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    ๋‹ค์ค‘ ์ธ๊ณต์œ„์„ฑ ์„ผ์„œ ๋ฐ ๊ธฐํ›„ ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ๋‚จ๊ทน ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”์˜ ์ดํ•ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ณผํ•™๊ต์œก๊ณผ(์ง€๊ตฌ๊ณผํ•™์ „๊ณต), 2021.8. ์„œ๊ธฐ์›.์ง€๋‚œ ์ˆ˜ ์‹ญ ๋…„ ๊ฐ„, ๋‚จ๊ทน์˜ ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”์— ๋Œ€ํ•œ ์šฐ๋ฆฌ์˜ ์ง€์‹์€ ์ธ๊ณต์œ„์„ฑ ๊ด€์ธก๊ณผ ์ง€๊ตฌ ๋ฌผ๋ฆฌ ๋ชจ๋ธ๋ง ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์— ์˜ํ•ด ๋น„์•ฝ์ ์œผ๋กœ ํ–ฅ์ƒ๋˜์–ด ์™”๋‹ค. ์ธ๊ณต์œ„์„ฑ ๊ด€์ธก์€ ์ง„ํ–‰์ค‘์ธ ๋‚จ๊ทน ์–ผ์Œ ์งˆ๋Ÿ‰ ์†์‹ค๊ณผ ๊ฐ€์†ํ™”๋ฅผ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜๋“ค์„ ์ง€์†์ ์œผ๋กœ ์ œ์•ˆํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋“ค์„ ๊ณ ๋ คํ•œ ๋ชจ๋ธ๋ง์€ ๋ฏธ๋ž˜์— ์ง„ํ–‰๋  ๋‚จ๊ทน ๋น™ํ•˜ ์†์‹ค์„ ์ •๋Ÿ‰์ ์œผ๋กœ ์‚ฐ์ถœํ•˜๊ณ  ์žˆ๋‹ค. ํ˜„์žฌ์˜ ๊ด€์ธก๊ณผ ๋ชจ๋ธ๋ง ๋ชจ๋‘๋Š” ๋‚จ๊ทน์˜ ์–ผ์Œ ๋ฐฐ์ถœ์ด ํ–ฅํ›„์— ์ ์ฐจ ๊ฐ€์†ํ™” ๋  ๊ฒƒ์ด๋ผ๊ณ  ์˜ˆ์ธกํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ฆ๊ฐ€์œจ์ด ์ง€์†๋œ๋‹ค๋ฉด, ๋‚จ๊ทน์€ ๊ฐ€๊นŒ์šด ๋ฏธ๋ž˜์— ํ•ด์ˆ˜๋ฉด ์ƒ์Šน์„ ์œ ๋ฐœ์‹œํ‚ค๋Š” ์ฒซ๋ฒˆ์งธ ๊ธฐ์—ฌ์ž๊ฐ€ ๋  ๊ฒƒ์ด๋‹ค. ๋‚จ๊ทน์—์„œ ๋ฐฐ์ถœ๋  ๋น™ํ•˜์˜ ์งˆ๋Ÿ‰์„ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ง„ํ–‰์ค‘์ธ ์–ผ์Œ ์งˆ๋Ÿ‰ ์†์‹ค์— ๋Œ€ํ•œ ์ง€์†์ ์ธ ๊ด€์ฐฐ๊ณผ ํ•จ๊ป˜, ๊ทธ๊ฒƒ์˜ ์›์ธ ๊ธฐ์ž‘์„ ๊ทœ๋ช…ํ•˜๋Š” ์ผ์ด ์š”๊ตฌ๋œ๋‹ค. ๋‚จ๊ทน์˜ ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”๋Š” ๊ฐ ๋น™ํ•˜๋งˆ๋‹ค ๋น„๊ท ์งˆํ•˜๊ฒŒ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ฐœ๋ณ„ ๋น™ํ•˜์˜ ๋™๋ ฅํ•™์€ ๋Œ€๊ธฐ์™€ ํ•ด์–‘ ์ˆœํ™˜, ๊ทธ๋ฆฌ๊ณ  ๊ณ ์ฒด ์ง€๊ตฌ์˜ ๋ณ€๋™์„ฑ ๋“ฑ ๋‹ค์–‘ํ•œ ์ง€๊ตฌ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ ์š”์†Œ๋“ค์˜ ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ฐ ์š”์†Œ๋“ค์ด ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ๋ฌผ๋ฆฌ์  ๊ธฐ์ž‘์„ ๋ณด๋‹ค ์ •ํ™•ํžˆ ์ดํ•ดํ•˜๊ณ , ๋ฏธ๋ž˜ ์งˆ๋Ÿ‰ ๋ณ€ํ™” ์˜ˆ์ธก์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ด๋“ค์„ ์ด ๋ง๋ผํ•˜๋Š” ๋‹คํ•™์ œ๊ฐ„ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ํ๋ฆ„์˜ ์ผํ™˜์œผ๋กœ, ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐํ›„ ๋ชจ๋ธ๋“ค๊ณผ ์›๊ฒฉ ํƒ์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋‚จ๊ทน์˜ ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•œ ์„ธ ๊ฐœ์˜ ์—ฐ๊ตฌ๋“ค์ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ฒซ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”์™€ ๊ฐ•์„ค๋Ÿ‰์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌํ•œ ๊ฒƒ์œผ๋กœ, ์ง€๊ตฌ ์‹œ์Šคํ…œ ๋‚ด์˜ ๊ธฐ๊ถŒ๊ณผ ๋น™๊ถŒ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์— ๋Œ€ํ•ด ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. ์กฐ์‚ฌ ๊ฒฐ๊ณผ, ์ตœ๊ทผ ์ˆ˜ ์‹ญ ๋…„ ๊ฐ„ ๋ฐœ์ƒํ•œ ๋‚จ๊ทน์˜ ๊ฐ•์„ค์€ ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”์˜ ๊ฒฝ๋…„ ๋ณ€๋™์„ฑ์˜ ๋Œ€๋ถ€๋ถ„์„ ์„ค๋ช…ํ•˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ, ๋™ ์‹œ๊ธฐ ์ง„ํ–‰๋œ ๋‚จ๊ทน ์–ผ์Œ ์งˆ๋Ÿ‰ ์†์‹ค์˜ ๊ฐ€์†ํ™”์˜ ์•ฝ 30%๊ฐ€ ๊ฐ•์„ค๋Ÿ‰ ๋ณ€ํ™”์˜ ๊ธฐ์—ฌ์ž„์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ถ”๊ฐ€์ ์ธ ํ†ต๊ณ„๋ถ„์„์„ ํ†ตํ•ด, ์ด๋Ÿฌํ•œ ๊ฐ•์„ค๋Ÿ‰ ๋ณ€ํ™”๊ฐ€ ๋‚จ๋ฐ˜๊ตฌ ๊ทน์ง„๋™ (Southern Annular Mode, SAM) ์ด๋ผ๊ณ  ๋ถˆ๋ฆฌ์šฐ๋Š” ๋‚จ๋ฐ˜๊ตฌ ๊ณ ์œ„๋„์˜ ์ฃผ๊ธฐ์  ๊ธฐํ›„๋ณ€ํ™”์™€ ๋ฐ€์ ‘ํ•œ ๊ด€๋ จ์ด ์žˆ์Œ๋„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‚จ๊ทน ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™” ๊ด€์ธก์˜ ํ•ด์ƒ๋„๋ฅผ ๋†’์ด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋Š” ๋น™ํ•˜ ๋™๋ ฅํ•™ ๋ชจ๋ธ๋“ค์˜ ์ดˆ๊ธฐ ์กฐ๊ฑด์„ ๋‹จ์ผ ๋น™ํ•˜์™€ ๊ฐ™์€ ์ž‘์€ ๊ทœ๋ชจ์—์„œ ํšจ๊ณผ์ ์œผ๋กœ ์ œ์•ฝํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์ด๋‹ค. ํ•ด์ƒ๋„ ์ฆ๊ฐ€๋ฅผ ์œ„ํ•ด, ์ธ๊ณต์œ„์„ฑ ์ค‘๋ ฅ๊ณ„์™€ ๊ณ ๋„๊ณ„ ๊ด€์ธก ๋ฐ์ดํ„ฐ๋ฅผ ์œตํ•ฉํ•˜๋Š” ์ƒˆ๋กœ์šด ์„ ํ˜• ์—ญ์‚ฐ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์—ญ์‚ฐ๋ฒ•์˜ ์ ์šฉ ๊ฒฐ๊ณผ, ๋‚จ๊ทน ๋Œ€๋ฅ™ ์ „์ฒด์˜ ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™” (2003-2016) ๋ฅผ ์•ฝ 27km์˜ ๋†’์€ ๊ณต๊ฐ„ ํ•ด์ƒ๋„์™€ ํ•จ๊ป˜ ํ•œ ๋‹ฌ์˜ ์งง์€ ์ƒ˜ํ”Œ๋ง ๊ฐ„๊ฒฉ์œผ๋กœ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ๋งŒ๋“  ๋ฐ์ดํ„ฐ๋Š” ์ธ๊ณต์œ„์„ฑ ์ค‘๋ ฅ๊ณ„๋‚˜ ๊ณ ๋„๊ณ„๋ฅผ ๋…๋ฆฝ์ ์œผ๋กœ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์— ๋น„ํ•ด ๋” ๋†’์€ ์ •ํ™•๋„๋ฅผ ๊ฐ€์งˆ ๊ฒƒ์ด๋ผ ์ถ”์ธก๋œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณ„์‚ฐํ•œ ๋‚จ๊ทน์˜ ๋น™ํ•˜ ๋ณ„ ์งˆ๋Ÿ‰ ๋ณ€ํ™”๋Š” ๊ฐ ์„ผ์„œ๋ฅผ ๋”ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์— ๋น„ํ•ด, Input-Output ๋ฐฉ๋ฒ•์ด๋ผ๋Š” ๋…๋ฆฝ์ ์ธ ๊ด€์ธก ๊ฒฐ๊ณผ์™€ ๋” ๋†’์€ ์œ ์‚ฌ์„ฑ์„ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‚จ๊ทน ๋น™ํ•˜ ํ•˜๋ถ€์˜ ๊ณ ์ฒด ์ง€๊ตฌ๊ฐ€ ์œ ๋ฐœํ•˜๋Š” ํ›„๋น™๊ธฐ ๋ฐ˜๋™ (Glacial Isostatic Adjustment, GIA) ํšจ๊ณผ๋ฅผ ์ถ”์ •ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋Š” ํ˜„์žฌ์˜ ๊ธฐ์ˆ ๋กœ ๊ด€์ธก์ด ๋ถˆ๊ฐ€๋Šฅํ•œ GIA ํšจ๊ณผ๊ฐ€ ์–ผ์Œ ์งˆ๋Ÿ‰ ๊ด€์ธก์— ๋ฏธ์น˜๋Š” ๋ถˆํ™•์‹ค์„ฑ๋ฅผ ๊ฒฝ๊ฐ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. GIAํšจ๊ณผ๋ฅผ ๋ถ„๋ฆฌ์‹œํ‚ค๊ธฐ ์œ„ํ•ด, ์•ž์„œ ์ˆ˜ํ–‰ํ•œ ๊ณ ํ•ด์ƒ๋„ ์งˆ๋Ÿ‰ ์ถ”์‚ฐ ๋ฐ์ดํ„ฐ์™€ ๋‹ค์ˆ˜์˜ ๊ธฐํ›„๋ชจ๋ธ์„ ์„œ๋กœ ๋น„๊ตํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์„œ๋‚จ๊ทน ๋กœ์Šค ๋น™๋ถ• ๊ทผ์ฒ˜์— ์œ„์น˜ํ•œ ์บ  ๋น™๋ฅ˜ (Kamb Ice Stream) ํ•˜๋ถ€์˜ GIA ํšจ๊ณผ๊ฐ€ ํšจ๊ณผ์ ์œผ๋กœ ๋ถ„๋ฆฌ๋  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ณ„์‚ฐ ๊ฐ’์„ ์„ ํ–‰ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœ๋œ ํ›„๋น™๊ธฐ ๋ฐ˜๋™ ๋ชจ๋ธ๋“ค๊ณผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ๋Œ€๋ถ€๋ถ„์˜ ๋ชจ๋ธ๋“ค์ด ์บ  ๋น™๋ฅ˜์˜ ํ›„๋น™๊ธฐ ๋ฐ˜๋™์„ ๊ณผ๋Œ€์ถ”์ •ํ•˜๊ณ  ์žˆ์Œ๋„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ํ˜„์กดํ•˜๋Š” ๋‹ค์ˆ˜์˜ GIA ๋ชจ๋ธ๋“ค์—์„œ ์บ  ๋น™๋ฅ˜ ํ•˜๋ถ€์˜ ํ›„๋น™๊ธฐ ๋ฐ˜๋™ ํšจ๊ณผ๊ฐ€ ๋‚จ๊ทน์—์„œ ๊ฐ€์žฅ ๋†’๊ฒŒ ๋ชจ์˜๋˜๊ณ  ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๊ฐ์•ˆํ•  ๋•Œ, ์ด ๋ฐœ๊ฒฌ์€ ๋ชจ๋ธ๋“ค์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ์žฌ๊ณ ํ•œ๋‹ค๋Š” ์ ์—์„œ ๋‚จ๊ทน ์–ผ์Œ ์งˆ๋Ÿ‰ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๊ธฐ์กด ๊ด€์ธก ๊ฒฐ๊ณผ์— ์‹œ์‚ฌํ•˜๋Š” ๋ฐ”๊ฐ€ ํฌ๋‹ค. ์„ธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•œ ๋‚จ๊ทน ๋น™ํ•˜ ๋ฐฐ์ถœ๋Ÿ‰ ์ถ”์ •๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ํ•ด์ˆ˜๋ฉด ์ƒ์Šน ์˜ˆ์ธก์ด ๋…ผ๋ฌธ์˜ ๋งˆ์ง€๋ง‰ ์žฅ์— ์ œ์‹œ๋˜์–ด ์žˆ๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ๋Œ€๊ธฐ์™€ ๊ณ ์ฒด ์ง€๊ตฌ์˜ ๋ณ€๋™์„ฑ์„ ๊ณ ๋ คํ•จ๊ณผ ๋™์‹œ์—, ๊ฐœ๋ณ„ ๋น™ํ•˜์˜ ํ•ด์ˆ˜๋ฉด ์ƒ์Šน ๊ธฐ์—ฌ๋„๋ฅผ ์˜ˆ์ธกํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์ด์ „์˜ ์—ฐ๊ตฌ๋“ค๊ณผ ์ฐจ๋ณ„๋œ๋‹ค.Over the past few decades, understanding of ice mass changes in Antarctica has been greatly improved by advances in satellite observation and geophysical modeling techniques. Satellite observations have clearly shown evidence of ongoing Antarctic ice mass loss, and numerical models have quantitatively estimated future ice mass loss. Both observation and modeling have found that Antarctic ice mass loss is accelerating and this would continue in the future. Within this century, Antarctica is expected to be the most important contributor to sea-level rise. To accurately predict Antarctic ice mass loss, continuous Antarctic observation is required, and the cause of Antarctic ice mass loss should be understood. Ice mass variations over Antarctic glaciers are determined by many factors, and their magnitudes differ significantly from glaciers to glaciers. Understanding ice mass variations at individual glaciers are important to project future Antarctic ice mass losses and subsequent sea level rise. Because glacier mass balances are affected by different physical mechanisms associated with atmospheric and oceanic circulations and solid earth deformation, multidisciplinary studies have been required for the accurate understanding of the interaction between Antarctic Ice Sheet (AIS) and the entire Earth system. In this dissertation, three studies are carried out using multiple climate models and remote sensing data to understand the current status of glacier mass balance in AIS. The first study examines the role of precipitation in AIS ice mass changes, identifying the interaction between atmosphere and cryosphere. It is found that the precipitation accounts for most of the inter-annual ice mass variability in recent decades and about 30% of the acceleration in contemporary ice mass loss can be explained by precipitation decrease. EOF analysis suggests that such precipitation variability is closely related to periodic climate change in the high altitude of the Southern Hemisphere, named Southern Annular Mode (SAM). After removing effects associated with precipitation decrease, Antarctic ice mass loss associated with glacier dynamics can be obtained. The second study is to develop a new method to improve the spatial resolution of the Antarctic ice mass change by combining two different satellite observations. Antarctic ice mass change in higher resolution can be estimated by a new linear inversion technique using satellite altimetry and gravimetry observations together. The new method provides monthly ice mass changes (2003-2016) for all Antarctic glaciers with a spatial resolution