ณต, 2016. 8. ๊น€ํ•œํ˜ธ.2000๋…„๋Œ€ ํ›„๋ฐ˜ ์ดํ›„ ๊ณก๋ฌผ ์„ ๋ฌผ์‹œ์žฅ์ด ๋ถˆ์•ˆ์ •ํ•ด์ ธ ์„ ๋ฌผ๊ฐ€๊ฒฉ์ด ๊ธ‰๋“ฑ๊ณผ ๊ธ‰๋ฝ์„ ๋ฐ˜๋ณตํ•˜๋Š” ๋“ฑ ํฐ ํญ์œผ๋กœ ๋ณ€ํ™”ํ•˜๊ณ  ๋ณ€๋™์ฃผ๊ธฐ๊ฐ€ ๋นจ๋ผ์ง€๋ฉด์„œ ์„ ๋ฌผ์‹œ์žฅ์˜ ๋ถˆํ™•์‹ค์„ฑ์ด ํ™•๋Œ€๋œ ๊ฒฐ๊ณผ ๊ฐ€๊ฒฉ ์˜ˆ์ธก์ด ์ด์ „๊ณผ๋Š” ๋น„ํ•  ๋ฐ” ์—†์ด ์–ด๋ ค์›Œ์กŒ๋‹ค. ์ตœ๊ทผ์˜ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ ๋ถˆํ™•์‹ค์„ฑ์„ ์•ผ๊ธฐํ•œ ์ฃผ ์›์ธ์„ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ ๋งŽ์€ ์—ฐ๊ตฌ๋“ค์ด ๊ตญ๋‚ด์™ธ์—์„œ ์ˆ˜ํ–‰๋˜์—ˆ๊ณ , ์ธ๋ฑ์ŠคํŽ€๋“œ ํ™œ์„ฑํ™”๋กœ ์ธํ•œ ํˆฌ๊ธฐ์  ์ˆ˜์š”์˜ ์ฆ๊ฐ€, ๋ฏธ๊ตญ ์–‘์ ์™„ํ™”์กฐ์น˜ ์ดํ›„ ๊ฑฐ์‹œ๊ฒฝ์ œ๋ณ€์ˆ˜(๊ธˆ๋ฆฌ, ํ™˜์œจ, ์œ ๊ฐ€)์˜ ์˜ํ–ฅ, ๋ฐ”์ด์˜ค์—ฐ๋ฃŒ ์ •์ฑ…, ์‹ ํฅ๊ตญ์˜ ๊ฒฝ์ œ์„ฑ์žฅ์œผ๋กœ ์ธํ•œ ์œก๋ฅ˜ ์ˆ˜์š” ์ฆ๊ฐ€, ์ด์ƒ๊ธฐํ›„ํ˜„์ƒ์ด ์š”์ธ์œผ๋กœ ์ง€๋ชฉ๋˜์—ˆ๋‹ค. ๋ณธ๊ณ ๋Š” ๋ฏธ๊ตญ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ ๊ฒฐ์ •์š”์ธ๋“ค์— ๋Œ€ํ•ด ํฌ๊ด„์ ์ธ ๋น„๊ต๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์ฒซ์งธ, ๊ธฐ๋Œ€ ๊ณต๊ธ‰๋Ÿ‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ(์œ ๊ฐ€, ๊ธฐํ›„)๊ณผ ๊ธฐ๋Œ€ ์ˆ˜์š”๋Ÿ‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ(ํ™˜์œจ, ๋ฐ”์ด์˜ค์—ฐ๋ฃŒ, ์‹ ํฅ๊ตญ ์ˆ˜์š”) ์ค‘ ์–ด๋Š ์š”์ธ์ด ๋” ์ค‘์š”ํ•œ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ ๊ฒฐ์ •์š”์ธ์ด ๋˜๋Š”์ง€, ๋‘˜์งธ, ํˆฌ๊ธฐ์  ์š”์ธ์ด ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์‹ค๋ฌผ์  ์š”์ธ๊ณผ ๋น„๊ตํ•˜์—ฌ ํ™•์ธํ•ด ๋ณด์•˜๋‹ค. ๋ถ„์„ ๋ฐฉ๋ฒ•์œผ๋กœ ํ•œ์ •๋œ ๋ณ€์ˆ˜๋งŒ์„ ๊ณ ๋ คํ•˜๋Š” ๋‹จ์ผ ๋ชจํ˜•์˜ ์ œ์•ฝ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋ฒ ์ด์ง€์•ˆ ๋ชจํ˜•ํ‰๊ท ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์‹ค์ฆ๋ถ„์„ํ•˜์˜€๋Š”๋ฐ, BMA ๋ถ„์„๋ชจํ˜• ์„ค์ •์‹œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์ฐจ๊ตฌ์กฐ์˜ ์ •ํ•ฉ์„ฑ ๋ฌธ์ œ๋ฅผ ๊ฒ€ํ† ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ณก๋ฌผ ๊ฐ€๊ฒฉ ๋ถ„์•ผ ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ๊ธฐ์กด์— ๋„๋ฆฌ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋Š” VAR๋ชจํ˜•์„ ํ†ตํ•œ ์ถ”์ •๊ฒฐ๊ณผ์™€ ์˜ˆ์ธก๋ ฅ์„ ๋ถ„์„ยทํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ BMA๋Š” ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ๋ถ„์•ผ์—์„œ VAR์— ๋น„ํ•ด ์ค‘์žฅ๊ธฐ ์˜ˆ์ธก์‹œ๊ณ„์—์„œ ์˜ˆ์ธก๋ ฅ์ด ์šฐ์›”ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ BMA๋Š” ์™ธ์ƒ๋ณ€์ˆ˜๋ฅผ ๋Œ€๊ทœ๋ชจ๋กœ ํฌํ•จํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ํ–ฅํ›„ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ๊ณผ ์—ฐ๊ด€์ด ์žˆ๋Š” ๋ณ€์ˆ˜๋ฅผ ๋ฐœ๊ตดํ•˜์—ฌ ํˆฌ์ž…ํ•จ์œผ๋กœ์จ ์˜ˆ์ธก๋ ฅ์„ ๊ฐœ์„ ํ•  ์—ฌ์ง€๋„ ์ถฉ๋ถ„ํžˆ ์žˆ๋‹ค. ๋ณธ๊ณ ๋Š” ๋˜ํ•œ ์„ ํ–‰์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„ํ™”๋˜๋Š” ๋ถ„์„์ž๋ฃŒ์™€ ๋ชจํ˜•์„ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์–‘์ ์™„ํ™”์กฐ์น˜ ์ดํ›„์˜ ์ตœ๊ทผ ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์˜€๊ณ , ๋‘˜์งธ, ๊ฑฐ์‹œ๊ฒฝ์ œ๋ณ€์ˆ˜๊ฐ€ ์„ ๋ฌผ์‹œ์žฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ธˆ๋ฆฌ, ๋‹ฌ๋Ÿฌํ™”๊ฐ€์น˜, ์œ ๊ฐ€๋ฅผ ํ†ตํ•ด ํฌ๊ด„์ ์œผ๋กœ ์ธก์ •ํ•จ์œผ๋กœ์จ ๋‹จํŽธ์  ์ธ๊ณผ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•œ ๊ธฐ์กด ์„ ํ–‰์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์„ฑ์„ ๋‘์—ˆ๊ณ , ์…‹์งธ, ์˜ฅ์ˆ˜์ˆ˜, ๋ฐ€ ์ฝฉ ๋ชจํ˜•์„ ๊ฐ๊ฐ ์„ค์ •ํ•จ์œผ๋กœ์จ ์ตœ๊ทผ ์ธ๋ฑ์ŠคํŽ€๋“œ ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ์›์ž์žฌ์ƒํ’ˆ ์ „์ฒด, ๋˜๋Š” ๊ณก๋ฌผ ์ „์ฒด๊ฐ€ ์ข…ํ•ฉ์ ์œผ๋กœ ๋ฐ›๋Š” ์˜ํ–ฅ(aggregate effect)๋งŒ ๊ฐ€๋Š ํ•  ์ˆ˜ ์žˆ์—ˆ๋˜ ๊ฒƒ๊ณผ๋Š” ๋‹ฌ๋ฆฌ ์ธ๋ฑ์ŠคํŽ€๋“œ(๋น„์ƒ์—…์ˆœ๋งค์ˆ˜ํฌ์ง€์…˜)๊ฐ€ ๊ฐ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ตฌ์ฒด์ ์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋„ท์งธ, ๋ฏธ๊ตญ ๋ฐ”์ด์˜ค์—ฐ๋ฃŒ ์ •์ฑ…์ด ๊ณก๋ฌผ๊ฐ€๊ฒฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ์—ํƒ„์˜ฌ๊ณผ ๋ฐ”์ด์˜ค๋””์ ค ์ˆ˜์š”๋Ÿ‰์„ ๋ฐ”์ด์˜ค์—ฐ๋ฃŒ ์ง€์›์ •์ฑ…(๋ณด์กฐ๊ธˆ, ์˜๋ฌด์†Œ๋น„๋Ÿ‰)์„ ๋ฐ˜์˜ํ•˜๋Š” ๋Œ€๋ฆฌ๋ณ€์ˆ˜๋กœ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๋‹ค์„ฏ์งธ, ๋ณธ๊ณ ์—์„œ๋Š” ์ค‘๊ตญ(๋ณธํ† , ํ™์ฝฉ, ๋Œ€๋งŒ)์˜ ๋ฏธ๊ตญ์‚ฐ ๋ผ์ง€๊ณ ๊ธฐ ์›”๊ฐ„ ์ˆ˜์ž…๋Ÿ‰์„ ์ค‘๊ตญ ๊ฒฝ์ œ์„ฑ์žฅ์œผ๋กœ ์ธํ•ด ์ฆ๊ฐ€ํ•˜๋Š” ์œก๋ฅ˜ ์ˆ˜์š”์˜ ๋Œ€๋ฆฌ๋ณ€์ˆ˜๋กœ ๊ตฌ์ถ•ํ•จ์œผ๋กœ์จ ๊ฒฝ์ œํ™œ๋™์ง€์ˆ˜๋‚˜ BDI๋ฅผ ์ด์šฉํ•œ ์„ ํ–‰์—ฐ๊ตฌ๋ณด๋‹ค ๊ณก๋ฌผ๋ถ€๋ฌธ์—์„œ ์ผ์–ด๋‚˜๋Š” ์˜ํ–ฅ์„ ๊ตฌ์ฒด์ ์ด๊ณ  ์ง์ ‘์ ์œผ๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ์—ฌ์„ฏ์งธ, ๊ธฐํ›„๋ณ€ํ™”์˜ ์˜ํ–ฅ ์ธก์ •์‹œ ๋”๋ฏธ๋ณ€์ˆ˜๋ฅผ ์ด์šฉํ•œ ๋ถ„์„์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์—ฐ์†๋ณ€์ˆ˜์ธ ๋‚จ๋ฐฉ์ง„๋™๊ณ„์ˆ˜๋ฅผ ์ด์šฉํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๊ฐ ๊ฒฐ์ •์š”์ธ์— ๋Œ€ํ•ด ์˜ํ–ฅ์˜ ํ™•์‹ค์„ฑ๊ณผ ํฌ๊ธฐ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ธฐ๋Œ€๊ณต๊ธ‰๋Ÿ‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ(์œ ๊ฐ€, ๊ธฐํ›„)๊ณผ ๊ธฐ๋Œ€ ์ˆ˜์š”๋Ÿ‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ(ํ™˜์œจ, ๋ฐ”์ด์˜ค์—ฐ๋ฃŒ, ์‹ ํฅ๊ตญ ์ˆ˜์š”)์œผ๋กœ ์„ค์ •ํ•œ ์š”์ธ๋“ค์„ ๋น„๊ตยท ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ ์ด์ค‘ ์œ ๊ฐ€๋งŒ ์˜ฅ์ˆ˜์ˆ˜, ๋ฐ€, ์ฝฉ ์„ ๋ฌผ๊ฐ€๊ฒฉ์— ๋Œ€ํ•ด ๋กœ๋ฒ„์ŠคํŠธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์œผ๋กœ ๋„์ถœ๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์€ ์—๋„ˆ์ง€๊ฐ€๊ฒฉ๊ณผ ์—ฐ๊ณ„(linkage)๊ฐ€ ์žˆ๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๊ณ , ์ €์œ ๊ฐ€๋‚˜ ๊ณ ์œ ๊ฐ€ ์ถฉ๊ฒฉ์€ ๊ณก๋ฌผ ์‹œ์žฅ์œผ๋กœ ์ „์ด๋  ํ™•๋ฅ ์ด ๋งค์šฐ ๋†’์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ์œ ๊ฐ€๊ฐ€ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์˜ ํฌ๊ธฐ๋ฅผ ์ถ”์ •๋œ ๊ณ„์ˆ˜๋กœ ํ™•์ธํ•ด ๋ณด๋ฉด ์˜ฅ์ˆ˜์ˆ˜์™€ ๋ฐ€์˜ ๊ฒฝ์šฐ ๋‹ค๋ฅธ ์š”์ธ๋“ค์— ๋น„ํ•ด ํฌ๋‚˜ ์ฝฉ์˜ ๊ฒฝ์šฐ ๋‹ฌ๋Ÿฌํ™” ๊ฐ€์น˜๋ณด๋‹ค ์ ์€ ๊ฒƒ์œผ๋กœ ๋„์ถœ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋˜ํ•œ ํˆฌ๊ธฐ์  ์š”์ธ๊ณผ ์‹ค๋ฌผ์  ์š”์ธ์ด ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํฌ๊ด„์ ์œผ๋กœ ๋น„๊ตยทํ‰๊ฐ€ํ•ด ๋ณด์•˜๋Š”๋ฐ, ๋ถ„์„๊ฒฐ๊ณผ ํˆฌ๊ธฐ์  ์ˆ˜์š”์˜ ์ฆ๊ฐ€๋Š” ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์„ ์ƒ์Šน์‹œํ‚ค๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์˜คํžˆ๋ ค ์•ˆ์ •ํ™”์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•œํŽธ ๊ธˆ๋ฆฌ ์ƒ์Šน์€ ๊ณก๋ฌผ ๊ฐ€๊ฒฉ์„ ํ•˜๋ฝ์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ๋„์ถœ๋˜์—ˆ๋Š”๋ฐ, ์•ž์„œ ์–ธ๊ธ‰ํ•œ ๋ฐ”์™€ ๊ฐ™์ด ๋น„์ƒ์—…์ˆœ๋งค์ˆ˜ํฌ์ง€์…˜๊ณผ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์€ ์Œ์˜ ๊ด€๊ณ„๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋„์ถœ๋˜์—ˆ์œผ๋ฏ€๋กœ, ๋ณธ๊ณ ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋„์ถœ๋œ ๊ฒฐ๋ก ์— ์˜ํ•˜๋ฉด ๊ธˆ๋ฆฌ๊ฐ€ ๊ฐ€๊ฒฉ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ ๊ฒฝ๋กœ๋Š” ํˆฌ๊ธฐ์  ์ˆ˜์š”๊ฐ€ ์•„๋‹ˆ๋ผ ์‹ค๋ฌผ ์ˆ˜์š”๋ฅผ ํ†ตํ•œ ๊ฒƒ์ด๋‹ค. ์ฆ‰, ๊ธˆ๋ฆฌ ํ•˜๋ฝ์€ ์‹ค๋ฌผ์‹œ์žฅ์—์„œ ์žฌ๊ณ ๋ณด์œ ๋น„์šฉ์„ ํ•˜๋ฝ์‹œํ‚ค๋ฏ€๋กœ ์žฌ๊ณ ์šฉ ์ˆ˜์š”๋Ÿ‰์„ ์ฆ๊ฐ€์‹œํ‚จ ๊ฒฐ๊ณผ ๊ฐ€๊ฒฉ์„ ์ƒ์Šน์‹œํ‚จ ๊ฒƒ์œผ๋กœ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ๋ฏธ ์—ฐ์ค€์ด ๊ธˆ๋ฆฌ๋ฅผ ๋†’์ผ ๊ฒฝ์šฐ ๊ณก๋ฌผ ์„ ๋ฌผ์‹œ์žฅ์€ ๊ฐ€๊ฒฉ์ƒ์Šน ์˜ํ–ฅ์„ ๋ฐ›์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•  ์ˆ˜ ์žˆ์œผ๋‚˜, ์ด๋Š” ์—ฌ๋Ÿฌ ์„ ํ–‰์—ฐ๊ตฌ์˜ ์ฃผ์žฅ๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๊ธˆ๋ฆฌ๊ฐ€ ์‹ค๋ฌผ ์žฌ๊ณ  ์ˆ˜์š”๋Ÿ‰์„ ๋ณ€๋™์‹œํ‚ด์œผ๋กœ์จ ์„ ๋ฌผ๊ฐ€๊ฒฉ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ๊ณ ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๊ทผ๊ฑฐํ•˜๋ฉด ํ–ฅํ›„ ๋ฏธ ์—ฐ์ค€์ด ๊ธˆ๋ฆฌ๋ฅผ ๋†’์ผ ๊ฒฝ์šฐ ํŠนํžˆ ์˜ฅ์ˆ˜์ˆ˜์™€ ๋ฐ€ ์„ ๋ฌผ์‹œ์žฅ์€ ๋†’์€ ํ™•๋ฅ ๋กœ ๊ฐ€๊ฒฉ์ƒ์Šน ์˜ํ–ฅ์„ ๋ฐ›์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๊ฐ€ ์ด์ „์˜ ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„๋˜๋Š” ์ ์€ ํ†ตํ™”์ •์ฑ…์ด ๊ณก๋ฌผ ์„ ๋ฌผ์‹œ์žฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๊ฑฐํ’ˆ, ์ฆ‰ ํˆฌ๊ธฐ์  ์ˆ˜์š”์˜ ์ผ์‹œ์  ์ฆ๊ฐ€๊ฐ€ ์•„๋‹Œ ์‹ค๋ฌผ ์žฌ๊ณ  ์ˆ˜์š”๋ฅผ ๋งค๊ฐœ๋กœ ํ•˜์—ฌ ์ด๋ฃจ์–ด์ง„๋‹ค๋Š” ๊ฒƒ์„ ๊ณ„๋Ÿ‰์  ๋ถ„์„์„ ํ†ตํ•ด ๋ฐํžŒ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๊ณก๋ฌผ์˜ ์ˆ˜์ž…์˜์กด๋„๊ฐ€ ๋งค์šฐ ๋†’์€ ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ ๊ณก๋ฌผ๊ฐ€๊ฒฉ์„ ์กฐ๊ธฐ์— ์˜ˆ์ธกํ•  ๋•Œ ๊ฐ ์š”์ธ์„ ์–ด๋–ป๊ฒŒ ๊ณ ๋ คํ•ด์•ผ ํ•  ๊ฒƒ์ธ๊ฐ€์— ๋Œ€ํ•ด ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์‹œ์‚ฌ์ ์„ ๋‚จ๊ธด๋‹ค. ์ข…ํ•ฉํ•ด ๋ณด๋ฉด 2000๋…„๋Œ€ ํ›„๋ฐ˜ ์ดํ›„ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ์˜ ๋ถˆํ™•์‹ค์„ฑ์ด ํ™•๋Œ€๋œ ์ฃผ ์š”์ธ์€ ๊ณก๋ฌผ ๊ฐ€๊ฒฉ๊ณผ ์œ ๊ฐ€์˜ ์—ฐ๊ณ„๋กœ ์ธํ•ด ์—๋„ˆ์ง€ ๋ถ€๋ฌธ์˜ ๋ณ€๋™์š”์ธ๋“ค์ด ๊ณก๋ฌผ ๋ถ€๋ฌธ์œผ๋กœ ํŒŒ๊ธ‰๋œ ๊ฒƒ, ๊ทธ๋ฆฌ๊ณ  ๋ฏธ๊ตญ ๊ธˆ์œต์œ„๊ธฐ์™€ ๊ฑฐ์‹œ๊ฒฝ์ œ์ •์ฑ…์œผ๋กœ ์ธํ•ด ๊ธˆ๋ฆฌ์™€ ๋‹ฌ๋Ÿฌํ™”๊ฐ€์น˜๊ฐ€ ๋ณ€๋™ํ•œ ๋ณตํ•ฉ์ ์ธ ๊ฒฐ๊ณผ๋ผ๊ณ  ๊ฒฐ๋ก ์„ ๋‚ด๋ฆด ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ๊ณ ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๊ณก๋ฌผ ๊ฐ€๊ฒฉ ์˜ˆ์ธก์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์—ฌ๋Ÿฌ ์ •๋ณด๋“ค์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์‹ค์ฆ์  ๊ทผ๊ฑฐ๊ฐ€ ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.After the late 2000s, the change cycle length of grains futures price is shortened and the boom and the bust repeats, which makes grains price outlook unprecedentedly difficult. Numerous studies are conducted to find out the main cause of the recent increase of uncertainty in grains futures price and the followings are pointed out: increase of speculative demand due to index fundchange of macroeconomic factors (interest rate, dollar value, and oil price) due to US quantitative easingbiofuel policyincrease of meat demand due to economic growth of Chinaand abnormal climate. The aim of this paper is to answer the following questions. First, which is relatively more important determinant of US grains futures price between expected supply (oil price and climate) and expected demand (dollar value, biofuel policy, and meat demand of China)? Second, how does speculative factors influence grains futures price in comparison with the tangible factors? The estimating method is Bayesian Model Averaving, which overcomes the weakness of single model consisting of restricted number of regressors. To consider the time lag structure, Vector Auto Regressive models, which are frequently utilized in the grains futures price outlook, are estimated so as to evaluate BMAs predictive performance in comparison with VAR. As a result, it is discovered that BMA have less mean square prediction error compared to VAR in the long run. Therefore, BMA has the full potential of complementary use in the study of grains futures price as BMA can include unlimited number of informative variables and there is a plenty of room to improve its predictive performance by discovering regressors highly related to grains futures price. According to the study results, the certainty and the size of impact of each determinant for each grain is discovered as followings. The