53 research outputs found

    ๊ทผ์‚ฌ ์—ฐ์‚ฐ์— ๋Œ€ํ•œ ๊ณ„์‚ฐ ๊ฒ€์ฆ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€,2020. 2. ์ฒœ์ •ํฌ.Verifiable Computing (VC) is a complexity-theoretic method to secure the integrity of computations. The need is increasing as more computations are outsourced to untrusted parties, e.g., cloud platforms. Existing techniques, however, have mainly focused on exact computations, but not approximate arithmetic, e.g., floating-point or fixed-point arithmetic. This makes it hard to apply them to certain types of computations (e.g., machine learning, data analysis, and scientific computation) that inherently require approximate arithmetic. In this thesis, we present an efficient interactive proof system for arithmetic circuits with rounding gates that can represent approximate arithmetic. The main idea is to represent the rounding gate into a small sub-circuit, and reuse the machinery of the Goldwasser, Kalai, and Rothblum's protocol (also known as the GKR protocol) and its recent refinements. Specifically, we shift the algebraic structure from a field to a ring to better deal with the notion of ``digits'', and generalize the original GKR protocol over a ring. Then, we represent the rounding operation by a low-degree polynomial over a ring, and develop a novel, optimal circuit construction of an arbitrary polynomial to transform the rounding polynomial to an optimal circuit representation. Moreover, we further optimize the proof generation cost for rounding by employing a Galois ring. We provide experimental results that show the efficiency of our system for approximate arithmetic. For example, our implementation performed two orders of magnitude better than the existing system for a nested 128 x 128 matrix multiplication of depth 12 on the 16-bit fixed-point arithmetic.๊ณ„์‚ฐ๊ฒ€์ฆ ๊ธฐ์ˆ ์€ ๊ณ„์‚ฐ์˜ ๋ฌด๊ฒฐ์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•œ ๊ณ„์‚ฐ ๋ณต์žก๋„ ์ด๋ก ์  ๋ฐฉ๋ฒ•์ด๋‹ค. ์ตœ๊ทผ ๋งŽ์€ ๊ณ„์‚ฐ์ด ํด๋ผ์šฐ๋“œ ํ”Œ๋žซํผ๊ณผ ๊ฐ™์€ ์ œ3์ž์—๊ฒŒ ์™ธ์ฃผ๋จ์— ๋”ฐ๋ผ ๊ทธ ํ•„์š”์„ฑ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ๊ณ„์‚ฐ๊ฒ€์ฆ ๊ธฐ์ˆ ์€ ๋น„๊ทผ์‚ฌ ์—ฐ์‚ฐ๋งŒ์„ ๊ณ ๋ คํ–ˆ์„ ๋ฟ, ๊ทผ์‚ฌ ์—ฐ์‚ฐ (๋ถ€๋™ ์†Œ์ˆ˜์  ๋˜๋Š” ๊ณ ์ • ์†Œ์ˆ˜์  ์—ฐ์‚ฐ)์€ ๊ณ ๋ คํ•˜์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ์งˆ์ ์œผ๋กœ ๊ทผ์‚ฌ ์—ฐ์‚ฐ์ด ํ•„์š”ํ•œ ํŠน์ • ์œ ํ˜•์˜ ๊ณ„์‚ฐ (๊ธฐ๊ณ„ ํ•™์Šต, ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ฐ ๊ณผํ•™ ๊ณ„์‚ฐ ๋“ฑ)์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๋ฐ˜์˜ฌ๋ฆผ ๊ฒŒ์ดํŠธ๋ฅผ ์ˆ˜๋ฐ˜ํ•˜๋Š” ์‚ฐ์ˆ  ํšŒ๋กœ๋ฅผ ์œ„ํ•œ ํšจ์œจ์ ์ธ ๋Œ€ํ™”ํ˜• ์ฆ๋ช… ์‹œ์Šคํ…œ์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฐ์ˆ  ํšŒ๋กœ๋Š” ๊ทผ์‚ฌ ์—ฐ์‚ฐ์„ ํšจ์œจ์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ๊ทผ์‚ฌ ์—ฐ์‚ฐ์— ๋Œ€ํ•œ ํšจ์œจ์ ์ธ ๊ณ„์‚ฐ ๊ฒ€์ฆ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ฃผ์š” ์•„์ด๋””์–ด๋Š” ๋ฐ˜์˜ฌ๋ฆผ ๊ฒŒ์ดํŠธ๋ฅผ ์ž‘์€ ํšŒ๋กœ๋กœ ๋ณ€ํ™˜ํ•œ ํ›„, ์—ฌ๊ธฐ์— Goldwasser, Kalai, ๋ฐ Rothblum์˜ ํ”„๋กœํ† ์ฝœ (GKR ํ”„๋กœํ† ์ฝœ)๊ณผ ์ตœ๊ทผ์˜ ๊ฐœ์„ ์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋Œ€์ˆ˜์  ๊ฐ์ฒด๋ฅผ ์œ ํ•œ์ฒด๊ฐ€ ์•„๋‹Œ ``์ˆซ์ž''๋ฅผ ๋ณด๋‹ค ์ž˜ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ํ™˜์œผ๋กœ ์น˜ํ™˜ํ•œ ํ›„, ํ™˜ ์œ„์—์„œ ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋„๋ก ๊ธฐ์กด์˜ GKR ํ”„๋กœํ† ์ฝœ์„ ์ผ๋ฐ˜ํ™”ํ•˜์˜€๋‹ค. ์ดํ›„, ๋ฐ˜์˜ฌ๋ฆผ ์—ฐ์‚ฐ์„ ํ™˜์—์„œ ์ฐจ์ˆ˜๊ฐ€ ๋‚ฎ์€ ๋‹คํ•ญ์‹์œผ๋กœ ํ‘œํ˜„ํ•˜๊ณ , ๋‹คํ•ญ์‹ ์—ฐ์‚ฐ์„ ์ตœ์ ์˜ ํšŒ๋กœ ํ‘œํ˜„์œผ๋กœ ๋‚˜ํƒ€๋‚ด๋Š” ์ƒˆ๋กญ๊ณ  ์ตœ์ ํ™”๋œ ํšŒ๋กœ ๊ตฌ์„ฑ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ฐˆ๋ฃจ์•„ ํ™˜์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ฐ˜์˜ฌ๋ฆผ์„ ์œ„ํ•œ ์ฆ๋ช… ์ƒ์„ฑ ๋น„์šฉ์„ ๋”์šฑ ์ตœ์ ํ™”ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์‹คํ—˜์„ ํ†ตํ•ด ์šฐ๋ฆฌ์˜ ๊ทผ์‚ฌ ์—ฐ์‚ฐ ๊ฒ€์ฆ ์‹œ์Šคํ…œ์˜ ํšจ์œจ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์šฐ๋ฆฌ์˜ ์‹œ์Šคํ…œ์€ ๊ตฌํ˜„ ์‹œ, 16 ๋น„ํŠธ ๊ณ ์ • ์†Œ์ˆ˜์  ์—ฐ์‚ฐ์„ ํ†ตํ•œ ๊นŠ์ด 12์˜ ๋ฐ˜๋ณต๋œ 128 x 128 ํ–‰๋ ฌ ๊ณฑ์…ˆ์˜ ๊ฒ€์ฆ์— ์žˆ์–ด ๊ธฐ์กด ์‹œ์Šคํ…œ๋ณด๋‹ค ์•ฝ 100๋ฐฐ ๋” ๋‚˜์€ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค.1 Introduction 1 1.1 Verifiable Computing 2 1.2 Verifiable Approximate Arithmetic 3 1.2.1 Problem: Verification of Rounding Arithmetic 3 1.2.2 Motivation: Verifiable Machine Learning (AI) 4 1.3 List of Papers 5 2 Preliminaries 6 2.1 Interactive Proof and Argument 6 2.2 Sum-Check Protocol 7 2.3 The GKR Protocol 10 2.4 Notation and Cost Model 14 3 Related Work 15 3.1 Interactive Proofs 15 3.2 (Non-)Interactive Arguments 17 4 Interactive Proof for Rounding Arithmetic 20 4.1 Overview of Our Approach and Result 20 4.2 Interactive Proof over a Ring 26 4.2.1 Sum-Check Protocol over a Ring 27 4.2.2 The GKR Protocol over a Ring 29 4.3 Verifiable Rounding Operation 31 4.3.1 Lowest-Digit-Removal Polynomial over Z_{p^e} 32 4.3.2 Verification of Division-by-p Layer 33 4.4 Delegation of Polynomial Evaluation in Optimal Cost 34 4.4.1 Overview of Our Circuit Construction 35 4.4.2 Our Circuit for Polynomial Evaluation 37 4.4.3 Cost Analysis 40 4.5 Cost Optimization 45 4.5.1 Galois Ring over Z_{p^e} and a Sampling Set 45 4.5.2 Optimization of Prover's Cost for Rounding Layers 47 5 Experimental Results 50 5.1 Experimental Setup 50 5.2 Verifiable Rounding Operation 51 5.2.1 Effectiveness of Optimization via Galois Ring 51 5.2.2 Efficiency of Verifiable Rounding Operation 53 5.3 Comparison to Thaler's Refinement of GKR Protocol 54 5.4 Discussion 57 6 Conclusions 60 6.1 Towards Verifiable AI 61 6.2 Verifiable Cryptographic Computation 62 Abstract (in Korean) 74Docto

    ๋น™ํ•˜ํ›„ํ‡ด์— ๋”ฐ๋ฅธ ์„œ๋‚จ๊ทน๋ฐ˜๋„ ํ‚น์กฐ์ง€์„ฌ ๋งˆ๋ฆฌ์•ˆ์†Œ๋งŒ์˜ ์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ ๋ณ€ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2022. 8. ๊น€์ข…์„ฑ.๋น™ํ•˜ํ›„ํ‡ด๋Š” ํ‡ด์ ์ž‘์šฉ์ด๋‚˜ ์ž…๋„, ๋ฌผ๋ฆฌ์  ๊ต๋ž€๊ณผ ๊ฐ™์€ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๋ฅผ ์œ ๋ฐœํ•˜์—ฌ ๊ทน์ง€ ์—ฐ์•ˆ์˜ ์ €์„œ ๋ฌด์ฒ™์ถ”๋™๋ฌผ๋“ค์—๊ฒŒ ๊ฐ•ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋‚จ๊ทน์˜ ์ฒœํ•ด์—์„œ ์‹ฌํ•ด์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๋„๋ฆฌ ๋ถ„ํฌํ•˜๊ณ  ์žˆ๋Š” ์ €์„œ๋™๋ฌผ๋“ค์€ ์œก์ƒ์˜ ๋Œ€๋ถ€๋ถ„์ด ๋น™ํ•˜๋กœ ๋’ค๋ฎ์ธ ๊ทน์ง€์—์„œ ์ €์„œ ์ƒํƒœ๊ณ„์˜ ์ค‘์š”์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค. ํ•˜์ง€๋งŒ ์˜จ๋‚œํ™”๋กœ ์ธํ•œ ๋น™ํ•˜ํ›„ํ‡ด์— ๋Œ€ํ•œ ์ €์„œ ์ƒํƒœ๊ณ„์˜ ๋ฐ˜์‘๊ณผ ๋ณ€ํ™”์— ๋Œ€ํ•ด์„œ๋Š” ๋งŽ์€ ๊ฒƒ์ด ์•Œ๋ ค์ง€์ง€ ์•Š์•˜๋‹ค. ์ด๋ฅผ ๋ฐํžˆ๊ธฐ ์œ„ํ•ด ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€ ์งˆ๋ฌธ์— ๋‹ตํ•˜๊ณ ์ž ๋น™ํ•˜๊ฐ€ ํ›„ํ‡ดํ•œ ๋‚จ๊ทน ์—ฐ์•ˆ ํ”ผ์š”๋ฅด๋“œ๋งŒ์—์„œ ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘์˜ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. 1) ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘์€ ๋น™ํ•˜ํ›„ํ‡ด ์ดํ›„์— ์–ด๋–ป๊ฒŒ ๋ณ€ํ•˜๋Š”๊ฐ€? 2) ๋‚จ๊ทน ๋น™ํ•˜ํ›„ํ‡ด ์ง€์—ญ์˜ ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๋ถ„ํฌ๋ฅผ ์ง€ํ‘œํ•˜๋Š” ๋ถ„๋ฅ˜๊ตฐ ๋ฐ ๊ด€๋ จ ํ™˜๊ฒฝ ์š”์ธ์€ ๋ฌด์—‡์ธ๊ฐ€? 3) ๋‚จ๊ทน์˜ ์šฐ์  ๋ถ„๋ฅ˜๊ตฐ ์ค‘ ํ•˜๋‚˜์ธ ๋ฉ๊ฒŒ์˜ ์„ญ์‹ ์–‘์ƒ์€ ๋น™ํ•˜์˜ ์˜ํ–ฅ์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๋Š”๊ฐ€? ๋น™ํ•˜๊ฐ€ ํ›„ํ‡ดํ•œ ๋‚จ๊ทน ํ”ผ์š”๋ฅด๋“œ๋งŒ์˜ ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘ ๋ถ„ํฌ์™€ ์„ญ์‹ ์–‘์ƒ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ๋งˆ๋ฆฌ์•ˆ์†Œ๋งŒ์—์„œ ์ฒ˜์Œ์œผ๋กœ ์ˆ˜์ค‘ ROV ์˜์ƒ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋ฉ๊ฒŒ ์„ญ์‹ ์–‘์ƒ์€ ํƒ„์†Œ ๋ฐ ์งˆ์†Œ ๋™์œ„์›์†Œ ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ๋น™ํ•˜ํ›„ํ‡ด ์ง€์—ญ์—์„œ ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘์˜ ๊ตฌ์กฐ์ , ๊ธฐ๋Šฅ์  ๋‹ค์–‘์„ฑ์€ ๊ณต๊ฐ„์— ๋”ฐ๋ผ ๋šœ๋ ทํ•˜๊ฒŒ ๋‹ฌ๋ž๋‹ค. ์ข… ๋‹ค์–‘์„ฑ์€ ๋น™ํ•˜์˜ ์˜ํ–ฅ์ด ๊ฐ์†Œํ•˜๋Š” ๋งŒ์˜ ์™ธ์ธก์œผ๋กœ ๊ฐˆ์ˆ˜๋ก ์ฆ๊ฐ€ํ–ˆ์œผ๋‚˜ ์„œ์‹๋ฐ€๋„๋Š” ๊ฐœ์ฒ™์ข…๋“ค(Molgula pedunculata ๋ฐ Cnemidocarpa verrucosa)์˜ ๊ธ‰๊ฒฉํ•œ ์ฆ๊ฐ€๋กœ ๋น™๋ฒฝ ์ธ๊ทผ์—์„œ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ์ €์„œ ๊ตฐ์ง‘์€ ๋น™ํ•˜ํ›„ํ‡ด ์ดํ›„์— ์ƒ์œ„๋ถ„๋ฅ˜๋‹จ๊ณ„ ์ˆ˜์ค€์—์„œ๋Š” ๋น ๋ฅด๊ฒŒ ์„ฑ์ˆ™๋˜์—ˆ๋‹ค(์•ฝ 10๋…„ ์ด๋‚ด). ๋ฐ˜๋ฉด, ๊ธฐ๋Šฅ์  ๋‹ค์–‘์„ฑ์€ ์™ธ์ธก์œผ๋กœ ๊ฐˆ์ˆ˜๋ก ์ฆ๊ฐ€ํ–ˆ์œผ๋ฉฐ, ๋น„๊ต์  ๋ฌผ๋ฆฌ์  ๊ต๋ž€์ด ์ ๊ณ  ๋จน์ด ๊ณต๊ธ‰์€ ํ’๋ถ€ํ•œ 30 m์—์„œ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋‚จ๊ทน ์—ฐ์•ˆ์—์„œ ๋น™ํ•˜ํ›„ํ‡ด ์ดํ›„์˜ 3๋‹จ๊ณ„ ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘ ๋ณ€ํ™” ๊ณผ์ •(๊ฐ€์ž…, ์ „ํ™˜, ์„ฑ์ˆ™)์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋ฉ๊ฒŒ ๋ถ„ํฌ๋Š” ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๋ณ€ํ™”์˜ 64%๋ฅผ ๋ฐ˜์˜ํ•˜์—ฌ ๋‚จ๊ทน ๋น™ํ•˜ํ›„ํ‡ด์— ๋Œ€ํ•œ ์ €์„œ ์ƒํƒœ๊ณ„ ๋ฐ˜์‘์„ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ธฐ ์œ„ํ•œ ์ง€ํ‘œ๊ตฐ์œผ๋กœ ์ ํ•ฉํ–ˆ๋‹ค. ๋ฉ๊ฒŒ์˜ ๊ณต๊ฐ„๋ถ„ํฌ๋Š” ๋น™ํ•˜๋กœ๋ถ€ํ„ฐ์˜ ๊ฑฐ๋ฆฌ์™€ ์ˆ˜์‹ฌ์— ๋”ฐ๋ผ ๋šœ๋ ทํ•˜๊ฒŒ ๋‹ฌ๋ž๋‹ค. ์„œ์‹๋ฐ€๋„๋Š” ๊ฐœ์ฒ™์ข…๋“ค(M. pedunculata ๋ฐ C. verrucosa)์˜ ๊ธ‰๊ฒฉํ•œ ์ฆ๊ฐ€๋กœ ๋น™๋ฒฝ ์ธ๊ทผ์—์„œ ์ตœ๋Œ€์˜€์œผ๋‚˜ ์ข…๋‹ค์–‘์„ฑ์€ ๋น™ํ•˜์˜ ์˜ํ–ฅ์ด ๊ฐ์†Œํ•˜๋Š” ์™ธ์ธก์œผ๋กœ ๊ฐˆ์ˆ˜๋ก ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ด๋Ÿฐ ๊ณต๊ฐ„๋ถ„ํฌ ์–‘์ƒ์€ ๋น„๊ต์  ๊ต๋ž€์ด ์‹ฌํ•œ ์–•์€ ์ˆ˜์‹ฌ(10โ€“30 m)์—์„œ๋Š” ๋‘๋“œ๋Ÿฌ์ง€์ง€ ์•Š์•˜๋‹ค. ๋ฌผ๋ฆฌ์  ๊ต๋ž€ ์ •๋„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ํ‡ด์ ๋ฌผ ํŠน์„ฑ๊ณผ ๋น™๋ฒฝ์œผ๋กœ๋ถ€ํ„ฐ์˜ ๊ฑฐ๋ฆฌ๊ฐ€ ๋ฉ๊ฒŒ ๋ถ„ํฌ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ์ฃผ์š”์ธ์ด์—ˆ๋‹ค. ฮด13C์™€ ฮด15N ๋ถ„์„์€ ๋‚จ๊ทน ์—ฐ์•ˆ์—์„œ ๋น™ํ•˜์˜ ์˜ํ–ฅ์— ๋”ฐ๋ฅธ ๋ฉ๊ฒŒ ์šฐ์ ์ข… 3๊ฐœ์˜ ์„ญ์‹ ์–‘์ƒ ๋ณ€ํ™”๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋งˆ๋ฆฌ์•ˆ์†Œ๋งŒ์—์„œ ๋ฉ๊ฒŒ์˜ ์ฃผ์š” ๋จน์ด๋Š” ์ €์„œ๊ทœ์กฐ๋ฅ˜์˜€์œผ๋ฉฐ(30โ€“70%), ๋ฉ๊ฒŒ ๋จน์ด์— ๋Œ€ํ•œ ์ €์„œ๊ทœ์กฐ๋ฅ˜์˜ ๊ธฐ์—ฌ๋„๋Š” ์ˆ˜์‹ฌ 100 m๊นŒ์ง€ ์œ ํšจํ–ˆ๋‹ค. ๋ฉ๊ฒŒ์˜ ์„ญ์‹์€ ์ข…์— ๋”ฐ๋ผ ๋‹ฌ๋ž๋‹ค. ๋น„์„ ํƒ์  ์„ญ์‹์„ ํ•˜๋Š” ๊ธฐ๋‘ฅํ˜• ๋ฉ๊ฒŒ์ธ M. pedunculata์˜ ๋จน์ด ๋Œ€ํ•œ ์ˆ˜์ธต ์ƒ์‚ฐ์˜ ๊ธฐ์—ฌ๋Š” ์‹๋ฌผํ”Œ๋ž‘ํฌํ†ค์ด ํ’๋ถ€ํ•œ ์™ธ์ธก์œผ๋กœ ๊ฐˆ์ˆ˜๋ก ์ฆ๊ฐ€ํ–ˆ๋‹ค. ํ•˜์ง€๋งŒ ์—ฌ์ „ํžˆ ์ €์„œ๊ทœ์กฐ๋ฅ˜๊ฐ€ ์ฃผ๋œ ๋จน์ด์› ์ค‘ ํ•˜๋‚˜์˜€๋‹ค. ๋ฐ˜๋ฉด, ๋ถ„์ถœ ๊ธฐ์ž‘์„ ๊ฐ€์ง„ C. verrucosa์™€ ๋‚ฉ์ž‘ํ•œ ํ˜•ํƒœ์ธ Ascidia challengeri๋Š” ๋น™ํ•˜์˜ ์˜ํ–ฅ๊ณผ ๋ฌด๊ด€ํ•˜๊ฒŒ ์ €์„œ๊ทœ์กฐ๋ฅ˜๋ฅผ ์ฃผ๋กœ ์„ญ์‹ํ–ˆ๋‹ค. ์ด ๊ฒฐ๊ณผ๋“ค์€ ๋‚จ๊ทน ์—ฐ์•ˆ์—์„œ ์ €์„œ๊ทœ์กฐ๋ฅ˜๊ฐ€ ์—ฌ๊ณผ์„ญ์‹์ž๋“ค์˜ ์ฃผ๋œ ๋จน์ด์›์ด๋ฉฐ, ํŠนํžˆ ๋น™ํ•˜ํ›„ํ‡ด๋กœ ์ธํ•œ ๋†’์€ ํƒ๋„๋กœ ์ˆ˜์ธต ์ƒ์‚ฐ๋ ฅ์ด ๋‚ฎ์€ ์ง€์—ญ์—์„œ ํ•ต์‹ฌ ๋จน์ด์›์ž„์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น™ํ•˜ํ›„ํ‡ด์— ๋Œ€ํ•œ ๋‚จ๊ทน ์ €์„œ์ƒํƒœ๊ณ„์˜ ๋ฐ˜์‘์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋งˆ๋ฆฌ์•ˆ์†Œ๋งŒ ๋น™ํ•˜๋กœ๋ถ€ํ„ฐ์˜ ๊ฑฐ๋ฆฌ๋Š” ํ•ด์ €๊ฐ€ ๋…ธ์ถœ๋œ ์‹œ๊ฐ„๊ณผ ๋น„๋ก€ํ•˜์—ฌ, ๊ฑฐ๋Œ€์ €์„œ๋™๋ฌผ ๊ตฐ์ง‘์˜ ๊ณต๊ฐ„ ๋ถ„ํฌ๋กœ๋ถ€ํ„ฐ ๋น™ํ•˜ ํ›„ํ‡ด ์ดํ›„์— ๋ฐœ์ƒํ•œ ๊ตฐ์ง‘์˜ ์ฒœ์ด ๊ณผ์ •์„ ์œ ์ถ”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋‚จ๊ทน ํ•ด์–‘ ์ƒํƒœ๊ณ„์˜ ๋ณ€ํ™”๋ฅผ ์˜ˆ์ธกํ•˜๊ณ  ๋Œ€๋น„ํ•˜๊ธฐ ์œ„ํ•œ ํ† ๋Œ€๋ฅผ ์ œ๊ณตํ•œ๋‹ค.On polar coasts, glacial retreat strongly affects benthic invertebrates by oceanographic settings, including physical disturbance, habitat alteration, and food availability. Benthic organisms widely distributed from shallow to deep seabeds of the Antarctic represent an important polar benthic ecosystem, but ecological processes and the impacts of recent glacial retreat remain unanswered. The community structure and function of benthic megafauna were investigated in a glacial retreated fjord in Antarctica to answer the following questions: 1) how do the benthic megafauna communities shift after glacial retreat? 2) what are the sentinel taxa and environmental factors for benthic megafauna distribution in the deglaciated Antarctic nearshore? 3) do the diets of ascidians differ under the influence of the glaciers? To confirm the distributions of the benthic megafauna community in a deglaciated fjord of Antarctica, underwater imagery survey using a ROV was conducted for the first time in Marian Cove (MC). The diets of ascidians were determined from C and N stable isotope analyses In the glacial retreated fjord, the structural and functional diversities of benthic megafauna communities varied greatly in space. Species diversity increased towards the outer sites where the glacial influence decreased, but the density peaked near the glaciers by the rapid increase of pioneer species (Molgula pedunculata and Cnemidocarpa verrucosa). Benthic communities matured rapidly at higher taxonomic levels after the glacial retreat (~10 years after seabed exposure). Functional diversity, on the other hand, increased toward the outside of the cove and peaked at 30 m as a result of a lesser disturbance and more food-availability. This study showed that three stages (colonization, transition, and maturing) represent the shift process of the benthic megafauna community after the glacial retreat in the Antarctic nearshore. The spatial distribution of ascidians explaining 64% of the benthic megafaunal variations indicates that ascidians are suitable indicator taxa for monitoring the responses of the benthic ecosystem to the glacial retreat in Antarctica. The spatial distribution of ascidians was significantly changed with the distance from the glacier and water depth. The density peaked near the glacier by a rapid increase of pioneer species (M. pedunculata and C. verrucosa), but diversity increased toward the outer site where the glacial influence decreased. The spatial pattern was not distinct at shallow depths (10 to 30 m) which had relatively severe disturbances. Sediment properties and distance from the glacier indicating the physical disturbance level by the deglaciation were key factors determining the ascidian distributions. ฮด13C and ฮด15N analysis showed changes in the diets of the three dominant ascidians according to the effects of the glaciers in the Antarctic nearshore. Benthic diatoms were the primary food (30โ€“70%) for the ascidians in MC, and their contribution to the diets of the ascidians was significant up to 100 m. The diets of the ascidians differed depending on the species. The contribution of pelagic production to M. pedunculata with non-selective feeding and cylindrical body form increased toward the outer site abundant in phytoplanktons, but benthic diatoms were still one of the major food sources. On the other hand, benthic diatoms were the major food for C. verrucosa, which had a squirting behavior, and Ascidia challengeri, which had a laterally flat body, regardless of the influence of the glaciers. These results indicate that benthic diatoms were the primary food for filter feeders in the Antarctic nearshore, and their contribution was particularly high in areas with low pelagic production due to high turbidity by the glacial retreats. Overall, the present study provides information on benthic ecosystem responses to glacial retreats in Antarctica. Given that the distance from the glacier was proportional to the seabed exposure time in MC, the spatial variation in the benthic megafauna community across the cove indicates the successional processes that occurred in the past after the glacial retreats. Therefore, this study provides a basis for predicting and preparing for changes in the Antarctic marine ecosystem caused by climate change.CHAPTER. 1. Introduction 1 1.1. Backgrounds 2 1.2. Objectives 10 CHAPTER. 2. Shifts in benthic megafauna communities after glacial retreat in an Antarctic fjord 13 2.1. Introduction 14 2.2. Materials and methods 16 2.2.1. Study area 16 2.2.2. ROV data acquisition 22 2.2.3. Megafaunal community analysis 24 2.2.4. Taxonomic and functional diversities 25 2.2.5. Statistical analysis 38 2.3. Results 39 2.3.1. Assemblages of benthic megafauna 39 2.3.2. Distribution characteristics of benthic megafauna 42 2.3.3. Relationship between the benthic megafauna community and environmental parameters 46 2.4. Discussion 48 2.4.1. Spatial variation in the structure of the benthic megafauna community 48 2.4.2. Changes to the functional diversity of the benthic megafauna community in the deglaciated fjord 55 2.4.3. Impact of glacial retreat on the benthic community 58 2.5. Conclusions 63 CHAPTER. 3. Patterns, drivers and implications of ascidian distributions in a rapidly deglaciating fjord, King George Island, West Antarctic Peninsula 64 3.1. Introduction 65 3.2. Materials and methods 68 3.2.1. Study area 68 3.2.2. ROV survey and sampling 70 3.2.3. ROV images and sample analysis 87 3.2.4. Statistical analyses 90 3.2.5. Supporting datasets 91 3.3. Results 92 3.3.1. Environmental characteristics of the study area 92 3.3.2. Ascidian contribution to the spatial variations of total epibenthic megafauna 99 3.3.3. Spatial patterns of ascidian distribution 104 3.3.3.1 Abundance, species composition and diversity 104 3.3.3.2 Differences in body size among stations 112 3.3.4. Relationship between ascidian assemblages and environmental parameters 114 3.4. Discussion 116 3.4.1. Ascidians as a key megabenthic community in an Antarctic fjord 116 3.4.2. Ascidian assemblages in the ice-proximal zone 118 3.4.3. Physical disturbance structuring ascidian communities in shallow habitats 120 3.4.4. Successional shifts of ascidian communities in deep habitats 125 3.5. Conclusions 129 CHAPTER. 4. Changes in ascidian diets under the influence of glacial retreat in a fjord, Antarctic nearshore 130 4.1. Introduction 131 4.2. Materials and methods 133 4.2.1. Study area 133 4.2.2. Potential food sources selection 136 4.2.3. Sample collection 138 4.2.4. Stable isotope analysis 139 4.2.5. Data analysis and statistics 140 4.3. Results 141 4.3.1. Stable isotope signatures of ascidians and potential food sources 141 4.3.2. Variations of relative contributions of the potential food sources to the diets of the ascidians among the stations and the species 144 4.4. Discussion 146 4.4.1. Diet change of ascidians 146 4.4.2. Effects of glacial retreat on food sources and distribution of ascidians 149 4.5. Conclusions 152 CHAPTER. 5. Conclusions 153 5.1. Summary 154 5.2. Environmental implications and limitations 158 5.3. Future research directions 160 BIBLIOGRAPHY 162 ABSTRACT (IN KOREAN) 175 APPENDIX 177๋ฐ•

    ์‚ฌ๋ฌผ ํ†ต์‹ ์—์„œ ๋”ฅ๋Ÿฌ๋‹์„ ์ด์šฉํ•œ ํ™•์‚ฐ์ฝ”๋“œ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2019. 8. ์ด๊ด‘๋ณต.Massive machine-type communications (mMTC) have been drawing a lot of attentions because the number of MTC devices is expected to be increasing in the next generation (5G) communication systems with a variety of Internet-of-Things (IoT) applications. For effective uplink transmission in the mMTC, the grant-free non-orthogonal multiple access (NOMA) scheme has been a promising solution to overcome high signaling overhead and latency problems. Due to instant transmissions, active user detection (AUD) is an important task for grant-free NOMA. In the transmitter, data symbols are spread by user-specific spreading sequences. However, the most research papers have focused on designing the effective detection algorithms, but not given much attention to the transmitter design. In this dissertation, the generation of spreading sequences via deep learning is proposed. With sufficient training data, the proposed spreading sequences show the close performance to the mathematically optimized sequences. In particular, we show the capabilities of learning sequences by demonstrating that learned sequences can have different cross-correlations depending on the activity probability of each user.5์„ธ๋Œ€ ์ด๋™ํ†ต์‹ ์—์„œ ์‚ฌ๋ฌผ ํ†ต์‹ ๊ธฐ๊ธฐ๋“ค์˜ ์ˆ˜๊ฐ€ ํญ๋ฐœ์ ์œผ๋กœ ์ฆ๊ฐ€ํ• ๊ฒƒ์ด๋ผ ์˜ˆ์ƒ๋˜๋ฉด์„œ, ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹ (massive machine-type communications, mMTC)์€ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ ์žˆ๋‹ค. ํšจ๊ณผ์ ์ธ ์ƒํ–ฅ๋งํฌ๋ฅผ ์œ„ํ•ด์„œ ์ตœ๊ทผ ๋ฌดํ—ˆ๊ฐ€ ๋ฐฉ์‹์˜ ๋น„์ง๊ต ๋‹ค์ค‘์ ‘์†(non-orthogonal multiple access, NOMA) ๊ธฐ์ˆ ์ด ๋†’์€ ์‹ ํ˜ธ ์˜ค๋ฒ„ํ—ค๋“œ์™€ ์ง€์—ฐ ์‹œ๊ฐ„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋Œ€์•ˆ์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋ฌดํ—ˆ๊ฐ€ ๋น„์ง๊ต ๋‹ค์ค‘์ ‘์†์—์„œ๋Š” ์Šค์ผ€์ฅด๋ง ์—†์ด ์ฆ‰๊ฐ์ ์ธ ์ „์†ก์ด ์ด๋ฃจ์–ด์ง€๊ธฐ ๋•Œ๋ฌธ์— ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ(active user detection, AUD)์ด ์ค‘์š”ํ•œ ๋ฌธ์ œ๊ฐ€ ๋œ๋‹ค. ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹ ์˜ ์ƒํ–ฅ๋งํฌ์—์„œ ์†ก์‹ ๊ธฐ๋Š” ๋ฐ์ดํ„ฐ ์‹ฌ๋ณผ์— ๊ธฐ๊ธฐ๋งˆ๋‹ค ๋‹ค๋ฅธ ํ™•์‚ฐ ์ฝ”๋“œ(spreading sequence)๋ฅผ ์ด์šฉํ•ด ๋ฐ์ดํ„ฐ๋ฅผ ํ™•์‚ฐํ•ด์„œ ๋ณด๋‚ธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€๋ถ€๋ถ„์˜ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ˆ˜์‹ ๊ธฐ์˜ ๊ฒ€์ถœ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์—ฐ๊ตฌ์— ์น˜์šฐ์ณ์ ธ ์žˆ๊ณ  ์†ก์‹ ๊ธฐ์—์„œ ์–ด๋– ํ•œ ํ™•์‚ฐ ์ฝ”๋“œ๋ฅผ ์„ค๊ณ„ํ•ด์„œ ๋ณด๋‚ด์•ผ ํ•˜๋Š”์ง€๋Š” ๋ฏธํกํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋”ฅ๋Ÿฌ๋‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ™•์‚ฐ ์ฝ”๋“œ๋ฅผ ์„ค๊ณ„ํ•˜๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ถฉ๋ถ„ํ•œ ํ•™์Šต ๋ฐ์ดํ„ฐ์„ ์ด์šฉํ•ด์„œ ํ•™์Šต๋œ ํ™•์‚ฐ ์ฝ”๋“œ๋Š” ์ˆ˜ํ•™์ ์œผ๋กœ ์ƒ๊ด€ ๊ด€๊ณ„๊ฐ€ ์ตœ์ ํ™”๋œ ํ™•์‚ฐ ์ฝ”๋“œ์™€ ๋น„์Šทํ•œ ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํŠนํžˆ ๊ธฐ๊ธฐ๋“ค์ด ์„œ๋กœ ๋‹ค๋ฅธ ํ™œ์„ฑ ํ™•๋ฅ ์„ ๊ฐ€์ง€๋Š” ํ™˜๊ฒฝ์—์„œ๋Š” ํ™•์‚ฐ ์ฝ”๋“œ๊ฐ€ ํ™œ์„ฑ ํ™•๋ฅ ์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ค๋ฅธ ์ƒํ˜ธ์ƒ๊ด€๊ด€๊ณ„์„ ๊ฐ€์ง€๋„๋ก ํ•™์Šต๋˜๊ณ , ๋†’์€ SNR์—์„œ ์•ฝ 1.4๋ฐฐ์—์„œ 2๋ฐฐ์˜ ์„ฑ๋Šฅ์˜ ํ–ฅ์ƒ์„ ๋ณด์—ฌ์ค€๋‹ค.1 Introduction 1 2 System Model 4 3 Design of Spreading Sequences using Deep Learning 6 3.1 Entire Network Structure of the System 6 3.2 Spreading Layer 7 3.3 Active User Detection Layer 7 4 Simulation Results 12 4.1 Simulation Setup 12 4.2 Simulation Results and Interpretation 13 5 Conclusion 19 Abstract (In Korean) 22 Acknowlegement 23Maste

    ์‹ค์‹œ๊ฐ„ ๊ทผ๊ฑฐ๋ฆฌ ์˜์ƒํ™”๋ฅผ ์œ„ํ•œ MIMO ์—ญํ•ฉ์„ฑ ๊ฐœ๊ตฌ ๋ ˆ์ด๋” ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022. 8. ๋‚จ์ƒ์šฑ.Microwave and millimeter wave (micro/mmW) imaging systems have advantages over other imaging systems in that they have penetration properties over non-metallic structures and non-ionization. However, these systems are commercially applicable in limited areas. Depending on the quality and size of the images, a system can be expensive and images cannot be provided in real-time. To overcome the challenges of the current micro/mmW imaging system, it is critical to suggest a new system concept and prove its potential benefits and hazards by demonstrating the testbed. This dissertation presents Ku1DMIC, a wide-band micro/mmW imaging system using Ku-band and 1D-MIMO array, which can overcome the challenges above. For cost-effective 3D imaging capabilities, Ku1DMIC uses 1D-MIMO array configuration and inverse synthetic aperture radar (ISAR) technique. At the same time, Ku1DMIC supports real-time data acquisition through a system-level design of a seamless interface with frequency modulated continuous wave (FMCW) radar. To show the feasibility of 3D imaging with Ku1DMIC and its real-time capabilities, an accelerated imaging algorithm, 1D-MIMO-ISAR RSA, is proposed and demonstrated. The detailed contributions of the dissertation are as follows. First, this dissertation presents Ku1DMIC โ€“ a Ku-band MIMO frequency-modulated continuous-wave (FMCW) radar experimental platform with real-time 2D near-field imaging capabilities. The proposed system uses Ku-band to cover the wider illumination area given the limited number of antennas and uses a fast ramp and wide-band FMCW waveform for rapid radar data acquisition while providing high-resolution images. The key design aspect behind the platform is stability, reconfigurability, and real-time capabilities, which allows investigating the exploration of the systemโ€™s strengths and weaknesses. To satisfy the design aspect, a digitally assisted platform is proposed and realized based on an AMD-Xilinx UltraScale+ Radio Frequency System on Chip (RFSoC). The experimental investigation for real-time 2D imaging has proved the ability of video-rate imaging at around 60 frames per second. Second, a waveform digital pre-distortion (DPD) method and calibration method are proposed to enhance the image quality. Even if a clean FMCW waveform is generated with the aid of the optimized waveform generator, the signal will inevitably suffer from distortion, especially in the RF subsystem of the platform. In near-field imaging applications, the waveform DPD is not effective at suppressing distortion in wide-band FMCW radar systems. To solve this issue, the LO-DPD architecture and binary search based DPD algorithm are proposed to make the waveform DPD effective in Ku1DMIC. Furthermore, an image-domain optimization correction method is proposed to compensate for the remaining errors that cannot be eliminated by the waveform DPD. For robustness to various unwanted signals such as noise and clutter signals, two regularized least squares problems are applied and compared: the generalized Tikhonov regularization and the total variation (TV) regularization. Through various 2D imaging experiments, it is confirmed that both methods can enhance the image quality by reducing the sidelobe level. Lastly, the research is conducted to realize real-time 3D imaging by applying the ISAR technique to Ku1DMIC. The realization of real-time 3D imaging using 1D-MIMO array configuration is impactful in that this configuration can significantly reduce the costs of the 3D imaging system and enable imaging of moving objects. To this end, the signal model for the 1D-MIMO-ISAR configuration is presented, and then the 1D-MIMO-ISAR range stacking algorithm (RSA) is proposed to accelerate the imaging reconstruction process. The proposed 1D-MIMO-ISAR RSA can reconstruct images within hundreds of milliseconds while maintaining almost the same image quality as the back-projection algorithm, bringing potential use for real-time 3D imaging. It also describes strategies for setting ROI, considering the real-world situations in which objects enter and exit the field of view, and allocating GPU memory. Extensive simulations and experiments have demonstrated the feasibility and potential benefits of 1D-MIMO-IASR configuration and 1D-MIMO-ISAR RSA.๋งˆ์ดํฌ๋กœํŒŒ ๋ฐ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ(micro/mmW) ์˜์ƒํ™” ์‹œ์Šคํ…œ์€ ๋น„๊ธˆ์† ๊ตฌ์กฐ ๋ฐ ๋น„์ด์˜จํ™”์— ๋น„ํ•ด ์นจํˆฌ ํŠน์„ฑ์ด ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋‹ค๋ฅธ ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์— ๋น„ํ•ด ์žฅ์ ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์€ ์ œํ•œ๋œ ์˜์—ญ์—์„œ๋งŒ ์ƒ์—…์ ์œผ๋กœ ์ ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋ฏธ์ง€์˜ ํ’ˆ์งˆ๊ณผ ํฌ๊ธฐ์— ๋”ฐ๋ผ ์‹œ์Šคํ…œ์ด ๋งค์šฐ ๊ณ ๊ฐ€์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด๋ฏธ์ง€๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ œ๊ณตํ•  ์ˆ˜ ์—†๋Š” ํ˜„ํ™ฉ์ด๋‹ค. ํ˜„์žฌ์˜ micro/mmW ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์˜ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๋ ค๋ฉด ์ƒˆ๋กœ์šด ์‹œ์Šคํ…œ ๊ฐœ๋…์„ ์ œ์•ˆํ•˜๊ณ  ํ…Œ์ŠคํŠธ๋ฒ ๋“œ๋ฅผ ์‹œ์—ฐํ•˜์—ฌ ์ž ์žฌ์ ์ธ ์ด์ ๊ณผ ์œ„ํ—˜์„ ์ž…์ฆํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Ku-band์™€ 1D-MIMO ์–ด๋ ˆ์ด๋ฅผ ์ด์šฉํ•œ ๊ด‘๋Œ€์—ญ micro/mmW ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์ธ Ku1DMIC๋ฅผ ์ œ์•ˆํ•˜์—ฌ ์œ„์™€ ๊ฐ™์€ ๋ฌธ์ œ์ ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ๋น„์šฉ ํšจ์œจ์ ์ธ 3์ฐจ์› ์˜์ƒํ™” ๊ธฐ๋Šฅ์„ ์œ„ํ•ด Ku1DMIC๋Š” 1D-MIMO ๋ฐฐ์—ด ๊ธฐ์ˆ ๊ณผ ISAR(Inverse Synthetic Aperture Radar) ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋™์‹œ์— Ku1DMIC๋Š” ์ฃผํŒŒ์ˆ˜ ๋ณ€์กฐ ์—ฐ์†ํŒŒ (FMCW) ๋ ˆ์ด๋”์™€์˜ ์›ํ™œํ•œ ์ธํ„ฐํŽ˜์ด์Šค์˜ ์‹œ์Šคํ…œ ์ˆ˜์ค€ ์„ค๊ณ„๋ฅผ ํ†ตํ•ด ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์„ ์ง€์›ํ•œ๋‹ค. Ku1DMIC๋ฅผ ์‚ฌ์šฉํ•œ 3์ฐจ์› ์˜์ƒํ™”์˜ ๊ตฌํ˜„ ๋ฐ ์‹ค์‹œ๊ฐ„ ๊ธฐ๋Šฅ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ๊ธฐ ์œ„ํ•ด, 2์ฐจ์› ์˜์ƒํ™”๋ฅผ ์œ„ํ•œ 1D-MIMO RSA๊ณผ 3์ฐจ์› ์˜์ƒํ™”๋ฅผ ์œ„ํ•œ 1D-MIMO-ISAR RSA๊ฐ€ ์ œ์•ˆ๋˜๊ณ  Ku1DMIC์—์„œ ๊ตฌํ˜„๋œ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๊ธฐ์—ฌ๋Š” Ku-band 1D-MIMO ๋ฐฐ์—ด ๊ธฐ๋ฐ˜ ์˜์ƒํ™” ์‹œ์Šคํ…œ ํ”„๋กœํ† ํƒ€์ž…์„ ๊ฐœ๋ฐœ ๋ฐ ํ…Œ์ŠคํŠธํ•˜๊ณ , ISAR ๊ธฐ๋ฐ˜ 3์ฐจ์› ์˜์ƒํ™” ๊ธฐ๋Šฅ์„ ๊ฒ€์‚ฌํ•˜๊ณ , ์‹ค์‹œ๊ฐ„ 3์ฐจ์› ์˜์ƒํ™” ๊ฐ€๋Šฅ์„ฑ์„ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด์— ๋Œ€ํ•œ ์„ธ๋ถ€์ ์ธ ๊ธฐ์—ฌ ํ•ญ๋ชฉ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์‹ค์‹œ๊ฐ„ 2D ๊ทผ๊ฑฐ๋ฆฌ์žฅ ์ด๋ฏธ์ง• ๊ธฐ๋Šฅ์„ ๊ฐ–์ถ˜ Ku ๋Œ€์—ญ MIMO ์ฃผํŒŒ์ˆ˜ ๋ณ€์กฐ ์—ฐ์†ํŒŒ(FMCW) ๋ ˆ์ด๋” ์‹คํ—˜ ํ”Œ๋žซํผ์ธ Ku1DMIC๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์‹œ์Šคํ…œ์€ ์ œํ•œ๋œ ์ˆ˜์˜ ์•ˆํ…Œ๋‚˜์—์„œ ๋” ๋„“์€ ์กฐ๋ช… ์˜์—ญ์„ ์ปค๋ฒ„ํ•˜๊ธฐ ์œ„ํ•ด Ku ๋Œ€์—ญ์„ ์‚ฌ์šฉํ•˜๊ณ  ๊ณ ํ•ด์ƒ๋„ ์ด๋ฏธ์ง€๋ฅผ ์ œ๊ณตํ•˜๋ฉด์„œ ๋น ๋ฅธ ๋ ˆ์ด๋” ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์„ ์œ„ํ•ด ๊ณ ์† ๋žจํ”„ ๋ฐ ๊ด‘๋Œ€์—ญ FMCW ํŒŒํ˜•์„ ์‚ฌ์šฉํ•œ๋‹ค. ํ”Œ๋žซํผ์˜ ํ•ต์‹ฌ ์„ค๊ณ„ ์›์น™์€ ์•ˆ์ •์„ฑ, ์žฌ๊ตฌ์„ฑ ๊ฐ€๋Šฅ์„ฑ ๋ฐ ์‹ค์‹œ๊ฐ„ ๊ธฐ๋Šฅ์œผ๋กœ ์‹œ์Šคํ…œ์˜ ๊ฐ•์ ๊ณผ ์•ฝ์ ์„ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํƒ์ƒ‰ํ•œ๋‹ค. ์„ค๊ณ„ ์›์น™์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•ด AMD-Xilinx UltraScale+ RFSoC(Radio Frequency System on Chip)๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋””์ง€ํ„ธ ์ง€์› ํ”Œ๋žซํผ์„ ์ œ์•ˆํ•˜๊ณ  ๊ตฌํ˜„ํ•œ๋‹ค. ์‹ค์‹œ๊ฐ„ 2D ์ด๋ฏธ์ง•์— ๋Œ€ํ•œ ์‹คํ—˜์  ์กฐ์‚ฌ๋Š” ์ดˆ๋‹น ์•ฝ 60ํ”„๋ ˆ์ž„์—์„œ ๋น„๋””์˜ค ์†๋„ ์ด๋ฏธ์ง•์˜ ๋Šฅ๋ ฅ์„ ์ž…์ฆํ–ˆ๋‹ค. ๋‘˜์งธ, ์˜์ƒ ํ’ˆ์งˆ ํ–ฅ์ƒ์„ ์œ„ํ•œ ํŒŒํ˜• ๋””์ง€ํ„ธ ์ „์น˜์™œ๊ณก(DPD) ๋ฐฉ๋ฒ•๊ณผ ๋ณด์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ตœ์ ํ™”๋œ ํŒŒํ˜• ๋ฐœ์ƒ๊ธฐ์˜ ๋„์›€์œผ๋กœ ๊นจ๋—ํ•œ FMCW ํŒŒํ˜•์ด ์ƒ์„ฑ๋˜๋”๋ผ๋„ ํŠนํžˆ ํ”Œ๋žซํผ์˜ RF ํ•˜์œ„ ์‹œ์Šคํ…œ์—์„œ ์‹ ํ˜ธ๋Š” ํ•„์—ฐ์ ์œผ๋กœ ์™œ๊ณก์„ ๊ฒช๊ฒŒ๋œ๋‹ค. ๊ทผ๊ฑฐ๋ฆฌ ์˜์ƒํ™” ์‘์šฉ ๋ถ„์•ผ์—์„œ๋Š” ํŒŒํ˜• DPD๋Š” ๊ด‘๋Œ€์—ญ FMCW ๋ ˆ์ด๋” ์‹œ์Šคํ…œ์˜ ์™œ๊ณก์„ ์–ต์ œํ•˜๋Š” ๋ฐ ํšจ๊ณผ์ ์ด์ง€ ์•Š๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด Ku1DMIC์—์„œ ํŒŒํ˜• DPD๊ฐ€ ์œ ํšจํ•˜๋„๋ก LO-DPD ์•„ํ‚คํ…์ฒ˜์™€ ์ด์ง„ ํƒ์ƒ‰ ๊ธฐ๋ฐ˜ DPD ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ํŒŒํ˜• DPD๋กœ ์ œ๊ฑฐํ•  ์ˆ˜ ์—†๋Š” ๋‚˜๋จธ์ง€ ์˜ค๋ฅ˜๋ฅผ ๋ณด์ƒํ•˜๊ธฐ ์œ„ํ•ด ์ด๋ฏธ์ง€ ์˜์—ญ ์ตœ์ ํ™” ๋ณด์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ๋ฐ ํด๋Ÿฌํ„ฐ ์‹ ํ˜ธ์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ์›์น˜ ์•Š๋Š” ์‹ ํ˜ธ์— ๋Œ€ํ•œ ๊ฒฌ๊ณ ์„ฑ์„ ์œ„ํ•ด ์ผ๋ฐ˜ํ™”๋œ Tikhonov ์ •๊ทœํ™” ๋ฐ ์ „์ฒด ๋ณ€๋™(TV) ์ •๊ทœํ™”๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ์ •๊ทœํ™”๋œ ์ตœ์†Œ ์ž์Šน ๋ฌธ์ œ๋ฅผ ์ ์šฉ ํ›„ ๋น„๊ตํ•œ๋‹ค. ๋‹ค์–‘ํ•œ 2์ฐจ์› ์˜์ƒํ™” ์‹คํ—˜์„ ํ†ตํ•ด ๋‘ ๋ฐฉ๋ฒ• ๋ชจ๋‘ ๋ถ€์—ฝ ๋ ˆ๋ฒจ์„ ์ค„์—ฌ ํ™”์งˆ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ISAR ๊ธฐ๋ฒ•์„ 2์ฐจ์› ์˜์ƒ ํ”Œ๋žซํผ์— ์ ์šฉํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ 3์ฐจ์› ์˜์ƒ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•œ๋‹ค. 1D-MIMO-ISAR ๊ตฌ์„ฑ์—์„œ ์‹ค์‹œ๊ฐ„ 3D ์ด๋ฏธ์ง•์˜ ๊ตฌํ˜„์€ ์ด๋Ÿฌํ•œ ๊ตฌ์„ฑ์ด 3D ์ด๋ฏธ์ง• ์‹œ์Šคํ…œ์˜ ๋น„์šฉ์„ ํฌ๊ฒŒ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์˜ํ–ฅ๋ ฅ์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋…ผ๋ฌธ์—์„œ๋Š” 1D-MIMO-ISAR ๊ตฌ์„ฑ์— ๋Œ€ํ•œ ์ด๋ฏธ์ง• ์žฌ๊ตฌ์„ฑ์„ ๊ฐ€์†ํ™”ํ•˜๊ธฐ ์œ„ํ•ด 1D-MIMO-ISAR ๋ฒ”์œ„ ์Šคํƒœํ‚น ์•Œ๊ณ ๋ฆฌ์ฆ˜(RSA)์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ 1D-MIMO-ISAR RSA๋Š” ๋„๋ฆฌ ์•Œ๋ ค์ง„ Back-Projection ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ฑฐ์˜ ๋™์ผํ•œ ์ด๋ฏธ์ง€ ํ’ˆ์งˆ์„ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ ์ˆ˜๋ฐฑ ๋ฐ€๋ฆฌ์ดˆ ์ด๋‚ด์— ์ด๋ฏธ์ง€๋ฅผ ์žฌ๊ตฌ์„ฑํ•จ์œผ๋กœ์จ ์‹ค์‹œ๊ฐ„ ์˜์ƒํ™”์— ๋Œ€ํ•œ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ ๋ฌผ์ฒด๊ฐ€ ์‹œ์•ผ์— ๋“ค์–ด์˜ค๊ณ  ๋‚˜๊ฐ€๋Š” ์‹ค์ œ ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•œ ROI ์„ค์ •, ๊ทธ๋ฆฌ๊ณ  ๋ฉ”๋ชจ๋ฆฌ ํ• ๋‹น์— ๋Œ€ํ•œ ์ „๋žต์„ ์„ค๋ช…ํ•œ๋‹ค. ๊ด‘๋ฒ”์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์„ ํ†ตํ•ด 1D-MIMO-IASR ๊ตฌ์„ฑ ๋ฐ 1D-MIMO-ISAR RSA์˜ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์ž ์žฌ์  ์ด์ ์„ ํ™•์ธํ•œ๋‹ค.1 INTRODUCTION 1 1.1 Microwave and millimeter-wave imaging 1 1.2 Imaging with radar system 2 1.3 Challenges and motivation 5 1.4 Outline of the dissertation 8 2 FUNDAMENTAL OF TWO-DIMENSIONAL IMAGING USING A MIMO RADAR 9 2.1 Signal model 9 2.2 Consideration of waveform 12 2.3 Image reconstruction algorithm 16 2.3.1 Back-projection algorithm 16 2.3.2 1D-MIMO range-migration algorithm 20 2.3.3 1D-MIMO range stacking algorithm 27 2.4 Sampling criteria and resolution 31 2.5 Simulation results 36 3 MIMO-FMCW RADAR IMPLEMENTATION WITH 16 TX - 16 RX ONE- DIMENSIONAL ARRAYS 46 3.1 Wide-band FMCW waveform generator architecture 46 3.2 Overall system architecture 48 3.3 Antenna and RF transceiver module 53 3.4 Wide-band FMCW waveform generator 55 3.5 FPGA-based digital hardware design 63 3.6 System integration and software design 71 3.7 Testing and measurement 75 3.7.1 Chirp waveform measurement 75 3.7.2 Range profile measurement 77 3.7.3 2-D imaging test 79 4 METHODS OF IMAGE QUALITY ENHANCEMENT 84 4.1 Signal model 84 4.2 Digital pre-distortion of chirp signal 86 4.2.1 Proposed DPD hardware system 86 4.2.2 Proposed DPD algorithm 88 4.2.3 Measurement results 90 4.3 Robust calibration method for signal distortion 97 4.3.1 Signal model 98 4.3.2 Problem formulation 99 4.3.3 Measurement results 105 5 THREE-DIMENSIONAL IMAGING USING 1-D ARRAY SYSTEM AND ISAR TECHNIQUE 110 5.1 Formulation for 1D-MIMO-ISAR RSA 111 5.2 Algorithm implementation 114 5.3 Simulation results 120 5.4 Experimental results 122 6 CONCLUSIONS AND FUTURE WORK 127 6.1 Conclusions 127 6.2 Future work 129 6.2.1 Effects of antenna polarization in the Ku-band 129 6.2.2 Forward-looking near-field ISAR configuration 130 6.2.3 Estimation of the movement errors in ISAR configuration 131 Abstract (In Korean) 145 Acknowlegement 148๋ฐ•

    The role of graphene patterning in field-effect transistor sensors to detect the tau protein for Alzheimer's disease: Simplifying the immobilization process and improving the performance of graphene-based immunosensors

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    We report the improvement in the sensing performance of electrolyte-gated graphene field-effect transistor (FET) sensors capable of detecting tau protein through a simplified, linker-free, anti-tau antibody immobilization process. For most of the graphene-based immunosensor, linkers, such as pyrenebutanoic acid, succinimidyl ester (PSE) must be used to the graphene surface, while the other side of linkers serves to capture the antibodies that can specifically interact with the target biomarker. In this study, graphene was patterned into eight different types and linker-free patterned graphene FET sensors were fabricated to verify their detection performance. The linker-free antibody immobilization to patterned graphene exhibited that the antibody was immobilized to the edge defect and had a doping-like behaviors on graphene. As the tau protein concentration in the electrolyte increased from 10 fg/ml to 1 ng/ml, the performances, charge neutral point shift and current change rate of the patterned graphene sensors without linkers were enhanced 2-3 times compared to a pristine graphene sensor with the PSE linker. Moreover, tau protein in the plasma of five Alzheimer's disease patients was measured using a linker-free patterned graphene sensor. It shows a 3-4 times higher current change rate than that of pristine graphene sensor with the PSE linker. Since the antibody is immobilized directly without a linker, a patterned graphene sensor without a linker can operate more sensitively in higher ionic concentration electrolyte.ope

    ์„œ์šธ์‹œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2022.2. ๋ฐ•์ธ๊ถŒ.Recently, the housing market in Seoul has been unstable. The rise in housing rent often leads low-income class to migrate involuntarily. In order to support housing stability on the low-income class, it is necessary to follow their involuntary housing movement mechanism. Therefore, this study analyzed the effect of changes in housing rent on the moving intention of tenants in Seoul by income. Based on household characteristics data derived from 2012-2017 Seoul Survey, this study added additional data on regional characteristics such as jeonse price, long-term rent with lump-sum deposit, jeonse increase rate, and public rental housing rate to analyze how household characteristics and regional characteristics affect moving intentions. After conducting the analysis on all tenants, the same analysis was conducted again by dividing them by income. The research results are as follows. First, in relation to the effect of changes in housing rent on the moving intention in tenants, the analysis conducted on all tenants showed that the rate of increase in housing rent was not significant statistically. However, when analyzing the division by income class, it was demonstrated that the change in housing rent affects the moving intention in the case of low-income class. Second, as to which household characteristics affect the decision of moving intention, the integrated analysis of all tenants showed that age, age square, housing satisfaction, main cause of debt, and number of household members had a statistically significant effect on moving intention. In the case of age, it was consistent with the life cycle theory, and the lower the housing satisfaction and the higher the number of household members, the higher the willingness to move. On the other hand, households who responded that the main cause of household debt was housing costs showed lower intention to move, and middle and high-income classes had higher intention to move than low-income class. This can show structural housing instability to tenants. Third, as a result of analyzing whether the difference in characteristics between regions affects the tenantโ€™s moving intention, the lower the jeonse price, the lower the public rental housing ratio, and the higher the worker ratio, the higher the willingness to move. In districts with low jeonse prices, poor living infrastructure is believed to have been a factor in the moving intention. It has been demonstrated that public rental housing plays a role in inducing local settlement of tenants and preventing external leaks in accordance with the policy purpose. Finally, the factors affecting the moving intention for each income class were different as follows. In the case of low-income class, changes in rent and residential infrastructure, such as the rate of increase in jeonse prices and public rental housing, have a significant impact on the moving intention. However, for middle and high-income classes, household characteristics such as residential housing type and housing rental type have a greater impact.