of 27 km. The high-resolution ice mass data agree better with the ice mass change from the Input-Output method than data conventionally obtained either from gravimetry or altimetry satellite. The third study estimates the Glacial Isostatic Adjustment (GIA) effect beneath the Antarctic glaciers. This aims to minimize the GIA error in ice mass observations. By comparing the high-resolution mass estimates with multiple climate models, the GIA effect beneath the Kamb Ice Stream (which is located near the Ross Ice Shelf in West Antarctica) is estimated. The estimated GIA effect is then compared with many GIA models. It is found that most of the GIA models overestimate the GIA effect at the Kamb Ice Stream. Given that a number of models simulate the highest GIA rate beneath the Kamb Ice Stream within Antarctic glaciers, this finding has significant implications to improve the accuracy of Antarctic ice mass change by reducing the GIA uncertainty. Lastly, we aggregate the results of the three studies to project the future mass loss of Antarctic glaciers. This result is distinct from previous studies in that it provides glacial-scale projections of ice mass changes based on ice dynamic effects after removing effects of precipitation and solid earth deformation from glacial-scale ice mass observations.Chapter 1. Introduction 1 Chapter 2. Backgrounds 5 2.1 Satellite gravimetry 5 2.1.1 Overview & Principle 5 2.1.2 Estimation of surface mass densities from GRACE gravity data 6 2.1.3 Spatial filtering 8 2.2 Satellite altimetry 11 2.2.1 Overview & Principle 11 2.2.2 Laser & radar altimetry 12 2.2.3 Data types 13 2.3 Least squares inversion 14 2.3.1 Simple least squares for linear inverse problem 14 2.3.2 Application of least square inversion to GRACE data 16 Chapter 3. Surface mass balance contributions to Antarctic ice mass change investigated by climate models and GRACE gravity data 19 3.1 Introduction 19 3.2 Data & Methods 20 3.2.1 Precipitation models 20 3.2.2 EOF analysis of SMB 21 3.2.3 REOF analysis of SMB 21 3.3 AIS SMB from 1979 to 2017 23 3.4 Observation of AIS SMB 29 3.5 Implications of SMB to present-day ice mass loss in AIS 34 3.6 Conclusion 35 Chapter 4. Estimation of high-resolution Antarctic ice mass balance using satellite gravimetry and altimetry 38 4.1 Introduction 38 4.2 Data 39 4.2.1 GRACE gravity data 39 4.2.2 Satellite altimetry data 40 4.3 Methods 43 4.3.1 Forward Modeling (FM) solution 43 4.3.2 Joint estimation using constrained linear deconvolution 46 4.3.3 Uncertainties 50 4.3.3.1 Uncertainty of GRACE observation 52 4.3.3.2 Uncertainty of FM solution 52 4.3.3.3 Uncertainty of altimetry-based mass loads 54 4.3.3.4 Uncertainty of CLD solution 57 4.4 High resolution Antarctic ice mass loads 59 4.5 AIS glacier mass balance 62 4.6 Conclusion 66 Chapter 5. Estimation of GIA effect beneath the Antarctic Glacier using multiple remote sensing and climate models 68 5.1 Introduction 68 5.2 Data & Method 69 5.2.1 Method 69 5.2.2 Basin boundary 71 5.2.3 SMB models 73 5.2.4 Mass densities from GRACE data 73 5.2.5 Mass densities from satellite altimetry data 74 5.2.6 High-resolution GRACE data and its sensitivity to GIA estimates 75 5.3 Result & Discussion 77 5.3.1 Estimated mass rates 77 5.3.2 GIA mass rate beneath the KIS 80 5.4 Conclusion 81 Chapter 6. Sea-level projections 82 Chapter 7. Conclusion 86 Appendix: Glacial mass variability calculated by satellite gravimetry, altimetry, and their joint estimation 89 References 112 Abstract in Korean 122๋ฐ•

    ๊ณ ์šฉ๋Ÿ‰ ์ธต์ƒ๊ตฌ์กฐ ์‚ฐํ™”๋ฌผ ๊ธฐ๋ฐ˜ ๋ฆฌํŠฌ ์ด์ฐจ์ „์ง€ ์–‘๊ทน ์†Œ์žฌ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2021.8. ๊ฐ•๊ธฐ์„.์—๋„ˆ์ง€ ์ˆ˜์š”๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ  ํ™˜๊ฒฝ ๋ฌธ์ œ์— ๋Œ€ํ•œ ์ธ์‹์ด ์ œ๊ณ ๋˜๋ฉด์„œ, ์ „๊ธฐ์ž๋™์ฐจ ๋ฐ ์—๋„ˆ์ง€์ €์žฅ์‹œ์Šคํ…œ์˜ ์‹œ์žฅ์ด ๊ธ‰์†๋„๋กœ ์„ฑ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๋ฐœ๋งž์ถ”์–ด, ์—๋„ˆ์ง€ ์ €์žฅ์žฅ์น˜์˜ ์„ฑ๋Šฅํ–ฅ์ƒ์— ๋Œ€ํ•œ ์ˆ˜์š” ์—ญ์‹œ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ๋‹ค. ๋‹ค์–‘ํ•œ ์—๋„ˆ์ง€ ์ €์žฅ์žฅ์น˜ ์ค‘, ๋ฆฌํŠฌ์ด์˜จ ์ด์ฐจ์ „์ง€๋Š” ๋†’์€ ์—๋„ˆ์ง€ ๋ฐ€๋„, ์šฐ์ˆ˜ํ•œ ์ถœ๋ ฅ ํŠน์„ฑ ๋ฐ ์ˆ˜๋ช… ํŠน์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ์ง€๋‚œ ์ˆ˜์‹ญ ๋…„๊ฐ„ ์ด๋™์‹ ์ „์ž์žฅ์น˜์™€ ์ „๊ธฐ์ž๋™์ฐจ์˜ ํ‘œ์ค€ ์—๋„ˆ์ง€ ์žฅ์น˜๋กœ ํ™œ์šฉ๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ์ฐจ์„ธ๋Œ€ ์—๋„ˆ์ง€ ๊ธฐ์ˆ ๋กœ์˜ ์™„์ „ํ™˜ ์ „ํ™˜์„ ์œ„ํ•ด์„œ๋Š”, ํ˜„ ๋ฐฐํ„ฐ๋ฆฌ ์‹œ์Šคํ…œ์—์„œ ์—๋„ˆ์ง€ ๋ฐ€๋„์˜ ๋น„์•ฝ์ ์ธ ํ–ฅ์ƒ์ด ์š”๊ตฌ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฐ๊ฒฝ์—์„œ ๋‹ค์–‘ํ•œ ์ฐจ์„ธ๋Œ€ ์ „๊ทน์„ ๊ฐœ๋ฐœํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ์ด์–ด์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋ฆฌํŠฌ๊ณผ์ž‰ ์–‘๊ทน ์†Œ์žฌ (lithium-rich layered oxides)๋Š” ์—๋„ˆ์ง€ ๋ฐ€๋„๊ฐ€ ๊ธฐ์กด์˜ ์–‘๊ทน์žฌ๋ณด๋‹ค ํ˜„์ €ํ•˜๊ฒŒ ๋†’์•„ ์ฐจ์„ธ๋Œ€ ์–‘๊ทน ์†Œ์žฌ๋กœ์จ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋ฆฌํŠฌ๊ณผ์ž‰ ์–‘๊ทน ์†Œ์žฌ๋Š” ์—๋„ˆ์ง€ ๋ณด์กด ์„ฑ๋Šฅ ์ธก๋ฉด์—์„œ ๋ช…ํ™•ํ•œ ํ•œ๊ณ„๊ฐ€ ์žˆ์–ด, ์—๋„ˆ์ง€ ๋ณด์กด ๋ฐ ์ˆ˜๋ช… ํŠน์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์ด ์‹œ๊ธ‰ํ•œ ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋ฆฌํŠฌ๊ณผ์ž‰ ์–‘๊ทน์žฌ์˜ ์ „์•• ๊ฐ•ํ•˜ ํ˜„์ƒ์— ๋Œ€ํ•œ ์ด๋ก ์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋‚˜์•„๊ฐ€ ์ถฉยท๋ฐฉ์ „ ๋™์•ˆ ์ „๊ทน์˜ ์ „๊ธฐํ™”ํ•™์  ๊ฐ€์—ญ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋””์ž์ธ ์ „๋žต์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ œ 2์žฅ์—์„œ๋Š” ๋ฆฌํŠฌ๊ณผ์ž‰ ์–‘๊ทน์†Œ์žฌ์—์„œ ์‚ฐํ™”ํ™˜์› ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ๊ตฌ์กฐ์  ๊ฒฐํ•จ์˜ ์ƒ๊ด€๊ด€๊ณ„์— ๋Œ€ํ•œ ํ†ตํ•ฉ์ ์ธ ์ด๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ๋ฆฌํŠฌ ๋ฐ ์†Œ๋“ ๊ณผ์ž‰ ์–‘๊ทน ์†Œ์žฌ์˜ ์‚ฐ์†Œ ์‚ฐํ™” ํ™˜์›์€ ์ „๊ธฐํ™”ํ•™์  ๋น„๊ฐ€์—ญ์„ฑ๊ณผ ์ „์•• ๊ฐ•ํ•˜๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ์ œ๊ธฐ๋˜์–ด ์™”๋‹ค. ๋น„๊ฐ€์—ญ์ ์ธ ์‚ฐ์†Œ ํ™˜์›๊ณผ ๊ตฌ์กฐ์  ๊ฒฐํ•จ์˜ ํ˜„์ƒ์  ์ƒ๊ด€๊ด€๊ณ„์— ๋Œ€ํ•œ ์‹คํ—˜์  ๊ด€์ฐฐ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๊ทธ ์ƒ๊ด€๊ด€๊ณ„๋Š” ์•„์ง ์ด๋ก ์ ์œผ๋กœ ์„ค๋ช…๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตฌ์กฐ์  ๊ฒฐํ•จ, ๊ฒฐํ•ฉ ๋ฐฐ์—ด, ์‚ฐ์†Œ ์‚ฐํ™”ํ™˜์› ๋ฉ”์ปค๋‹ˆ์ฆ˜ ๊ฐ„์˜ ๋‹ค์ฐจ์›์  ์ƒ๊ด€์„ฑ์„ ์ข…ํ•ฉ์ ์œผ๋กœ ์—ฐ๊ตฌํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„“์€ ๋ฒ”์œ„์˜ ๋ฆฌํŠฌ ๊ณผ์ž‰ ์–‘๊ทน ์†Œ์žฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋ฉฐ, ์–‘์ด์˜จ์„ฑ ๊ตฌ์กฐ ๊ฒฐํ•จ๊ณผ ์Œ์ด์˜จ์„ฑ ๊ตฌ์กฐ ๊ฒฐํ•จ์„ ๋ชจ๋‘ ํฌํ•จํ•œ๋‹ค. ์–‘์ด์˜จ์„ฑ ๊ตฌ์กฐ ๊ฒฐํ•จ์˜ ๊ฒฝ์šฐ, ๊ฐ•ํ•œ ์‚ฐ์†Œ-์‚ฐ์†Œ, ๊ธˆ์†-์‚ฐ์†Œ ํ˜ผ์„ฑ์„ ๊ฐ•ํ™”์‹œ์ผœ ์‚ฐ์†Œ๋ฅผ ์•ˆ์ •์‹œํ‚ค๋ฉฐ, ๊ทธ ํ˜ผ์„ฑ์˜ ์ •๋„๋Š” ์‚ฐ์†Œ์˜ ์ „ํ•˜๋Ÿ‰๊ณผ ๊ธˆ์†-์‚ฐ์†Œ ๊ณต์œ ๊ฒฐํ•ฉ์„ฑ์— ์˜ํ•ด ๊ฒฐ์ •๋˜๋Š” ๊ฒƒ์„ ์ฆ๋ช…ํ•œ๋‹ค. ๋˜ํ•œ ๊ตฌ์กฐ๊ฒฐํ•จ์œผ๋กœ ์ธํ•œ ์‚ฐ์†Œ ํ˜ผ์„ฑ์ด ๊ตฌ์กฐ์  ๊ฐ€์—ญ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ๊ตฌ์กฐ๋‚ด ์ƒ์„ฑ๋˜๋Š” ์‚ฐ์†Œ ์ด๋Ÿ‰์ฒด๊ฐ€ ์‹ฌ๊ฐํ•œ ๊ตฌ์กฐ์  ๋น„๊ฐ€์—ญ์„ฑ์„ ์•ผ๊ธฐํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ค๋žœ ๊ธฐ๊ฐ„ ๋ณด๊ณ ๋˜์–ด์˜จ ๊ตฌ์กฐ์  ๊ฒฐํ•จ๊ณผ ์‚ฐ์†Œ ์‚ฐํ™”ํ™˜์› ์‚ฌ์ด์˜ ํ˜„์ƒ์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์„ค๋ช…ํ•˜๋ฉฐ, ์‚ฐ์†Œ ์‚ฐํ™” ํ™˜์›์˜ ๊ฐ€์—ญ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ด๋ก ์  ํ† ๋Œ€๋ฅผ ์ œ์‹œํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ์ œ 3์žฅ์—์„œ๋Š” ๋ฐ˜๋ณต๋œ ์ถฉ๋ฐฉ์ „ ๋™์•ˆ ๋ฆฌํŠฌ๊ณผ์ž‰ ์–‘๊ทน ์†Œ์žฌ์˜ ๊ตฌ์กฐ์  ๊ฐ€์—ญ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ „๋žต์€ ์†Œ์žฌ์˜ ์ „์••๊ฐ•ํ•˜ ํ˜„์ƒ์ด ์ฃผ๋กœ ์ „์ด๊ธˆ์†์˜ ๋น„๊ฐ€์—ญ์ ์ธ ์ด๋™์—์„œ ๊ธฐ์ธํ•œ๋‹ค๋Š” ๊ธฐ์กด์˜ ์ดํ•ด์— ๊ธฐ์ดˆํ•œ๋‹ค. ์•ž์„œ ์ „์ด๊ธˆ์†์˜ ์ด๋™ ์ž์ฒด๋ฅผ ์ค„์ด๋ ค๋Š” ์‹œ๋„๊ฐ€ ๋งŽ์•˜์ง€๋งŒ, ์ด๋™์˜ ์—ด์—ญํ•™์  ์•ˆ์ •์„ฑ ๋•Œ๋ฌธ์— ์žฅ์‚ฌ์ดํด ๋™์•ˆ์˜ ๊ตฌ์กฐ ๋ณด์กด์€ ๋ถˆ๊ฐ€๋Šฅ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตฌ์กฐ ๋ณ€ํ™” ์ž์ฒด๋ฅผ ์–ต์ œํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹Œ, ๊ตฌ์กฐ์˜ ๊ฐ€์—ญ์„ฑ์„ ๋†’์ž„์œผ๋กœ์จ ๋ฆฌํŠฌ ๊ณผ์ž‰์†Œ์žฌ์˜ ์ „์••๊ฐ•ํ•˜ ํ˜„์ƒ์„ ํ•ด๊ฒฐํ•œ๋‹ค. ๋‹ˆ์ผˆยท๋ง๊ฐ„ ๊ธฐ๋ฐ˜ ๋ฆฌํŠฌ๊ณผ์ž‰ ์‚ฐํ™”๋ฌผ์˜ ์‚ฐ์†Œ ๊ฒฉ์ž๋ฅผ O3 ํ˜•ํƒœ์—์„œ O2ํ˜•ํƒœ๋กœ ์กฐ์ ˆํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๊ตฌ์กฐ์  ๊ฐ€์—ญ์„ฑ์„ ์ƒ๋‹นํžˆ ํ–ฅ์ƒ์‹œํ‚ค๋ฉด์„œ ์ „์•• ๊ฐ•ํ•˜ ํ˜„์ƒ์„ ์–ต์ œํ•  ์ˆ˜ ์žˆ์Œ์„ ์ œ์‹œํ•œ๋‹ค. X์„  ํšŒ์ ˆ ๋ถ„์„, ์ฃผ์‚ฌํˆฌ๊ณผ ์ „์ž ํ˜„๋ฏธ๊ฒฝ, ๋ผ๋งŒ ๋ถ„๊ด‘๋ฒ•์„ ํ†ตํ•ด ์ถฉ์ „ ์ค‘ ๋ฆฌํŠฌ ์ธต์œผ๋กœ ์ด๋™ํ•œ ์ „์ด ๊ธˆ์†์ด, ๋ฐฉ์ „ ์‹œ ์›๋ž˜์˜ ์ž๋ฆฌ๋กœ ๊ฐ€์—ญ์ ์œผ๋กœ ๋Œ์•„๊ฐ€๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•œ๋‹ค. ๋‚˜์•„๊ฐ€ ์ œ์ผ ์›๋ฆฌ ๊ณ„์‚ฐ์„ ํ†ตํ•ด O2 ์‚ฐ์†Œ ๊ฒฉ์ž๋‚ด ์ „์ด๊ธˆ์† ์ž๋ฆฌ์™€ ๋ฆฌํŠฌ ์ž๋ฆฌ๊ฐ€ ์„œ๋กœ ๋ฉด์„ ๊ณต์œ ํ•˜๊ณ , ์ด๋กœ ์ธํ•œ ๋ฐ˜๋ฐœ๋ ฅ์ด ๊ตฌ์กฐ์˜ ๊ฐ€์—ญ์„ฑ์— ํฌ๊ฒŒ ๊ธฐ์—ฌํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์ฆ๋ช…ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์žฌ๋ฃŒ์˜ ๊ตฌ์กฐ๋ฅผ ๋ฆฌํŠฌ๊ณผ์ž‰ ์–‘๊ทน ์†Œ์žฌ์˜ ์ „์•• ๊ฐ•ํ•˜ ๋ฐ ์ „์•• ํžˆ์Šคํ…Œ๋ฆฌ์‹œ์Šค ํ˜„์ƒ์„ ํ•ด๊ฒฐํ•˜๋ฉฐ, ๊ตฌ์กฐ์  ๊ฐ€์—ญ์„ฑ์ด ์ค‘์š”ํ•œ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ๋„๋ฆฌ ์ ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.With the advent of