15 research outputs found

    A Study for Quality Assessment of Herbal Materials Using Chromatographic Fingerprints and Relative Quantification

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2017. 8. ๋ฐ•์ •์ผ.์ฒœ์—ฐ๋ฌผ์€ ๋‹ค์–‘ํ•œ ํ™œ์„ฑ์„ฑ๋ถ„์„ ํฌํ•จํ•˜๊ณ  ์žˆ๊ณ  ์ƒ๋ฌผ๋‹ค์–‘์„ฑ์œผ๋กœ ์ธํ•ด ์›๋ฃŒ๋งˆ๋‹ค ๊ทธ ์กฐ์„ฑ์˜ ์ฐจ์ด๊ฐ€ ํฌ๋ฏ€๋กœ ์ •ํ™•ํ•˜๊ฒŒ ์›๋ฃŒ์˜ ํ’ˆ์งˆ์„ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฐœ๋ฐœ์ด ์ค‘์š”์‹œ๋˜๊ณ  ์žˆ๋‹ค. ํ˜„์žฌ ์ด๋ฅผ ์œ„ํ•ด ๋‹ค์„ฑ๋ถ„์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜์—ฌ ํ‰๊ฐ€ํ•˜๋Š” ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ๋ฒ•๊ณผ ์†Œ์ˆ˜ ์ง€ํ‘œ์„ฑ๋ถ„์„ ์ •๋Ÿ‰ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ฐ™์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐ ๋ฐฉ์‹์—์„œ ๋ณดํŽธ์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ๋ฐฉ๋ฒ•๋“ค์„ ๊ฐœ์„ ํ•˜์—ฌ ์—„๊ฒฉํ•˜๋ฉด์„œ ํšจ์œจ์ ์œผ๋กœ ํ’ˆ์งˆ์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ € ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ์„ ์ด์šฉํ•œ ๊ธฐ์กด์˜ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ•์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ๋ณตํ•ฉ ์œ ์‚ฌ์„ฑ ์ง€ํ‘œ ์ฒด๊ณ„(CAM)๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋‹ค์–‘ํ•œ ๊ด€์ ์—์„œ ์ง€๋ฌธ ๊ฐ„์˜ ์œ ์‚ฌ์„ฑ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ  ๋‹จ์ผ ์œ ์‚ฌ์„ฑ ์ง€ํ‘œ๋ฅผ ํ™œ์šฉํ•˜์˜€์„ ๋•Œ ๋‚˜ํƒ€๋‚˜๋Š” ๋‹จ์ ์ด ๋ณด์™„๋˜์—ˆ๋‹ค. ์œ„ ์ฒด๊ณ„๋Š” ๋ฐฑ์ถœ ์‹œ๋ฃŒ 40์ข…์— ์ ์šฉํ•˜์˜€๊ณ  ํ’ˆ์งˆ ๊ธฐ์ค€ ์„ค์ •์—๋Š” ์ปค๋„๋ฐ€๋„์ถ”์ •์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๊ธฐ์ค€์— ์˜ํ•ด ํ’ˆ์งˆ์ด ๋ณด์žฅ๋œ ์›๋ฃŒ 22์ข…์€ ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ์˜ํ•ด ์„ ํƒ๋œ ์›๋ฃŒ 22์ข…์— ๋น„ํ•ด ์ƒ์ •ํ•œ ๊ธฐ์ค€ ์‹œ๋ฃŒ์™€ ๋” ํ™”ํ•™์ ์œผ๋กœ ๋™๋“ฑํ–ˆ๋‹ค. ๋‘˜์งธ๋กœ, ํ‘œ์ค€๋ฌผ์งˆ ํ™•๋ณด์˜ ์–ด๋ ค์›€์„ ๊ทน๋ณตํ•˜๊ณ  ํšจ์œจ์ ์œผ๋กœ ์„ฑ๋ถ„ ํ•จ๋Ÿ‰์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์ฆ๊ธฐํ™”๊ด‘์‚ฐ๋ž€๊ฒ€์ถœ๊ธฐ๋ฅผ ์ด์šฉํ•œ ์ƒ๋Œ€ ์ •๋Ÿ‰๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. HPLC์™€ ๊ฒฐํ•ฉํ•˜์˜€์„ ๋•Œ ๊ฒ€์ถœ๊ฐ๋„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ธ์ž๋“ค์„ ์กฐ์‚ฌํ•˜์˜€๊ณ  ์ด์— ๋”ฐ๋ฅธ ์ด๋™์ƒ ์กฐ๊ฑด์„ ์ตœ์ ํ™”ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์„ฑ๋ถ„์˜ ๊ฒ€์ถœ๊ฐ๋„๋ฅผ ํ‰์ค€ํ™” ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ธ์‚ผ ๋ฐ ๊ฐ€๊ณต์ธ์‚ผ์ œํ’ˆ์˜ ์ฃผ์š”ํ™œ์„ฑ์„ฑ๋ถ„์ธ Ginsenoside 9์ข… ๋ถ„์„์— ํ™œ์šฉํ•œ ๊ฒฐ๊ณผ, ์ตœ์  ์กฐ๊ฑด์—์„œ ์ง์ ‘ ์ •๋Ÿ‰๋ฒ• ๋Œ€๋น„ ์ตœ๋Œ€ ์ฐจ์ด๋Š” 8.26%, ์ „์ฒด ํ•จ๋Ÿ‰ ์ฐจ์ด๋Š” 0.91%์— ๋ถˆ๊ณผํ–ˆ๋‹ค.I. ์„œ๋ก  1 1. ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ์„ ์ด์šฉํ•œ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ• 1 2. ์ƒ๋Œ€์ •๋Ÿ‰์„ ์ด์šฉํ•œ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ• 5 II. ์‹คํ—˜๋ฐฉ๋ฒ• 7 1. ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ์„ ์ด์šฉํ•œ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ• 7 1.1. ์‹œ๋ฃŒ, ์‹œ์•ฝ 7 1.2. ๋ถ„์„๊ธฐ๊ธฐ ๋ฐ ์กฐ๊ฑด 7 1.3. ๋ฐ˜์‘ํ‘œ๋ฉด๋ถ„์„๋ฒ•์„ ์ด์šฉํ•œ ์ถ”์ถœ๋ฒ• ์ตœ์ ํ™” 8 1.4. ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ ํš๋“ 8 1.5. ๋ณตํ•ฉ ์œ ์‚ฌ์„ฑ ์ง€ํ‘œ(CAM) ๊ณ„์‚ฐ 9 1.6. ์ปค๋„๋ฐ€๋„์ถ”์ •์„ ์ด์šฉํ•œ ์ด์ƒ์น˜ ๊ฒ€์ถœ 9 2. ์ƒ๋Œ€์ •๋Ÿ‰์„ ์ด์šฉํ•œ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ• 10 2.1. ์‹œ๋ฃŒ, ์‹œ์•ฝ 10 2.2. ๋ถ„์„๊ธฐ๊ธฐ ๋ฐ ์กฐ๊ฑด 11 2.3. ์ด๋™์ƒ ์กฐ์„ฑ์ด ๊ฒ€์ถœ ๊ฐ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 11 2.4. Peak bandwidth๊ฐ€ ๊ฒ€์ถœ ๊ฐ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 12 2.5. ๋ถ„์„๋ฒ• ์ตœ์ ํ™” ๋ฐ ์œ ํšจ์„ฑ ๊ฒ€์ฆ 12 2.6. ์ง์ ‘ ์ •๋Ÿ‰๋ฒ•๊ณผ์˜ ๋น„๊ต ํ‰๊ฐ€ 13 III. ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 14 1. ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ์„ ์ด์šฉํ•œ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ• 14 1.1. ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”„์ง€๋ฌธ ํš๋“ 14 1.2. ๋ณตํ•ฉ ์œ ์‚ฌ์„ฑ ์ง€ํ‘œ(CAM) ๊ณ„์‚ฐ 23 1.3. ์ปค๋„๋ฐ€๋„์ถ”์ •์„ ์ด์šฉํ•œ ์ด์ƒ์น˜ ๊ฒ€์ถœ 27 1.4. ๊ธฐ์กด ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ•๊ณผ์˜ ๋น„๊ต 36 2. ์ƒ๋Œ€์ •๋Ÿ‰์„ ์ด์šฉํ•œ ํ’ˆ์งˆ ํ‰๊ฐ€๋ฒ• 39 2.1. ์ด๋™์ƒ ์กฐ์„ฑ์ด ๊ฒ€์ถœ ๊ฐ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 39 2.2. Peak bandwidth๊ฐ€ ๊ฒ€์ถœ ๊ฐ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 43 2.3. ์ˆœ์ฐจ์  Gradient ๋ณ€๊ฒฝ์„ ํ†ตํ•œ ๋ถ„์„ ์กฐ๊ฑด ์ตœ์ ํ™” 46 2.4. ์ƒ๋Œ€ ์ •๋Ÿ‰๋ฒ•์˜ ์œ ํšจ์„ฑ ๊ฒ€์ฆ 48 2.5. ์ง์ ‘ ์ •๋Ÿ‰๋ฒ•๊ณผ์˜ ๋น„๊ต 53 IV. ๊ฒฐ๋ก  55 V. ์ฐธ๊ณ ๋ฌธํ—Œ 56Docto

    ์œ ํšจ์„ฑ ์ถ”์ ์„ ํ†ตํ•œ ์ธ ๋ฉ”๋ชจ๋ฆฌ ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค ๋กœ๊ทธ ๊ด€๋ฆฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021.8. ์—ผํ—Œ์˜.์ธ-๋ฉ”๋ชจ๋ฆฌ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋Š” ๋ฉ”์ธ ๋ฉ”๋ชจ๋ฆฌ์— ์ƒ์ฃผํ•ด ์žˆ๋Š” ๋ฐ์ดํ„ฐ์…‹์—์„œ ํŠธ๋ Œ์ ์…˜์„ ์ฒ˜๋ฆฌํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํด๋ผ์ด์–ธํŠธ ์š”์ฒญ์— ๋Œ€ํ•œ ๋น ๋ฅธ ์‘๋‹ต์‹œ๊ฐ„์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฒ˜๋ฆฌ ์†๋„์˜ ํ–ฅ์ƒ์€ ์ธํ•ด ํŠธ๋ Œ์ ์…˜์˜ ๋‚ด๊ตฌ์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์กด์˜ ๋กœ๊น… ๊ธฐ๋ฒ•๊ณผ ์ฒดํฌํฌ์ธํŒ… ๊ธฐ๋ฒ•์˜ ๋น„์šฉ์„ ์ƒ๋Œ€์ ์œผ๋กœ ํฌ๊ฒŒ ๋งŒ๋“ ๋‹ค. ๋งŽ์€ ์ธ-๋ฉ”๋ชจ๋ฆฌ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๊ฐ€ ๋กœ๊ทธ์˜ ๋ถ€ํ”ผ๋ฅผ ์ค„์ด๋Š” ๊ฒƒ์œผ๋กœ ํ†ตํ•ด ๋กœ๊ทธ ์ƒ์„ฑ๊ณผ ๋กœ๊ทธ ์ €์žฅ IO์— ์˜ํ•œ ์˜ค๋ฒ„ํ—ค๋“œ๋ฅผ ๊ฐ์†Œ์‹œํ‚ค์ง€๋งŒ ๊ทธ๊ฒƒ์€ ๋ณต๊ตฌ ์‹œ๊ฐ„์˜ ์ฆ๊ฐ€๋ฅผ ๊ฐ€์ ธ์˜จ๋‹ค. ์ฃผ๊ธฐ์ ์ธ ์ฒดํฌํฌ์ธํŒ…์€ ๋ณต๊ตฌ ์‹œ๊ฐ„์„ ๊ฐ์†Œ์‹œํ‚ค๊ณ  ๋กœ๊ทธ์˜ ์ €์žฅ ๊ณต๊ฐ„์„ ์žฌ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ ์ฒดํฌํฌ์ธํŠธ ๋ฐฉ๋ฒ•์€ ์ข…์ข… ์‹œ์Šคํ…œ์˜ ์ž‘์—…๋Ÿ‰ ์ €ํ•˜, ์ง€์—ฐ ์ฆ๊ฐ€, ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ ์ฆ๊ฐ€๋กœ ์ธํ•ด ์ƒ๋‹นํ•œ ๋น„์šฉ์ด ๋ฐœ์ƒํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ํŒŒ์ผ ๋‚ด ๋กœ๊ทธ์˜ ์œ ํšจ์„ฑ์„ ์ถ”์ ํ•˜๊ณ  ๋ถˆํ•„์š”ํ•œ ๋กœ๊ทธ๋ฅผ ์ œ๊ฑฐํ•˜๋Š” ๊ธฐ์ˆ ์ธ validity tracking-based compaction (VTC)๋ฅผ ์‚ฌ์šฉํ•œ ์ฒดํฌํฌ์ธํŒ…์„ ์ œ์•ˆ ํ•œ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ์ œ์•ˆ ํ•˜๋Š” ๋ฐฉ์‹์€ ์Šค๋ƒ…์ƒท์„ ์‚ฌ์šฉํ•˜๋Š” ๊ธฐ์กด ์ฒดํฌ ํฌ์ธํŠธ ๋ฐฉ์‹์— ๋น„ํ•ด ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์ด ๋งค์šฐ ๋‚ฎ์ถœ ์ˆ˜ ์žˆ๋‹ค. ์šฐ๋ฆฌ์˜ ์‹คํ—˜์— ๋”ฐ๋ฅด๋ฉด ๊ธฐ์กด์˜ ์ฒดํฌํฌ์ธํŒ… ๋ฐฉ๋ฒ•์€ ์—…๋ฐ์ดํŠธ๊ฐ€ ์ง‘์ค‘๋˜๋Š” ์›Œํฌ๋กœ๋“œ์—์„œ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์ด ์ตœ๋Œ€ 2๋ฐฐ๊นŒ์ง€ ์ฆ๊ฐ€ ํ•˜๋Š” ๊ฒƒ์ด ๋น„ํ•˜์—ฌ VTC๋Š” 2% ๋ฏธ๋งŒ์˜ ์ฆ๊ฐ€๋ฅผ ๋ณด์ธ๋‹ค. ๊ทธ๊ฒƒ์€ ์‹œ์Šคํ…œ์ด ๋ฉ”๋ชจ๋ฆฌ์˜ ๋Œ€๋ถ€๋ถ„์„ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณด๊ด€ํ•˜๊ณ  ํŠธ๋ Œ์ ์…˜์„ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด์„œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค.With in-memory databases (IMDBs), where all data sets reside in main memory for fast processing speed, logging and checkpointing are essential for achieving persistence in data. Logging of IMDBs has evolved to reduce run-time overhead to suit the systems, but this causes an increase in recovery time. Checkpointing technique compensates for these problems with logging, but existing schemes often incur high costs due to reduced system throughput, increased latency, and increased memory usage. In this paper, we propose a checkpointing scheme using validity tracking-based compaction (VTC), the technique that tracks the validity of logs in a file and removes unnecessary logs. The proposed scheme shows extremely low memory usage compared to existing checkpointing schemes, which use consistent snapshots. Our experiments demonstrate that checkpoints using consistent snapshot increase memory footprint by up to two times in update-intensive workloads. In contrast, our proposed VTC only requires 2% additional memory for checkpointing. That means the system can use most of its memory to store data and process transactions.Abstract 1 1 Introduction 7 2 BACKGROUND AND MOTIVATION 12 2.1 Persistence in In-Memory Databases 12 2.2 Fork-Based Checkpointing 14 3 Design and Implementation 16 3.1 Design Overview 16 3.2 Distributed Logging and Log Data Format 18 3.3 Log File Compaction 19 3.4 Lazy Invalidation 24 3.5 Recovery 25 3.6 Correctness 27 3.7 Implementation 28 4 Evaluation 30 4.1 Experimental Setup 30 4.2 Performance 32 4.2.1 Throughput 32 4.2.2 Memory Footprint 33 4.2.3 Checkpointing Time 35 4.2.4 File Size 36 4.2.5 Restoring Time 37 5 Related Work 39 6 Conclusion 42 ์ดˆ๋ก 48์„

    Analysis on the Effect of the Venture Certification System on SMEs' financial Performances

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› : ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2014. 8. ๋ฐ•์ˆœ์• .๊ทธ๊ฐ„ ๋ฒค์ฒ˜๊ธฐ์—… ์ธ์ฆ์ œ๋„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ํ˜„ํ™ฉ์ง„๋‹จ ๋ฐ ๋ฌธ์ œ์ , ๊ฐœ์„ ๋ฐฉํ–ฅ ๋“ฑ์˜ ์ œ๋„์  ๋ถ„์„๊ณผ ํ•จ๊ป˜ ๋ฒค์ฒ˜๊ธฐ์—…์˜ ์ƒ์กด์—…๋ ฅ, ์žฌ๋ฌด์  ๊ฒฝ์˜์„ฑ๊ณผ ๋“ฑ ์‹ค์ฆ๋ถ„์„์„ ํ†ตํ•œ ๋‹ค์–‘ํ•œ ๊ฒ€ํ† ๊ฐ€ ์ด๋ฃจ์–ด์กŒ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ผ๋ฐ˜๊ธฐ์—…๊ณผ์˜ ๋น„๊ต๋ถ„์„์„ ๊ฐ„๊ณผํ•œ ์ฑ„ ๋‹จ์ผ๋Œ€์ƒ์˜ ๋ถ„์„์— ์น˜์ค‘ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•จ์œผ๋กœ์จ ๊ฐ๊ด€์  ์„ค๋ช…๋ ฅ์— ํ•œ๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ธ ๋ถ€๋ถ„์ด ์žˆ๋‹ค. ๋˜ํ•œ, ๋น„๊ต์—ฐ๊ตฌ๋ฅผ ํ•˜์˜€๋‹ค๊ณ  ํ•˜๋”๋ผ๋„ ๋™์ผ ์กฐ๊ฑด ์ฆ‰, ๋™์ผ ์‹œ๊ธฐ์— ์ •๋ถ€๋กœ๋ถ€ํ„ฐ ๊ธฐ์ˆ ๋ณด์ฆ์„ ๋ฐ›๊ฑฐ๋‚˜ ์‹ ์šฉ๋Œ€์ถœ์ง€์›์„ ๋ฐ›์•˜์Œ์—๋„ ๋ฒค์ฒ˜์ธ์ฆ์„ ๋ฐ›์ง€ ์•Š์€ ๊ธฐ์—…๊ณผ์˜ ์„ฑ๊ณผ๋น„๊ต๊ฐ€ ์•„๋‹Œ ์ผ๋ฐ˜ ์ค‘์†Œ๊ธฐ์—…์˜ ๋ฏธ์‹œ์  ํ†ต๊ณ„ ์ง€ํ‘œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋น„๊ต๋ถ„์„ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ฃผ๋ฅผ ์ด๋ฃจ๊ณ  ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋™์ผ ์—ฐ๋„์— ๋™์ผ ์กฐ๊ฑด์œผ๋กœ ์ •๋ถ€์˜ ๊ธˆ์œต์ง€์›์„ ๋ฐ›์€ ๋ฒค์ฒ˜๊ธฐ์—…๊ณผ ้ž ๋ฒค์ฒ˜๊ธฐ์—…(์ผ๋ฐ˜๊ธฐ์—…) ๊ฐ„ ๊ฒฝ์˜์„ฑ๊ณผ ๋น„๊ต ๋ถ„์„์„ ์‹ค์‹œํ•จ์œผ๋กœ์จ ๋ฒค์ฒ˜๊ธฐ์—… ์ธ์ฆ์˜ ์‹ค์งˆ์  ํšจ๊ณผ๋ฅผ ๋ฐํž ์ˆ˜ ์žˆ๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋˜๋„๋ก ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์‹คํ—˜์ง‘๋‹จ์€ 2009๋…„๋„์— ์ค‘์†Œ๊ธฐ์—…์ง„ํฅ๊ณต๋‹จ์œผ๋กœ๋ถ€ํ„ฐ ์ง์ ‘ยท์‹ ์šฉ๋Œ€์ถœ์„ ํ†ตํ•˜์—ฌ ์ž๊ธˆ์ง€์›์„ ๋ฐ›์€ ์—…์ฒด ์ค‘ ๋ฒค์ฒ˜๊ธฐ์—…ํ˜‘ํšŒ์˜ ๋ฒค์ฒ˜๊ธฐ์—… ์ธ์ฆ์„ ํš๋“ํ•œ ๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€์œผ๋ฉฐ, ํ†ต์ œ์ง‘๋‹จ์€ ๋™์ผํ•œ ์ ˆ์ฐจ์™€ ์กฐ๊ฑด์œผ๋กœ ์ค‘์†Œ๊ธฐ์—…์ง„ํฅ๊ณต๋‹จ์œผ๋กœ๋ถ€ํ„ฐ ์ž๊ธˆ๋Œ€์ถœ์„ ๋ฐ›์•˜์œผ๋‚˜ ๋ฒค์ฒ˜๊ธฐ์—… ์ธ์ฆ์„ ํš๋“ํ•˜์ง€ ์•Š์€ ๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฒค์ฒ˜๊ธฐ์—…์˜ ๊ฒฝ์˜์„ฑ๊ณผ๋ฅผ ์ˆ˜์ต์„ฑ, ์•ˆ์ •์„ฑ, ์„ฑ์žฅ์„ฑ์˜ ๋Œ€ํ‘œ์  ์„ธ๊ฐ€์ง€ ๋ณ€์ˆ˜๋กœ ๊ตฌ๋ถ„ํ•œ ํ›„, ๋‹ค์‹œ ์ด์ž์‚ฐ์ด์ต๋ฅ , ์ž๊ธฐ์ž๋ณธ์ด์ต๋ฅ , ๋ถ€์ฑ„๋น„์œจ, ์œ ๋™๋น„์œจ, ์ด์ž์‚ฐ์ฆ๊ฐ€์œจ, ์˜์—…์ด์ต์ฆ๊ฐ€์œจ์˜ 6๊ฐ€์ง€ ์ข…์†๋ณ€์ˆ˜๋กœ ๋‚˜๋ˆ„์–ด ๋ฒค์ฒ˜๊ธฐ์—…๋งŒ์„ ๋Œ€์ƒ์œผ๋กœํ•œ ์ธ์ฆ์ „ยทํ›„์˜ ๊ฒฝ์˜์„ฑ๊ณผ ๋น„๊ต์™€ ํ•จ๊ป˜ ์ผ๋ฐ˜๊ธฐ์—…๊ณผ์˜ ๋น„๊ต๋ถ„์„์„ ํ†ตํ•ด ํšจ๊ณผ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ถ„์„์˜ ๋ฐฉ๋ฒ•์€ t-test์™€ ํ•จ๊ป˜ ์ด์ค‘์ฐจ๋ถ„์ถ”์ •๊ธฐ๋ฒ•(D.I.D)์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ, ๋จผ์ € ๋ฒค์ฒ˜๊ธฐ์—…๋งŒ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์ธ์ฆ์ „ยทํ›„ ๊ฒฝ์˜์„ฑ๊ณผ ๋ถ„์„์—์„œ๋Š” ๋งค์ถœ์•ก ๋ฐ ์ด์ž์‚ฐ ๊ทœ๋ชจ๋ฉด์—์„œ๋Š” ๋šœ๋ ทํ•œ ์ฆ๊ฐ€๋ฅผ ๋ณด์ด๊ณ  ์žˆ์–ด ์„ฑ์žฅ์„ธ๋ฅผ ๋‚˜ํƒ€๋‚ด๊ณ  ์žˆ์œผ๋‚˜ ์ˆ˜์ต์„ฑ ์ง€ํ‘œ์ธ ์ž๊ธฐ์ž๋ณธ์ด์ต๋ฅ  ๋ฉด์—์„œ๋Š” ์ฐจ์ด์ ์„ ์ฐพ๊ธฐ ์–ด๋ ค์› ๋‹ค. ๋˜ํ•œ, ์ผ๋ฐ˜๊ธฐ์—…๊ณผ์˜ ๋น„๊ต์— ์žˆ์–ด์„œ๋Š” ์ด์ž์‚ฐ ์ด์ต๋ฅ  ๋ฉด์—์„œ๋Š” ๊ธ์ •์  ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด๊ณ  ์žˆ์œผ๋‚˜ ์„ฑ์žฅ์„ฑ ํ•ญ๋ชฉ์ธ ์ด์ž์‚ฐ์ฆ๊ฐ€์œจ์— ์žˆ์–ด์„œ๋Š” ๊ฐœ์„ ํšจ๊ณผ๋ฅผ ์ฐพ๊ธฐ ์–ด๋ ค์› ๋‹ค. ํ•œํŽธ, ์•ˆ์ •์„ฑ ๋ฉด์—์„œ๋Š” ์ž์ฒด์ง‘๋‹จ ๋น„๊ต ๋ฐ ์–‘ ์ง‘๋‹จ๊ฐ„ ๋น„๊ต ๋ชจ๋‘ ์„ฑ๊ณผ์—ฌ๋ถ€์— ๋Œ€ํ•œ ํ†ต๊ณ„์  ์œ ์˜์ ์ด ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ •์ฑ…์  ์‹œ์‚ฌ์ ์œผ๋กœ๋Š” ์ฒซ์งธ, ๋ฏธ๋ž˜์„ฑ์žฅ์„ฑ ์ค‘์‹ฌ์˜ ๋ฒค์ฒ˜๊ธฐ์—… ์„ ๋ณ„์ œ๋„ ๊ฐœํŽธ, ๋‘˜์งธ, ์ •๋ถ€์ง€์›๋‚ด์šฉ์˜ ๊ธฐ์ˆ ์  ๋ถ„์•ผ ๊ฐ•ํ™”, ์…‹์งธ, ์œ ์‚ฌ ์ธ์ฆ์ œ๋„์™€์˜ ํ†ตํ•ฉ, ๋„ท์งธ, ๋ฒค์ฒ˜๊ธฐ์—…์— ๋Œ€ํ•œ ๋ฒค์ฒ˜์บํ”ผํƒˆ ํˆฌ์žํ™•๋Œ€ ๋“ฑ์„ ๋“ค ์ˆ˜ ์žˆ๋‹ค.The certification system of venture company has been studied for many areas such history of sustenance and financial performance analysis as well as institutional analysis like diagnosis of current status and problem and improvement. Nonetheless, a comparative study hasnt been duly attempted between venture company and non-venture company, so those studies have limitation in objective explanation. If any studies carried out a comparative research, most of them did so borrowing the statistical data of the entire Small and Middle-sized Enterprises(SMEs), rather than comparing the performance of companies that were certified as venture company and those that wasnt after they were granted technology guarantee or credit loan from the government under the same conditions and in the same year. The present study attempted to conduct a comparative analysis of the performance of venture companies and non-venture companies that received credit loan from the government in the same year and under the same conditions. By doing so, it aims to verify the effectiveness of the certification system of venture company. For