์ตœ๊ทผ ์„œ์šธ์‹œ์˜ ์ฃผํƒ์‹œ์žฅ์ด ๋ถˆ์•ˆ์ •ํ•˜๋‹ค. ์ฃผํƒ ์ž„์ฐจ๋ฃŒ ์ƒ์Šน์€ ์ €์†Œ๋“์ธต์—๊ฒŒ ํŠนํžˆ ํฐ ์˜ํ–ฅ์„ ์ฃผ์–ด ์ด๋“ค์˜ ๋น„์ž๋ฐœ์  ์ด์ฃผ๋กœ ์ด์–ด์ง€๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ์ €์†Œ๋“์ธต์˜ ์ฃผ๊ฑฐ ๋ถ€๋‹ด์„ ๋œ๊ณ  ์ฃผ๊ฑฐ์•ˆ์ •์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทธ๋“ค์˜ ๋น„์ž๋ฐœ์  ์ฃผ๊ฑฐ ์ด๋™ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ •ํ™•ํ•˜๊ฒŒ ํŒŒ์•…ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ์‹œ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ฃผํƒ ์ž„์ฐจ๋ฃŒ ๋ณ€ํ™”๊ฐ€ ์†Œ๋“๊ณ„์ธต๋ณ„ ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ์ด์‚ฌ์˜ํ–ฅ์— ๋ฏธ์นœ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์„œ์šธ์„œ๋ฒ ์ด์˜ 2012๋…„~2017๋…„ ์ด์‚ฌ์˜ํ–ฅ ์„ค๋ฌธ์กฐ์‚ฌ ๊ฒฐ๊ณผ์—์„œ ๋„์ถœ๋œ ๊ฐ€๊ตฌ์  ํŠน์„ฑ ์ž๋ฃŒ๋ฅผ ํ† ๋Œ€๋กœ ํ•˜๊ณ , ์—ฌ๊ธฐ์— ์ „์„ธ๊ฐ€๊ฒฉ, ์ „์„ธ๊ฐ€ ์ƒ์Šน๋ฅ , ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๋น„์œจ ๋“ฑ์˜ ์ง€์—ญ์  ํŠน์„ฑ ์ž๋ฃŒ๋ฅผ ์ถ”๊ฐ€๋กœ ๊ตฌ์ถ•ํ•ด ๊ฐ€๊ตฌํŠน์„ฑ๊ณผ ์ง€์—ญํŠน์„ฑ์ด ์ด์‚ฌ์˜ํ–ฅ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ „์ฒด ์ž„์ฐจ๊ฐ€๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•œ ํ›„, ์†Œ๋“๊ณ„์ธต๋ณ„๋กœ ๋ถ„ํ• ํ•ด ๋™์ผํ•œ ๋ถ„์„์„ ๋‹ค์‹œ ์‹ค์‹œํ–ˆ๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ฃผํƒ ์ž„์ฐจ๋ฃŒ์˜ ๋ณ€ํ™”๊ฐ€ ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ์ด์‚ฌ์˜ํ–ฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ด€๋ จ, ์ „์ฒด ์ž„์ฐจ๊ฐ€๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰ํ•œ ํ†ตํ•ฉ ๋ถ„์„์—์„œ๋Š” ์ฃผํƒ ์ž„์ฐจ๋ฃŒ์˜ ์ƒ์Šน๋ฅ ์ด ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ์ด์‚ฌ์˜ํ–ฅ์— ์œ ์˜ํ•˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์†Œ๋“๊ณ„์ธต๋ณ„ ๋ถ„ํ•  ๋ถ„์„ ์‹œ์—๋Š” ์ €์†Œ๋“์ธต์˜ ๊ฒฝ์šฐ ์ฃผํƒ ์ž„์ฐจ๋ฃŒ์˜ ๋ณ€ํ™”๊ฐ€ ์ด์‚ฌ์˜ํ–ฅ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์ด ์‹ค์ฆ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ์ด์‚ฌ์˜ํ–ฅ์˜ ๊ฒฐ์ •์—๋Š” ์–ด๋– ํ•œ ๊ฐ€๊ตฌ์  ํŠน์„ฑ์ด ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€์— ๋Œ€ํ•ด์„œ๋Š”, ๋จผ์ € ์ „์ฒด ์ž„์ฐจ๊ฐ€๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ†ตํ•ฉ ๋ถ„์„ ๊ฒฐ๊ณผ ์—ฐ๋ น๊ณผ ์—ฐ๋ น ์ œ๊ณฑ, ์ฃผ๊ฑฐ๋งŒ์กฑ๋„, ๋ถ€์ฑ„ ์ฃผ ์›์ธ, ๊ฐ€๊ตฌ์› ์ˆ˜ ๋ณ€์ˆ˜๊ฐ€ ์ด์‚ฌ์˜ํ–ฅ์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์—ฐ๋ น์˜ ๊ฒฝ์šฐ ์ƒ์• ์ฃผ๊ธฐ์ด๋ก ์— ๋ถ€ํ•ฉํ–ˆ์œผ๋ฉฐ, ์ฃผ๊ฑฐ๋งŒ์กฑ๋„๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก, ๊ฐ€๊ตฌ์› ์ˆ˜๊ฐ€ ๋งŽ์„์ˆ˜๋ก ์ด์‚ฌ์˜ํ–ฅ์ด ์ฆ๊ฐ€ํ–ˆ๋‹ค. ๋ฐ˜๋ฉด ๊ฐ€๊ตฌ๋ถ€์ฑ„์˜ ์ฃผ ์›์ธ์ด ์ฃผ๊ฑฐ๋น„๋ผ๊ณ  ์‘๋‹ตํ•œ ๊ฐ€๊ตฌ์ผ์ˆ˜๋ก ์ด์‚ฌ์˜ํ–ฅ์ด ๋–จ์–ด์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ค‘ยท๊ณ ์†Œ๋“์ธต์˜ ์ด์‚ฌ์˜ํ–ฅ์ด ์ €์†Œ๋“์ธต๋ณด๋‹ค, ๊ณต๋™์ฃผํƒ์— ๊ฑฐ์ฃผํ•˜๋Š” ๊ฒฝ์šฐ ๋‹จ๋…/๋‹ค๊ฐ€๊ตฌ์ฃผํƒ์— ๊ฑฐ์ฃผํ•˜๋Š” ๊ฒฝ์šฐ๋ณด๋‹ค ์ด์‚ฌ์˜ํ–ฅ์ด ๋” ๋†’์€ ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Š” ์†Œ๋“์— ์ƒ๊ด€์—†์ด ์ž„์ฐจ๋ฃŒ์˜ ๋ถ€๋‹ด ๋˜๋Š” ์ฃผํƒ ๊ตฌ์ž…์˜ ์‚ฌ์œ ๋กœ ์ด์‚ฌ์˜ํ–ฅ์ด ๋ฐœ์ƒํ•˜๋Š” ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ๊ตฌ์กฐ์  ์ฃผ๊ฑฐ๋ถˆ์•ˆ์ •์„ฑ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค๊ณ  ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ์ง€์—ญ ๊ฐ„์˜ ํŠน์„ฑ ์ฐจ์ด๊ฐ€ ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ์ด์‚ฌ์˜ํ–ฅ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ง€์—ญํŠน์„ฑ ๋ณ€์ˆ˜์˜ ๊ฒฝ์šฐ ์ „์„ธ๊ฐ€๊ฒฉ์ด ๋‚ฎ์€ ์ง€์—ญ์ผ์ˆ˜๋ก, ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๋น„์œจ์ด ๋‚ฎ์€ ์ง€์—ญ์ผ์ˆ˜๋ก, ๊ทผ๋กœ์ž ๋น„์œจ์ด ๋†’์€ ์ง€์—ญ์ผ์ˆ˜๋ก ์ด์‚ฌ์˜ํ–ฅ์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ „์„ธ๊ฐ€๊ฒฉ์ด ๋‚ฎ์€ ์ง€์—ญ์€ ๊ฐ์ข… ์ƒํ™œ ์ธํ”„๋ผ๊ฐ€ ์—ด์•…ํ•œ ๊ฒƒ์ด ์ด์‚ฌ์˜ํ–ฅ์˜ ์š”์ธ์ด ๋œ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ๋‹ค. ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์€ ์ •์ฑ… ์ทจ์ง€์— ๋ถ€ํ•ฉํ•˜๊ฒŒ ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ์ง€์—ญ ์ •์ฐฉ์„ ์œ ๋„ํ•˜๊ณ  ์™ธ๋ถ€ ์œ ์ถœ์„ ๋ฐฉ์ง€ํ•˜๋Š” ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ์Œ์„ ์‹ค์ฆํ–ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์†Œ๋“๊ณ„์ธต๋ณ„๋กœ ์ด์‚ฌ์˜ํ–ฅ์— ๋ฏธ์น˜๋Š” ์š”์†Œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ƒ์ดํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ €์†Œ๋“์ธต์˜ ๊ฒฝ์šฐ ์ „์„ธ๊ฐ€๊ฒฉ ์ƒ์Šน๋ฅ ์ด๋‚˜ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๋น„์œจ๊ณผ ๊ฐ™์€ ์ง€์—ญ์˜ ์ž„์ฐจ๋ฃŒ ๋ณ€๋™ ๋ฐ ์ฃผ๊ฑฐ ์ธํ”„๋ผ์  ์ง€์—ญํŠน์„ฑ์ด ์ด์‚ฌ์˜ํ–ฅ์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€๋งŒ, ์ค‘ยท๊ณ ์†Œ๋“์ธต์—๊ฒŒ๋Š” ๊ทธ๋Ÿฌํ•œ ์ง€์—ญํŠน์„ฑ๋ณด๋‹ค๋Š” ๊ฑฐ์ฃผ ์ฃผํƒ์œ ํ˜•, ์ฃผํƒ ์ž„์ฐจ์œ ํ˜• ๋“ฑ ๊ฐ€๊ตฌํŠน์„ฑ์ด ๋”์šฑ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ €์†Œ๋“์ธต์˜ ๊ฒฝ์šฐ ์ค‘ยท๊ณ ์†Œ๋“์ธต๊ณผ๋Š” ๋‹ฌ๋ฆฌ ์ฃผํƒ ์ž„์ฐจ๋ฃŒ์˜ ์ƒ์Šน์ด ์ด์‚ฌ์˜ํ–ฅ์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค๋Š” ์ ์„ ์‹ค์ฆํ•จ์— ๋”ฐ๋ผ ์ด์— ๋Œ€ํ•œ ์ •์ฑ… ๋งˆ๋ จ์ด ์‹œ๊ธ‰ํ•˜๋‹ค๋Š” ์ , ์ง€์—ญ ๊ฐ„์˜ ์—ฌ๊ฑด ์ฐจ์ด๊ฐ€ ์ด์‚ฌ์˜ํ–ฅ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฏ€๋กœ ์ •๋ถ€์˜ ์ฃผํƒ์ •์ฑ…์€ ์ง€์—ญ๋ณ„ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋งž์ถคํ˜•์œผ๋กœ ์ถ”์ง„๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ์ , ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๋น„์œจ์ด ๋‚ฎ์€ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๋Š” ์ €์†Œ๋“์ธต ๊ฐ€๊ตฌ๋Š” ์ด์‚ฌ์˜ํ–ฅ์ด ๋†’์€ ๋ฐ˜๋ฉด, ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๋น„์œจ์ด ๋†’์€ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๋Š” ์ €์†Œ๋“์ธต ๊ฐ€๊ตฌ๋Š” ์ด์‚ฌ์˜ํ–ฅ์ด ๋‚ฎ์•˜๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์ด ์ €์†Œ๋“ ์ž„์ฐจ๊ฐ€๊ตฌ์˜ ์ฃผ๊ฑฐ์•ˆ์ •์— ๊ธฐ์—ฌํ•˜๊ณ  ์žˆ์Œ์„ ํ†ต๊ณ„์ ์œผ๋กœ ์‹ค์ฆํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค.์ œ๏ผ‘์žฅ ์„œ๋ก  1 ์ œ๏ผ‘์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ๏ผ’์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 5 ๏ผ‘. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 5 ๏ผ’. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 6 ๏ผ“. ์—ฐ๊ตฌ์˜ ๊ตฌ์„ฑ 7 ์ œ๏ผ’์žฅ ์ฃผ๊ฑฐ์ด๋™์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 9 ์ œ๏ผ‘์ ˆ ์ฃผ๊ฑฐ์ด๋™์— ๊ด€ํ•œ ์ด๋ก  9 ๏ผ‘. ์ฃผ๊ฑฐ์ด๋™์˜ ๊ฐœ๋… ๋ฐ ๊ฒฐ์ •์š”์ธ 9 ๏ผ’. ๋น„์ž๋ฐœ์  ์ฃผ๊ฑฐ์ด๋™๊ณผ ์  ํŠธ๋ฆฌํ”ผ์ผ€์ด์…˜ 13 ์ œ๏ผ’์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ๋ฐ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 16 ๏ผ‘. ์ฃผ๊ฑฐ์ด๋™์— ๊ด€ํ•œ ์—ฐ๊ตฌ 16 ๏ผ’. ๊ณ„์ธต๋ณ„ ์ฃผ๊ฑฐ์ด๋™ ๊ฒฐ์ •์š”์ธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 19 ๏ผ“. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 24 ์ œ๏ผ“์žฅ ์ž๋ฃŒ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 26 ์ œ๏ผ‘์ ˆ ๋ถ„์„์ž๋ฃŒ ๋ฐ ํ‘œ๋ณธ 26 ์ œ๏ผ’์ ˆ ์—ฐ๊ตฌ ๊ฐ€์„ค ๋ฐ ๋ถ„์„ ๋ฐฉ๋ฒ• 30 ์ œ๏ผ“์ ˆ ๋ณ€์ˆ˜์„ค์ • ๋ฐ ๊ธฐ์ดˆํ†ต๊ณ„ 32 ์ œ๏ผ”์žฅ ์„œ์šธ์‹œ ์ž„์ฐจ๊ฐ€๊ตฌ ์ด์‚ฌ์˜ํ–ฅ ์‹ค์ฆ๋ถ„์„ 39 ์ œ๏ผ‘์ ˆ ์ „ ์ž„์ฐจ๊ฐ€๊ตฌ ํ†ตํ•ฉ ๋ถ„์„ 39 ๏ผ‘. ์˜๋ชจํ˜• ์ถ”์ • 39 ๏ผ’. ๋‹ค์ˆ˜์ค€ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 39 ์ œ๏ผ’์ ˆ ์†Œ๋“๊ณ„์ธต๋ณ„ ๋ถ„์„ 43 ๏ผ‘. ๊ธฐ์ดˆํ†ต๊ณ„๋Ÿ‰ ๋ฐ ์˜๋ชจํ˜• ์ถ”์ • 43 ๏ผ’. ์†Œ๋“๊ณ„์ธต๋ณ„ ๋‹ค์ˆ˜์ค€ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 46 ์ œ๏ผ•์žฅ ๊ฒฐ๋ก  53 ์ œ๏ผ‘์ ˆ ์—ฐ๊ตฌ์˜ ์š”์•ฝ ๋ฐ ์‹œ์‚ฌ์  53 ์ œ๏ผ’์ ˆ ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ํ•œ๊ณ„ 55 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 57 Abstract 62์„

    Design Method of a Multiport MIMO Antenna on a Bilaterally Symmetric Conductor Using Characteristic Mode Theory