new market segment aiming at societal energy and environmental concerns such as electrified transportation and grid-scale energy storage applications, there has been the pressing demand for the improvements in the performance of energy storage systems. Among energy storage systems, rechargeable lithium-ion batteries have been the de facto standards for portable electronic devices and electrified transportation for decades owing to their high energy density, power capability and stable cyclability. However, the full-fledged placement of green energy technologies requires a significant breakthrough in the energy density of current battery systems, which has prompted the search for alternative battery electrode materials. In this regard, lithium-rich layered oxide electrodes have garnered tremendous research attention as a next-generation cathode system with exceptionally high energy density. But, the supply of high capacity from lithium-rich layered oxides has been known to compromise energy retention properties, thus it is of great importance to enhance the cycling performance of those electrodes. In this thesis, I present a theoretical investigation on the voltage depression problem of lithium-rich layered oxide electrodes, and propose a design strategy to improve electrochemical reversibility of electrodes during cycling. In Chapter 2, I designed a unified a theoretical picture of the relations between redox chemistry and structural disorders in lithium-rich layered oxide electrodes. Oxygen redox provides high energy density for lithium- and sodium-rich layered oxides electrodes, but simultaneously leads to electrochemical irreversibility and voltage depression. Despite the observation of the associations between the irreversible oxygen redox and structural disorders, their intrinsic relations have yet been fully understood because there has been little consideration of bonding rearrangements involved with structural disordering. In this respect, I comprehensively address the multifaceted connections between structural disorder, bonding arrangement, and oxygen redox chemistry. My work encompasses a wide range of lithium-rich electrodes in charge-transfer systems and Mott-Hubbard systems, and covers both cation and anion disorders. It is unraveled that cation disorders stabilize oxygen redox by driving strong oxygen-oxygen and/or metal-oxygen hybridization, and the nature of bonding reorganization depends on the occupancy of oxygen non-bonding states and metal-oxygen covalency. I further answer how the formation of short covalent bonds affects electrochemical and structural reversibility. And importantly, the free movement of oxygen dimer is spotted, suggesting poor structural resilience of oxygen dimers. On the other hand, anion disorder is found to compensate for the electron deficiency of oxygen network without significantly regulating bonding arrangements. My findings rationalize long-reported phenomenological correlations between structural disorders and oxygen redox, and offer a scientific basis for optimizing the reversibility of oxygen redox considering structural disorders. In Chapter 3, I present a design strategy to improve the structural reversibility of lithium-rich layered oxide electrodes during charging and discharging. There has been a consensus that the voltage decay is mainly originated from structural transformations involving irreversible cation migration. While many previous studies have succeeded in inhibiting cation migration itself to some extent, the thermodynamically spontaneous nature of cation migration requires a paradigm shift toward managing the reversibility of inevitable cation migration. I demonstrate for cobalt-free lithium-rich nickel manganese oxides that by tweaking the oxygen lattice of compounds from typical O3 to O2 staking, the reversibility of cation migration can be remarkably improved, thereby dramatically suppressing the voltage decay. Preeminent intra-cycle reversibility of cation migration is visualized via scanning transmission electron microscope, and such reversibility is proved to aid in the preservation of pristine structure over extended cycles. First-principle calculations verify that a large electrostatic repulsion between face-sharing cations restricts the movements of transition metals in the lithium layer, thereby streamlining the returning migration path of transition metals. Furthermore, I prove that the enhanced