117 research outputs found

    ์˜คํ”„์…‹ ์ œ๊ฑฐ๊ธฐ์˜ ์ ์‘ ์ œ์–ด ๋“ฑํ™”๊ธฐ์™€ ๋ณด์šฐ-๋ ˆ์ดํŠธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋ฅผ ํ™œ์šฉํ•œ ์ˆ˜์‹ ๊ธฐ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021.8. ์—ผ์ œ์™„.In this thesis, designs of high-speed, low-power wireline receivers (RX) are explained. To be specific, the circuit techniques of DC offset cancellation, merged-summer DFE, stochastic Baud-rate CDR, and the phase detector (PD) for multi-level signal are proposed. At first, an RX with adaptive offset cancellation (AOC) and merged summer decision-feedback equalizer (DFE) is proposed. The proposed AOC engine removes the random DC offset of the data path by examining the random data stream's sampled data and edge outputs. In addition, the proposed RX incorporates a shared-summer DFE in a half-rate structure to reduce power dissipation and hardware complexity of the adaptive equalizer. A prototype chip fabricated in 40 nm CMOS technology occupies an active area of 0.083 mm2. Thanks to the AOC engine, the proposed RX achieves the BER of less than 10-12 in a wide range of data rates: 1.62-10 Gb/s. The proposed RX consumes 18.6 mW at 10 Gb/s over a channel with a 27 dB loss at 5 GHz, exhibiting a figure-of-merit of 0.068 pJ/b/dB. Secondly, a 40 nm CMOS RX with Baud-rate phase-detector (BRPD) is proposed. The RX includes two PDs: the BRPD employing the stochastic technique and the BRPD suitable for multi-level signals. Thanks to the Baud-rate CDRโ€™s advantage, by not using an edge-sampling clock, the proposed CDR can reduce the power consumption by lowering the hardware complexity. Besides, the proposed stochastic phase detector (SPD) tracks an optimal phase-locking point that maximizes the vertical eye opening. Furthermore, despite residual inter-symbol interference, proposed BRPD for multi-level signal secures vertical eye margin, which is especially vulnerable in the multi-level signal. Besides, the proposed BRPD has a unique lock point with an adaptive DFE, unlike conventional Mueller-Muller PD. A prototype chip fabricated in 40 nm CMOS technology occupies an active area of 0.24 mm2. The proposed PAM-4 RX achieves the bit-error-rate less than 10-11 in 48 Gb/s and the power efficiency of 2.42 pJ/b.๋ณธ ๋…ผ๋ฌธ์€ ๊ณ ์†, ์ €์ „๋ ฅ์œผ๋กœ ๋™์ž‘ํ•˜๋Š” ์œ ์„  ์ˆ˜์‹ ๊ธฐ์˜ ์„ค๊ณ„์— ๋Œ€ํ•ด ์„ค๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋งํ•˜๋ฉด, ์˜คํ”„์…‹ ์ƒ์‡„, ๋ณ‘ํ•ฉ๋œ ์„œ๋จธ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฐ์ • ํ”ผ๋“œ๋ฐฑ ๋“ฑํ™”๊ธฐ ๊ธฐ์ˆ , ํ™•๋ฅ ์  ๋ณด์šฐ ๋ ˆ์ดํŠธ ํด๋Ÿญ๊ณผ ๋ฐ์ดํ„ฐ ๋ณต์›๊ธฐ, ๊ทธ๋ฆฌ๊ณ  ๋‹ค์ค‘ ๋ ˆ๋ฒจ ์‹ ํ˜ธ์— ์ ํ•ฉํ•œ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ์งธ๋กœ, ์ ์‘ ์˜คํ”„์…‹ ์ œ๊ฑฐ ๋ฐ ๋ณ‘ํ•ฉ๋œ ์„œ๋จธ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฐ์ • ํ”ผ๋“œ๋ฐฑ ๋“ฑํ™”๊ธฐ๋ฅผ ๊ฐ–์ถ˜ ์ˆ˜์‹ ๊ธฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ ์‘ ์˜คํ”„์…‹ ์ œ๊ฑฐ ์—”์ง„์€ ์ž„์˜์˜ ๋ฐ์ดํ„ฐ ์ŠคํŠธ๋ฆผ์˜ ์ƒ˜ํ”Œ๋ง ๋ฐ์ดํ„ฐ, ์—์ง€ ์ถœ๋ ฅ์„ ๊ฒ€์‚ฌํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๊ฒฝ๋กœ ์ƒ์˜ ์˜คํ”„์…‹์„ ์ œ๊ฑฐํ•œ๋‹ค. ๋˜ํ•œ ํ•˜ํ”„ ๋ ˆ์ดํŠธ ๊ตฌ์กฐ์˜ ๋ณ‘ํ•ฉ๋œ ์„œ๋จธ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฐ์ • ํ”ผ๋“œ๋ฐฑ ๋“ฑํ™”๊ธฐ๋Š” ์ „๋ ฅ์˜ ์‚ฌ์šฉ๊ณผ ํ•˜๋“œ์›จ์–ด์˜ ๋ณต์žก์„ฑ์„ ์ค„์ธ๋‹ค. 40 nm CMOS ๊ธฐ์ˆ ๋กœ ์ œ์ž‘๋œ ํ”„๋กœํ† ํƒ€์ž… ์นฉ์€ 0.083 mm2 ์˜ ๋ฉด์ ์„ ๊ฐ€์ง„๋‹ค. ์ ์‘ ์˜คํ”„์…‹ ์ œ๊ฑฐ๊ธฐ ๋•๋ถ„์— ์ œ์•ˆ๋œ ์ˆ˜์‹ ๊ธฐ๋Š” 10-12 ๋ฏธ๋งŒ์˜ BER์„ ๋‹ฌ์„ฑํ•œ๋‹ค. ๋˜ํ•œ ์ œ์•ˆ๋œ ์ˆ˜์‹ ๊ธฐ๋Š” 5GHz์—์„œ 27 dB์˜ ๋กœ์Šค๋ฅผ ๊ฐ–๋Š” ์ฑ„๋„์—์„œ 10 Gb/s์˜ ์†๋„์—์„œ 18.6 mW๋ฅผ ์†Œ๋น„ํ•˜๋ฉฐ 0.068 pJ/b/dB์˜ FoM์„ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ๋กœ, ๋ณด์šฐ ๋ ˆ์ดํŠธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๊ฐ€ ์žˆ๋Š” 40 nm CMOS ์ˆ˜์‹ ๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ˆ˜์‹ ๊ธฐ์—๋Š” ๋‘๊ฐœ์˜ ๋ณด์šฐ ๋ ˆ์ดํŠธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋ฅผ ํฌํ•จํ•œ๋‹ค. ํ•˜๋‚˜๋Š” ํ™•๋ฅ ๋ก ์  ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š” ๋ณด์šฐ ๋ ˆ์ดํŠธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ์ด๋‹ค. ๋ณด์šฐ ๋ ˆ์ดํŠธ ํด๋Ÿญ ๋ฐ์ดํ„ฐ ๋ณต์›๊ธฐ์˜ ์žฅ์  ๋•๋ถ„์— ์—์ง€ ์ƒ˜ํ”Œ๋ง ํด๋Ÿญ์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š์Œ์œผ๋กœ์„œ ํŒŒ์›Œ์˜ ์†Œ๋ชจ์™€ ํ•˜๋“œ์›จ์–ด์˜ ๋ณต์žก์„ฑ์„ ์ค„์˜€๋‹ค. ๋˜ํ•œ ํ™•๋ฅ ์  ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋Š” ์ˆ˜์ง ์•„์ด ์˜คํ”„๋‹์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ์ตœ์ ์˜ ์œ„์ƒ ์ง€์ ์„ ์ฐพ์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค๋ฅธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋Š” ๋‹ค์ค‘ ๋ ˆ๋ฒจ ์‹ ํ˜ธ์— ์ ํ•ฉํ•œ ๋ฐฉ์‹์ด๋‹ค. ์‹ฌ๋ณผ ๊ฐ„ ๊ฐ„์„ญ์ด ๋‹ค์ค‘ ๋ ˆ๋ฒจ ์‹ ํ˜ธ์— ๋งค์šฐ ์ทจ์•ฝํ•œ ๋ฌธ์ œ๊ฐ€ ์žˆ๋”๋ผ๋„ ์ œ์•ˆ๋œ ๋‹ค์ค‘ ๋ ˆ๋ฒจ ์‹ ํ˜ธ์šฉ ๋ณด์šฐ ๋ ˆ์ดํŠธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋Š” ์ˆ˜์ง ์•„์ด ๋งˆ์ง„์„ ํ™•๋ณดํ•œ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ์ œ์•ˆ๋œ ๋ณด์šฐ ๋ ˆ์ดํŠธ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ๋Š” ๊ธฐ์กด์˜ ๋ฎฌ๋Ÿฌ-๋ฎ๋Ÿฌ ์œ„์ƒ ๊ฒ€์ถœ๊ธฐ์™€ ๋‹ฌ๋ฆฌ ์ ์‘ํ˜• ๊ฒฐ์ • ํ”ผ๋“œ๋ฐฑ ๋“ฑํ™”๊ธฐ๊ฐ€ ์žˆ๋”๋ผ๋„ ์œ ์ผํ•œ ๋ฝ ์ง€์ ์„ ๊ฐ–๋Š”๋‹ค. ํ”„๋กœํ† ํƒ€์ž… ์นฉ์€ 0.24mm2์˜ ๋ฉด์ ์„ ๊ฐ€์ง„๋‹ค. ์ œ์•ˆ๋œ PAM-4 ์ˆ˜์‹ ๊ธฐ๋Š” 48 Gb/s์˜ ์†๋„์—์„œ 10-11 ๋ฏธ๋งŒ์˜ BER์„ ๊ฐ€์ง€๊ณ , 2.42 pJ/b์˜ FoM์„ ๊ฐ€์ง„๋‹ค.CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATION 1 1.2 THESIS ORGANIZATION 5 CHAPTER 2 BACKGROUNDS 6 2.1 BASIC ARCHITECTURE IN SERIAL LINK 6 2.1.1 SERIAL COMMUNICATION 6 2.1.2 CLOCK AND DATA RECOVERY 8 2.1.3 MULTI-LEVEL PULSE-AMPLITUDE MODULATION 10 2.2 EQUALIZER 12 2.2.1 EQUALIZER OVERVIEW 12 2.2.2 DECISION-FEEDBACK EQUALIZER 15 2.2.3 ADAPTIVE EQUALIZER 18 2.3 CLOCK RECOVERY 21 2.3.1 2X OVERSAMPLING PD ALEXANDER PD 22 2.3.2 BAUD-RATE PD MUELLER MULLER PD 25 CHAPTER 3 AN ADAPTIVE OFFSET CANCELLATION SCHEME AND SHARED SUMMER ADAPTIVE DFE 28 3.1 OVERVIEW 28 3.2 AN ADAPTIVE OFFSET CANCELLATION SCHEME AND SHARED-SUMMER ADAPTIVE DFE FOR LOW POWER RECEIVER 31 3.3 SHARED SUMMER DFE 37 3.4 RECEIVER IMPLEMENTATION 42 3.5 MEASUREMENT RESULTS 45 CHAPTER 4 PAM-4 BAUD-RATE DIGITAL CDR 51 4.1 OVERVIEW 51 4.2 OVERALL ARCHITECTURE 53 4.2.1 PROPOSED BAUD-RATE CDR