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2017. 8. ๋‚จ์ƒ์šฑ.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŠน์„ฑ ๋ชจ๋“œ ์ด๋ก ์— ๊ธฐ๋ฐ˜ํ•œ ๋‹ค์ค‘ ํฌํŠธ MIMO ์•ˆํ…Œ๋‚˜ ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณด๋‹ค ๊ตฌ์ฒด์ ์œผ๋กœ๋Š”, ํ•˜๋‚˜์˜ ๋Œ€์นญ ์ถ•์ด ์žˆ๋Š” ๋„์ฒด๋ฅผ ๋‹ค์ค‘ ํฌํŠธ MIMO ์•ˆํ…Œ๋‚˜๋กœ ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. MIMO ํ†ต์‹  ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์ •ํ•ด์ง„ ๊ณต๊ฐ„๋‚ด์—์„œ์˜ ๋‹ค์ค‘์˜ ๋…๋ฆฝ์ ์ธ ์•ˆํ…Œ๋‚˜ ์„ค๊ณ„๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ํŠน์„ฑ ๋ชจ๋“œ ์ด๋ก ์€ ์ •ํ•ด์ง„ ๋„์ฒด์—์„œ์˜ ์ง๊ตํ•˜๋Š” ๊ณต์ง„ ๋ชจ๋“œ๋“ค์„ ์ œ๊ณตํ•ด์ฃผ๊ณ , ์ด์™€ ๋ชจ๋“œ ๋””์ปคํ”Œ๋ง ๋„คํŠธ์›Œํฌ๋ฅผ ์ด์šฉํ•˜๋ฉด ์ฃผ์–ด์ง„ ๋„์ฒด๋ฅผ ์ด์šฉํ•œ MIMO ์•ˆํ…Œ๋‚˜ ์„ค๊ณ„๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์•ˆํ…Œ๋‚˜์˜ ์ˆ˜๊ฐ€ ๋Š˜์–ด๋‚จ์— ๋”ฐ๋ผ ๋ชจ๋“œ ๋””์ปคํ”Œ๋ง ๋„คํŠธ์›Œํฌ๋Š” ๋ณต์žกํ•œ ์„ค๊ณ„๊ฐ€ ์š”๊ตฌ๋œ๋‹ค. ์ด์ „์—๋Š” ์ด๋ฅผ ๋ณด๋‹ค ๊ฐ„ํ•œํ•˜๊ฒŒ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ์ƒํ•˜์ขŒ์šฐ๋Œ€์นญ ๋„์ฒด์—์„œ์˜ MIMO ์•ˆํ…Œ๋‚˜์˜ ์„ค๊ณ„๊ฐ€ ์š”๊ตฌ๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ขŒ์šฐ๋Œ€์นญ ๋„์ฒด์—์„œ์˜ ๋ณด๋‹ค ๊ฐ„๋‹จํ•œ ๋‹ค์ค‘ ํฌํŠธ MIMO ์•ˆํ…Œ๋‚˜ ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณด๋‹ค ์ž์„ธํ•˜๊ฒŒ๋Š”, ๊ฐ„๋‹จํ•œ ๋ชจ๋“œ ๋””์ปคํ”Œ๋ง ๋„คํŠธ์›Œํฌ ๊ตฌํ˜„์€ ํŠน์„ฑ ๋ชจ๋“œ๋ฅผ ๊ธ‰์ „ํ•˜๋Š” ์ปคํ”Œ๋ง ์š”์†Œ์˜ ์„ค๊ณ„์— ์˜์กด์ ์ด๊ธฐ์—, ์ด์— ๋Œ€ํ•œ ์œ„์น˜ ๋ฐ ๋ชจ์–‘์— ๋Œ€ํ•œ ์„ค๊ณ„ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด์„œ, 2.4GHz- ISM ๋Œ€์—ญ์—์„œ ๋™์ž‘ํ•˜๋Š” ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋“œ๋ก ์— ์‚ฌ์šฉ๋˜๋Š” ์‚ผ์ค‘ ์•ˆํ…Œ๋‚˜๋ฅผ ์ œ์ž‘ ๋ฐ ์ธก์ •ํ•˜์˜€๋‹ค. ์ด ์•ˆํ…Œ๋‚˜๋Š” 50 mm 61.5 mm 10 mm ์˜ ํฌ๊ธฐ๋ฅผ ๊ฐ€์ง€๋ฉฐ, FR-4 ๋‹จ์ธต ๊ธฐํŒ์„ ์ด์šฉํ•˜์—ฌ ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ธก์ • ๊ฒฐ๊ณผ, mutual coupling์€ -20 dB ์ดํ•˜๋กœ ๋‚ฎ์€ ์ปคํ”Œ๋ง์„ ์ œ๊ณตํ•˜์˜€๊ณ , ์•ˆํ…Œ๋‚˜ ํŒจํ„ด ๊ฐ„์˜ ์ƒ๊ด€๋„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ง€ํ‘œ์ธ envelope correlation coefficient๋Š” 0.001๋ณด๋‹ค ๋‚ฎ์€ ๊ฐ’์„ ์ œ๊ณตํ•˜์˜€๊ธฐ์— MIMO ํ†ต์‹ ์šฉ์œผ๋กœ์จ ์ ํ•ฉํ•จ์„ ๋ณด์˜€๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 2 ์žฅ ํŠน์„ฑ ๋ชจ๋“œ ์ด๋ก  4 ์ œ 1 ์ ˆ ํŠน์„ฑ ๋ชจ๋“œ ์ด๋ก ์˜ ๊ฐœ์š” 4 ์ œ 2 ์ ˆ ํŠน์„ฑ ์ „๋ฅ˜ ์ƒ๊ด€๋„ 6 ์ œ 3 ์ ˆ ์Šฌ๋กฏ ํ˜•ํƒœ์˜ ์œ ๋„์„ฑ ์ปคํ”Œ๋ง ๋ถ„์„ 8 ์ œ 3 ์žฅ ์ขŒ์šฐ๋Œ€์นญ ๋„์ฒด์—์„œ์˜ ์ œ์•ˆ๋œ ์„ค๊ณ„ ๋ฐฉ๋ฒ• 11 ์ œ 1 ์ ˆ ๋‹ค๋ฅธ ๊ตฐ์˜ ํŠน์„ฑ ๋ชจ๋“œ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ์•ˆํ…Œ๋‚˜ ์„ค๊ณ„ 12 ์ œ 2 ์ ˆ ๊ฐ™์€ ๊ตฐ์˜ ๋‹ค๋ฅธ ํŠน์„ฑ ๋ชจ๋“œ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ์•ˆํ…Œ๋‚˜ ์„ค๊ณ„ 13 ์ œ 3 ์ ˆ ์ œ์•ˆ๋œ ๋‹ค์ค‘ ํฌํŠธ MIMO ์•ˆํ…Œ๋‚˜์˜ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 18 ์ œ 4 ์žฅ ์˜ˆ์‹œ ์ƒ์ฒด ๋ชจ๋ฐฉ์˜ ๋ฒŒ๋ ˆ ๋ชจ์–‘ MIMO ์•ˆํ…Œ๋‚˜ 20 ์ œ 1 ์ ˆ ๋ฒŒ๋ ˆ ๋ชจ์–‘ ๋„์ฒด์˜ ํŠน์„ฑ ๋ชจ๋“œ ๋ถ„์„ 21 ์ œ 2 ์ ˆ ์ œ์•ˆ๋œ ๋ฐฉ์‹์— ๊ธฐ๋ฐ˜ํ•œ ICE ์„ค๊ณ„ 22 ์ œ 3 ์ ˆ MDN์„ ํฌํ•จํ•˜๋Š” ์‹œ์Šคํ…œ์ ์ธ ์„ค๊ณ„ 25 ์ œ 4 ์ ˆ ์ธก์ • ๋ฐ ๊ฒฐ๊ณผ 27 ์ œ 5 ์žฅ ๊ฒฐ๋ก  31 ๋ถ€๋ก 34 ์ฐธ๊ณ ๋ฌธํ—Œ 37 Abstract 40Maste

    Estimation of Usual Intakes of Foods and Nutrients in Korean Adults

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‹ํ’ˆ์˜์–‘ํ•™๊ณผ, 2013. 2. ๋ฐฑํฌ์˜.์‹์‚ฌ์„ญ์ทจ์กฐ์‚ฌ์˜ ์ฃผ๋œ ๋ชฉ์ ์€ ์ง‘๋‹จ์ด๋‚˜ ๊ฐœ์ธ์˜ ์„ญ์ทจ๋Ÿ‰์„ ์ธก์ •ํ•˜์—ฌ ๋งŒ์„ฑ์งˆ๋ณ‘๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๊ทœ๋ช…ํ•˜๊ฑฐ๋‚˜, ์‹์‚ฌ์„ญ์ทจ๊ธฐ์ค€ ๋“ฑ๊ณผ์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์„ญ์ทจ์ˆ˜์ค€์˜ ์ ์ ˆ์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ธ๊ฐ„์˜ ์„ญ์ทจ๋Ÿ‰์€ ๋‚ ์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์žฅ๊ธฐ๊ฐ„์˜ ํ‰๊ท  ์„ญ์ทจ๋Ÿ‰์œผ๋กœ ์ •์˜๋˜๋Š” ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์‹์‚ฌ์„ญ์ทจ์กฐ์‚ฌ๋ฐฉ๋ฒ• ์ค‘ 24์‹œ๊ฐ„ ํšŒ์ƒ๋ฒ•(24HR)์ด๋‚˜ ์‹์‚ฌ๊ธฐ๋ก๋ฒ•(DR)์€ ์‹ํ’ˆ์˜ ๋ชฉ๋ก ๋ฐ ๋ถ„๋Ÿ‰์„ ์ •ํ™•ํ•˜๊ฒŒ ์กฐ์‚ฌํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ์กฐ์‚ฌ์ผ์ˆ˜๊ฐ€ ์ ์„ ๊ฒฝ์šฐ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ํŒŒ์•…ํ•˜๊ธฐ ์–ด๋ ต๊ณ  ์—ฌ๋Ÿฌ ๋‚ ์„ ์กฐ์‚ฌํ•˜๋ฉด ์กฐ์‚ฌ๋น„์šฉ์ด ์ฆ๊ฐ€ํ•  ๋ฟ ์•„๋‹ˆ๋ผ ์กฐ์‚ฌ์˜ ๋ถ€๋‹ด์œผ๋กœ ์ธํ•œ ์˜ค๋ฅ˜๊ฐ€ ์ƒ๊ธธ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์ ์ ˆํ•œ ์กฐ์‚ฌ์ผ์ˆ˜๋ฅผ ์‚ฐ์ •ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์‹ํ’ˆ์„ญ์ทจ๋นˆ๋„์กฐ์‚ฌ(FFQ)๋Š” ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์กฐ์‚ฌํ•  ๋ชฉ์ ์œผ๋กœ ๊ณ ์•ˆ๋œ ๊ฒƒ์ด์ง€๋งŒ, ์กฐ์‚ฌํ•ญ๋ชฉ์ด ์ œํ•œ๋˜๊ณ  1ํšŒ์กฐ์‚ฌ๋กœ ์žฅ๊ธฐ๊ฐ„์˜ ์„ญ์ทจ๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜๋ฏ€๋กœ ํƒ€๋‹น๋„ ๊ฒ€์ฆ ๋ฐ ๋ณด์ •์„ ํ†ตํ•ด ์‚ฌ์šฉํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋˜ํ•œ ๋Œ€๋ถ€๋ถ„์˜ ์‚ฌ๋žŒ์ด ๋งค์ผ ์„ญ์ทจํ•˜๋Š” ์—๋„ˆ์ง€ ๋ฐ ์˜์–‘์†Œ์™€ ๋‹ฌ๋ฆฌ ํŠน์ • ์Œ์‹์ด๋‚˜ ์‹ํ’ˆ์€ ์กฐ์‚ฌ๋œ ๋‚ ์— ์„ญ์ทจํ•˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, FFQ์—์„œ ์–ป์€ ์„ญ์ทจ๋นˆ๋„์™€ 24HR ๋˜๋Š” DR์˜ ์„ญ์ทจ๋Ÿ‰์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜์—ฌ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ถŒ์žฅ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ˆ˜๋„๊ถŒ์— ๊ฑฐ์ฃผํ•˜๋Š” 20~65์„ธ ํ•œ๊ตญ ์„ฑ์ธ์˜ ์ผ์ƒ์ ์ธ ์˜์–‘์†Œ ์„ญ์ทจ๋Ÿ‰์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋Š” DR์˜ ์กฐ์‚ฌ์ผ์ˆ˜ ์‚ฐ์ • ๋ฐ FFQ์˜ ํƒ€๋‹น๋„ ๊ฒ€์ฆ, ๊ทธ๋ฆฌ๊ณ  ์Œ์‹ ์ˆ˜์ค€์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ • ๋ชจํ˜•์„ ํƒ์ƒ‰ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ด 414๋ช…์˜ 12์ผ DR(12DR) ์ž๋ฃŒ๋กœ ์˜์–‘์†Œ ์„ญ์ทจ๋Ÿ‰์˜ ๋ณ€์ด์š”์†Œ๋ฅผ ๋ถ„์„ํ•˜์˜€์„ ๋•Œ, ๋Œ€๋ถ€๋ถ„์˜ ๋ณ€์ด๋Š” ๊ฐœ์ธ๋‚ด ๋ณ€์ด(60~85%)์™€ ๊ฐœ์ธ๊ฐ„ ๋ณ€์ด(14~38%)๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์—ˆ์œผ๋ฉฐ, ๊ณ„์ ˆ, ์กฐ์‚ฌํšŸ์ˆ˜, ์ฃผ์ค‘/์ฃผ๋ง์— ์˜ํ•œ ๋ณ€์ด๋Š” ์ด ๋ณ€์ด ์ค‘ ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ์ด ๋‚ฎ์•˜๋‹ค(<3%). ๊ฐœ์ธ๋‚ด ๋ณ€์ด์™€ ๊ฐœ์ธ๊ฐ„ ๋ณ€์ด์˜ ๋น„์œจ์€ ๋‚จ์„ฑ์ด ์—ฌ์„ฑ์— ๋น„ํ•ด 20~45์„ธ ์„ฑ์ธ์ด 46~65์„ธ ์„ฑ์ธ์— ๋น„ํ•ด ๋†’์•˜๋‹ค. ๊ฐœ์ธ๋‚ด ๋ณ€์ด`์™€ ๊ฐœ์ธ๊ฐ„ ๋ณ€์ด์˜ ๋น„์œจ์„ ์ด์šฉํ•˜์—ฌ ์กฐ์‚ฌ์ผ์ˆ˜๋ฅผ ์‚ฐ์ •ํ•œ ๊ฒฐ๊ณผ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰๊ณผ์˜ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ 0.7 ์ด์ƒ์ธ ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์˜์–‘์†Œ์—์„œ 3~4์ผ์˜ DR์ด ํ•„์š”ํ•˜์˜€๊ณ , 0.8 ์ด์ƒ์˜ ์ˆ˜์ค€์„ ๋ชฉ์ ์œผ๋กœ ํ•˜๋Š” ๊ฒฝ์šฐ 6~7์ผ์˜ DR์„ ์ˆ˜ํ–‰ํ•  ํ•„์š”๊ฐ€ ์žˆ์—ˆ๋‹ค. ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ์ž๋ฃŒ๋กœ๋ถ€ํ„ฐ ๊ฐœ๋ฐœ๋œ FFQ์˜ ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น๋„๋ฅผ 12DR๊ณผ 2ํšŒ์˜ FFQ ์กฐ์‚ฌ๋ฅผ ๋งˆ์นœ 126๋ช…์˜ ์„ญ์ทจ๋Ÿ‰ ์ž๋ฃŒ๊ฐ„ ์ƒ๊ด€๊ณ„์ˆ˜๋กœ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ, ๋น„๊ต์  ๋†’์€ ์ˆ˜์ค€์˜ ์‹ ๋ขฐ๋„(ํ‰๊ท ์ƒ๊ด€๊ณ„์ˆ˜=0.55)์™€ ๋ณดํ†ต ์ˆ˜์ค€์˜ ํƒ€๋‹น๋„(ํ‰๊ท ์ƒ๊ด€๊ณ„์ˆ˜=0.41)๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์‚ฌ๋ถ„์œ„ ๋ถ„๋ฅ˜๊ฒฐ๊ณผ ํ‰๊ท ์ ์œผ๋กœ ์•ฝ 77%์˜ ๋Œ€์ƒ์ž๋“ค์ด ๋™์ผ ๋˜๋Š” ์ธ์ ‘ํ•œ ์‚ฌ๋ถ„์œ„์— ๋ถ„๋ฅ˜๋˜์—ˆ์œผ๋ฉฐ, 4%์˜ ๋Œ€์ƒ์ž๋“ค์ด ๋ฐ˜๋Œ€ ์‚ฌ๋ถ„์œ„์— ์œ„์น˜ํ•˜์˜€๋‹ค. 12DR ๋Œ€๋น„ FFQ์˜ ์„ญ์ทจ๋น„์œจ ๋ฐ ๊ทธ 95% ์‹ ๋ขฐ๊ตฌ๊ฐ„์œผ๋กœ ์ผ์น˜๋„๋ฅผ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ ์—๋„ˆ์ง€, ํƒ„์ˆ˜ํ™”๋ฌผ, ๋น„ํƒ€๋ฏผ C๋Š” FFQ์—์„œ ๊ณผ๋Œ€ํ‰๊ฐ€๊ฐ€, ์ง€๋ฐฉ, ๋‚˜ํŠธ๋ฅจ, ๋น„ํƒ€๋ฏผ A, ๋ฆฌ๋ณดํ”Œ๋ผ๋นˆ์€ ๊ณผ์†Œํ‰๊ฐ€๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—๋„ˆ์ง€ ์กฐ์ • ๊ฒฐ๊ณผ ์ผ๋ถ€ ์˜์–‘์†Œ์—์„œ ์ƒ๊ด€๊ณ„์ˆ˜๊ฐ€ ์ƒ์Šนํ•˜๊ณ  ๋™์ผ ๋˜๋Š” ์ธ์ ‘ ์‚ฌ๋ถ„์œ„์— ๋ถ„๋ฅ˜๋˜๋Š” ๋Œ€์ƒ์ž์˜ ๋น„์œจ์ด ์ฆ๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ํšŒ๊ท€๋ชจํ˜•์— ์˜ํ•ด ๋ณด์ •๋œ FFQ์˜ ํ‰๊ท ์€ 12DR์˜ ํ‰๊ท ์— ์œ ์‚ฌํ•ด์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์˜์–‘์†Œ์—์„œ ์ฒซ ๋ฒˆ์งธ FFQ์™€ ๋‘ ๋ฒˆ์งธ FFQ์˜ ์ƒ๋Œ€์ ์ธ ํƒ€๋‹น๋„๋Š” ์œ ์‚ฌํ•˜์˜€์œผ๋ฉฐ, 2ํšŒ FFQ๋ฅผ ํ‰๊ท ํ•œ ๊ฒฝ์šฐ๊ฐ€ ๊ฐ FFQ๋ณด๋‹ค ํƒ€๋‹น๋„๊ฐ€ ๋†’์•˜๋‹ค. ์Œ์‹์ˆ˜์ค€์˜ FFQ์™€ 12DR์„ 288๋ช…์—๊ฒŒ ์ˆ˜ํ–‰ํ•œ ํ›„, FFQ์—์„œ ์‘๋‹ต๋œ ์„ญ์ทจ๋นˆ๋„์™€ 12DR์—์„œ ๋ณด๊ณ ๋œ ์Œ์‹์˜ ์„ญ์ทจ๋นˆ๋„๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, 106๊ฐœ ์Œ์‹ํ•ญ๋ชฉ ์ค‘ 89๊ฐœ ํ•ญ๋ชฉ(84%)์—์„œ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์„ญ์ทจ๋นˆ๋„๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„๋Š” ์ž์ฃผ ์„ญ์ทจํ•˜๋Š” ์Œ์‹๋“ค์ด ๋” ๋†’์•˜์œผ๋ฉฐ, ์Œ์‹๊ตฐ ์ˆ˜์ค€์œผ๋กœ ํ†ตํ•ฉํ•˜์—ฌ ๋ถ„์„ํ•œ ๊ฒฝ์šฐ์—๋Š” 7๊ฐœ๊ตฐ ๋ชจ๋‘์—์„œ ์œ ์˜ํ•œ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณ„์ ˆ๋ณ„ 2์ผ์”ฉ ์ž„์˜ ์ถ”์ถœํ•œ DR์˜ ์„ญ์ทจ๋Ÿ‰์— FFQ์˜ ์„ญ์ทจ๋นˆ๋„๋ฅผ ๊ณต๋ณ€์ˆ˜๋กœ ํ™œ์šฉํ•˜๋Š” ๋ชจํ˜•(Multiple Source Method)์œผ๋กœ ์Œ์‹(๊ตฐ)์ˆ˜์ค€์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์ถ”์ •ํ•œ ๊ฒฐ๊ณผ, ๋Œ€๋ถ€๋ถ„์˜ ์Œ์‹ํ•ญ๋ชฉ๊ณผ 7๊ฐœ ์Œ์‹๊ตฐ ๋ชจ๋‘์—์„œ ์„ญ์ทจ๋Ÿ‰์˜ ๋ถ„ํฌ๊ฐ€ ์–‘ ๊ทน๋‹จ์—์„œ 12DR์— ์œ ์‚ฌํ•ด์กŒ์œผ๋ฉฐ ๋น„์„ญ์ทจ์ž์˜ ๋น„์œจ๋„ ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜์–‘์†Œ์˜ ์กฐ์‚ฌ์ผ์ˆ˜๋ฅผ ์‚ฐ์ •ํ•˜๋Š” ๊ธฐ์กด ๊ตญ๋‚ด์„ ํ–‰์—ฐ๊ตฌ๋ณด๋‹ค ๋‹ค์–‘ํ•œ ๋ณ€์ด์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๋” ๋„“์€ ๋ฒ”์œ„์˜ ์—ฐ๋ น๊ตฐ(20~65์„ธ)์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ์œผ๋ฉฐ, FFQ์˜ ์„ญ์ทจ๋นˆ๋„๋ฅผ 2DR์˜ ์„ญ์ทจ๋Ÿ‰๊ณผ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜๋Š” ๋ชจํ˜•์œผ๋กœ ์Œ์‹์ˆ˜์ค€์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์ฒ˜์Œ์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค๋Š” ๋ฐ์„œ ๊ทธ ์˜์˜๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ FFQ๋Š” ์ˆ˜ํ–‰ ์ „ํ›„์˜ ์„ญ์ทจ๋Ÿ‰๊ณผ ๋ชจ๋‘ ์œ ์‚ฌํ•œ ํƒ€๋‹น๋„๋ฅผ ๋ณด์˜€์œผ๋ฏ€๋กœ ์ถ”ํ›„ FFQ์˜ ์ˆ˜ํ–‰์‹œ์ ์„ ์ •ํ•˜๋Š”๋ฐ ์ฐธ๊ณ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ์—์„œ DR์„ 3์ผ์—์„œ 4์ผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒฝ์šฐ ๋Œ€๋ถ€๋ถ„ ์˜์–‘์†Œ์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์ƒ๊ด€๊ด€๊ณ„ 0.7์˜ ์ˆ˜์ค€์œผ๋กœ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ FFQ๋Š” ์˜์–‘์†Œ์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์ถ”์ •ํ•˜๋Š”๋ฐ ๋น„๊ต์  ๋†’์€ ์ˆ˜์ค€์˜ ์‹ ๋ขฐ๋„์™€ ๋ณดํ†ต ์ˆ˜์ค€์˜ ํƒ€๋‹น๋„๋ฅผ ๋ณด์˜€๋‹ค. ์˜์–‘์†Œ์™€ ๋‹ฌ๋ฆฌ ์„ญ์ทจํ™•๋ฅ ์„ ๊ณ ๋ คํ•ด์•ผ ํ•˜๋Š” ์Œ์‹์˜ ๊ฒฝ์šฐ์—๋Š” FFQ๋กœ๋ถ€ํ„ฐ ์กฐ์‚ฌ๋œ ์„ญ์ทจ๋นˆ๋„๋ฅผ ๊ณต๋ณ€์ˆ˜๋กœ ํฌํ•จํ•˜๋Š” ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ • ๋ชจํ˜•์˜ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ตœ์†Œ 2DR์— 1ํšŒ์˜ FFQ๊ฐ€ ๋ณ‘ํ–‰๋œ๋‹ค๋ฉด ๋” ์ •ํ™•ํ•œ ์ˆ˜์ค€์œผ๋กœ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ˆ˜๋„๊ถŒ ์„ฑ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋œ ๊ฒฐ๊ณผ์ด๋ฏ€๋กœ ๋‹ค๋ฅธ ์—ฐ๋ น๊ตฐ/์ง€์—ญ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง€์†๋˜์–ด ํ•œ๊ตญ์ธ์˜ ์‹์ƒํ™œ ๋ถ„์„๊ณผ ํ‰๊ฐ€๊ฐ€ ๋ณด๋‹ค ๋‹ค์–‘ํ•˜๊ฒŒ ์ˆ˜ํ–‰๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.The major purpose of dietary assessment is to assess the relationship between chronic disease and dietary intake and to evaluate the dietary intake of an individual or a group by using certain standards (Dietary Reference Intakes). Since dietary intake of humans can vary greatly on a day to day basis, it is essential to know the usual intake, which is defined as long-term average intake. 