reversibility help mitigate the asymmetry of anion redox, which arises from the intra-cycle asymmetry of transition metal locations, ameliorating voltage hysteresis concurrently.Chapter 1. Introduction 1 1.1 Motivation and outline 1 1.2 References 6 Chapter 2. Trilateral correlation of structural disorder, bond covalency, and oxygen redox chemistry in lithium-rich layered oxide electrodes 9 2.1 Introduction 9 2.2 Computational details 13 2.3 Result and Discussion 15 2.3.1 Cation disordering in charge-transfer systems 15 2.3.2 Cation disordering in Mott-Hubbard systems 36 2.3.3 Reversibility and asymmetry of the oxygen redox 63 2.3.4 Anionic disorder and oxygen redox chemistry 94 2.3.5 Theoretical voltage profiles considering structural disorder 108 2.3.6 Electronic structure of electrodes 111 2.3.7 Effects of metal-oxygen decoordination on the electronic structure 114 2.3.8 Types of oxygen dimer 116 2.3.9 Cation migration in Na0.6[Li0.2Mn0.8]O2 and Na2/3[Mg1/3Mn2/3]O2 119 2.3.10 Effects of metal substitution on bond rearrangements 122 2.4 Concluding remarks 143 2.5 References 144 Chapter 3. Voltage decay and redox asymmetry mitigation by reversible cation migration in lithium-rich layered oxide electrodes 156 3.1 Introduction 156 3.2 Experimental and computational details 162 3.3 Results and discussion 167 3.3.1 Electrochemistry of O2-LLNMOs 167 3.3.2 Reversible cation migration in O2-LLNMOs 174 3.3.3 High-potential O redox behavior preserved in O2-LLNMOs 190 3.3.4 Synthesis of O2-LLNMOs 198 3.3.5 Structural characterization of O2-LLNMOs 204 3.3.6 Theoretical investigation of cation migration pathways 205 3.3.7 Partial manganese reduction during discharge 217 3.4 Concluding remarks 218 3.5 References 220 Chapter 4. Summary 231 Abstract in Korean 233๋ฐ•

    Crowdsourcing Based WiFi Radio Map Management with Magnetic Landmark and PDR

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2015. 8. ๊ถŒํƒœ๊ฒฝ.๋ชจ๋ฐ”์ผ ๋””๋ฐ”์ด์Šค๋ฅผ ์ด์šฉํ•˜๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ๊ธ‰๊ฒฉํ•˜๊ฒŒ ๋Š˜์–ด๋‚˜๋ฉด์„œ ์‚ฌ์šฉ์ž์˜ ์œ„์น˜๋‚˜ ์ƒํ™ฉ์— ์•Œ๋งž์€ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ์œ„์น˜ ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค(LBS, Location Based Service)๊ฐ€ ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์‹ค์™ธ์—์„œ๋Š” GPS ์‹ ํ˜ธ๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์ธก์œ„ ์„œ๋น„์Šค๊ฐ€ ๊ฐ€๋Šฅํ•œ ๋ฐ˜๋ฉด, ์‹ค๋‚ด์—์„œ๋Š” GPS ์‹ ํ˜ธ๋ฅผ ์ˆ˜์‹ ํ•˜๊ธฐ์— ์–ด๋ ค์›€์ด ์žˆ์–ด, ๋‹ค๋ฅธ ์ธก์œ„ ์ž์›๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ์ธก์œ„๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. WiFi๋Š” ๊ณ ์† ๋ฌด์„ ํ†ต์‹ ์„ ์œ„ํ•˜์—ฌ ๋งŽ์€ AP๋“ค์ด ์กด์žฌ ์‹ค๋‚ด์— ๋งŽ์ด ์„ค์น˜๊ฐ€ ๋˜์–ด ์žˆ์–ด์„œ, ์„œ๋น„์Šค ์ง€์—ญ์—์„œ์˜ ๋ผ๋””์˜ค๋งต์„ ์ˆ˜์ง‘ํ•˜๋Š” ํ•‘๊ฑฐํ”„๋ฆฐํŒ…(Fingerprinting) ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•  ๊ฒฝ์šฐ ์•ˆ์ •์ ์ธ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค [1][2]. ํ•˜์ง€๋งŒ, ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ํ™˜๊ฒฝ์ ์ธ ์š”์ธ์— ์˜ํ•ด์„œ ์‹ ํ˜ธ์˜ ํŠน์„ฑ์ด ๋ณ€ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ์ง€์†์ ์œผ๋กœ ์ „์ฒด ์„œ๋น„์Šค ์ง€์—ญ์˜ ๋ผ๋””์˜ค๋งต์„ ์žฌ์ˆ˜์ง‘ํ•˜๋Š” ๋“ฑ์˜ ๊ด€๋ฆฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ WiFi ๋ผ๋””์˜ค๋งต์„ ์ง€์†์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ์šฉ์ž๋“ค์˜ ์œ„์น˜์—์„œ ๊ด€์ฐฐํ•œ ํ•‘๊ฑฐํ”„๋ฆฐํŠธ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ผ๋””์˜ค๋งต์˜ ์–‘์ƒ์ด ๋ณ€ํ•˜๋Š” ๊ฒƒ์„ ๊ฐ์ง€ํ•˜์—ฌ ์ตœ์ข…์ ์œผ๋กœ ์—…๋ฐ์ดํŠธ๊นŒ์ง€ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์‚ฌ์šฉ์ž์˜ ์ถ”์ •๋œ ์œ„์น˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ผ๋””์˜ค๋งต์˜ ๊ด€๋ฆฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ผ๋””์˜ค๋งต์˜ ์˜ค์—ผ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์˜ค์—ผ์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด์„œ PDR ๋ฐ ์ง€์ž๊ธฐ ๋žœ๋“œ๋งˆํฌ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์‚ฌ์šฉ์ž ์œ„์น˜ ์ถ”์ •์„ ํ•  ์ˆ˜ ์žˆ๋‹ค.Recently, the portion of people using smartphone are continuously increasing, applications of location-based services (LBS) have been exploding to release and studied. Outdoor positioning may not be a big deal by exploiting triangulation of GPS signals, which offers reliable service in general. However, in indoor, GPS signal does not reachable inside of building or not enough to estimating a position with triangulation. Therefore, other sensor modules should be used to indoor localization. As WiFi