ARCHITECTURE 53 4.2.2 PROPOSED ANALOG FRONT-END STRUCTURE 59 4.3 STOCHASTIC PHASE DETECTION PAM-4 CDR 64 4.3.1 PROPOSED STOCHASTIC PHASE DETECTION 64 4.3.2 COMPARISON OF THE STOCHASTIC PD WITH SS-MMPD 70 4.4 PHASE DETECTION FOR MULTI-LEVEL SIGNALING 73 4.4.1 PROPOSED BAUD-RATE PHASE DETECTOR FOR MULTI-LEVEL SIGNAL 73 4.4.2 DATA LEVEL AND DFE COEFFICIENT ADAPTATION 79 4.4.3 PROPOSED PHASE DETECTOR 84 4.5 MEASUREMENT RESULT 88 4.5.1 MEASUREMENT OF THE PROPOSED STOCHASTIC BAUD-RATE PHASE DETECTION 94 4.5.2 MEASUREMENT OF THE PROPOSED BAUD-RATE PHASE DETECTION FOR MULTI-LEVEL SIGNAL 97 CHAPTER 5 CONCLUSION 103 BIBLIOGRAPHY 105 ์ดˆ ๋ก 109๋ฐ•

    ๋‹คํŒŒ์žฅ ๊ด€์ธก ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•œ ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ์€ํ•˜ ์ง„ํ™” ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€, 2017. 8. ์ด๋ช…๊ท .์€ํ•˜์˜ ํ˜•ํƒœ์™€ ๋ฌผ๋ฆฌ์ ์ธ ํŠน์„ฑ๋“ค์€ ์€ํ•˜๊ฐ€ ์†ํ•œ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง„๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์€ ํ•˜์˜ ์ง„ํ™”๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์€ํ•˜์— ์ž‘์šฉํ•˜๋Š” ํ™˜๊ฒฝ ํšจ๊ณผ์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ๋™๋ฐ˜๋˜์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์— ์œ„์น˜ํ•˜๋Š” ์€ํ•˜๋“ค์˜ ๋‹คํŒŒ์žฅ(0.2 โˆ’ 25 ฮผm) ํŠน์„ฑ๊ณผ ํ™˜๊ฒฝ ํšจ๊ณผ์— ๋Œ€ํ•œ ๋„ค ํŽธ์˜ ์—ฐ๊ตฌ๋“ค๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. 2์žฅ์—์„œ ์šฐ๋ฆฌ๋Š” SDSS์™€ WISE ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•œ Abell 2199 ์ดˆ์€ํ•˜๋‹จ์— ๊ด€ํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ด์•ผ๊ธฐํ•œ๋‹ค. Abell 2199 ์ดˆ์€ํ•˜๋‹จ์€ ์—ฌ๋Ÿฌ ์€ํ•˜๋‹จ๊ณผ ์€ํ•˜๊ตฐ์ด ๋ชจ์—ฌ์žˆ๋Š”, ๊ฐ€๊นŒ์šด ์šฐ์ฃผ(z โˆผ 0.03)์—์„œ ๊ฐ€์žฅ ๋ฌด๊ฑฐ์šด ์€ํ•˜ ๊ตฌ์กฐ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” Abell 2199 ์ดˆ์€ํ•˜๋‹จ์— ์†Œ์†๋œ ์€ํ•˜๋“ค์˜ ์ค‘์ ์™ธ์„ (MIR) ์ƒ‰์ง€์ˆ˜-๊ด‘๋„ ๋ถ„ํฌ๋กœ๋ถ€ํ„ฐ, ์€ํ•˜์˜ ์ค‘๊ฐ„ ์ง„ํ™” ๋‹จ๊ณ„์— ํ•ด๋‹นํ•˜ ๋Š” MIR Green Valley(MGV)๋ฅผ ์ •์˜ํ•˜์˜€๋‹ค. MGV์— ์žˆ๋Š” ์€ํ•˜๋“ค์€ ๊ฐ€์‹œ๊ด‘ ์ƒ‰๋“ฑ๊ธ‰ ๋„์—์„œ๋Š” Red Sequence์— ์œ„์น˜ํ•œ๋‹ค. MGV ์€ํ•˜๋“ค์€ Red Sequence์— ํ•จ๊ป˜ ์œ„์น˜ํ•˜๋Š” MIR Blue ์€ํ•˜๋“ค๊ณผ ๋น„์Šทํ•œ Dn4000๊ณผ ๋ณ„ํ˜•์„ฑ๋ฅ (Star Formation Rates)๋ฅผ ๊ฐ–๋Š”๋‹ค. ํ•ญ ์„ฑ ์ข…์กฑ ๋ชจํ˜•(stellar population model)์ด ์˜ˆ์ธกํ•˜๋Š” MGV ์€ํ•˜์— ์†ํ•œ ๋ณ„๋“ค์˜ ํ‰๊ท  ๋‚˜์ด๋Š” 10โˆ’100์–ต๋…„์œผ๋กœ, MIR Blue ์€ํ•˜๋“ค(> 100์–ต๋…„)๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ์ Š๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ์€ํ•˜๊ฐ€ ๋ณ„ํ˜•์„ฑ ํ™œ๋™์ด ๊ฑฐ์˜ ์™„์ „ํžˆ ๋ฉˆ์ถ˜ ๋’ค์— MGV๋กœ ์ง„์ž…ํ•˜๋ฉฐ, MGV์—์„œ ์ˆ˜์‹ญ์–ต๋…„์˜ ์‹œ๊ฐ„์„ ๋ณด๋‚ธ ๋’ค์— MIR Blue ์€ํ•˜๋กœ ์ง„ํ™”ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋งํ•ด์ค€๋‹ค. ์€ํ•˜์˜ ํ˜• ํƒœ๋ฅผ ์‚ดํŽด๋ณด์•˜์„ ๋•Œ, MGV์—๋Š” ์กฐ๊ธฐํ˜• ์€ํ•˜์™€ ๋งŒ๊ธฐํ˜• ์€ํ•˜๊ฐ€ ๋น„์Šทํ•œ ๋น„์œจ๋กœ ์กด์žฌํ•œ๋‹ค. MGV ์กฐ๊ธฐํ˜• ์€ํ•˜๋Š” MGV ๋งŒ๊ธฐํ˜• ์€ํ•˜๋ณด๋‹ค ๊ณ ๋ฐ€๋„ ํ™˜๊ฒฝ(์€ํ•˜๋‹จ/์€ํ•˜๊ตฐ์˜ ์ค‘์‹ฌ๋ถ€)์— ์ง‘์ค‘๋˜์–ด ์žˆ๋‹ค. ์ด๋Š” ์€ํ•˜๋‹จ๊ณผ ์€ํ•˜๊ตฐ ์ค‘์‹ฌ๋ถ€ ํ™˜๊ฒฝ์ด MGV ๋งŒ๊ธฐํ˜• ์€ํ•˜์—์„œ MIR ์กฐ๊ธฐ ํ˜• ์€ํ•˜๋กœ์˜ ํ˜•ํƒœ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ์Œ์„ ์˜๋ฏธํ•˜๋Š” ๊ด€์ธก์ ์ธ ์ฆ๊ฑฐ์ด๋‹ค. MGV ๋งŒ๊ธฐํ˜• ์€ํ•˜๊ฐ€ ๋งค์šฐ ๋‚ฎ์€ ๋ณ„ํ˜•์„ฑ ํ™œ๋™์„ ๋ณด์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ทธ ํ˜•ํƒœ๋ฅผ ์œ ์ง€ํ•˜๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์€ MGV ๋งŒ๊ธฐํ˜• ์€ํ•˜์˜ ๋ณ„ํ˜•์„ฑ๋ฅ  ๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ํ™˜๊ฒฝ ํšจ๊ณผ๊ฐ€ ๊ธด ์‹œ๊ฐ„์— ๊ฑธ์ณ ์ฒœ์ฒœํžˆ ์ž‘์šฉ ํ–ˆ๋‹ค๋Š” ๊ฒƒ์„ ๋งํ•ด์ค€๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ MGV ๋งŒ๊ธฐํ˜• ์€ํ•˜๋ฅผ ํ˜•์„ฑํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•œ ํ™˜๊ฒฝ ํšจ๊ณผ๋Š” Strangulation ํ˜น์€ Starvation์ผ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค. ๋ฐ˜๋ฉด, MGV ๋งŒ๊ธฐํ˜• ์€ํ•˜์—์„œ MGV ์กฐ๊ธฐํ˜• ์€ํ•˜๋กœ์˜ ํ˜•ํƒœ ๋ณ€ํ™”๋Š” ์€ํ•˜๋‹จ/์€ํ•˜๊ตฐ ์ค‘์‹ฌ๋ถ€์—์„œ ๋นˆ๋ฒˆํžˆ ์ผ์–ด๋‚˜๋Š” ์€ํ•˜ ๋ณ‘ํ•ฉ (merging) ๋ฐ ์กฐ์„ ์ƒํ˜ธ์ž‘์šฉ(tidal interaction)์— ์˜ํ•œ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์šฐ๋ฆฌ๋Š” 3์žฅ์—์„œ ๋ฐ€์ง‘์€ํ•˜๊ตฐ(Compact Groups of Galaxies)์—์„œ ์ผ์–ด๋‚˜๋Š” ์€ํ•˜ ์ง„ ํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ด์•ผ๊ธฐํ•œ๋‹ค. ๋ฐ€์ง‘์€ํ•˜๊ตฐ์€ ์•ฝ 100 kpc ์ดํ•˜์˜ ์ข์€ ๊ณต๊ฐ„์— 3โˆ’10๊ฐœ์˜ ์€ํ•˜๋“ค์ด ๋ฐ€์ง‘ํ•ด ์žˆ์œผ๋ฉฐ, ์€ํ•˜ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์ด ๋งค์šฐ ํ™œ๋ฐœํ•œ ํ™˜๊ฒฝ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” Sohn et al. (2016)์ด ๋งŒ๋“  ๋ฐ€์ง‘์€ํ•˜๊ตฐ ๋ชฉ๋ก๊ณผ WISE ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฐ€์ง‘์€ํ•˜๊ตฐ์— ์†ํ•œ ์€ํ•˜๋“ค์˜ MIR ํŠน์„ฑ๋“ค์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ฐ€์ง‘์€ํ•˜๊ตฐ ์€ํ•˜๋“ค์˜ ์ค‘์ ์™ธ์„  [3.4] โˆ’ [12] ์ƒ‰์ง€ ์ˆ˜๋Š” ์€ํ•˜๋‹จ ์€ํ•˜๋“ค์— ๋น„ํ•ด ํ‰๊ท ์ ์œผ๋กœ ์ž‘๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ๋ฐ€์ง‘์€ํ•˜๊ตฐ์— ์œ„์น˜ํ•œ ์€ํ•˜๋“ค์˜ ํ‰๊ท  ๋ณ„ ๋‚˜์ด(mean stellar age)๊ฐ€ ์ข€ ๋” ๋งŽ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๋˜ํ•œ, ๋ฐ€์ง‘์€ํ•˜๊ตฐ์€ ์€ํ•˜๋‹จ์— ๋น„ํ•ด ๋‚ฎ์€ MGV ์€ํ•˜ ๋น„์œจ์„ ๋ณด์ธ๋‹ค. ์ด๋Ÿฌํ•œ ์ž‘์€ MGV ์€ํ•˜ ๋น„์œจ์€ ์ €๋ฐ€๋„ ํ™˜๊ฒฝ์— ์œ„์น˜ํ•œ ๋ฐ€์ง‘์€ํ•˜๊ตฐ๊ณผ ๊ณ ๋ฐ€๋„ ํ™˜๊ฒฝ์— ์œ„์น˜ํ•œ ๋ฐ€์ง‘์€ํ•˜๊ตฐ ๋ชจ๋‘์—์„œ ๋ฐœ๊ฒฌ๋œ๋‹ค. ์ด ๋Š” ๋ฐ€์ง‘์€ํ•˜๊ตฐ์—์„œ ์€ํ•˜ ์ง„ํ™”๊ฐ€ ๋น ๋ฅด๊ฒŒ ์ง„ํ–‰๋˜์—ˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ณ ๋ฐ€๋„ ํ™˜๊ฒฝ์— ์œ„์น˜ํ•œ ๋ฐ€์ง‘์€ํ•˜๊ตฐ์€ ์ €๋ฐ€๋„ ํ™˜๊ฒฝ์— ์œ„์น˜ํ•œ ๋ฐ€์ง‘์€ํ•˜๊ตฐ๋ณด๋‹ค ๋†’์€ ๋น„์œจ์˜ ์กฐ๊ธฐํ˜• ์€ํ•˜๋“ค๊ณผ ์ž‘ ์€ MIR ์ƒ‰์ง€์ˆ˜๋ฅผ ๊ฐ–๋Š” ์€ํ•˜๋“ค๋กœ ์ด๋ฃจ์–ด์ ธ์žˆ๋‹ค. ์ด๋Š” ๋ฐ€์ง‘์€ํ•˜๊ตฐ์ด ์ฃผ๋ณ€ ํ™˜๊ฒฝ์œผ๋กœ๋ถ€ํ„ฐ ๋ฉค๋ฒ„ ์€ํ•˜๋ฅผ ์ƒˆ๋กญ๊ฒŒ ๊ณต๊ธ‰๋ฐ›๋Š”๋‹ค๋Š” ๊ฒƒ์„ ๋งํ•ด์ฃผ๋Š” ๊ด€์ธก์  ์ฆ๊ฑฐ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋ฐ€์ง‘์€ํ•˜๊ตฐ์ด ๋‹ค๋ฅธ ์€ํ•˜ ํ™˜๊ฒฝ๋ณด๋‹ค ์€ํ•˜ ์ง„ํ™”๊ฐ€ ๋น ๋ฅด๊ฒŒ ์ง„ํ–‰๋˜๋Š” ํ™˜๊ฒฝ์ด๋ฉฐ, ๋ฌด๊ฑฐ์šด ํƒ€์›์€ํ•˜ ํ˜•์„ฑ์— ํฌ๊ฒŒ ๊ธฐ์—ฌํ•˜๋Š” ํ™˜๊ฒฝ์ž„์„ ๋ฐํ˜€๋ƒˆ๋‹ค. 4์žฅ์—์„œ๋Š” Gemini-North ๋ง์›๊ฒฝ์˜ GMOS IFU๋ฅผ ์ด์šฉํ•œ E+A ์€ํ•˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ด์•ผ๊ธฐํ•œ๋‹ค. E+A ์€ํ•˜ ์ŠคํŽ™ํŠธ๋Ÿผ์—์„œ ๋ณด์ด๋Š” ๊ฐ•ํ•œ ๋ฐœ๋จธํก์ˆ˜์„ (Hฮณ, Hฮด, Hฮฒ)์€ ์ Š์€ A ํ˜• ๋ณ„๋“ค์˜ ์กด์žฌ๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ Š์€ ๋ณ„๋“ค์ด ์ฃผ๋กœ ๋ถ„ํฌํ•˜๋Š” ์€ํ•˜๋“ค๊ณผ๋Š” ๋‹ฌ๋ฆฌ, E+A ์€ํ•˜์—์„œ๋Š” Hฮฑ์™€ ๊ฐ™์€ ๋ฐฉ์ถœ์„ ์ด ๊ฑฐ์˜ ๋ณด์ด์ง€ ์•Š๋Š”๋‹ค. ์ด๋Ÿฌํ•œ ์ŠคํŽ™ํŠธ๋Ÿผ์˜ ํŠน์„ฑ์€ E+A ์€ํ•˜๊ฐ€ 10์–ต๋…„ ์ „ ์ฏค์— ๊ฐ•ํ•œ ๋ณ„ํญ๋ฐœ์„ ๊ฒช์—ˆ์ง€๋งŒ, ์–ด๋– ํ•œ ์ด์œ  ๋•Œ๋ฌธ์— ํ˜„์žฌ๋Š” ๋ณ„ํ˜•์„ฑ ํ™œ๋™์ด ๊ฑฐ์˜ ๋ฉˆ์ถ˜ ์ƒํƒœ์ž„์„ ๋งํ•ด์ค€๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ E+A ์€ํ•˜๋Š” Post-Starburst ์€ํ•˜์ด๋ฉฐ, ์€ํ•˜ ์ง„ํ™”์˜ ์ค‘๊ฐ„ ๋‹จ๊ณ„์— ์žˆ๋Š” ๋Œ€ํ‘œ์ ์ธ ์€ํ•˜ ์ข…์กฑ์œผ๋กœ ์ถ”์ •๋œ๋‹ค. E+A ์€ํ•˜์˜ ์ Š์€ Aํ˜• ๋ณ„๋“ค์˜ ๊ณต๊ฐ„ ๋ถ„ํฌ๋Š” ๋ณ„ํญ๋ฐœ ํ˜„์ƒ์ด ์ผ์–ด๋‚˜๋Š” ์˜์—ญ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ง์ ‘์ ์œผ๋กœ ๋ณด ์—ฌ์ค€๋‹ค. ๊ธฐ์กด์˜ ์ˆ˜์น˜์‹คํ—˜์—์„œ๋Š” ์€ํ•˜ ๋ณ‘ํ•ฉ ์‹œ ๊ฐ€์Šค๊ฐ€ ์€ํ•˜ ์ค‘์‹ฌ๋ถ€๋กœ ๋ชจ์—ฌ๋“ค๊ณ , ์€ํ•˜ ์ค‘์‹ฌ๋ถ€๋กœ๋ถ€ํ„ฐ ๋ฐ˜๊ฒฝ 1 kpc ์ด๋‚ด์˜ ์˜์—ญ์—์„œ ๊ฐ•ํ•œ ๋ณ„ํญ๋ฐœ์ด ์ƒ๊ธด๋‹ค๊ณ  ์˜ˆ์ธกํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฐ€๊นŒ์šด(0.03 < z < 0.05) ๋‹ค์„ฏ๊ฐœ์˜ E+A ์€ํ•˜๋“ค์— ๋Œ€ํ•œ ์šฐ๋ฆฌ์˜ ๊ด€์ธก ๊ฒฐ๊ณผ๋Š” ๋‹ค์„ฏ ์€ํ•˜ ๋ชจ๋‘์—์„œ Aํ˜• ๋ณ„๋“ค์ด 1 kpc ์˜์—ญ ๋ฐ”๊นฅ๊นŒ์ง€ ๋„“๊ฒŒ ๋ถ„ํฌํ•˜๊ณ  ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ์šฐ๋ฆฌ๋Š” Aํ˜• ๋ณ„๋“ค์˜ ์€ํ•˜ ์ง‘์ค‘๋„(central concentration)์™€ ๋ณ„ํญ๋ฐœ ์„ธ๊ธฐ ์‚ฌ์ด์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ฐœ๊ฒฌ ํ•˜์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” Aํ˜• ๋ณ„๋“ค์˜ ๊ณต๊ฐ„ ๋ถ„ํฌ๊ฐ€ E+A ์€ํ•˜๊ฐ€ ๊ณผ๊ฑฐ์— ๊ฒช์€ ๋ณ„ํญ๋ฐœ์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ์Œ์„ ๋งํ•ด์ค€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์šฐ๋ฆฌ๋Š” 5์žฅ์—์„œ ๋‚˜์„ ์€ํ•˜์˜ ๋ง‰๋Œ€ ๊ตฌ์กฐ์™€ ํ™œ๋™์„ฑ ์€ํ•˜ํ•ต(Active Galactic Nuclei, AGN) ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ด์•ผ๊ธฐํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์•ฝ 9์ฒœ๊ฐœ์˜ ๋‚˜์„ ์€ํ•˜๋“ค๊ณผ Lee et al. (2012a)์˜ ๋ถ„๋ฅ˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์˜€๋‹ค. AGN๋ฅผ ๋ณด์œ ํ•œ ์€ํ•˜๋“ค์€ ๊ทธ๋ ‡์ง€ ์•Š์€ ์€ํ•˜๋“ค์— ๋น„ํ•ด 2.5๋ฐฐ ๋†’์€ ํ™•๋ฅ ๋กœ ๋ง‰๋Œ€ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, AGN ๋น„ ์œจ๋„ ๋ง‰๋Œ€๋ฅผ ๊ฐ€์ง€์ง€ ์•Š์€ ์€ํ•˜๋ณด๋‹ค ๋ง‰๋Œ€์€ํ•˜์—์„œ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์€ํ•˜์˜ u โˆ’ r ์ƒ‰์ง€์ˆ˜์™€ ๋ณ„์งˆ๋Ÿ‰(stellar mass)๋ฅผ ๊ณ ์ •ํ•˜๊ณ  ๋น„๊ตํ–ˆ์„ ๋•Œ๋Š” ์•ž์˜ ๊ฒฐ๊ณผ๊ฐ€ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š๋Š” ๋‹ค. ๋˜ํ•œ, AGN์˜ ํ™œ๋™ ์„ธ๊ธฐ๋ฅผ ์˜๋ฏธํ•˜๋Š” Eddington ratio๋ฅผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ๋ง‰๋Œ€์€ํ•˜์™€ ๋ง‰๋Œ€๊ฐ€ ์—†๋Š” ์€ํ•˜ ์‚ฌ์ด์˜ ํฐ ์ฐจ์ด๋ฅผ ๋ฐœ๊ฒฌํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋“ค์€ AGN ํ™œ๋™์ด ๋ง‰ ๋Œ€๊ฐ€ ์žˆ๋Š” ๊ฒฝ์šฐ์—๋„ ๊ฐ•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์œผ๋ฉฐ, ๋ง‰๋Œ€์— ์˜ํ•ด์„œ AGN์˜ ์„ธ๊ธฐ๊ฐ€ ์ฆ๊ฐ€๋˜์ง€ ์•Š์Œ์„ ๋ณด์—ฌ์ค€๋‹ค.The role of environment in galaxy evolution is an important issue in recent astronomy. In order to better understand how environment affects galaxy transition from star-forming galaxies to quiescent galaxies, we conduct several studies using multi-wavelength data, from near-ultraviolet to mid-infrared (MIR), of nearby galaxies in various environments. First, using the Wide-field Infrared Survey Explorer (WISE) data, we study the MIR properties of the galaxies in the A2199 supercluster at z = 0.03 to understand the star formation activity of galaxy groups and clusters in the supercluster environment. We classify the supercluster galaxies into three classes in the MIR color- luminosity diagram: MIR blue cloud (massive, quiescent and mostly early-type), MIR star-forming sequence (mostly late-type), and MIR green valley galaxies. These MIR green valley galaxies are distinguishable from the optical green valley galaxies, in the sense that they belong to the optical red sequence. We find that the fraction of each MIR class does not depend on virial mass of each group/cluster. We compare the cumulative distributions of surface galaxy number density and cluster/group-centric distance for the three MIR classes. MIR green valley galaxies show the distribution between MIR blue cloud and MIR star-forming sequence galaxies. However, if we fix galaxy morphology, early- and late-type MIR green valley galaxies show different distributions. Our results suggest a possible evolutionary scenario of these galaxies: 1) Late-type MIR star-forming sequence galaxies โ†’ 2) Late-type MIR green valley galaxies โ†’ 3) Early-type MIR green valley galaxies โ†’ 4) Early-type MIR blue cloud galaxies. In this sequence, star formation of galaxies is quenched before the galaxies enter the MIR green valley, and then morphological transformation occurs in the MIR green valley. Second, we study the MIR properties of galaxies in compact groups and their environmental dependence. We find that the MIR [3.4] โˆ’ [12] colors of compact group early-type galaxies are on average bluer than those of cluster early-type galaxies. When compact groups have both early- and late-type member galaxies, the MIR colors of the late-type members in those compact groups are bluer than the MIR colors of cluster late-type galaxies. As compact groups are located in denser regions, they tend to have larger early-type galaxy fractions and bluer mean MIR colors of member galaxies. These trends are also seen for neighboring galaxies around compact groups. However, compact group member galaxies always have larger early-type galaxy fractions and bluer MIR colors than their neighboring galaxies. Our findings suggest that the properties of compact group galaxies depend on both internal and external environments of compact groups, and that galaxy evolution is faster in compact groups than in the central regions of clusters. Furthermore, our findings suggest that there is a connection between compact group members and their neighboring galaxies, and that neighboring galaxies are sources of compact group members. Third, we present the two-dimensional distribution of stellar population in five E+A galaxies (0.03 kpc scales. In contrast, Pracy et al. (2013) found a central concentration of A-stars and strong negative Balmer absorption line gradients within 1 kpc scales for local (z < 0.04) E+A galaxies. They claimed that previous studies failed to detect the central concentration because the E+A galaxies samples in previous studies are too far (z โˆผ 0.1) to resolve the central kpc scales. To check Pracy et al.s argument and the expectation from simulations, we selected five E+A galaxies at 0.03 < z < 0.05. Thanks to good seeing (โˆผ 0.