robust determinants are oil price and interest rate and others only have stochastic effects. Corn futures price has greatest impact from expected supply (oil price) and the certainty of getting the impact is also high. Wheat futures price has great and almost certain impact from oil price and has greatest and less certain impact from lagged dollar index. Soybeans futures prices have greatest impact from dollar index, however, the impact is only stochastic. It is also discovered that the increase of noncommercial net long position does not raise grains futures price but rather stabilizes it. Interest rate is found to be negatively correlated to grains futures price but the channel of the interest rate is not speculative demand since it is shown that noncommercial net long position is negatively related to grains futures price. Therefore, higher interest rate affects grains futures price via increasing tangible demand by lowering stock cost. In summary, there is a high probability that grains futures price respond to the change of oil price and interest rate and lower probability that grains futures price be influenced from other determinants. The result of this study provides both the certainty and the size of the impact from determinants for corn, wheat and soybeans futures prices, which would serve as an empirical evidence of utilizing various information in the grains futures market and helps resolve the uncertainty in the grains price outlook.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ 1 ์ œ2์ ˆ ์„ ํ–‰์—ฐ๊ตฌ 6 ์ œ3์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 9 ์ œ4์ ˆ ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ๊ณผ ๊ตฌ์„ฑ 13 ์ œ2์žฅ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ ๊ฒฐ์ •์š”์ธ 14 ์ œ1์ ˆ ์–‘์ ์™„ํ™”์™€ ๊ฑฐ์‹œ๊ฒฝ์ œ๋ณ€์ˆ˜์˜ ์˜ํ–ฅ 14 ์ œ2์ ˆ ์ธ๋ฑ์ŠคํŽ€๋“œ์™€ ํˆฌ๊ธฐ์  ์ˆ˜์š”์˜ ์˜ํ–ฅ 19 ์ œ3์ ˆ ๋ฐ”์ด์˜ค์—ฐ๋ฃŒ ์‚ฐ์—…๊ณผ ์ •์ฑ… 24 ์ œ4์ ˆ ์ค‘๊ตญ์˜ ๊ฒฝ์ œ์„ฑ์žฅ๊ณผ ์œก๋ฅ˜์†Œ๋น„๋Ÿ‰ ์ฆ๊ฐ€ 31 ์ œ5์ ˆ ๊ธฐํ›„๋ณ€ํ™”์˜ ์˜ํ–ฅ 39 ์ œ3์žฅ ๋ถ„์„๋ฐฉ๋ฒ•๊ณผ ์ž๋ฃŒ 44 ์ œ1์ ˆ ๊ณก๋ฌผ ์„ ๋ฌผ๊ฐ€๊ฒฉ ๊ฒฐ์ •์ฒด๊ณ„ 44 ์ œ2์ ˆ ๋ถ„์„์ž๋ฃŒ 48 ์ œ3์ ˆ ๋ฒ ์ด์ง€์•ˆ ๋ชจํ˜• ํ‰๊ท  54 ์ œ1ํ•ญ ๋ฒ ์ด์ง€์•ˆ ๋ถ„์„๊ณผ ๊ณ ์ „์  ๊ณ„๋Ÿ‰๊ฒฝ์ œํ•™์˜ ๋น„๊ต 54 ์ œ2ํ•ญ ๋ฒ ์ด์ง€์•ˆ ๋ชจํ˜• ํ‰๊ท  58 ์ œ3ํ•ญ ์‹ค์ฆ๋ชจํ˜•์˜ ๊ตฌ์ถ• 62 ์ œ4์ ˆ ๋ฒกํ„ฐ์ž๊ธฐํšŒ๊ท€๋ชจํ˜• 65 ์ œ1ํ•ญ ๊ฐœ์š” 65 ์ œ2ํ•ญ ์‹ค์ฆ๋ชจํ˜•์˜ ๊ตฌ์ถ• 67 ์ œ5์ ˆ ์˜ˆ์ธก๋ ฅ ํ‰๊ฐ€ 69 ์ œ4์žฅ ๋ถ„์„ ๊ฒฐ๊ณผ 70 ์ œ1์ ˆ BMA ๋ถ„์„๊ฒฐ๊ณผ 70 ์ œ2์ ˆ VAR ๋ถ„์„๊ฒฐ๊ณผ 81 ์ œ3์ ˆ ์˜ˆ์ธก๋ ฅ ํ‰๊ฐ€ 95 ์ œ5์žฅ ์‹œ์‚ฌ์ ๊ณผ ๊ฒฐ๋ก  98 ์ œ1์ ˆ ๊ฒฐ๊ณผ์˜ ํ•ด์„๊ณผ ์‹œ์‚ฌ์  98 ์ œ2์ ˆ ๊ฒฐ๋ก  105 ์ฐธ๊ณ  ๋ฌธํ—Œ 108 ๋ถ€๋ก: ์˜ฅ์ˆ˜์ˆ˜, ๋ฐ€, ์ฝฉ์˜ ์ˆ˜ํ™•๊ธฐ์™€ ์„ ๋ฌผ๊ณ„์•ฝ ์›” 114Docto