is popular wireless communication technology, a lot of APs (Access Points) are deployed in building. For example, average of observable APs on a scan is about 36.8 in Seoul station located in South Korea. Thus, WIFi may be a good source of estimating position due to its technological infrastructure. WiFi fingerprinting schemes has a good performance when it comes to performing positioning at initial site survey moment. Localization accuracy depends on radio map similarity of positioning moment. Therefore, continuous survey of radio map is required to accurate localization. In this paper, motivated by these limitation, we suggest crowdsourcing based WiFi radio map management system. Users observe WiFi APs information to estimating position and transfer the fingerprint (estimated position and corresponding AP information) to fingerprint management server. Then, the server manages reported fingerprints to add/delete/change to radio map. Because management of fingerprints is based on estimated position, however, the radio map could be polluted by un-accurate position. Therefore, we adapt landmark by using magnetic field sequence to calibrate users position. Moreover, PDR (Pedestrian dead-reckoning) trajectory coordinates reported by user are used to scoring quality of radio map. Finally, our system reduce the cost of continuous site survey.์ œ 1์žฅ ์„œ ๋ก  8 ์ œ 1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 8 ์ œ 2์ ˆ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 13 ์ œ 2์žฅ WiFi ์‹ ํ˜ธ์— ๋Œ€ํ•œ ๋ถ„์„ 14 ์ œ 1์ ˆ ๋น„์ปจ ํ”„๋ ˆ์ž„ 14 ์ œ 2์ ˆ ๋ฌด์„  ์‹ ํ˜ธ ์†์‹ค 14 ์ œ 3์ ˆ ๋ฌธ์ œ ์ •์˜ ๋ฐ ํ•ด๊ฒฐ 18 ์ œ 3์žฅ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ๊ธฐ๋ฐ˜ ๋ผ๋””์˜ค๋งต ๊ด€๋ฆฌ ์‹œ์Šคํ…œ 20 ์ œ 1์ ˆ ๊ตฌ์กฐ ๋ฐ ์‹œ์Šคํ…œ 20 1. ์ดˆ๊ธฐ ๋ผ๋””์˜ค๋งต ๊ตฌ์ถ• 20 2. ํด๋ผ์ด์–ธํŠธ 21 3. ์„œ๋ฒ„ 23 ์ œ 2์ ˆ ์ธก์œ„ ํ™˜๊ฒฝ ๋ณ€ํ™” ๊ฐ์ง€ 23 4. AP ์ œ๊ฑฐ ๊ฐ์ง€ 26 5. AP ๋ณ€ํ™” ๊ฐ์ง€ 27 ์ œ 3์ ˆ ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ด์šฉํ•œ ์—๋Ÿฌ ๋ณด์ • 28 ์ œ 4์ ˆ ๋ผ๋””์˜ค๋งต ํ’ˆ์งˆ ์ถ”์ • 30 ์ œ 4์žฅ ์‹œ์Šคํ…œ ๋ถ„์„ ๋ฐ ํ‰๊ฐ€ 33 ์ œ 1์ ˆ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 33 ์ œ 2์ ˆ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์„ค์ • 34 1. ์ดˆ๊ธฐ ๋ผ๋””์˜ค๋งต ๊ตฌ์ถ• 34 2. ์ด๋™ ๋ฐ ์„ผ์„œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 34 3. AP ์ถ”๊ฐ€/์ œ๊ฑฐ/๋ณ€ํ™” ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 35 ์ œ 3์ ˆ ์„ฑ๋Šฅ ํ‰๊ฐ€ 36 1. ๋žœ๋“œ๋งˆํฌ ํšจ์šฉ์„ฑ 36 2. AP ์–‘์ƒ ๋ณ€ํ™” ๊ฐ์ง€ ์„ฑ๋Šฅ 38 3. ๋ผ๋””์˜ค๋งต ํ’ˆ์งˆ ์ถ”์ • ์„ฑ๋Šฅ 41 ์ œ 5์žฅ ๊ฒฐ ๋ก  43 ์ฐธ๊ณ  ๋ฌธํ—Œ 44 Abstract 47Maste

    Geometric control using geodesics and parallel transport

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2013. 8. ๊น€์œ ๋‹จ.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ธก์ง€์„  ๋ฐ ํ‰ํ–‰์ด๋™(geodesics and parallel transport)๋ฅผ ์ด์šฉํ•œ ๋ฏธ ๋ถ„ ๊ธฐํ•˜ํ•™์  ์ž์„ธ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ธก์ง€์„ ์˜ ๊ธธ์ด๋กœ ํ‘œํ˜„๋œ ์ž์„ธ ์˜ค ์ฐจ๋Š” ์„ค๊ณ„์ƒ ์‹ค์ œ ์˜ค์ฐจ์— ์ •ํ™•ํ•˜๊ฒŒ ๋น„๋ก€ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ถ”์ข… ์˜ค์ฐจ์ด๋ฉฐ, ์ด๋ฅผ ๋ฏธ๋ถ„ํ•˜ ์—ฌ์„œ ์ƒˆ๋กœ์šด ์˜ค์ฐจ ๋ฒกํ„ฐ๋ฅผ ๊ตฌํ•˜์˜€๋‹ค. ์ด ์˜ค์ฐจ ๋ฒกํ„ฐ์˜ ํฌ๊ธฐ๊ฐ€ ์‹ค์ œ ์˜ค์ฐจ์˜ ํฌ๊ธฐ์™€ ์„  ํ˜•์ ์œผ๋กœ ๋น„๋ก€ํ•œ๋‹ค๋Š” ํŠน์ง•์€, ํฐ ์˜ค์ฐจ๊ฐ€ ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ์— ๋น ๋ฅธ ์ œ์–ด ๋ฐ˜์‘์„ ๊ฐ€์ ธ์˜ค ๋Š” ํŠน์„ฑ์ด ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์ด ์ •์˜๋œ ์˜ค์ฐจํ•จ์ˆ˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ค๊ณ„๋œ ๋ฅด์•ผํ”„๋…ธํ”„ ๊ธฐ๋ฐ˜ ์˜ ์ œ์–ด๊ธฐ๋Š” ์ „์—ญ์ ์ด๋ฉฐ ์ง€์ˆ˜ํ•จ์ˆ˜์ ์œผ๋กœ ์•ˆ์ •ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๊ธฐํ•˜ํ•™์  ์ œ์–ด ๊ธฐ๋ฒ•์€ ์ง€์—ญ์ ์ธ ์ขŒํ‘œ๊ณ„๋ฅผ ์ž„์˜๋กœ ๋„์ž…ํ•˜์ง€ ์•Š ์Œ์œผ๋กœ ์ธํ•˜์—ฌ ์ƒ๋Œ€์ ์œผ๋กœ ํฐ ๊ฑฐ๋ฆฌ ์˜ค์ฐจ์™€ ์†๋„ ์˜ค์ฐจ์— ์œ ์—ฐํ•˜๊ฒŒ ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋‹ค ๋Š” ์ธก๋ฉด์—์„œ ๊ณผ๊ฑฐ์˜ ์—ฐ๊ตฌ๋ณด๋‹ค ์ง„์ „๋œ ๊ฒฐ๊ณผ๋กœ ์•ž์œผ๋กœ ๊ณ„์†๋  ์—ฐ๊ตฌ์˜ ๊ธฐ์ดˆ ์ž๋ฃŒ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค.Abstract 1 Introduction 2 Mathematical Preliminaries 2.1 Riemannian Geometry 2.2 Lie Group 2.3 Geodesics and Parallel Transport 2.3.1 Geodesics and Parallel Transport on a Riemannian Manifold 2.3.2 Geodesic and Parallel Transport on a Lie Group 3 Geometric control using Geodesics and Parallel Transport 3.1 Equations of Motion 3.2 Geometric tracking control on R3 3.2.1 Error function by geodesic distance 3.2.2 Transport map by parallel transport 3.2.3 Tracking controller 3.3 Geometric tracking control on SO(3) 3.3.1 Error function by geodesic distance 3.3.2 Transport map by parallel transport 3.3.3 Tracking controller 4 Numerical Simulations 4.1 Assumptions and Simulation Environment 4.2 Simulation Results 4.2.1 Large initial attitude error 4.2.2 Large initial attitude error with large velocity error 5 Conclusion ReferencesMaste