โ€ฒโ€ฒ8 โ‰ƒ 0.7 kpc) of our observations, we are able to resolve the central 1 kpc region of our targets. We find that all five galaxies have negative Balmer line gradients, but that three galaxies have flatter gradients than those reported in Pracy et al. We discuss the results in relation with galaxy merger history. Finally, we investigate the connection between the presence of bars and AGN activity, using a volume-limited sample of โˆผ9,000 late-type galaxies with axis ratio b/a > 0.6 and Mr < โˆ’19.5 at low redshift (0.02 โ‰ค z โ‰ฒ 0.055), selected from Sloan Digital Sky Survey Data Release 7. We find that the bar fraction in AGN-host galaxies (42.6%) is โˆผ2.5 times higher than in non-AGN galaxies (15.6%), and that the AGN fraction is a factor of two higher in strong-barred galaxies (34.5%) than in non-barred galaxies (15.0%). However, these trends are simply caused by the fact that AGN-host galaxies are on average more massive and redder than non-AGN galaxies because the fraction of strong-barred galaxies (fSB) increases with u โˆ’ r color and stellar velocity dispersion. When u โˆ’ r color and velocity dispersion (or stellar mass) are fixed, both the excess of fSB in AGN-host galaxies and the enhanced AGN fraction in strong-barred galaxies disappears. Among AGN-host galaxies we find no strong difference of the Eddington ratio distributions between barred and non-barred systems. These results indicate that AGN activity is not dominated by the presence of bars, and that AGN power is not enhanced by bars. In conclusion we do not find a clear evidence that bars trigger AGN activity.1 Introduction 1 1.1 Environmental Effects on Galaxy Evolution 1 1.2 Why Do We Need Multi-Wavelength Data? 4 1.3 A Nearby Supercluster of Galaxies, the A2199 Supercluster 6 1.4 The Densest Environment, Compact Groups of Galaxies 8 1.5 Galaxies in Transition, E+A Galaxies 12 1.6 Properties of Barred Galaxies and Environment 15 1.7 Purpose of this Thesis 18 2 Galaxy Evolution in the MIR Green Valley: A Case of the A2199 Supercluster 21 2.1 Introduction 21 2.2 Data 24 2.3 Galaxy Groups and Clusters in the A2199 Supercluster 25 2.4 Integrated SFRs of Galaxy Systems in the A2199 Supercluster 30 2.5 MIR Properties of Galaxies in Galaxy Systems of the A2199 Supercluster 33 2.5.1 Galaxy Classification in the MIR Color-Luminosity Diagram 33 2.5.2 Galaxies in Groups/Clusters and their MIR Classes 41 2.5.3 Environmental Dependence based on ฮฃ5 and R/R200 44 2.6 Discussion 50 2.6.1 Star Formation Activity of Galaxy Systems and their Dependence on Virial Mass 50 2.6.2 Morphology Dependence of the Galaxies in the MIR Green Valley 53 2.7 Summary and Conclusions 58 3 A WISE MIR View on the Highway for Galaxy Evolution in Compact Groups 