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    ๊ฐ€์ƒํ˜„์‹ค(Virtual reality) ๊ธฐ๊ธฐ๋ฅผ ํ™œ์šฉํ•œ ๋ฐฐํŠธ ์Šค์œ™ ํ›ˆ๋ จ์˜ ์Šค์œ™ ์ •ํ™•๋„ ํ•™์Šตํšจ๊ณผ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ฒด์œก๊ต์œก๊ณผ, 2018. 2. ๊น€์„ ์ง„.๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์•ผ๊ตฌ ๋ฐฐํŠธ ์Šค์œ™ ๊ณผ์ œ์—์„œ VR(Virtual Reality) ๊ธฐ๊ธฐ๋ฅผ ํ™œ์šฉํ•œ ์ง‘๋‹จ๊ณผ ์‚ฌ์šฉํ•˜์ง€ ์•Š์€ ์ง‘๋‹จ๊ณผ์˜ ์Šค์œ™ ์ •ํ™•๋„์˜ ์ฐจ์ด๋ฅผ ๋น„๊ตยท๋ถ„์„ ํ•˜๋Š”๋ฐ ์žˆ๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž๋Š” ๋งŒ 20์„ธ ๏ฝž 26์„ธ์˜ ์„ฑ์ธ ๋‚จ์„ฑ๋“ค๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์‚ฌ์ „ ๊ฒ€์‚ฌ ์ดํ›„ ๋ฌด์ž‘์œ„๋กœ ์Šค์œ™ ์ •ํ™•๋„๊ฐ€ ๋น„์Šทํ•œ ๋‘ ๊ฐœ์˜ ์ง‘๋‹จ(ํ”„๋ฆฌ ๋ฐฐํŒ… ํ›ˆ๋ จ , ํ”„๋ฆฌ ๋ฐฐํŒ… ํ›ˆ๋ จ + VR ํ›ˆ๋ จ)์œผ๋กœ ๊ตฌ์„ฑ ํ•˜์˜€๋‹ค. ์‹คํ—˜ ์ฐธ๊ฐ€์ž๋“ค์€ 17m ๊ฑฐ๋ฆฌ์—์„œ 100km/h๏ฝž110km/h ์†๋„๋กœ ํ”ผ์นญ ๋จธ์‹ ์—์„œ ์˜ค๋Š” ๊ณต์„ 20ํšŒ ์Šค์œ™ํ•˜๋Š” ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ•™์Šต ๊ธฐ๊ฐ„์€ 3์ฃผ ๊ฐ„ ์ฃผ 3ํšŒ ์ด 9ํšŒ ์‹ค์‹œ ํ•˜์˜€๋‹ค. VR๊ณผ ํ”„๋ฆฌ๋ฐฐํŒ…์„ ๋ณ‘ํ–‰ํ•œ ์ง‘๋‹จ์ด ํƒ€๊ฒฉ ํšŸ์ˆ˜, ์œ ํšจ ํƒ€๊ฒฉ ํšŸ์ˆ˜, ๋น„๊ฑฐ๋ฆฌ์—์„œ ํ”„๋ฆฌ๋ฐฐํŒ…์„ ํ•œ ์ง‘๋‹จ๋ณด๋‹ค ์œ ์˜ํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ๋ฐœ์‚ฌ๊ฐ๋„์˜ ์ผ๊ด€์„ฑ์—์„œ VR๊ณผ ํ”„๋ฆฌ๋ฐฐํŒ…์„ ๋ณ‘ํ–‰ํ•œ ์ง‘๋‹จ์˜ ์ผ๊ด€์„ฑ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฐฐํŠธ ์Šคํ”ผ๋“œ์—์„œ๋Š” ๋‘ ์ง‘๋‹จ์˜ ์œ ์˜ํ•œ ์ฐจ์ด๋Š” ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด VR ์Šค์œ™ ํ›ˆ๋ จ์„ ๋ณ‘ํ–‰ํ•œ ์ง‘๋‹จ์ด ๊ทธ๋ ‡์ง€ ์•Š์€ ์ง‘๋‹จ๋ณด๋‹ค ์Šค์œ™ ์ •ํ™•์„ฑ์—์„œ ๋” ๋‚˜์€ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚จ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ๋ชฉ์  5 3. ์—ฐ๊ตฌ๊ฐ€์„ค 5 4. ์šฉ์–ด์˜ ์ •์˜ 6 5. ์—ฐ๊ตฌ์˜ ์ œํ•œ์  7 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 8 1. ์šด๋™ํ•™์Šต์— ๋Œ€ํ•œ ์ดํ•ด 8 1) ์šด๋™ ํ•™์Šต์˜ ์ •์˜ 8 2) ์šด๋™ ์ˆ˜ํ–‰๊ณผ ํŒŒ์ง€๊ฒ€์‚ฌ 9 3) ํ”ผ๋“œ๋ฐฑ์˜ ๊ฐœ๋… 10 2. ์•ผ๊ตฌ ์Šค์œ™์— ๋Œ€ํ•œ ์ดํ•ด 12 1) ์šด๋™ ๊ธฐ์ˆ  12 2) ์•ผ๊ตฌ ์Šค์œ™์˜ ์—ญํ•™ ์›๋ฆฌ 13 3) ์Šค์œ— ์ŠคํŒŸ์˜ ์ •์˜ 14 3. ์‹œ๊ฐ ํƒ์ƒ‰ 15 1) ์‹œ๊ฐ ํƒ์ƒ‰์˜ ์ •์˜ 15 2) ์ ‘์ด‰ ์‹œ๊ฐ„ ์ •๋ณด 16 4. ๊ฐ€์ƒ ํ˜„์‹ค ์—ฐ๊ตฌ ํ˜„ํ™ฉ 19 1) ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•™์Šต ํšจ๊ณผ 19 2) ์•ผ๊ตฌ ๊ณผ์ œ ์—ฐ๊ตฌ ํ˜„ํ™ฉ 20 โ…ข. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 22 1. ์—ฐ๊ตฌ๋Œ€์ƒ 22 2. ์‹คํ—˜๋„๊ตฌ ๋ฐ ์ธก์ •๋ฐฉ๋ฒ• 23 3. ์‹คํ—˜์ ˆ์ฐจ 27 4. ์‹คํ—˜์„ค๊ณ„ 31 5. ์ž๋ฃŒ๋ถ„์„ 32 6. ํ†ต๊ณ„์ฒ˜๋ฆฌ 34 โ…ฃ. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 35 1. ํƒ€๊ฒฉ ํšŸ์ˆ˜ 35 2. ์œ ํšจ ํƒ€๊ฒฉ ํšŸ์ˆ˜ 38 3. ๋น„๊ฑฐ๋ฆฌ 40 4. ๋ฐœ์‚ฌ ๊ฐ๋„ 43 5. ์Šค์œ™ ์Šคํ”ผ๋“œ 46 โ…ค. ๋…ผ์˜ 48 1. ์•ผ๊ตฌ ์Šค์œ™์˜ ์ •ํ™•์„ฑ ํ•™์Šต ํšจ๊ณผ์— ๋Œ€ํ•œ ๋น„๊ต 50 2. VR๋ฅผ ํ™œ์šฉํ•œ ์Šค์œ™ ์ •ํ™•์„ฑ ํ•™์Šตํšจ๊ณผ ๋น„๊ต 54 3. ์ข…ํ•ฉ ๋…ผ์˜ 55 โ…ฅ. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 56 1. ๊ฒฐ๋ก  57 2. ์ œ์–ธ 58 ์ฐธ๊ณ ๋ฌธํ—Œ 59 Abstract 64Maste

    A Study on Augmented Reality Putting Training Using Machine Learning Technology : Focusing on Changes According to Skill Level

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ฒด์œก๊ต์œก๊ณผ, 2023. 2. ๊น€์„ ์ง„.ํผํŒ… ๊ณผ์ œ๋Š” ๋‹ค์–‘ํ•œ ํŒจํ„ด์˜ ๊ฐœ์ธ์ฐจ๊ฐ€ ์กด์žฌํ•˜๋ฉฐ ์˜ฌ๋ฐ”๋ฅธ ์ธ์ง€-์ˆ˜ํ–‰ ๋Šฅ๋ ฅ์— ๋”ฐ๋ผ ์ˆ˜ํ–‰ ์ˆ˜์ค€์ด ๊ฒฐ์ •๋œ๋‹ค. ๊ฐœ์ธ์ฐจ๋Š” ๊ณง ๊ณ ์œ ๊ฐ๊ฐ ์š”์ธ์ด๋ฉฐ, ์ด๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์–ด๋–ป๊ฒŒ ํ•™์Šต์‹œ์ผœ์•ผ ์„ฑ๊ณต์ ์ธ ํผํŒ…์ด ๊ฐ€๋Šฅํ•œ์ง€์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ฐœ์ธ์ฐจ๋ฅผ ๊ณ ๋ คํ•œ ๋จธ์‹ ๋Ÿฌ๋‹ ํ”„๋กœ๊ทธ๋žจ์„ ํ™œ์šฉํ•˜์—ฌ ์ฆ๊ฐ•ํ˜„์‹ค ํ”ผ๋“œ๋ฐฑ์„ ์ ์šฉํ•œ ์ง‘๋‹จ๊ณผ ์ž๊ธฐ์กฐ์ ˆ ํ”ผ๋“œ๋ฐฑ(self-controlled feedback) ์„ ํ™œ์šฉํ•œ ์ผ๋ฐ˜ ํผํŒ… ํ›ˆ๋ จ ์ง‘๋‹จ๊ณผ์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ํšจ์œจ์ ์ธ ํผํŒ… ํ•™์Šต๋ฒ•์„ ์ œ์‹œํ•˜๋Š” ๋ฐ ์žˆ๋‹ค. ์—ฐ๊ตฌ์— ์ฐธ์—ฌํ•œ ๋Œ€์ƒ์€ ๋งŒ 20์„ธ์—์„œ 40์„ธ ์‚ฌ์ด์˜ ์„ฑ์ธ 36๋ช…์œผ๋กœ ์ˆ™๋ จ์ž(ํ•ธ๋””์บก 10 ์ดํ•˜, ๊ตฌ๋ ฅ 3๋…„ ์ด์ƒ) 18๋ช…๊ณผ ๋น„์ˆ™๋ จ์ž(ํ•ธ๋””์บก 30 ์ด์ƒ, ๊ตฌ๋ ฅ 2๋…„ ์ด๋‚ด) 18๋ช…์œผ๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ˆ™๋ จ์ž ๋˜๋Š” ๋น„์ˆ™๋ จ์ž์—์„œ 9๋ช…์”ฉ ๋จธ์‹ ๋Ÿฌ๋‹ ์ง‘๋‹จ๊ณผ ์ž๊ธฐ์กฐ์ ˆ ์ง‘๋‹จ์œผ๋กœ ๋‚˜๋ˆ„์—ˆ๋‹ค(4๊ทธ๋ฃน). ์—ฐ๊ตฌ์ฐธ์—ฌ์ž๋“ค ์€ ๊ฑฐ๋ฆฌ(1m, 2.5m 3.5m)์™€ ๋ฐฉํ–ฅ(ํ™€์ปต ๊ธฐ์ค€ 12๊ฐ€์ง€)์— ๋”ฐ๋ผ ํผํŒ… ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‚ฌ์ „๊ฒ€์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋จธ์‹ ๋Ÿฌ๋‹ ์ง‘๋‹จ์€ ๊ฐœ์ธ์ฐจ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฐœ์ธ๋ณ„ ํผํŒ… ํ…œํฌ ๋ฐ ์Šค์œ™ ํฌ๊ธฐ์— ๋Œ€ํ•œ ์‹œ๊ฐ ๋ฐ ์ฒญ๊ฐ ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ›์•˜์œผ๋ฉฐ ์ž๊ธฐ์กฐ์ ˆ ์ง‘๋‹จ์€ ์„ฑ๊ณต๋ฅ ๊ณผ ์˜ค์ฐจ๋ฒ”์œ„์— ๋Œ€ํ•œ ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ›์•˜๋‹ค. 3ํšŒ์˜ ํ•™์Šต ๊ณผ์ • ๋’ค ์‚ฌํ›„ ๊ฒ€์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ, 48์‹œ๊ฐ„์ด ์ง€๋‚œ ๋’ค ํŒŒ์ง€ ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ํผํŒ… ํ•™์Šต ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์„ฑ๊ณต๋ฅ , ์˜ค์ฐจ๋ฒ”์œ„(MRE), ํผํ„ฐ ํ—ค๋“œ์˜ ์›€์ง์ž„(์ž„ํŒฉํŠธ ๊ฐ๋„, ์†๋„, ํ…œํฌ, ํฌ๊ธฐ)์„ ํ™•์ธํ•˜์—ฌ ํ‰๊ท ๊ณผ ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ์‚ฐ์ถœํ•˜์—ฌ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํผํŒ… ์„ฑ๊ณต๋ฅ ์€ ๋จธ์‹ ๋Ÿฌ๋‹์„ ์ ์šฉํ•œ ์ˆ™๋ จ์ž ์ง‘๋‹จ์—์„œ ํšจ๊ณผ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์˜ค์ฐจ๋ฒ”์œ„์—์„œ๋Š” ๋จธ์‹ ๋Ÿฌ๋‹์„ ์ ์šฉํ•œ ๋น„์ˆ™๋ จ์ž์™€ ์ˆ™๋ จ์ž ์ง‘๋‹จ ๋ชจ๋‘์—์„œ ์ž๊ธฐ์กฐ์ ˆ ์ง‘๋‹จ๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํผํ„ฐ ํ—ค๋“œ์˜ ์›€์ง์ž„์—์„œ๋Š” ์ž„ํŒฉํŠธ ๊ฐ๋„์˜ ์ผ๊ด€์„ฑ์„ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ ๋จธ์‹ ๋Ÿฌ๋‹ ๋น„์ˆ™๋ จ์ž ์ง‘๋‹จ์—์„œ ํšจ๊ณผ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ž๊ธฐ์กฐ์ ˆ ์ง‘๋‹จ์€ ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ํผํŒ… ์†๋„์˜ ์ผ๊ด€์„ฑ์—์„œ๋Š” ์ž๊ธฐ์กฐ์ ˆ ์ง‘๋‹จ์€ ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜์œผ๋ฉฐ ๋จธ์‹ ๋Ÿฌ๋‹ ์ง‘๋‹จ์€ ์ˆ™๋ จ์ž, ๋น„์ˆ™๋ จ์ž ๋ชจ๋‘ ํ–ฅ์ƒ๋œ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ…œํฌ์˜ ์ผ๊ด€์„ฑ์—์„œ๋Š” ๊ฐœ์ธ๋ณ„ ์„ฑ๊ณตํ•œ ํผํŒ…์„ ๊ธฐ์ค€์œผ๋กœ ํ…œํฌ์˜ ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•œ ๊ฒฐ๊ณผ ๋จธ์‹ ๋Ÿฌ๋‹ ์ง‘๋‹จ์ด ํฌ๊ฒŒ ํ–ฅ์ƒ๋˜์—ˆ๋‹ค. ๋น„์ˆ™๋ จ์ž์˜ ๊ฒฝ์šฐ ์ผ๊ด€์„ฑ์ด ์•ฝ 20% ํ–ฅ์ƒ๋˜์—ˆ์œผ๋ฉฐ, ์ˆ™๋ จ์ž๋Š” ์•ฝ15% ํ–ฅ์ƒ๋˜์—ˆ๋‹ค. ๋ฐฑ์Šค์œ™๊ณผ ํŒ”๋กœ์Šค๋ฃจ์˜ ์Šค์œ™ํฌ๊ธฐ ์ผ๊ด€์„ฑ์—์„œ๋Š” ์ž๊ธฐ์กฐ์ ˆ ์ง‘๋‹จ์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง„ ์•Š์•˜์œผ๋‚˜ ํ–ฅ์ƒ๋œ ๊ฒฝํ–ฅ์„ฑ์„ ๊ฐ€์ ธ์™”๊ณ  ๋จธ์‹ ๋Ÿฌ๋‹ ์ง‘๋‹จ์€ ์ˆ™๋ จ์ž, ๋น„์ˆ™๋ จ์ž ๋ชจ๋‘ ์ผ๊ด€์„ฑ์ด ํ–ฅ์ƒ๋˜์—ˆ๋‹ค.์ข…ํ•ฉํ•˜๋ฉด ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐœ์ธ์˜ ๊ณ ์œ ๊ฐ๊ฐ ์ค‘ ๋‚ด์ ํ…œํฌ(internal tempo) ์š”์ธ์„ ๋จธ์‹ ๋Ÿฌ๋‹ ํ”„๋กœ๊ทธ๋žจ์„ ํ™œ์šฉํ•˜์—ฌ ์ธ์ง€-์ˆ˜ํ–‰ ๋ถ€ํ•˜๊ฐ€ ๋†’์€ ๊ฑฐ๋ฆฌX๊ฒฝ์‚ฌ ํผํŒ…์ˆ˜ํ–‰ ์ƒํ™ฉ์— ์ ์šฉํ•˜์˜€๋‹ค. ํผํŒ… ๊ณผ์ œ์˜ ํŠน์„ฑ์ƒ ๊ฐœ์ธ ๊ณ ์œ ๊ฐ๊ฐ์„ ๊ณ ๋ คํ•˜์—ฌ ์ผ๊ด€์„ฑ์„ ํ‚ค์šฐ๋Š” ํ•™์Šต์„ ํ†ตํ•ด ํ•™์Šตํšจ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์‹ค์ œ ํ˜„์žฅ์—์„œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์šฉํ•˜์—ฌ ํผํŒ… ํ•™์Šต์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ฉฐ, ์ˆ™๋ จ๋„์— ๋”ฐ๋ฅธ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋ฒ”์ฃผํ™”๋ฅผ ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค.There are various patterns of individual differences in the putting task, and the performance level is determined according to the correct cognitive ability and performance ability. Individual difference is a proprioceptive factor, and it is necessary to study how to learn how to putt successfully. The purpose of this study is to suggest an efficient putting learning method through comparison between a group applying augmented reality feedback using a machine learning program considering individual differences and a general putting training group using self-controlled feedback. The subjects who participated in the study were 36 adults between the ages of 20 and 40, 18 expert (handicap less than 10, experience more than 3 years) and 18 novice(handicap more than 30, experience less than 2 years). 