92 research outputs found

    Cataract Surgery Practices in the Republic of Korea: A Survey of the Korean Society of Cataract and Refractive Surgery 2018

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    PURPOSE: To describe current cataract surgery practice patterns and changing trends among Korean ophthalmologists. METHODS: A survey of members of the Korean Society of Cataract and Refractive Surgery was performed in July 2018. One hundred and two (12.7%) of 801 questionnaires were returned for analysis. The data were analyzed using descriptive statistics and compared with previous surveys. RESULTS: Most of the respondents (75%) had been in practice for 6 or more years and performed an average of 31 cataract surgeries per month. The preferred method for cataract surgery was phacoemulsification (95%); 5% used a femtosecond laser. The use of topical anesthesia markedly increased from 69% (2012) to 80% (2018). The use of optical biometry exceeded that of ultrasound A-scan biometry. A multifocal intraocular lens was used by 76% of the respondents compared with 44% of the respondents in 2012. Topical nonsteroidal anti-inflammatory drugs were used by 70% of the respondents postoperatively. Most (59%) of these anti-inflammatory drugs were prescribed for 4 weeks. CONCLUSIONS: This survey provided a comprehensive update of the present cataract surgery practices in the Republic of Korea. The results emphasized the increasing use of premium intraocular lenses, optical biometry, and topical anesthesia.ope

    ๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜๋ฅผ ์œ„ํ•œ ๊ณ ์ • ๋น„์œจ ์••์ถ• ํ•˜๋“œ์›จ์–ด ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2016. 2. ์ดํ˜์žฌ.๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜์—์„œ์˜ ์••์ถ• ๋ฐฉ์‹์€ ์ผ๋ฐ˜์ ์ธ ๋น„๋””์˜ค ์••์ถ• ํ‘œ์ค€๊ณผ๋Š” ๋‹ค๋ฅธ ๋ช‡ ๊ฐ€์ง€ ํŠน์ง•์ด ์žˆ๋‹ค. ์ฒซ์งธ, ํŠน์ˆ˜ํ•œ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋‘˜์งธ, ์••์ถ• ์ด๋“, ์†Œ๋น„ ์ „๋ ฅ, ์‹ค์‹œ๊ฐ„ ์ฒ˜๋ฆฌ ๋“ฑ์„ ์œ„ํ•ด ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ๊ฐ€ ์ž‘๊ณ , ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์••์ถ•๋ฅ ์ด ๋‚ฎ๋‹ค. ์…‹์งธ, ๋ž˜์Šคํ„ฐ ์ฃผ์‚ฌ ์ˆœ์„œ์— ์ ํ•ฉํ•ด์•ผ ํ•œ๋‹ค. ๋„ท์งธ, ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ํฌ๊ธฐ๋ฅผ ์ œํ•œ์‹œํ‚ค๊ฑฐ๋‚˜ ์ž„์˜ ์ ‘๊ทผ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์••์ถ• ๋‹จ์œ„๋‹น ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ •ํ™•ํžˆ ๋งž์ถœ ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์™€ ๊ฐ™์€ ํŠน์ง•์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์„ธ ๊ฐ€์ง€ ์••์ถ• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. LCD ์˜ค๋ฒ„๋“œ๋ผ์ด๋ธŒ๋ฅผ ์œ„ํ•œ ์••์ถ• ๋ฐฉ์‹์œผ๋กœ๋Š” BTC(block truncation coding) ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์••์ถ• ์ด๋“์„ ์ฆ๊ฐ€์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋ชฉํ‘œ ์••์ถ•๋ฅ  12์— ๋Œ€ํ•œ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋Š”๋ฐ, ์••์ถ• ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ด์›ƒํ•˜๋Š” ๋ธ”๋ก๊ณผ์˜ ๊ณต๊ฐ„์  ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜์—ฌ ๋น„ํŠธ๋ฅผ ์ ˆ์•ฝํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‘ ๋ฒˆ์งธ๋Š” ๋‹จ์ˆœํ•œ ์˜์—ญ์€ 2ร—16 ์ฝ”๋”ฉ ๋ธ”๋ก, ๋ณต์žกํ•œ ์˜์—ญ์€ 2ร—8 ์ฝ”๋”ฉ ๋ธ”๋ก์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. 2ร—8 ์ฝ”๋”ฉ ๋ธ”๋ก์„ ์ด์šฉํ•˜๋Š” ๊ฒฝ์šฐ ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ๋งž์ถ”๊ธฐ ์œ„ํ•˜์—ฌ ์ฒซ ๋ฒˆ์งธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ ˆ์•ฝ๋œ ๋น„ํŠธ๋ฅผ ์ด์šฉํ•œ๋‹ค. ์ €๋น„์šฉ ๊ทผ์ ‘-๋ฌด์†์‹ค ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์••์ถ•์„ ์œ„ํ•œ ๋ฐฉ์‹์œผ๋กœ๋Š” 1D SPIHT(set partitioning in hierarchical trees) ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. SPIHT์€ ๊ณ ์ • ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ๋งž์ถ”๋Š”๋ฐ ๋งค์šฐ ํšจ๊ณผ์ ์ธ ์••์ถ• ๋ฐฉ์‹์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 1D ํ˜•ํƒœ์ธ 1D SPIHT์€ ๋ž˜์Šคํ„ฐ ์ฃผ์‚ฌ ์ˆœ์„œ์— ์ ํ•ฉํ•จ์—๋„ ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ง„ํ–‰๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 1D SPIHT์˜ ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ์ ์ธ ์†๋„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด 1D SPIHT ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ณ‘๋ ฌ์„ฑ์„ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ˜•ํƒœ๋กœ ์ˆ˜์ •๋œ๋‹ค. ์ธ์ฝ”๋”์˜ ๊ฒฝ์šฐ ๋ณ‘๋ ฌ ์ฒ˜๋ฆฌ๋ฅผ ๋ฐฉํ•ดํ•˜๋Š” ์˜์กด ๊ด€๊ณ„๊ฐ€ ํ•ด๊ฒฐ๋˜๊ณ , ํŒŒ์ดํ”„๋ผ์ธ ์Šค์ผ€์ฅด๋ง์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋œ๋‹ค. ๋””์ฝ”๋”์˜ ๊ฒฝ์šฐ ๋ณ‘๋ ฌ๋กœ ๋™์ž‘ํ•˜๋Š” ๊ฐ ํŒจ์Šค๊ฐ€ ๋””์ฝ”๋”ฉํ•  ๋น„ํŠธ์ŠคํŠธ๋ฆผ์˜ ๊ธธ์ด๋ฅผ ๋ฏธ๋ฆฌ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ˆ˜์ •๋œ๋‹ค. ๊ณ ์ถฉ์‹ค๋„(high-fidelity) RGBW ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ ๋ฐฉ์‹์œผ๋กœ๋Š” ์˜ˆ์ธก ๊ธฐ๋ฐ˜์˜ ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆ ์˜ˆ์ธก ๋ฐฉ์‹์€ ๋‘ ๋‹จ๊ณ„์˜ ์ฐจ๋ถ„ ๊ณผ์ •์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๊ณต๊ฐ„์  ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜๋Š” ๋‹จ๊ณ„์ด๊ณ , ๋‘ ๋ฒˆ์งธ๋Š” ์ธํ„ฐ-์ปฌ๋Ÿฌ ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•˜๋Š” ๋‹จ๊ณ„์ด๋‹ค. ์ฝ”๋”ฉ์˜ ๊ฒฝ์šฐ ์••์ถ• ํšจ์œจ์ด ๋†’์€ VLC(variable length coding) ๋ฐฉ์‹์„ ์ด์šฉํ•˜๋„๋ก ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ VLC ๋ฐฉ์‹์€ ๋ชฉํ‘œ ์••์ถ•๋ฅ ์„ ์ •ํ™•ํžˆ ๋งž์ถ”๋Š”๋ฐ ์–ด๋ ค์›€์ด ์žˆ์—ˆ์œผ๋ฏ€๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Golomb-Rice ์ฝ”๋”ฉ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๊ณ ์ • ๊ธธ์ด ์••์ถ• ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆ ์ธ์ฝ”๋”๋Š” ํ”„๋ฆฌ-์ฝ”๋”์™€ ํฌ์Šคํ„ฐ-์ฝ”๋”๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ํ”„๋ฆฌ-์ฝ”๋”๋Š” ํŠน์ • ์ƒํ™ฉ์— ๋Œ€ํ•˜์—ฌ ์‹ค์ œ ์ธ์ฝ”๋”ฉ์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ๋‹ค๋ฅธ ๋ชจ๋“  ์ƒํ™ฉ์— ๋Œ€ํ•œ ์˜ˆ์ธก ์ธ์ฝ”๋”ฉ ์ •๋ณด๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ํฌ์Šคํ„ฐ-์ฝ”๋”์— ์ „๋‹ฌํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํฌ์ŠคํŠธ-์ฝ”๋”๋Š” ์ „๋‹ฌ๋ฐ›์€ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹ค์ œ ๋น„ํŠธ์ŠคํŠธ๋ฆผ์„ ์ƒ์„ฑํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ 4 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 8 ์ œ 2 ์žฅ ์ด์ „ ์—ฐ๊ตฌ 9 2.1 BTC 9 2.1.1 ๊ธฐ๋ณธ BTC ์•Œ๊ณ ๋ฆฌ์ฆ˜ 9 2.1.2 ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ BTC ์•Œ๊ณ ๋ฆฌ์ฆ˜ 10 2.2 SPIHT 13 2.2.1 1D SPIHT ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 2.2.2 SPIHT ํ•˜๋“œ์›จ์–ด 17 2.3 ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ 19 2.3.1 ์˜ˆ์ธก ๋ฐฉ๋ฒ• 19 2.3.2 VLC 20 2.3.3 ์˜ˆ์ธก ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ ํ•˜๋“œ์›จ์–ด 22 ์ œ 3 ์žฅ LCD ์˜ค๋ฒ„๋“œ๋ผ์ด๋ธŒ๋ฅผ ์œ„ํ•œ BTC 24 3.1 ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 24 3.1.1 ๋น„ํŠธ-์ ˆ์•ฝ ๋ฐฉ๋ฒ• 25 3.1.2 ๋ธ”๋ก ํฌ๊ธฐ ์„ ํƒ ๋ฐฉ๋ฒ• 29 3.1.