61 3.1 Introduction 61 3.2 Data 66 3.3 Results 69 3.3.1 Galaxy Color Distributions 69 3.3.2 MIR Color-Luminosity Diagram 77 3.3.3 Environments of Compact Group Galaxies 80 3.4 Discussion 93 3.4.1 Fast Galaxy Evolution in Compact Groups 93 3.4.2 Relation between Compact Group Member Galaxies and Neighboring Galaxies 99 3.4.3 Hydrodynamic Interactions in Compact Groups 101 3.5 Summary and Conclusions 103 4 A GMOS-IFU Spectroscopy of E+A Galaxies: On the Spatial Distribution of Young Stellar Population 107 4.1 Introduction 107 4.2 Observations and Data Reduction 110 4.2.1 TargetSelection 110 4.2.2 Observations 112 4.2.3 DataReduction 114 4.3 Spatial Distribution of the E+A Signatures 115 4.3.1 2D Image Construction from the 3D Datacube 115 4.3.2 Comparison with Pracy et al. (2013)s E+A Sample 120 4.4 Discussion 125 4.5 Summary and Conclusions 129 5 Do Bars Trigger AGN Activity? 131 5.1 Introduction 131 5.2 Data and Methods 134 5.2.1 SDSS Galaxy Sample 134 5.2.2 Classification of Spectral Types 136 5.3 Results 138 5.3.1 Dependence of Bar Fraction on AGN Activity 139 5.3.2 Dependence of AGN Fraction on Bar Presence 145 5.3.3 Comparison of Eddington Ratio between Barred and Non-barred AGN-host Galaxies 151 5.4 Discussion 156 5.4.1 Do AGNs Favor Barred Galaxies? 156 5.4.2 Dependence on the MBHโˆ’ฯƒ Relation 157 5.4.3 What Triggers AGNs? 160 5.5 Summary and Conclusions 163 6 Summary and Conclusions 165 Bibliography 169 ์š”์•ฝ 195Docto

    ์™ธ๋ž˜์–ด ์š”์†Œ๊ฐ€ ํฌํ•จ๋œ ๋‹จ์–ด ํ˜•์„ฑ ์—ฐ๊ตฌ

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    ํ•œ๊ตญ์–ด ์—ฐ์ƒ์–ด ์‚ฌ์ „ ๊ตฌ์ถ•์„ ์œ„ํ•œ ์‹œํ—˜์  ์—ฐ๊ตฌ

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    The aim of this paper is to find a methodology for building a Korean associative thesaurus. Associative words are believed to be a vocabulary which frequently co-occurs on a account of their meaning adjacency. Associative words belong to unconscious implicit human memory. Therefore for identifying the substantiality of associative words, it is said to be more efficient to collect them by corpus based approach which can catch frequently co-occurring words rather than to collect them by questionnaire which asks conscious reflection. On this point of view, this paper attempts to extract associative words from adjacent words to a target word in the corpus. It shows that 75% of associative words collected by questionnaire appear in the adjacent word list, which is made by extracting left and right three words adjacent to a target word. This method also catches associative words extensively, which are not included by questionnaire. It means that corpus based approach to Korean associative thesaurus can be more efficient and more economical. A underlying methodology for building a Korean associative thesaurus is established

    ์€ํ•˜์˜ ๋ฌผ๋ฆฌ์ ์ธ ํŠน์„ฑ๊ณผ ํ™˜๊ฒฝ์  ์š”์ธ์ด ๋งŒ๊ธฐํ˜• ์€ํ•˜์˜ ๋ง‰๋Œ€๊ตฌ์กฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(์ฒœ๋ฌธํ•™์ „๊ณต),2010.2.Maste
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