9 participants were divided into a machine learning group and a self-controlled group (4 groups). Participants performed putting tasks according to the distance (1m, 2.5m, 3.5m) and direction (12 hole cup standards). Based on the pretest data, the machine learning group received visual and auditory feedback on individual putting tempo and swing size in consideration of individual differences, and the self-adjustment group received feedback on the success rate and mean radial error. A post-test was conducted after the learning process three times, and a gripping test was conducted after 48 hours. To confirm the change in putting learning, the success rate, mean radial error (MRE), and putter head movement (impact angle, speed, tempo) were checked, and the average and standard deviation were calculated and analyzed for comparison. The results are as follow. First, the putting success rate was effectively shown in the expert group to which machine learning was applied. In the mean radial error, both the novice and expert groups to which machine learning was applied appeared more effective than the self-controlled group. As a result of examining the consistency of the impact angle in the movement of the putter head, it appeared effective in the machine learning novice group, but did not appear in the self-adjustment group. Regarding the consistency of putting speed, the self-controlled group showed no effect, and the machine learning group showed improved results for both expert and novice. In the consistency of tempo, the machine learning group improved significantly as a result of checking the standard deviation of tempo based on individual successful putting. Consistency improved by about 20% for the novice, and about 15% for the expert. Regarding the consistency of the swing size of the backswing and follow-through, the self-controlled group showed an improved tendency, although it was not statistically significant, and the machine learning group showed improved consistency for both the expert and novice. In summary, this study applied the proprioceptive (internal tempo) factor considering individual differences to the distance x incline putting performance situation with high cognitive-performance load using a machine learning program. Due to the nature of the putting task, the learning effect was confirmed through learning to develop consistency in consideration of individual unique senses. Through this, it will be possible to apply the research results in the actual field and use them for putting learning, and we tried to categorize them through the results according to the level of proficiency.I. ์„œ ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  8 3. ์—ฐ๊ตฌ ๊ฐ€์„ค 9 4. ์šฉ์–ด์˜ ์ •์˜ 10 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 14 1. ์šด๋™ํ•™์Šต์˜ ์ดํ•ด 14 1) ์šด๋™ํ•™์Šต์˜ ๊ฐœ๋… 14 2) ์šด๋™ํ•™์Šต์˜ ์ด๋ก  15 3) ์šด๋™ํ•™์Šต์˜ ๋‹จ๊ณ„ 16 4) ์šด๋™ํ•™์Šต๊ณผ ํŒŒ์ง€ 17 2. ํ”ผ๋“œ๋ฐฑ์— ๋Œ€ํ•œ ์ดํ•ด 19 1) ํ”ผ๋“œ๋ฐฑ์˜ ๊ฐœ๋… 19 2) ํ”ผ๋“œ๋ฐฑ์˜ ๊ธฐ๋Šฅ 20 3) ์™ธ์žฌ์  ํ”ผ๋“œ๋ฐฑ์˜ ์ดํ•ด 20 4) ์™ธ์žฌ์  ํ”ผ๋“œ๋ฐฑ์˜ ํ™œ์šฉ 22 3. ๊ณจํ”„ํผํŒ…(golf putting) 23 1) ๊ณจํ”„ ํผํŒ…์˜ ์ดํ•ด 23 2) ํผํ„ฐ ์ŠคํŠธ๋กœํฌ์— ๋Œ€ํ•œ ์ดํ•ด 23 3) ๊ฐœ์ธ ๋‚ด์  ํ…œํฌ ๋‹ค์–‘์„ฑ๊ณผ ํผํŒ… ํ…œํฌ 25 4. AR(augmented reality)๊ณผ ๋จธ์‹ ๋Ÿฌ๋‹(machine learning) 26 1) AR์— ๋Œ€ํ•œ ์ดํ•ด 26 2) ๋จธ์‹ ๋Ÿฌ๋‹์— ๋Œ€ํ•œ ์ดํ•ด 27 3) ๋จธ์‹ ๋Ÿฌ๋‹ ์„ ํ–‰์—ฐ๊ตฌ 28 โ…ข. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 30 1. ์—ฐ๊ตฌ๋Œ€์ƒ 30 2. ์‹คํ—˜์žฅ๋น„ ๋ฐ ์ธก์ •๋ฐฉ๋ฒ• 31 3. ์‹คํ—˜์ ˆ์ฐจ 39 4. ์‹คํ—˜์„ค๊ณ„ 45 5. ์ž๋ฃŒ๋ถ„์„ 46 6. ํ†ต๊ณ„๋ถ„์„ 50 โ…ฃ. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 51 1. ์ˆ˜ํ–‰ ์ •ํ™•์„ฑ 51 1) ํผํŒ… ์„ฑ๊ณต๋ฅ  51 2) ํ‰๊ท  ์˜ค์ฐจ๋ฒ”์œ„(MRE) 54 3) ๊ฑฐ๋ฆฌ๋ณ„ ํ™€์ปต ์„ฑ๊ณต๋ฅ ๊ณผ ์˜ค์ฐจ๋ฒ”์œ„ 58 2. ์ˆ˜ํ–‰ ์ˆ˜์ค€ 61 1) ํผํ„ฐ ํ—ค๋“œ ์›€์ง์ž„ 61 1-1) ์ž„ํŒฉํŠธ ๊ฐ๋„์˜ ๋ณ€ํ™” 61 1-2) ํผํŒ… ์†๋„ ์ผ๊ด€์„ฑ 64 2) ํ…œํฌ ์ผ๊ด€์„ฑ 68 3) ๊ฑฐ๋ฆฌ๋ณ„ ํ…œํฌ ์ผ๊ด€์„ฑ 71 4) ์Šค์œ™ ํฌ๊ธฐ ์ผ๊ด€์„ฑ(๋ฐฑ์Šค์œ™) 72 5) ์Šค์œ™ ํฌ๊ธฐ ์ผ๊ด€์„ฑ(ํŒ”๋กœ์Šค๋ฃจ) 76 โ…ค. ๋…ผ์˜ 80 1. ํผํŒ… ์ˆ˜ํ–‰ ์ •ํ™•์„ฑ์˜ ๋ณ€ํ™” ๋น„๊ต 81 2. ํผํŒ… ์ˆ˜ํ–‰ ์ˆ˜์ค€์˜ ๋ณ€ํ™” ๋น„๊ต 83 1) ์ž„ํŽ™ํŠธ ํ—ค๋“œ ๊ฐ๋„ ์ผ๊ด€์„ฑ 83 2) ํผํŒ… ํ…œํฌ์™€ ์Šค์œ™ ํฌ๊ธฐ ์ผ๊ด€์„ฑ 84 3) ํผํŒ… ์ˆ˜ํ–‰ ์ˆ˜์ค€ ๋ณ€ํ™”๋Ÿ‰ 87 โ…ฅ. ๊ฒฐ ๋ก  91 โ…ฆ. ์ œ ์–ธ 94 ์ฐธ๊ณ ๋ฌธํ—Œ 95 abstact 104๋ฐ•

    The Epistemology and Glocalization of Hallyu

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    ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™” ๊ณผ์ •์—์„œ ๋‚˜ํƒ€๋‚œ ์‹œ๋ฏผ์ฐธ์—ฌ ๋™๊ธฐ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› : ํ–‰์ •ํ•™๊ณผ(ํ–‰์ •ํ•™์ „๊ณต), 2013. 8. ์ด์Šน์ข….๊ตญ๋ฌธ์ดˆ๋ก ์‹œ๋ฏผ ์˜์ง€์˜ ์ •์น˜์  ์‹คํ˜„์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๋ฏผ์ฃผ์ฃผ์˜ ๊ธฐ๋ฐ˜ ํ˜„๋Œ€ ํ–‰์ •ํ™˜๊ฒฝ์—์„œ ์‹œ๋ฏผ ์ฐธ์—ฌ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ทธ ์ค‘์š”์„ฑ์ด ๋‚˜๋‚ ์ด ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์‹œ๋ฏผ ์ฐธ์—ฌ์˜ ๋Œ€๋ถ€๋ถ„์ด ์ผ์„  ์ˆ˜์ค€์—์„œ ์ด๋ฃจ์–ด์ง„๋‹ค๋Š” ์ ์—์„œ ์ด์— ๋Œ€ํ•œ ๊ด€์ฐฐ์€ ์‹œ๋ฏผ์ฐธ์—ฌ๊ฐ€ ์ •์ฑ…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ํญ ๋„“์€ ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๊ฒฝ์šฐ ์ด๋Ÿฌํ•œ ์‹œ๊ฐ์—์„œ ์ „๋ผ๋ถ๋„ ์ต์‚ฐ ์ง€์—ญ์˜ ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™” ๊ณผ์ •์—์„œ ๋‚˜ํƒ€๋‚œ ์‹œ๋ฏผ ์ฐธ์—ฌ์˜ ๋™๊ธฐ๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™”์— ์žˆ์–ด ์ด๋ก ์ , ์ •์ฑ…์  ์‹œ์‚ฌ์ ์„ ๋ฐœ๊ตดํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. 