24-hour recall (24HR) and dietary record (DR) provide rich detail about the types and amount of foods consumed. However, due to the day-to-day variation, observed intake obtained from a small number of days is a poor estimator of usual intake. In addition, high burden on respondent make the multiple 24HR/DR infeasible. In contrast, food frequency questionnaire (FFQ) is designed to measure long-term intake but is limited to a finite list and has the disadvantage of not being able to accurately report individual intake over a long period of time. Unlike most nutrients, which is consumed daily, estimating usual intake of foods or dishes that are consumed only occasionally need to be estimated by calculating the probability of consumption and the amount consumed for those days. The aim of this study is to calculate the required number of DR needed and to validate the FFQ in order to assess usual nutrient intake and to evaluate the application of an analytical model to estimate the distribution of usual intake of occasionally-consumed foods among adults residing in Seoul metropolitan area. To determine the number of days, 12DR were collected from four seasons of 1 year from 414 adults aged 20~65y. The sources of variation and the variation ratio were calculated to determine the number of DR. Upon examining the data, variations attributable to the day of the week, recording sequence and seasonality were generally small (<3%), although the degree of variation differed by sex and age (20~45y and 46~65y). The correlation coefficient between the true intake and the observed intake increased with additional DR, reaching 0.7 at 3~4 days and 0.8 at 6~7 days. To examine the validity and reproducibility of the FFQ developed for Korean National Health and Nutrition Examination Survey (KNHANES), the 109-item FFQ was administered twice to 126 adults in a 1-year period and 3DR for each of the four seasons were conducted as a reference method. The mean Pearson correlation coefficient was 0.55 for reproducibility and 0.42 for validity. On average, 77% of the participants were classified into the same or adjacent quartiles, while 4% of the participants were grossly misclassified. The FFQ overestimated the nutrient intake of energy, carbohydrate and vitamin C, and underestimated the nutrient intake of fat, sodium, vitamin A and riboflavin. Adjusting for energy increased the correlation between FFQ and 12DR for most nutrients, and the mean nutrient intakes estimated by the calibrated FFQ were similar to the means estimated by the 12DR. The validity of the first and second FFQ was similar, while the validity of mean FFQ (average of two FFQ) was generally higher than for each FFQ. To estimate the usual intake of foods, whether or not increasing the FFQ frequency is associated with consumption frequency of 12DR was assessed from 288 adults. For all seven food groups, and 89 of 106 individual foods (84%), there was a significant correlation between FFQ frequency and consumption frequency of 12DR. The Multiple Source Method (MSM) was applied to a randomly selected 2DR using the FFQ frequency as a covariate. Usual food intake distributions that were estimated by MSM showed similar extreme estimates (5th, 95th percentiles) when compared with 12DR, more so than when 12DR was compared with 2DR. In addition, the proportion of non-consumer was reduced for most individual foods and food groups and was comparable with those of 12DR. This is the first study to estimate usual food intake distribution among Korean adults, and comprehensively examine the components of within-individual variation compared to previous Korean studies. In conclusion, this study suggests that 3~4 days of DR may be sufficient to achieve modest precision (rโ‰ฅ0.7), and 6~7 days may be needed for high precision (rโ‰ฅ0.8). The FFQ developed for assessing nutrient intakes in KNHANES has acceptable reproducibility and modest validity over a 1-year period. A significant correlation between FFQ frequency and consumption frequency of DR was observed (84%). Using at least 2 days of DR with frequency information such as a FFQ, usual food intake and the proportion of true-nonconsumers could be estimated more accurately. Since this study was performed to assess and evaluate the usual dietary intake in Korean adults residing in Seoul metropolitan area, further studies targeting subjects in different age groups and living areas are required in the future.๊ตญ๋ฌธ์ดˆ๋ก List of Tables List of Figures I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  5 II. ๋ฌธํ—Œ๊ณ ์ฐฐ 6 1. ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ •์˜ ์ค‘์š”์„ฑ 6 2. ์˜์–‘์†Œ์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ •์„ ์œ„ํ•œ ์กฐ์‚ฌ์ผ์ˆ˜์˜ ์‚ฐ์ • 11 3. ์‹ํ’ˆ์„ญ์ทจ๋นˆ๋„์กฐ์‚ฌ์ง€์˜ ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น๋„ ๊ฒ€์ฆ ๋ฐฉ๋ฒ• 15 4. ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ •์„ ์œ„ํ•œ ํ†ต๊ณ„์  ๋ชจํ˜• 20 (1) ์˜์–‘์†Œ์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ • 21 (2) ์‹ํ’ˆ(์Œ์‹)์˜ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ • 27 5. ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ •์„ ์œ„ํ•œ ํ†ต๊ณ„์  ๋ฐฉ๋ฒ•๋“ค์˜ ๋น„๊ต 40 6. ๊ตญ๋‚ด์˜ ์—ฐ๊ตฌ ๋™ํ–ฅ 43 (1) ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ •์„ ์œ„ํ•œ ์กฐ์‚ฌ์ผ์ˆ˜์˜ ์‚ฐ์ • 43 (2) ์‹ํ’ˆ์„ญ์ทจ๋นˆ๋„์กฐ์‚ฌ์ง€์˜ ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น๋„ ๊ฒ€์ฆ 44 (3) ํ•œ๊ตญ์ธ์˜ ์ผ์ƒ ์„ญ์ทจ๋Ÿ‰ ์ถ”์ • 46 III. ์—ฐ๊ตฌ 1: ํ•œ๊ตญ ์„ฑ์ธ์˜ ์˜์–‘์†Œ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ •์— ํ•„์š”ํ•œ ์กฐ์‚ฌ์ผ์ˆ˜์˜ ์‚ฐ์ • 47 1. ์„œ๋ก  47 2. ์—ฐ๊ตฌ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 50 (1) ์—ฐ๊ตฌ ๋Œ€์ƒ์ž 50 (2) ์‹์‚ฌ๊ธฐ๋ก๋ฒ• 50 (3) ํ†ต๊ณ„ ์ฒ˜๋ฆฌ 51 3. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 53 (1) ๋Œ€์ƒ์ž๋“ค์˜ ์ผ๋ฐ˜ ์‚ฌํ•ญ ๋ฐ ์˜์–‘์†Œ ์„ญ์ทจ๋Ÿ‰ 53 (2) ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ๋ณ€์ด ๊ตฌ์„ฑ ๋น„์œจ 55 (3) ์—ฐ๋ น๊ทธ๋ฃน์— ๋”ฐ๋ฅธ ๋ณ€์ด ๊ตฌ์„ฑ ๋น„์œจ 58 (4) ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์กฐ์‚ฌ์ผ์ˆ˜์˜ ์‚ฐ์ • 63 (5) ์—ฐ๋ น๊ทธ๋ฃน์— ๋”ฐ๋ฅธ ์กฐ์‚ฌ์ผ์ˆ˜์˜ ์‚ฐ์ • 66 (6) ์กฐ์‚ฌ์ผ์ˆ˜์˜ ์ฆ๊ฐ€์— ๋”ฐ๋ฅธ ์ƒ๊ด€๊ณ„์ˆ˜์˜ ๋ณ€ํ™” 69 4. ๊ณ ์ฐฐ 71 5. ๊ฒฐ๋ก  76 IV. ์—ฐ๊ตฌ 2: ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ์‹ํ’ˆ์„ญ์ทจ๋นˆ๋„์กฐ์‚ฌ์ง€์˜ ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น๋„ ๊ฒ€์ฆ 77 1. ์„œ๋ก  77 2. ์—ฐ๊ตฌ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 80 (1) ์—ฐ๊ตฌ ๋Œ€์ƒ์ž 80 (2) ์‹ํ’ˆ์„ญ์ทจ๋นˆ๋„์กฐ์‚ฌ์ง€ 82 (3) ์‹์‚ฌ๊ธฐ๋ก๋ฒ• 83 (4) ์‹์‚ฌ์„ญ์ทจ์ž๋ฃŒ ๋ถ„์„ 84 (5) ํ†ต๊ณ„ ์ฒ˜๋ฆฌ 86 3. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 88 (1) ์‹ ๋ขฐ๋„์˜ ๊ฒ€์ฆ 88 (2) ํƒ€๋‹น๋„ ๊ฒ€์ฆ 92 (3) 12DR์„ ์ด์šฉํ•œ FFQ์˜ ๋ณด์ • 99 4. ๊ณ ์ฐฐ 103 5. ๊ฒฐ๋ก  109 V. ์—ฐ๊ตฌ 3: ํ•œ๊ตญ ์„ฑ์ธ์˜ ์ฃผ์š” ์ƒ์šฉ ์Œ์‹ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰ ์ถ”์ • 110 1. ์„œ๋ก  110 2. ์—ฐ๊ตฌ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 113 (1) ์—ฐ๊ตฌ ๋Œ€์ƒ์ž 113 (2) ์‹์‚ฌ์„ญ์ทจ์กฐ์‚ฌ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ์—ฐ๋™ 114 (3) ์„ญ์ทจ๋นˆ๋„์™€ ์„ญ์ทจ๋Ÿ‰์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 115 (4) ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์˜ ์‚ฐ์ถœ 116 (5) ํ†ต๊ณ„ ์ฒ˜๋ฆฌ 119 3. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 120 (1) FFQ์™€ 12DR์˜ ์„ญ์ทจ๋นˆ๋„๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋น„๊ต 120 (2) 2DR ์„ญ์ทจ๋Ÿ‰๊ณผ FFQ ๋นˆ๋„๋ฅผ ํ™œ์šฉํ•œ ์ผ์ƒ์„ญ์ทจ๋Ÿ‰์˜ ์ถ”์ • 131 4. ๊ณ ์ฐฐ 156 5. ๊ฒฐ๋ก  160 VI. ์ข…ํ•ฉ ๊ณ ์ฐฐ 161 VII. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  166 1. ์š”์•ฝ 166 2. ์ œํ•œ์  167 3. ์ œ์–ธ 168 ์ฐธ๊ณ ๋ฌธํ—Œ 169 Appendices 186 Abstract 236Docto

    Neuroprotection of active principles from the Cudrania tricuspidata in in vitro models of Parkinsons disease: Effect on the ubiquitin-proteasome system and Nrf2-ARE pathway

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2018. 2. ๋งˆ์‘์ฒœ.Abstract Neuroprotection of active principles from the Cudrania tricuspidata in in vitro models of Parkinsons disease: Effect on the ubiquitin-proteasome system and Nrf2- ARE pathway Dong-Woo Kim Natural Products Science Major College of Pharmacy Doctor Course in the Graduate School Seoul National University Parkinsons disease (PD) is characterized by severe motor deficits, cogwheel rigidity, bradykinesia, and the loss of dopaminergic neurons. The aetiology of PD has not been clearly identifiedhowever, oxidative stress is thought to be a common factor that leads to cellular dysfunction and neurodegeneration. In particular, the pathological events that occur in PD have been suggested to be linked to protein oxidation caused by oxidative stress, and excessive intracellular ROS induce apoptosis that is characterized by the cleavage of caspase-3, caspase-9 and poly ADP-ribose polymerase (PARP). The neurotoxin 6-hydroxydopamine (6-OHDA), Carbonyl cyanide 3-chlorophenylhydrazone (CCCP) destroys dopaminergic and noradrenergic neurons in the brain by inducing excessive ROS such as superoxide radicals, which leads to protein oxidation and neuronal cell death. Also, ubiquitin-proteasome system play a key role in the etiology of PD. The proteasome selective degrades oxidized proteins via ubiquitin-mediated processes, and its role is essential for cellular protein maintenance. However, dysfunctionsย in the ubiquitination machinery or in the proteolytic activities of theย proteasome induce the accumulation of polyubiquitinated misfolded proteins and oxidized proteins. Subsequently, this induces protein aggregation, further inhibits proteasome activity, generates additional cellular stress, and ultimately leads to neuronal cell death. Additionally, mitophagy play a major role in the etiology of PD. Mitophagy, a specialized autophagy pathway that mediates the clearance of damaged mitochondria by lysosomes, is important for mitochondrial quality control. Defective mitochondria, if left uncleared, can be a source of oxidative stress and compromise the health of the entire mitochondrial network. The several lines of evidence propose that mitochondrial dysfunction is central to the disease.