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์š”์•ฝ 31 3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 33 3.2.1 ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์ธํ„ฐํŽ˜์ด์Šค 34 3.2.2 ์ธ์ฝ”๋”์™€ ๋””์ฝ”๋”์˜ ๊ตฌ์กฐ 37 3.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 44 3.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ฑ๋Šฅ 44 3.3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ ๊ฒฐ๊ณผ 49 ์ œ 4 ์žฅ ์ €๋น„์šฉ ๊ทผ์ ‘-๋ฌด์†์‹ค ํ”„๋ ˆ์ž„ ๋ฉ”๋ชจ๋ฆฌ ์••์ถ•์„ ์œ„ํ•œ ๊ณ ์† 1D SPIHT 54 4.1 ์ธ์ฝ”๋” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 54 4.1.1 ์˜์กด ๊ด€๊ณ„ ๋ถ„์„ ๋ฐ ์ œ์•ˆํ•˜๋Š” ํŒŒ์ดํ”„๋ผ์ธ ์Šค์ผ€์ฅด 54 4.1.2 ๋ถ„๋ฅ˜ ๋น„ํŠธ ์žฌ๋ฐฐ์น˜ 57 4.2 ๋””์ฝ”๋” ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 59 4.2.1 ๋น„ํŠธ์ŠคํŠธ๋ฆผ์˜ ์‹œ์ž‘ ์ฃผ์†Œ ๊ณ„์‚ฐ 59 4.2.2 ์ ˆ๋ฐ˜-ํŒจ์Šค ์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ• 63 4.3 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ 65 4.4 ์‹คํ—˜ ๊ฒฐ๊ณผ 73 ์ œ 5 ์žฅ ๊ณ ์ถฉ์‹ค๋„ RGBW ์ปฌ๋Ÿฌ ์ด๋ฏธ์ง€ ์••์ถ•์„ ์œ„ํ•œ ๊ณ ์ • ์••์ถ•๋น„ VLC 81 5.1 ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 81 5.1.1 RGBW ์ธํ„ฐ-์ปฌ๋Ÿฌ ์—ฐ๊ด€์„ฑ์„ ์ด์šฉํ•œ ์˜ˆ์ธก ๋ฐฉ์‹ 82 5.1.2 ๊ณ ์ • ์••์ถ•๋น„๋ฅผ ์œ„ํ•œ Golomb-Rice ์ฝ”๋”ฉ 85 5.1.3 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์š”์•ฝ 89 5.2 ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 90 5.2.1 ์ธ์ฝ”๋” ๊ตฌ์กฐ 91 5.2.2 ๋””์ฝ”๋” ๊ตฌ์กฐ 95 5.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 101 5.3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์‹คํ—˜ ๊ฒฐ๊ณผ 101 5.3.2 ํ•˜๋“œ์›จ์–ด ๊ตฌํ˜„ ๊ฒฐ๊ณผ 107 ์ œ 6 ์žฅ ์••์ถ• ์„ฑ๋Šฅ ๋ฐ ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ ๋น„๊ต ๋ถ„์„ 113 6.1 ์••์ถ• ์„ฑ๋Šฅ ๋น„๊ต 113 6.2 ํ•˜๋“œ์›จ์–ด ํฌ๊ธฐ ๋น„๊ต 120 ์ œ 7 ์žฅ ๊ฒฐ๋ก  125 ์ฐธ๊ณ ๋ฌธํ—Œ 128 ABSTRACT 135Docto

    Simplified Nonlinear Static Progressive Collapse Analysis of Steel Moment Frmaes

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    ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„์„ ํ˜• ์œ ํ•œ์š”์†Œํ•ด์„์„ ๊ธฐ์ดˆ๋กœ ๊ธฐ๋‘ฅ์ด ์†์‹ค๋œ ์ฒ ๊ณจ๋ชจ๋ฉ˜ํŠธ๊ณจ์กฐ์˜ 2๊ฒฝ๊ฐ„ ๋ณด ๋ชจ๋ฉ˜ํŠธ-์ถ•์ธ์žฅ๋ ฅ ์ƒํ˜ธ์ž‘์šฉ์˜ ๋ชจํ˜•ํ™” ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ณธ ๋ชฉ์ ์„ ์œ„ํ•ด ๊ธฐ๋‘ฅ์ด ์†์‹ค๋œ 2๊ฒฝ๊ฐ„ ๋ถ€๋ถ„๊ณจ์กฐ ๋ชจ๋ธ์„ ๊ตฌ์„ฑํ•œ ํ›„ ๋ณด์ŠคํŒฌ๊ธธ์ด ๋Œ€ ๋ณด์ถค ๋น„ ๋ฐ ๋ณด ์‚ฌ์ด์ฆˆ๋ฅผ ๋ณ€์ˆ˜๋กœ ํ•˜์—ฌ ์žฌ๋ฃŒ์ /๊ธฐํ•˜ํ•™์  ๋น„์„ ํ˜•์ด ๊ณ ๋ ค๋œ ์œ ํ•œ์š”์†Œํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋น„์„ ํ˜• ํ•ด์„์„ ํ†ตํ•˜์—ฌ ๋ณด์ŠคํŒฌ๊ธธ์ด ๋Œ€ ๋ณด์ถค ๋น„๊ฐ€ ๋ณด์˜ ํ˜„์ˆ˜์ž‘์šฉ ๋ฐœํ˜„์— ๊ฐ€์žฅ ์ง€๋ฐฐ์ ์ธ ์š”์†Œ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•ด์„๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ์ดˆ๊ธฐ ํƒ„์„ฑ๊ฑฐ๋™์—์„œ๋ถ€ํ„ฐ ํ˜„์ˆ˜์ž‘์šฉ์— ์ด๋ฅด๊ธฐ๊นŒ์ง€์˜ ๋ณด์˜ ํ˜„ํšŒ์ „๊ฐ-์ˆ˜์ง์ €ํ•ญ๋ ฅ ๊ด€๊ณ„๋ฅผ ์ผ๋ จ์˜ ์„ ํ˜• ๋ชจ๋ธ๋กœ์„œ ๊ทผ์‚ฌํ™”ํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์•„์šธ๋Ÿฌ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ์•ˆ์„ ์—๋„ˆ์ง€ํ‰ํ˜•๋ฒ•๊ณผ ๊ฒฐํ•ฉํ•˜์—ฌ ์ฒ ๊ณจ๋ชจ๋ฉ˜ํŠธ๊ณจ์กฐ์˜ ๋น„์„ ํ˜• ์ •์  ์—ฐ์‡„๋ถ•๊ดดํ•ด์„ ๋ฐ ์„ค๊ณ„์— ํŽธ๋ฆฌํ•˜๊ฒŒ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์˜ˆ์‹œํ•˜์˜€๋‹ค.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฑด์„ค๊ตํ†ต๋ถ€๊ฐ€ ์ถœ์—ฐํ•˜๊ณ  ํ•œ๊ตญ๊ฑด์„ค๊ตํ†ต๊ธฐ์ˆ ํ‰๊ฐ€์›์—์„œ ์œ„ํƒ์‹œํ–‰ํ•œ 2003๋…„๋„ ๊ฑด์„คํ•ต์‹ฌ๊ธฐ์ˆ ์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…(03์‚ฐํ•™์—ฐC103A104001-03A0204-00110) ๋ฐ 2006๋…„๋„ ์„œ์šธ๋Œ€ํ•™๊ต ํ˜„๋Œ€ํ•™์ˆ ์—ฐ๊ตฌ๋น„ ์ง€์›์— ์˜ํ•œ ๊ฒƒ์ž…๋‹ˆ๋‹ค

    ์˜์–ด์˜ ์†Œ์ ˆ๊ณผ ์ฃผ์ˆ ๊ด€๊ณ„

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    ์ฃผ์ˆ ๊ด€๊ณ„(predication)๋Š” ๊ทธ๋ฆฌ์Šคยท๋กœ๋งˆ์˜ ๊ณ ์ „์  ๋ฌธ๋ฒ•์—ฐ๊ตฌ์—์„œ ์‹œ์ž‘ํ•˜์—ฌ ์ „ํ†ต๋ฌธ๋ฒ•์„ ๊ฑฐ์ณ ํ˜„๋Œ€์˜ GB์ด๋ก ์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ์–ด๋–ค ํ˜•ํƒœ, ์–ด๋–ค ์ข…๋ฅ˜์˜ ๋ฌธ๋ฒ•์—์„œ๋„ ๋‹น์—ฐํžˆ ์กด์žฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ฐ›์•„๋“ค์—ฌ์ ธ ๊ธฐ์ˆ ๋˜์–ด ์˜จ ๊ฐ„๋‹จํ•œ ๋‚ด์šฉ์˜ ๋ฌธ์žฅ๊ตฌ์กฐ๋กœ์„œ ํ•˜๋‚˜์˜ ์ ˆ(clause)์†์— ์กด์žฌํ•˜๋Š” ์ฃผ์–ด(subject)์™€ ์ˆ ๋ถ€(predicate)์˜ ๊ด€๊ณ„๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ๋ณธ๊ณ ์˜ ๋ชฉ์ ์€ Chomsky(1981)์˜ Lectures 0n Government and Binding(LGB)์ดํ›„ ์ˆ˜๋งŽ์€ ๋…ผ์˜ํ™” ๋…ผ์Ÿ๊ฑฐ๋ฆฌ๋ฅผ ์ œ๊ณตํ–ˆ๋˜ ์˜์–ด์˜ ์†Œ์ ˆ (small clause)๊ณผ ๊ด€๋ จ๋œ ๋ฌธ์ œ์ ์˜ ์ผ๋ถ€๋ฅผ ์ฃผ์ˆ ๊ด€๊ณ„ ์ด๋ก (predication theory)์— ์ž…๊ฐํ•œ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ด๊ณ ์ž ํ•˜๋Š”๊ฒƒ์ด๋‹ค. ๋” ๊ตฌ์ฒด์ ์œผ๋กœ, ๋‹ค์Œ์˜ ์˜ˆ๋ฌธ์„ ๋ณด์ž. (1) a. *John considers (a PRO sick] b. John left the room (a PRO angry] (la) ์˜ ๊ฒฝ์šฐ ๋ณด๋ฌธ์†Œ์ ˆ (complement small clause)์˜ ์ฃผ์–ด๋Š” PRO๊ฐ€ ๋  ์ˆ˜ ์—†์œผ๋‚˜, (16)์—์„œ ๋ณด๋“ฏ์ด ๋ถ€๊ฐ€์†Œ์ ˆ (adjunct small clause)์˜ ์ฃผ์–ด๋Š” PRO๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด๋ ‡๊ฒŒ PRO๋ฅผ ์„ค์ •ํ•จ์œผ๋กœ์จ ์ƒ๊ฒผ๋˜ ๋ฌธ์ œ, ์ฆ‰ ์˜์–ด์˜ ๋ณด๋ฌธ์†Œ์ ˆ์˜ ์ฃผ์–ด ์ž๋ฆฌ์—๋Š” PRO๊ฐ€ ์˜ฌ ์ˆ˜ ์—†์œผ๋‚˜ ๋ถ€๊ฐ€์†Œ์ ˆ์—๋Š”, ํŠน์ˆ˜ํ•œ ๊ฒฝ์šฐ๋ฅผ ์ œ์™ธํ•˜๊ณ ๋Š”, ๋Œ€์ฒด๋กœ ๊ทธ ์ฃผ์–ด ์ž๋ฆฌ์— PRO๊ฐ€ ์˜จ๋‹ค๋Š” ์‚ฌ์‹ค๊ณผ ๊ด€๋ จ๋œ ๋ฌธ๋ฒ•์„ฑ์˜ ๋ฌธ์ œ๋ฅผ PRO๋ฅผ ์„ค์ •ํ•˜์ง€ ์•Š๊ณ  ์ฃผ์ˆ ๊ด€๊ณ„์ด๋ก ์— ์ž…๊ฐํ•œ ๋ถ„์„์œผ๋กœ ์„ค๋ช…ํ•˜๊ณ ์ž ํ•˜๋Š” ๊ฒƒ์ด ๋ณธ๊ณ ์˜ ๋ชฉ์ ์ด๋‹ค

    A Feature Movement Analysis of English There-Construction

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    ์ด ์—ฐ๊ตฌ๋Š” ์ฒซ์งธ๏ผŒ ์˜์–ด์˜ ํ—ˆ์‚ฌ there ๊ตฌ๋ฌธ(expletive there construction)์„ ๋ถ„์„ํ•จ์— ์žˆ์–ด์„œ Chomsky (1995) ๋‚˜ Lasnik (1995b)์— ์˜ํ•ด ์ œ์•ˆ๋œ ์ž์งˆ ์ด๋™๋ถ„์„(Feature Movement Analysis)์ด Chomsky (1986)์ด๋‚˜ Chomsky (1991)์˜ ๋ฒ•์ฃผ์ด๋™๋ถ„์„(Category Movement Analysis)์ด๋‚˜ ์ตœ๊ทผ Boล›koviฤ‡ (1995)์— ์˜ํ•ด ์ œ์•ˆ๋œ ์ •์‚ฌํ•˜ํ–ฅ์ด๋™๋ถ„์„(Affix Hopping Analysis)๋ณด๋‹ค ๊ฐœ ๋…์ ์ธ ๋ฉด์— ์žˆ์–ด์„œ๋‚˜ ๊ฒฝํ—˜์ ์ธ ๋ณ€์— ์žˆ์–ด์„œ ์šฐ์›”ํ•จ์„ ๋ณด์ด๊ณ ๏ผŒ ๋‘˜์งธ๏ผŒ ์ด๋Ÿฌ ํ•œ ์„ธ ๊ฐ€์ง€ ๋ถ„์„๋“ค์˜ ๋‹จ์ ์„ ๋ณด์™„ํ•œ ์ƒˆ๋กœ์šด ๋ถ„์„์•ˆ์„ ์ œ์‹œํ•จ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. This study has a couple of aims as follows. First, it aims to show that in analyzing English there-construction, the Feature Movement Analysis pro posed on the basis of the Minimalist Program of Chomsky (1995) and Lasnik (1995b) is superior either to the Category Movement Analysis of Chomsky (1986, 1991) or to the Downward Movement Analysis of Boskoviรฉ (1995) both conceptually and empirically. Second, a new feature movement-based analysis will be proposed that can overcome the problems of the analyses proposed by Chomsky (1995) and Lasnik (1995b). . There have been tons of analyses proposed on English there-construction in the generatรญve grammar traditรญon. Especially with the launch of the Mini malist Program, its analysis entertains a more deepened scrutiny. By and large, proposed analyses can be divided into two major trends according to the development of the theory of generative grammar: one is the Category Movement Analysis (Chomsky (1 986, 1991, 1993), Lasnik (1995a)) and the other is the Feature