    Characteristics of Ambivalence of Self-image in Patients with Schizophrenia

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    Objectives: Ambivalence of self-image is considered to be important in patients with schizophrenia since impairment of self-referential processing and increment in ambivalence are illness-related symptoms. This study aimed to evaluate quantitative and qualitative properties of ambivalence of self-image in patients with schizophrenia. Methods: Twenty patients with schizophrenia and 20 normal controls performed a set of โ€™self-image reflection taskโ€™ and then the level of ambivalence towards actual and ideal self-image were numerically scored. Ambivalence scores were compared between groups and correlation analyses with psychometric scales were done in each group. Results: Patients with schizophrenia had higher level of ambivalence towards both actual and ideal self-image (p<0.001). Normal controls showed significant correlations with the scales representing level of self-concept clarity (r=-0.480, p=0.033), depression (r=0.479, p=0.033), and self-esteem (r=-0.555, p=0.011 ; R=-0.600, p=0.005) while the patients did not. Conclusion: Ambivalence towards oneโ€™s self-image is more intense in patients with schizophrenia. This symptom may be considered to exist as an independent symptom in schizophrenia.ope

    Feasibility of the virtual reality-based assessments in patients with panic disorder

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    Introduction: Recurrences and diagnostic instability of panic disorder (PD) are common and have a negative effect on its long-term course. Developing a novel assessment tool for anxiety that can be used in a multimodal approach may improve these problems in panic disorder patients. This study assessed the feasibility of virtual reality-based assessment in panic disorder (VRA-PD). Methods: Twenty-five patients with PD (ANX group) and 28 healthy adults (CON group) participated in the study. VRA-PD consisted of four modules based on the key components of cognitive behavior therapy for an anxiety disorder: โ€œBaseline evaluation moduleโ€ (M0), โ€œDaily environment exposure moduleโ€ (M1), โ€œRelaxation moduleโ€ (M2), and โ€œInteroceptive exposure moduleโ€ (M3). Multiple evaluations, including self-rating anxiety scores (AS) and physiological responses [heart rate variability (HRV) index], were performed in three steps at M1, M2, and M3, and once at M0. Comparisons between patients with PD and healthy controls, factor analysis of variables in VRA-PD, changes in responses within modules, and correlation analysis between variables in VRA-PD and anxiety symptoms assessed by psychological scales were performed. Results : All participants completed the VRA-PD without discontinuation. The ANX group reported significantly higher AS for all steps and a smaller HRV index in M1 (steps 1 and 2) and M2 (step 1). Repeated-measures analysis of covariance (ANCOVA) revealed significant interaction effects for AS in M1 (F = 4.09, p = 0.02) and M2 (F = 4.20, p = 0.02), and HRV index in M2 (F = 16.22, p < 0.001) and M3 (F = 21.22, p = 0.02). The HRV index only indicated a good model fit for the three-factor model, reflecting the construct of the VRA-PD. Both AS and HRV indexes were significantly correlated with anxiety and depression symptoms. Discussion : The current study provides preliminary evidence that the VRA-PD could be a valid anxiety behavior assessment tool.ope

    Development of water-window soft X-ray source using laser produced plasmas

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    Maste

    HVPE๋ฒ•์„ ์ด์šฉํ•œ GaN ๊ฒฐ์ •์˜ ์„ฑ์žฅ์— ๋ฏธ์น˜๋Š” ๊ทน์„ฑ์˜ ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2003.Maste

    Deep graph neural network-based prediction of acute suicidal ideation in young adults

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    Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855-0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.ope
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