the experiment group, this study chose the companies that were granted am official status of venture company from Korea Venture Business Association from those who were funded with credit loam from Small & medium Business Corporation(SBC) in 2009. In the meantime, the control group consists of the company that received loan from SBC through the same procedures and conditions as those of the experiment group, but failed in certification. In this study, the managerial performance of venture company was divided into 3 variables: profitability, stability and growth. Further, they were also subdivided into 6 independent variables: Return On Assets (ROA), Return On Equity Assets (ROE), Debt-Equity Ratio, Current Ratio, Growth Rate on Assets and Growth Rate of Operating Income. With respect to these variables, the experiment group (venture companies) was analyzed for their managerial performance before and after the certification of the title. And then the performance was compared with that of non-venture companies to verify the effectiveness of the certification system of venture company. T-test and difference-in-difference model(D.I.D) of regression analysis were employed as analysis method. The results demonstrated that the venture-certified companies showed apparent growth in total sales and assets before and after the certification, but there was not obvious difference in ROE, which is one of profit indexes. In the comparison with the performance of non-venture companies, venture companies showed positive growth in ROA while no visible improvement was observed in Growth Rate on Assets. In the meantime, both with-group and between-group analysis didnt show statistically significant difference in stability. The policy implications of the present study are that i) the certification system should be more relied on potentiality potential in certifying venture companyii) the government should reinforce technological assistanceiii) similar certification systems should be unifiedand iv) the investment of venture capital should be expanded on venture company.๋ชฉ ์ฐจ ์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ๊ณผ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ๊ณผ ๋ฒ”์œ„ 4 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 6 ์ œ 2 ์žฅ ์ด๋ก ์  ๋…ผ์˜์™€ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  7 ์ œ 1 ์ ˆ ๋ฒค์ฒ˜๊ธฐ์—…์— ๊ด€ํ•œ ์ด๋ก ์  ๋…ผ์˜ 7 ๊ฐ€. ๋ฒค์ฒ˜๊ธฐ์—…์˜ ๊ฐœ๋… ๋ฐ ์ œ๋„ 7 ๋‚˜. ๋ฒค์ฒ˜๊ธฐ์—…์˜ ํ˜„ํ™ฉ ๋ฐ ์ง€์›์ œ๋„ 12 ๋‹ค. ๊ธฐํƒ€ ํ˜์‹ ๊ธฐ์—… ์ธ์ฆ์ œ๋„ 15 ์ œ 2 ์ ˆ ๋ฒค์ฒ˜๊ธฐ์—…์˜ ๊ฒฝ์˜์„ฑ๊ณผ 19 ์ œ 3 ์ ˆ ์ค‘์†Œ๊ธฐ์—… ์ •์ฑ…์ž๊ธˆ์˜ ์ด๋ก ์  ๋…ผ์˜ 21 ๊ฐ€. ์ค‘์†Œ๊ธฐ์—… ์ •์ฑ…์ž๊ธˆ ๊ฐœ์š” 21 ๋‚˜. ์ค‘์†Œ๊ธฐ์—… ์ •์ฑ…์ž๊ธˆ์˜ ์ง€์›์„ฑ๊ณผ 23 ์ œ 4 ์ ˆ ์†Œ ๊ฒฐ 25 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์„ค๊ณ„ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 26 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์ ˆ์ฐจ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 26 ๊ฐ€. ์—ฐ๊ตฌ์ ˆ์ฐจ 26 ๋‚˜. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 27 ์ œ 2 ์ ˆ ์ž๋ฃŒ์˜ ์ˆ˜์ง‘ ๋ฐ ๋ณ€์ˆ˜์˜ ์ •์˜ 31 ๊ฐ€. ๋ถ„์„๋Œ€์ƒ ์ž๋ฃŒ 31 ๋‚˜. ๋ณ€์ˆ˜์˜ ํŠน์„ฑ๊ณผ ์ธก์ • 35 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ๊ฐ€์„ค ๋ฐ ์—ฐ๊ตฌ๋ชจํ˜• 40 ๊ฐ€. ์—ฐ๊ตฌ๊ฐ€์„ค 40 ๋‚˜. ๋ถ„์„๋ชจํ˜• 41 ์ œ 4 ์žฅ ์‹ค์ฆ๋ถ„์„ ๊ฒฐ๊ณผ 43 ์ œ 1 ์ ˆ ๋ฒค์ฒ˜๊ธฐ์—… ์ธ์ฆ์ „๊ณผ ์ธ์ฆํ›„์˜ ๊ฒฝ์˜์„ฑ๊ณผ ๋น„๊ต 43 ๊ฐ€. ์ˆ˜์ต์„ฑ ๋น„๊ต 45 ๋‚˜. ์•ˆ์ •์„ฑ ๋น„๊ต 47 ๋‹ค. ์„ฑ์žฅ์„ฑ ๋น„๊ต 49 ์ œ 2 ์ ˆ ๋ฒค์ฒ˜๊ธฐ์—…๊ณผ ์ผ๋ฐ˜๊ธฐ์—…๊ณผ์˜ ๊ฒฝ์˜์„ฑ๊ณผ ๋น„๊ต 51 ๊ฐ€. ์ˆ˜์ต์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 51 ๋‚˜. ์•ˆ์ •์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 56 ๋‹ค. ์„ฑ์žฅ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 60 ์ œ 5 ์žฅ ๊ฒฐ ๋ก  64 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ ๋ฐ ์ •์ฑ…์  ์‹œ์‚ฌ์  64 ๊ฐ€. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 64 ๋‚˜. ์ •์ฑ…์  ์‹œ์‚ฌ์  67 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๊ณผ์ œ 69 ์ฐธ๊ณ ๋ฌธํ—Œ 70 Abstract 75Maste

    ้•ทๆœŸๆณขๅ‹•้Ž็จ‹์— ์žˆ์–ด์„œ ๆŠ€่ก“่ฎŠๅŒ–์˜ ๅฝนๅ‰ฒ์— ๊ด€ํ•œ ็ก็ฉถ : ๋„ค์˜ค ์Š˜ํŽ˜ํ„ฐ๋ฆฌ์–ธ์˜ ๋…ผ์˜๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ๅคงๅญธ้™ข :็ถ“ๆฟŸๅญธ็ง‘ ็ถ“ๆฟŸๅญธๅฐˆๆ”ป,1995.Maste

    ํ™•๋ฅ ์  ๋ˆ„๋ฝ๊ณผ ์Šคํ‚ต ์—ฐ๊ฒฐ์„ ํ†ตํ•œ ์ง€์‹ ์ „๋‹ฌ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ์‹ฌ๋ณ‘ํšจ.๊นŠ์€ ์‹ ๊ฒฝ๋ง์€ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์—์„œ ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์™”์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๊นŠ์€ ์‹ ๊ฒฝ๋ง์€ ๊ณ„์‚ฐ ๋ฐ ๋ฉ”๋ชจ๋ฆฌ ์ง‘์•ฝ์ ์ด๋ฏ€๋กœ ์‹ค์ œ๋กœ ์‚ฌ์šฉ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทธ ๊ทœ๋ชจ๊ฐ€ ์ค„์—ฌ์งˆ ํ•„์š”๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ์‹ ๊ฒฝ๋ง์˜ ํฌ๊ธฐ๋ฅผ ์ค„์ด๋ฉด์„œ๋„ ๊ทธ ์„ฑ๋Šฅ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ์•ˆ์œผ๋กœ ์ง€์‹ ์ฆ๋ฅ˜ ๊ธฐ์ˆ ์ด ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. ์ด ๊ธฐ๋ฒ•์˜ ๊ธฐ๋ณธ ์•„์ด๋””์–ด๋Š” ํ•™์ƒ ์‹ ๊ฒฝ๋ง์„ ์„ ์ƒ๋‹˜ ์‹ ๊ฒฝ๋ง์˜ ๋„์›€์„ ๋ฐ›์•„ ํ•™์Šต์‹œํ‚ค๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๋‹ค์–‘ํ•œ ์ง€์‹ ์ถ”์ฒ  ๊ธฐ๋ฒ•๋“ค์ด ์ œ์‹œ๋˜์—ˆ๊ณ  ๊ทธ๊ฒƒ๋“ค ์ค‘ ํ•˜๋‚˜๊ฐ€ ๋‹ค์ค‘ ์„ ์ƒ ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ด๊ฒƒ์€ ์–ด๋Š ์ •๋„ ์ž์›์˜ ๋‚ญ๋น„๋ฅผ ์œ ๋ฐœํ•˜๋ฏ€๋กœ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›์ง€ ๋ชปํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ์šฐ๋ฆฌ๋Š” ํ™•๋ฅ ์  ๋ธ”๋ก๊ณผ ์Šคํ‚ต ์—ฐ๊ฒฐ์„ ํ™œ์šฉํ•˜์—ฌ ์ถ”๊ฐ€์ ์ธ ์ž์› ์—†์ด ํ•œ ๊ฐœ์˜ ์„ ์ƒ๋‹˜ ์‹ ๊ฒฝ๋ง์œผ๋กœ๋ถ€ํ„ฐ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์‹ ๊ฒฝ๋ง์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์ƒ์„ฑ๋œ ์‹ ๊ฒฝ๋ง๋“ค์€ ๋‹ค์ค‘ ์„ ์ƒ ์‹ ๊ฒฝ๋ง์˜ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๊ณ  ์ถ”๊ฐ€์ ์ธ ์ž์› ์—†์ด ํ•™์ƒ ์‹ ๊ฒฝ๋ง์— ์ถฉ๋ถ„ํ•œ ์ง€์‹์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ œ์•ˆํ•˜๋Š” ์ ‘๊ทผ๋ฒ•์œผ๋กœ ํ•™์ƒ ์‹ ๊ฒฝ๋ง์ด cifar-100๊ณผ tiny-imagenet ๋ฐ์ดํƒ€์…‹์— ๋Œ€ํ•˜์—ฌ ์„ฑ๋Šฅ ํ–ฅ์ƒ์ด ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€์Šต๋‹ˆ๋‹ค.Deep neural networks have achieved state-of-the-art performance in various fields. However, DNNs might need to be scaled down to fit real-word applications since they are computationally and memory intensive. As a means to compress the network yet still maintain the performance of the network, knowledge distillation has brought a lot of attention. This technique is based on the idea to train a student network using the provided output of a teacher network. %In this work, we propose a new distillation framework to provide the diverse knowledge to the student network using only one single teacher network. Various distillation methods have been proposed and one of them is deploying multiple teacher networks. However, it causes to some extent waste of resources, so did not receive much attention. In the proposed approach, we generate multiple sub-networks from one single teacher network by exploiting stochastic block and skip connection. Thus, they can play the role of multiple teacher networks and provide sufficient knowledge to the student network without additional resources. We observe the improved performance of student networks with the proposed approach for CIFAR-100 and tiny-imagenet dataset.1. Introduction 1 2. Related Works 5 2.1 Knowledge Transfer 5 2.2 Multiple Teacher Network 8 2.3 Regularizing Output 8 3. Proposed Framework 9 3.1 Generating Multiple networks 9 3.2 Availability of the proposed framework 10 3.3 Application to Other Distillation Techniques 12 4. Experiment 15 4.1 Dataset and Simulation Setting 15 4.2 CIFAR- 100 4.3 Tiny ImageNet 5. Ablation Study 23 6. Conclusion 25Maste
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