Movement Analysis (Chomsky (1995), Lasnik (1995b)). Both analyses basically assume upward movement (raising) of associate NP or its formal features to there. In particular, Chomsky (1986) proposes that the associate NP substitute there via raising, and Chomsky (1991) proposes that the associate NP adjoin to there. Recently, Chomsky (1995) proposes that the formal features of associate NP raise. These movements are done due to different reasons: for Chomsky (1986, 1991, 1993), the movement is just for Case-checking. For Lasnik (1995a, 1995b), it is for the satisfaction of theres morphological inadequacies. For Chomsky (1995), the movement is triggered by the unchecked feature of Agr. Boล›koviฤ‡ (1995), however, in his dissertation, points out problems of upward movement-based analyses and proposes that there as an affiรฌx move downward to associate NP. This paper aims to show that the formal features of there moves to the associate NP not upward but downward. In par ticular, this paper intends to draw conclusions as in (1)

    Face Detection and Tracking Using H.264/AVC Information

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 2. ์ดํ˜์žฌ.์–ผ๊ตด ํƒ์ƒ‰ ๋ฐ ์ถ”์ ์— ๊ด€๋ จ๋œ ์—ฐ๊ตฌ ๋ถ„์•ผ๋Š” ์นด๋ฉ”๋ผ ๋ฐ ์บ ์ฝ”๋” ๊ธฐ์ˆ ์˜ ๋ฐœ์ „, ๋กœ๋ด‡ ๋น„์ „ ๋ถ„์•ผ์˜ ๋Œ€๋‘, ๊ฐ์‹œ์šฉ ์‹œ์Šคํ…œ์˜ ๋ณด๊ธ‰ํ™” ๋“ฑ์˜ ์‹œ๋ฅ˜์™€ ํ•จ๊ป˜ ๋ฐœ์ „ํ•ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•„์š”๋กœ ํ•˜๋Š” ์ž์› ๋ฐ ์—ฐ์‚ฐ๋Ÿ‰์ด ๋งŽ์•„ ์†Œํ˜• ๋‚ด์žฅํ˜• ์‹œ์Šคํ…œ์— ๊ตฌํ˜„๋˜๊ธฐ๋Š” ํž˜๋“  ๋ฌธ์ œ์ ์ด ์กด์žฌํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ H.264/AVC ์ธ์ฝ”๋”๋กœ๋ถ€ํ„ฐ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ์›€์ง์ž„ ๋ฒกํ„ฐ (motion vector) ์ •๋ณด์™€ I-๋งคํฌ๋กœ๋ธ”๋ก (Intra-macroblock) ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ์˜์ƒ์— ์ƒˆ๋กญ๊ฒŒ ๋“ค์–ด์˜ค๋Š” ๊ฐ์ฒด๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ , ๊ทธ ๊ฐ์ฒด ์˜์—ญ ๋‚ด๋ถ€์—์„œ ์–ผ๊ตด์„ ํƒ์ƒ‰ํ•˜๋Š” ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์–ผ๊ตด์ด ํƒ์ƒ‰๋œ ์ดํ›„์—๋Š” ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ํ•ด๋‹น ์–ผ๊ตด์„ ํƒ์ƒ‰ ๋ฐ ์ถ”์ ํ•˜๋„๋ก ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์–ผ๊ตด ํƒ์ƒ‰ ๊ณผ์ •์—์„œ์˜ ์—ฐ์‚ฐ๋Ÿ‰์„ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์•ˆ์ •ํ™”๋œ ํƒ์ƒ‰ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๊ฐ์ฒด ํƒ์ƒ‰ ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ I-๋งคํฌ๋กœ๋ธ”๋ก์„ ์ด์šฉํ•˜์—ฌ ์˜์ƒ์— ์ƒˆ๋กญ๊ฒŒ ๋“ฑ์žฅํ•œ ๊ฐ์ฒด๋ฅผ ์ฐพ๊ณ , ์›€์ง์ž„ ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ํƒ์ƒ‰๋œ ๊ฐ์ฒด ์˜์—ญ์„ ํ™•์žฅ์‹œํ‚จ๋‹ค. ์ œ์•ˆํ•œ ๊ฐ์ฒด ํƒ์ƒ‰ ๋ฐฉ์‹์€ ํ‰๊ท  0.927 ์˜ precision, 0.903 ์˜ recall ์ˆ˜์ค€์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ ํ•„์š”๋กœ ํ•˜๋Š” ์ž์› ๋ฐ ์—ฐ์‚ฐ๋Ÿ‰ ์ธก๋ฉด์—์„œ๋Š” ์‹œ๊ฐ„ ์ฐจ๋ถ„๋ฒ• (temporal differencing) ๊ณผ ์œ ์‚ฌํ•˜๊ณ , ํƒ์ƒ‰ ๊ฒฐ๊ณผ ์ธก๋ฉด์—์„œ๋Š” ๋ฐฐ๊ฒฝ ์ฐจ๋ถ„๋ฒ• (background subtraction) ๊ณผ ์œ ์‚ฌํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ๋ฐฐ๊ฒฝ์˜ ํ˜ผ์žกํ•œ ์›€์ง์ž„์ด ์žˆ๋Š” ์˜์ƒ, ํŒจ๋‹ (panning) ์นด๋ฉ”๋ผ์˜ ์˜์ƒ์—์„œ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์šฐ์ˆ˜ํ•œ ํƒ์ƒ‰ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ž„์„ ํ™•์ธํ•˜๊ณ , ๊ทธ ์ด์œ ๋ฅผ ๋ถ„์„ํ•˜๋„๋ก ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ์–ผ๊ตด ํƒ์ƒ‰ ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ ๊ธฐ๋ณธ์ ์œผ๋กœ Viola and Jones ์–ผ๊ตด ํƒ์ƒ‰๊ธฐ๋ฅผ ์ด์šฉํ•œ๋‹ค. ์ด๋•Œ, ํ•„์š”๋กœ ํ•˜๋Š” ์—ฐ์‚ฐ๋Ÿ‰์„ ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ์Šคํ‚ต ๋ฐฉ์‹, ์Šค์ผ€์ผ ์ˆ˜ ์กฐ์ • ๋ฐฉ์‹ ๋“ฑ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋Œ€๋žต 75 ๋งŒ๊ฐœ ์ •๋„ ํ•„์š”๋กœ ํ–ˆ๋˜ ํƒ์ƒ‰ ์„œ๋ธŒ ์œˆ๋„์šฐ ์ˆ˜๊ฐ€ ์–ผ๊ตด ํ•˜๋‚˜ ๋‹น 1,000 ๊ฐœ ๋ฏธ๋งŒ์œผ๋กœ ๊ฐ์†Œํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ์–ผ๊ตด ์ถ”์  ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ ๊ธฐ๋ณธ์ ์œผ๋กœ H.264/AVC ์ธ์ฝ”๋”์˜ ์›€์ง์ž„ ๋ฒกํ„ฐ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜๋Š” Lee et al. ๋ฐฉ์‹์„ ์ด์šฉํ•œ๋‹ค. ์ด๋•Œ Lee et al. ์˜ ์ถ”์  ๋ฐฉ์‹์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ H.264/AVC ์ธ์ฝ”๋”์˜ ํƒ์ƒ‰ ์˜์—ญ (search range) ์„ ์ด์šฉํ•œ ์ถ”์  ๋ณด์™„ ๋ฐฉ์‹, I-๋งคํฌ๋กœ๋ธ”๋ก์„ ์ด์šฉํ•œ ์ถ”์  ๋ณด์™„ ๋ฐฉ์‹ ๋“ฑ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด์™€ ๊ฐ™์€ ๋ฐฉ์‹์„ ์ด์šฉํ•  ๊ฒฝ์šฐ Viola and Jones ๋ฐฉ์‹์œผ๋กœ ๊ตฌํ•œ ์–ผ๊ตด์˜ ์ค‘์‹ฌ์ ๊ณผ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ํƒ์ƒ‰๋œ ์–ผ๊ตด์˜ ์ค‘์‹ฌ์ ๊ฐ„์˜ ํ‰๊ท  ๊ฑฐ๋ฆฌ ์ฐจ์ด๊ฐ€ ๋Œ€๋žต 1-ํ”ฝ์…€ ์ •๋„์ž„์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค.Maste
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