    Automatic control of a miniature vehicle using GPS-RTK and single axis gyro

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2007.Maste

    Therapeutic monitoring of busulfan in patients undergoing hematopoietic stem cell transplantation

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    Thesis(master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์•ฝํ•™๊ณผ ์ž„์ƒ์•ฝํ•™์ „๊ณต,2004.Maste

    ํ•œ๊ตญ ํ•™์ƒ์˜ ๊ฐ€์กฑ๋ฐฐ๊ฒฝ๊ณผ ํ•™์—…์„ฑ์ทจ๋„์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๊ตญ์ œ๋Œ€ํ•™์› : ๊ตญ์ œํ•™๊ณผ, 2014. 8. ์€๊ธฐ์ˆ˜.This study investigates the family dynamics of the South Korean student and provides empirical evidence that family background and academic achievement are closely related. The research is based on an academic survey of 357 undergraduates from high achieving and low achieving tertiary institutions, conducting in 2007 and 2012. The first part of this thesis outlines the importance of education in South Korea and the research background. A literature review shows that much of the previous studies on family background and academic achievement have focused on single groups of achievers, and are based on American or European contexts. This study investigates the South Korean family during the notoriously tough 3rd year of high school. The family characteristics of higher and lower academic achievers are examined under four categories: financial capital, human capital, social capital, and personal effort. Following the conceptual framework and hypothesis section come the results of the survey. Various analytical procedures have been conducted for this study using statistical software. Results indicate a positive correlation between a students academic achievement and family wealthparents education leveland parental involvement. Each of these categories is further investigated with sub-categories such as place of residence, mothers employment status, and birth order. Results show that the higher achiever is usually the firstborn child whose father is highly-educated and mother is highly-educated but stays at home during the final year of high school. Either one of the parents may have received higher education from overseas. The family may also live in a lucrative neighborhood and belong to the upper-middle income bracket, being able to pay for costly private education and allowing the mother to stay at home. A summary of the findings concludes this study along with implications and propositions for future research.Chapter 1. Introduction โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 1 1.1 Research Background โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 1 1.2 Research Questions โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 3 1.3 Significance of Research โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 4 Chapter 2. Method & Data โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 5 Chapter 3. Literature Review โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 9 3.1 Family Background and Academic Achievement โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 9 3.2 Non-Family Factor and Academic Achievement โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 12 Chapter 4. Conceptual Framework & Hypothesis โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 14 Chapter 5. Results โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 19 5.1 Financial Capital: Family Wealth โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 19 5.1.1 Perceived Income Bracket โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 19 5.1.2 Place of Residence โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 22 5.1.3 Type of Residence โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 25 5.1.4 Study-Abroad Experience โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 27 5.2 Human Capital: Parents Education Level โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 34 5.2.1 Father: Years of Education โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 34 5.2.2 Mother: Years of Education โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 43 5.2.3 Parents Overseas Education โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 44 5.3 Social Capital: Parental Involvement โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 45 5.3.1 Mothers Employment Status โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 46 5.3.2 Parent-Student Conversation โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 49 5.3.3 Number of Siblings โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 50 5.3.4 Birth Order โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 51 5.4 Personal Effort โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 52 5.4.1 Hours of Sleep โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 52 5.4.2 Hagwon Fees โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 54 5.4.3 Private Tutoring Fees โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 55 Chapter 6. Conclusion โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 57 6.1 Summary of Findings โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 57 6.2 Implications of Research โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 59 6.3 Limitations & Propositions for Future Research โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 60 References โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 61 ๊ตญ๋ฌธ์ดˆ๋ก (Abstract in Korean) โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 66Maste

    ์ œ2๊ธ‰ ์™€๋™์—์„œ ๊ตฌ์น˜๋ถ€ ๋ณตํ•ฉ๋ ˆ์ง„์˜ ๋ฏธ์„ธ๋ณ€์—ฐ ๋ˆ„์ถœ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ ์น˜๊ณผ๋ณด์กดํ•™์ „๊ณต,2002.Maste

    AI and Text-Mining Applications for Analyzing Contractorโ€™s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects

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    Contractors responsible for the whole execution of engineering, procurement, and construction (EPC) projects are exposed to multiple risks due to various unbalanced contracting methods such as lump-sum turn-key and low-bid selection. Although systematic risk management approaches are required to prevent unexpected damage to the EPC contractors in practice, there were no comprehensive digital toolboxes for identifying and managing risk provisions for ITB and contract documents. This study describes two core modules, Critical Risk Check (CRC) and Term Frequency Analysis (TFA), developed as a digital EPC contract risk analysis tool for contractors, using artificial intelligence and text-mining techniques. The CRC module automatically extracts risk-involved clauses in the EPC ITB and contracts by the phrase-matcher technique. A machine learning model was built in the TFA module for contractual risk extraction by using the named-entity recognition (NER) method. The risk-involved clauses collected for model development were converted into a database in JavaScript Object Notation (JSON) format, and the final results were saved in pickle format through the digital modules. In addition, optimization and reliability validation of these modules were performed through Proof of Concept (PoC) as a case study, and the modules were further developed to a cloud-service platform for application. The pilot test results showed that risk clause extraction accuracy rates with the CRC module and the TFA module were about 92% and 88%, respectively, whereas the risk clause extraction accuracy rates manually by the engineers were about 70% and 86%, respectively. The time required for ITB analysis was significantly shorter with the digital modules than by the engineers.11Nsciescopu
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