2012๋…„ ๊ฒฝ์ฐฐ์ฒญ์—์„œ๋Š” ์น˜์•ˆ์ˆ˜์š”๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ์ ์€ ์๋ฉด ์ง€์—ญ์˜ ํŒŒ์ถœ์†Œ๋ฅผ ํ†ตํํ•ฉํ•˜์—ฌ ์ง€๊ตฌ๋Œ€ ์ฒด์ œ๋กœ ๊ฐœํŽธํ•˜๊ณ , ๋Œ€์‹  ์ด์›ƒ ์๋ฉด์˜ ์ง€๊ตฌ๋Œ€๋ฅผ ํ™•์žฅํ•˜๊ณ  ํŒŒ์ถœ์†Œ ํ์ง€ ์ง€์—ญ์— ๋ฏผ์›์„ ์ „๋‹ดํ•˜๋Š” ์กฐ์ง ์šด์˜์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๊ณผ์ •์—์„œ ์๋ฉด ์ง€์—ญ ์ฃผ๋ฏผ๋“ค์˜ ๊ฐ•๋ ฅํ•œ ๋ฐ˜๋ฐœ์ด ์žˆ์—ˆ๊ณ  ์ด์— ํ•ด๋‹น ์ •์ฑ…์ด ์ขŒ์ดˆ๋˜์–ด ํŒŒ์ถœ์†Œ ์ฒด์ œ๊ฐ€ ์กด์†ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์™€ ๊ฐ™์ด ์ฃผ๋ฏผ๋“ค์˜ ๊ฐ•๋ ฅํ•œ ๋ฐ˜๋ฐœ์€ ์ผ์ข…์˜ ์ฐธ์—ฌ ํ˜„์ƒ์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด์— ์ด๋Ÿฌํ•œ ์‹œ๋ฏผ ์ฐธ์—ฌ๊ฐ€ ๋ฐœ์ƒํ•œ ์›์ธ์— ๋Œ€ํ•˜์—ฌ ์‹œ๋ฏผ ์ฐธ์—ฌ์˜ ๋™๊ธฐ ์ด๋ก ๊ณผ ์ง€์—ญ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ํŒจ๋Ÿฌ๋‹ค์ž„ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๊ฒฝ์ฐฐํ•™ ์—ฐ๊ตฌ์— ๊ธฐ์ดˆํ•˜์—ฌ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ ๊ฒƒ์ด๋‹ค. ์ด์— ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™” ๊ณผ์ •์—์„œ ๋‚˜ํƒ€๋‚œ ์ง€์—ญ ์ฃผ๋ฏผ๋“ค์˜ ์ฐธ์—ฌ ๋™๊ธฐ, ์ฆ‰ ์ง€์—ญ ๋‚ด ๊ฒฝ์ฐฐ๊ด€์„œ์ธ ํŒŒ์ถœ์†Œ์˜ ์กด์†์— ๋Œ€ํ•œ ๋™๊ธฐ์— ๋Œ€ํ•˜์—ฌ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์ด ์ง€์—ญ ๋‚ด ์•ˆ์ „ ํ™•๋ณด๋กœ ๋‹จ์ˆœํ•˜๊ฒŒ ์‚ดํŽด๋ณธ ๋ฐ” ์ด๋ฅผ ๋ณด๋‹ค ์ •๋ฐ€ํ•˜๊ฒŒ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์šฐ์„  ์ฃผ๋ฏผ๋“ค์˜ ์ง€์—ญ์‚ฌํšŒ ์น˜์•ˆ์— ๋Œ€ํ•œ ์ง‘ํ•ฉ์  ์ด์ต ๋™๊ธฐ๋ฅผ ๊ฒฝ์ฐฐํ™œ๋™์— ๋Œ€ํ•œ ๊ณ ์ „์  ๋ชจํ˜•์ธ Crime Fighter Model์—์„œ ๊ฐ•์กฐํ•˜๋Š” ์ง€์—ญ ๋‚ด ๋ฒ•์ง‘ํ–‰ ๊ฐ•ํ™”์— ๋Œ€ํ•œ ์š”์ธ๊ณผ ์ง€์—ญ์‚ฌํšŒ ๊ฒฝ์ฐฐํ™œ๋™ ๋ชจ๋ธ(Community Policing Model)์— ๊ธฐ์ดˆํ•œ ์ง€์—ญ ๋‚ด ์ฃผ๋ฏผ์˜ ์•ˆ์ „์— ๋Œ€ํ•œ ๋งŒ์กฑ๊ณผ ์น˜์•ˆ์„œ๋น„์Šค ๊ณต๋™์ƒ์‚ฐ์— ์ดˆ์ ์„ ๋‘” ์ง€์—ญ ๋‚ด ์•ˆ์ „์ฒด๊ฐ ํ™•๋ณด๋กœ ๋‚˜๋ˆ„์—ˆ๋‹ค. ์•„์šธ๋Ÿฌ ์ด๋Ÿฌํ•œ ์ง‘ํ•ฉ์  ์š”์ธ๊ณผ ๊ตฌ๋ถ„๋˜๋Š” ๊ฐœ๋ณ„์  ์š”์ธ์œผ๋กœ, ์ฃผ๋ฏผ๊ณผ ์ƒ๋Œ€์ ์œผ๋กœ ๊ฐ€๊นŒ์šด ํŒŒ์ถœ์†Œ ์ฒด์ œ์˜ ํŠน์„ฑ์„ ์ด์šฉํ•˜์—ฌ ์ฃผ๋ฏผ์ด ์ง€์—ญ๊ฒฝ์ฐฐ๊ณผ์˜ ์‚ฌ์ ์ธ ๊ด€๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฒ•์  ์ผํƒˆ์˜ ์ด์ต์„ ํ–ฅ์œ ํ•˜๊ณ ์ž ํ•˜๋Š” ์ผํƒˆ์  ์ด์ต ์š”์ธ์„ ์ƒˆ๋กœ์ด ์ธ์ง€ํ•˜์˜€๋‹ค. ์ด๋“ค ์š”์ธ๋“ค์ด ์ง€์—ญ๊ฒฝ์ฐฐ ๊ฐœํŽธ์— ๋Œ€ํ•œ ์ฃผ๋ฏผ๋“ค์˜ ๋ฐ˜๋ฐœ, ์ฆ‰ ํŒŒ์ถœ์†Œ ์ฒด์ œ์˜ ์ง€์ง€ ์›์ธ์ด ๋œ ๊ฒƒ์œผ๋กœ ๋ณด๊ณ , ํŒŒ์ถœ์†Œ๋ฅผ ์ง€์ง€ํ•˜๋Š” ์ง‘๋‹จ์ด ๊ทธ๋ ‡์ง€ ์•Š์€ ์ง‘๋‹จ์— ๋น„ํ•˜์—ฌ ํŠน์ • ์š”์ธ์„ ๊ธฐ์ค€์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š”์ง€์— ๋Œ€ํ•˜์—ฌ ์ต์‚ฐ ์ง€์—ญ์˜ ์๋ฉด์ง€์—ญ ์ฃผ๋ฏผ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•˜์˜€์œผ๋‚˜, ํ†ต๊ณ„์ ์œผ๋กœ ๊ทธ๋ ‡๋‹ค๊ณ  ํ•  ์ˆ˜๋Š” ์—†์—ˆ๋‹ค. ์ด์— ์ฃผ๋ฏผ๋“ค์ด ์–ด๋– ํ•œ ์š”์ธ์—์„œ ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™”์— ์ฐธ์—ฌํ•˜์˜€๋Š”์ง€์— ๋Œ€ํ•˜์—ฌ ์‚ฌํšŒ ์ „์ฒดใ†์ง€์—ญ์‚ฌํšŒใ†๊ฐœ์ธ์˜ ๊ธฐ์ค€์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ์žฌ์ฐจ ์„ค๋ฌธํ•˜์˜€๋‹ค. ์„ค๋ฌธ ๊ฒฐ๊ณผ ์ง€์—ญ ์ฃผ๋ฏผ๋“ค์€ ๋ชจ๋“  ์๋ฉด์— ํŒŒ์ถœ์†Œ๊ฐ€ ์„ค์น˜๋˜์–ด ์žˆ๋Š” ๊ธฐ์กด ์ง€์—ญ๊ฒฝ์ฐฐ ์ฒด์ œ๋ฅผ ์ง€์ง€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๊ทธ ๊ฐ€์žฅ ํฐ ๋™๊ธฐ๋Š” ์ง€์—ญ์‚ฌํšŒ ์ˆ˜์ค€์˜ ์ด์ต์œผ๋กœ ํŠนํžˆ ์ง€์—ญ ๋‚ด ๊ฒฝ์ฐฐ๊ด€์„œ์˜ ์กด์žฌ์™€ ์ด์— ๋”ฐ๋ฅธ ์•ˆ์ „์ฒด๊ฐ์˜ ํ™•๋ณด์— ๋Œ€ํ•œ ์š•๊ตฌ๊ฐ€ ๊ฐ€์žฅ ํฌ๊ฒŒ ์ž‘์šฉํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•œํŽธ ์ผ์„  ๊ฒฝ์ฐฐ๊ด€๋“ค์˜ ๊ฒฝ์šฐ ์ด๋Ÿฌํ•œ ์ฃผ๋ฏผ๋“ค๊ณผ๋Š” ๋ฐ˜๋Œ€๋กœ ์๋ฉด์ง€์—ญ ์ง€์—ญ๊ฒฝ์ฐฐ ์ฒด์ œ๋กœ ํŒŒ์ถœ์†Œ๋ณด๋‹ค ์ง€๊ตฌ๋Œ€์™€ ์น˜์•ˆ์„ผํ„ฐ(๋ฏผ์›๋‹ด๋‹น๊ด€)์„ ๋ณ‘์„คํ•˜๋Š” ๊ฐœํŽธ์•ˆ์„ ์ง€์ง€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ์—ฌ๊ธฐ์—๋Š” ๊ฒฝ์ฐฐ์กฐ์ง์˜ ์ •์ฑ…์ถ”์ง„ ๋™๊ธฐ์ธ ํšจ์œจ์  ์กฐ์ง์šด์˜์˜ ์š”์ธ์ด ํฌ๊ฒŒ ์ž‘์šฉํ•˜๊ธฐ๋„ ํ•˜์˜€์œผ๋‚˜ ์—…๋ฌด ๋ถ€๋‹ด ๋“ฑ์˜ ๊ฐœ์ธ์  ์š”์ธ ๋˜ํ•œ ์ž‘์šฉํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ์๋ฉด ์ง€์—ญ์˜ ์ฃผ๋ฏผ๋“ค์ด ํŒŒ์ถœ์†Œ ์ฒด์ œ๋ฅผ ์ง€์ง€ํ•˜๋Š” ๊ฐ€์žฅ ํฐ ๋™๊ธฐ๋Š” ์ง€์—ญ ๋‚ด ์•ˆ์ „์˜ ํ™•๋ณด๋ผ๋Š” ์ง€์—ญ์‚ฌํšŒ ์ˆ˜์ค€์˜ ์ด์ต ๋™๊ธฐ๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ํŠนํžˆ ์ด ์ค‘ ์ง€์—ญ๊ฒฝ์ฐฐ ๊ด€์„œ์™€ ๊ฒฝ์ฐฐ๊ด€์˜ ์กด์žฌ ์ž์ฒด์— ๋Œ€ํ•œ ์š”๊ตฌ๊ฐ€ ๊ฐ€์žฅ ํฐ ์›์ธ์ด์—ˆ๋‹ค. ์•„์šธ๋Ÿฌ ๋น„๋ก ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™” ๊ณผ์ •์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€๋Š” ๋ชปํ–ˆ์œผ๋‚˜, ์ฃผ๋ฏผ์˜ ์ผํƒˆ์  ๋™๊ธฐ๋ผ๋Š” ์š”์ธ์„ ๊ฐ์ง€ํ•œ ์  ๋˜ํ•œ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ •์ฑ…์ ์œผ๋กœ๋Š” ์ถ”ํ›„ ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™” ๊ณผ์ •์—์„œ ์ ์–ด๋„ ์๋ฉด์ง€์—ญ ๋‹จ์œ„์— ์ผ์ • ์ •๋„์˜ ๊ฒฝ์ฐฐ๋ ฅ์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์„ ์›์น™์œผ๋กœ ํ•˜๋˜ ๊ทธ๋Ÿฌํ•œ ์›์น™ ํ•˜์—์„œ ํšจ์œจ์  ์šด์˜์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ๋Š” ์ •์ฑ…์„ ๊ณ ์•ˆํ•˜๋ฉฐ ์ด๋ฅผ ์ฃผ๋ฏผ ์ „์ฒด ์ง‘๋‹จ๊ณผ ํ† ๋ก ํ•˜๊ณ  ํ™๋ณดํ•  ํ•„์š”๊ฐ€ ์žˆ์–ด์•ผ ํ•œ๋‹ค๋Š” ํ•จ์˜๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ----------------------------------- ์ฃผ์š”์–ด : ์‹œ๋ฏผ์ฐธ์—ฌ, ์ฐธ์—ฌ ๋™๊ธฐ, ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์ง ํ•™ ๋ฒˆ : 2010-22139๋ชฉ ์ฐจ ์ œ1์žฅ ์„œ๋ก  ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  ์ œ2์ ˆ ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ๋ฒ”์œ„ 1. ์—ฐ๊ตฌ๋ฌธ์ œ 2. ์—ฐ๊ตฌ๋Œ€์ƒ ์„ ์ • 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ์ œ2์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ์ œ1์ ˆ ์ผ์„ ๊ด€๋ฃŒ ์ด๋ก  1. ์ผ์„ ๊ด€๋ฃŒ ์ด๋ก ์˜ ๊ฐœ์š” 2. ์ผ์„ ๊ด€๋ฃŒ๋กœ์„œ ๊ฒฝ์ฐฐ ์ œ2์ ˆ ์‹œ๋ฏผ์ฐธ์—ฌ ์ด๋ก  1. ์‹œ๋ฏผ์ฐธ์—ฌ์˜ ์˜์˜ 2. ์‹œ๋ฏผ์ฐธ์—ฌ์˜ ๊ฐœ๋… 3. ์‹œ๋ฏผ์ฐธ์—ฌ์— ๋Œ€ํ•œ ์‹œ๊ฐ 4. ์‹œ๋ฏผ์ฐธ์—ฌ์™€ ํ–‰์ •์ด๋… 5. ์ฐธ์—ฌ์˜ ๋™๊ธฐ ์ œ3์ ˆ ๊ณต์ต์ด๋ก  1. ๊ณต์ต ๋ถ€์กด์žฌ์„ค 2. ๊ณต์ต์ด ์กด์žฌํ•œ๋‹ค๋Š” ์ฃผ์žฅ 3. ์†Œ๊ฒฐ 34 ์ œ4์ ˆ ์ง€์—ญ์‚ฌํšŒ ๊ฒฝ์ฐฐํ™œ๋™ 1. ์ง€์—ญ์‚ฌํšŒ ๊ฒฝ์ฐฐํ™œ๋™์˜ ์˜๋ฏธ 2. ์ง€์—ญ๊ฒฝ์ฐฐ๊ธฐ๊ด€ 3. ์ง€์—ญ๊ฒฝ์ฐฐ์กฐ์ง์˜ ๋ณ€ํ™” 4. ์ง€์—ญ๊ฒฝ์ฐฐ๊ด€์„œ์˜ ๋ถ„๋ฅ˜ 5. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ์ œ3์žฅ ๋ฌธ์ œ์˜ ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ์„ค๊ณ„ ์ œ1์ ˆ ํŒŒ์ถœ์†Œ ๋ฐฐ์น˜์— ๋Œ€ํ•œ ๋…ผ์˜ ์ง„ํ–‰ 1. ์ต์‚ฐ์‹œ : ๋„๋†ํ†ตํ•ฉ์ง€์—ญ 2. ๋…ผ๋ž€์˜ ๊ฒฝ๊ณผ 3. ๋…ผ๋ž€์˜ ํ•ด์„ ์ œ2์ ˆ ์—ฐ๊ตฌ์„ค๊ณ„ 1. ์—ฐ๊ตฌ๊ฐ€์„ค 2. ๋ณ€์ˆ˜์„ ์ • ๋ฐ ์กฐ์ž‘์  ์ •์˜ 3. ์„ค๋ฌธ์ง€ ๊ตฌ์„ฑ 4. ์ž๋ฃŒ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• ์ œ4์žฅ ์„ค๋ฌธ์กฐ์‚ฌ ๊ฒฐ๊ณผ ๋ถ„์„ ์ œ1์ ˆ ์ฃผ๋ฏผ ์„ค๋ฌธ์กฐ์‚ฌ ๊ฒฐ๊ณผ๋ถ„์„ 1. ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ 2. ์ฃผ๋ฏผ๋“ค์˜ ํŒŒ์ถœ์†Œ ์กฐ์ง ๊ฐœํŽธ์— ๋Œ€ํ•œ ์ธ์‹ 3. ์ง€์—ญ๊ฒฝ์ฐฐ์— ๋Œ€ํ•œ ๊ธฐ๋Œ€ 4. ์ง‘๋‹จ ๊ฐ„ ์ฐจ์ด ๋ถ„์„ 5. ์ง€์—ญ๊ฒฝ์ฐฐ ์กฐ์งํ™” ์ฐธ์—ฌ ๋™๊ธฐ 6. ๋ถ„์„๊ฒฐ๊ณผ ์ œ2์ ˆ ์ผ์„  ๊ฒฝ์ฐฐ๊ด€ ์„ค๋ฌธ์กฐ์‚ฌ ๊ฒฐ๊ณผ๋ถ„์„ 1. ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ 2. ํŒŒ์ถœ์†Œ ๊ฐœํŽธ์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ๋ฐ ์ธ์‹ ์—ฌ๋ถ€ 3. ์ง€๊ตฌ๋Œ€ ์ฒด์ œ ์ง€์ง€ ์›์ธ 4. ํŒŒ์ถœ์†Œ ์ฒด์ œ ์ง€์ง€ ์›์ธ ์ œ5์žฅ ๊ฒฐ๋ก  ์ œ1์ ˆ ๋ถ„์„๊ฒฐ๊ณผ ์š”์•ฝ ์ œ2์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ํ•จ์˜ ์ œ3์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ์ฐธ๊ณ ๋ฌธํ—Œ ๋ถ€๋ก : ์„ค๋ฌธ์ง€Maste

    A Study on the sloshing of cargo tanks including hydroelastic effects

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ,1997.Docto

    ๊ณจ์ ˆ ์น˜์œ  ๋ฐ ์‹ ์—ฐ๊ณจ ํ˜•์„ฑ์ˆ  ์‹œ ๋‚ดํ”ผ ์ „๊ตฌ ์„ธํฌ์˜ ๋™์›

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    Thesis(doctor`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์ •ํ˜•์™ธ๊ณผํ•™์ „๊ณต,2007.Docto

    Analysis of Tip Vortex Generated at the Tip of a Rectangular Hydrofoil Using FLUENT

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    ์—ด๋ฆฐ์ถฉ๋‚จ 64ํ˜ธ-[๊ถŒ๋‘์–ธ]๋ฌธํ™”์œต์„ฑ์˜ ์‹œ๋Œ€, ์‹œ๋ฏผ๋“ค์˜ ๋ฌธํ™” ํ–‰๋ณต์€?

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    ๋ฌธํ™”์œต์„ฑ์ด ์ƒˆ๋กœ์šด ํ™”๋‘๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๋ฐ•๊ทผํ˜œ ๋Œ€ํ†ต๋ น์€ ์ทจ์ž„์‚ฌ์—์„œ ๋ฌธํ™”์œต์„ฑ์„ 3๋Œ€ ๊ตญ์ •๊ณผ์ œ๋กœ ์ œ์‹œํ–ˆ๊ณ  ์ฒญ์™€๋Œ€ ์ง์†์œผ๋กœ ๋ฌธํ™”์œต์„ฑ์œ„์›ํšŒ๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค. ์‚ฌ์‹ค ๋ฌธํ™”์œต์„ฑ์€ ๊ทธ๋™์•ˆ ๋ฌธํ™”์˜ˆ์ˆ ๊ณ„์—์„œ ์‚ฌ์šฉํ•˜์ง€ ์•Š์•˜๋˜ ๊ฐœ๋…์ด๊ณ  ์šฉ์–ด๋กœ๋งŒ ๋ณด๋ฉด ์ž์œจ๊ณผ ์ฐฝ์กฐ๋ฅผ ๊ทผ๊ฐ„์œผ๋กœ ํ•˜๋Š” ์ด ์‹œ๋Œ€์˜ ๋ฌธํ™”๊ด€์— ๋น„ํ•ด ์ง€๋‚˜์น˜๊ฒŒ ๊ณ„๋ชฝ์ฃผ์˜์ ์ธ ๊ด€์ ์ด ๊ฐ•ํ•˜๋‹ค. ์ทจ์ž„์‚ฌ์˜ ๋‚ด์šฉ์„ ์‚ดํŽด๋ณด๋ฉด ๋ฌธํ™”์œต์„ฑ์€ ๊ตญ๊ฐ€๋ฒˆ์˜์˜ ์ค‘์š”ํ•œ ๋™๋ ฅ์œผ๋กœ ๊ฐ„์ฃผ๋œ๋‹ค. ์ด๋ฅธ๋ฐ” ๋ฌธํ™”๋ฅผ ํ†ตํ•ด ์ƒˆ๋กœ์šด ๊ตญ๊ฐ€ ๋ฒˆ์˜์˜ ๊ณ„๊ธฐ๋ฅผ ๋งŒ๋“ค๊ฒ ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ฒˆ์˜์œผ๋กœ์„œ ๋ฌธํ™”์œต์„ฑ์€ "๋ฌธํ™”๊ฐ€ ๊ตญ๋ ฅ", "๋ฌธํ™”์™€ ์ฒจ๋‹จ๊ธฐ์ˆ ์ด ์œตํ•ฉ๋œ ์ฝ˜ํ…ํŠธ์‚ฐ์—… ์œก์„ฑ์„ ํ†ตํ•ด ์ฐฝ์กฐ๊ฒฝ์ œ๋ฅผ ๊ฒฌ์ธํ•˜๊ณ , ์ƒˆ ์ผ์ž๋ฆฌ๋ฅผ ๋งŒ๋“ค์–ด๋‚˜๊ฐˆ ๊ฒƒ"์ด๋ผ๋Š” ์ทจ์ž„์‚ฌ์— ์ž˜ ๋ฐ˜์˜๋˜์–ด ์žˆ๋‹ค. -์ดํ›„ ์ƒ๋žตN/
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