ย PD-associated mutations in PINK1 or parkin impair parkin recruitment, mitochondrial ubiquitination, and/or mitophagy. In the context of the inherently high mitochondrial oxidative stress in substantia nigra dopamine neurons, loss of parkin-mediated mitophagy could explain the greater susceptibility of substantia nigra neurons to neurodegeneration. Thus, promoting mitophagy and enhancing mitochondrial quality control could benefit dopaminergic neurons. The current therapeutic drugs are based on prohibiting the progress of PD through treatment of dopamine agonist or dopamine precursor. New therapies in development are aimed at protecting dopaminergic neurons. In this study, the effects of natural products on 6-OHDA, CCCP-mediated signaling in SH-SY5Y neuroblastoma cell were investigated to discover new lead compounds for the treatment of PD. Cudrania tricuspidata (Moraceae) is a subtropical tree that is widely distributed in Korea, China, and Japan. The fruits of C. tricuspidata are used in jams, juices, and a fermented alcoholic beverage with sugar, and they are commercially produced as food in Korea. Also, the cortex and root bark of C. tricuspidata have been used as a traditional medicine for inflammation and tumors. A recent study demonstrated that the extracts of C. tricuspidata protect neurons against oxidative stress-induced cytotoxicity and inhibitory effects on nitric oxide synthase (NOS). The compounds isolated from C. tricuspidata are primarily xanthones and flavones in addition to some alkaloids, lignins, coumarins, polysaccharides, and chromones. The isoflavones and chromones from C. tricuspidata have been reported to exert protective effects against 6-OHDA-induced neurotoxicity and to have inhibitory effects against IgE-mediated allergic and inflammatory responses. In first chapter, it was investigated that 5,7-dihydroxychromone (DHC) isolated from the roots of C. tricuspidata for its neuronal cell protection and inhibition of the generation of ROS in 6-OHDA-induced SH-SY5Y cells. Flow cytometric analysis revealed that DHC protected against the 6-OHDA-induced generation of ROS and protected against neuronal cell death. Additionally, DHC increased the nuclear translocation of Nrf2 and the binding of Nrf2 to ARE, which subsequently resulted in the up-regulation of the expression of Nrf2-dependent antioxidant genes, including heme oxygenase 1 (HO-1), NAD(P)H quinone oxidoreductase 1 (NQO1) and glutamate-cysteine ligase, catalytic subunit (GCLc). DHC inhibited the expression of cleaved caspase-3 and caspase-9 and PARP in 6-OHDA-induced SH-SY5Y cells. The addition of Nrf2 siRNA abolished the neuroprotective effect of DHC against 6-OHDA-induced cell death and the expression of Nrf2-mediated antioxidant genes. These findings suggest that the neuroprotective effect of DHC against 6-OHDA-induced toxicity is partly due to the induction of Nrf2-mediated antioxidant gene expression via the activation of the Nrf2-ARE signaling pathway in SH-SY5Y cells. In the second chapter, the effects of ethanol extract from the fruits of C. tricuspidata (CTE) and it active compounds were studied. Among the nine isolates from a 50% ethanol extract from C. tricuspidata fruits (CTE50), orobol (OB), 6-prenylorobol (POB), and 6,8-diprenylorobol (DPOB) showed neuroprotective effects in 6-OHDA-induced SH-SY5Y cell death. In addition, CTE50 and the three orobol derivatives (OB, POB, and DPOB) attenuated the cleavage of caspase-3, caspase-9, and PARP and inhibited the excessive generation of ROS. Furthermore, it enhanced the 6-OHDA-induced dysfunction of proteasome activity and reduced the accumulation of ubiquitin conjugated-proteins and the polyubiquitination of ฮฑ-synuclein and synphilin-1. The proteasome inhibitor MG132 blocked the neuroprotective effects and the enhanced proteasome activity produced by CTE50 and the three orobol derivatives. These results demonstrate that CTE50 and three orobol derivatives protect against 6-OHDA-induced neurotoxicity by enhancing the ubiquitin/proteasome-dependent degradation of ฮฑ-synuclein and synphilin-1, suggesting that they might be possible candidates for the treatment of neurodegenerative diseases. In the third chapter, the effects of active compounds from the C. tricuspidata extracts on deubiquitinating enzymes were studied. TH3-125-4 (TH20) isolated from the root barks of the C. tricuspidata protected against CCCP-induced neuronal cell death in Parkin K.D. SH-SY5Y cells. Also, TH20 significantly inhibited USP30 enzyme activity and disassembly of polyubiquitin chain in in vitro assay. Additionally, TH20 decreased protein expression of USP30. Based on the results, it was suggested that TH20 might be promising candidates for the therapy of familiar PD via restoring Parkin-mediated mitophagy.Chapter1 Neuroprotection against 6-OHDA-induced oxidative stress and apoptosis in SH-SY5Y cells by 5,7 Dihydroxychromone: Activation of the Nrf2/ARE pathway 1 1. Introduction 2 2. Material and methods 10 2.1. Chemicals and reagents 10 2.2. Preparation of 5,7-dihydroxychromone (DHC) 11 2.3 Cell cultures 11 2.4 Measurement of cell viability 12 2.5 Measurement of cell necrosis by propidium iodide staining 12 2.6 Measurement of intracellular ROS by Flow cytometry 13 2.7 Nuclear and cytosolic lysate preparations 13 2.8 Electrophoretic mobility shift assay (EMSA) 14 2.9 Nrf2 knockout via the transfection of small interfering RNA (siRNA) 14 2.10 Measurement of mRNA expression 14 2.11 Measurement of protein expression 16 2.12 Nuclear translocation of Nrf2 using fluorescence microscope 16 2.13 Statistical analysis 17 3 . Results 18 3.1 Protective effect of DHC against 6-OHDA-induced neuronal cell death 18 3.2 Inhibitory effect of DHC against 6-OHDA-induced intracellular ROS generation 27 3.3 Effects of DHC on induction of the nuclear Nrf2 and binding affinity of Nrf2/ARE in SH-SY5Y cells 29 3.4 Effects of DHC on HO-1, NQO1 and GCLc protein expression in SH-SY5Y cells 34 3.5 Effects of DHC on HO-1, NQO1 and GCLc mRNA expression in SH-SY5Y cells 37 3.6 The inhibitory effects of DHC on the 6-OHDA-induced apoptotic signal 41 4. Discussion 43 5. Conclusion 47 Chapter2 Orobol derivatives and extracts from Cudrania tricuspidata fruits protect against 6-hydroxydopamine-induced neuronal cell death by enhancing proteasome activity and the ubiquitin/proteasome-dependent degradation of ฮฑ-synuclein and synphilin- 1 49 1. Introduction 50 2. Material and methods 57 2.1. Chemicals and reagents 57 2.2. Preparation of ethanol extracts from the fruits of C. tricuspidata (CTE) 57 2.3 Ultra performance liquid chromatography (UPLC) analysis of CTE50 58 2.4 Isolation and identification of compounds from CTE50 59 2.5 Cell cultures 59 2.6 Measurement of cell viability 60 2.7 Measurement of intracellular ROS by flow cytometry 60 2.8 Measurement of proteasome activity 61 2.9 Measurement of mRNA expression 62 2.10 Measurement of protein expression 63 2.11 Immunoprecipitation assay 64 2.12 Statistical analysis 65 3. Results 66 3.1 Protective effects against 6-OHDA-induced neuronal cell death in SH-SY5Y cell 66 3.2 Inhibition of 6-OHDA-induced intracellular ROS generation 73 3.3 Neuroprotective effects against 6-OHDA-induced apoptosis 76 3.4 Protective effects against 6-OHDA-induced dysfunction of proteasome activity 78 3.5 Effects of CTE50 and three orobol derivatives on proteasome subunit mRNA expression 82 3.6 Inhibition of 6-OHDA-induced ubiquitin-conjugated proteins 84 3.7 Inhibition of 6-OHDA-induced poly-ubiquitination of ฮฑ-synuclein, and synphilin-1 86 3.8 A proteasome inhibitor (MG-132) diminished the protective effects of CTE50 and the three orobol derivatives against 6-OHDA-induced neuronal cell death and proteasome dysfunction 89 4. Discussion 96 5. Conclusion 103 Chapter3 Protective effects of TH3-125-4 (TH20) from the root barks of Cudrania tricuspidata on CCCP-induced neuronal cell death via the inhibition of USP30 deubiquitinating enzyme in Parkin knock down SH-SY5Y cells 105 1. Introduction 106 2. Material and methods 109 2.1 Chemicals and reagents 109 2.2 Preparation of TH3-125-4 (TH20) 109 2.3 Cell cultures 110 2.4 Measurement of cell viability 110 2.5 Measurement of mitochondrial membrane potential by JC-1 staining 111 2.6 Measurement of intracellular ROS by Flow cytometry 111 2.7 Mitochondrial and cytosolic fraction preparations 112 2.8 Parkin knock down via the transfection of small interfering RNA (siRNA) 112 2.9 Measurement of protein expression 113 2.10 Immunoprecipitation assay 113 2.11 Measurement of mitophagy by fluorescence microscope 114 2.12 Measurement of deubiquitinating enzyme activity 115 2.13 Measurement of ubiquitin chain disassembly 115 2.14 Statistical analysis 115 3. Results 117 3.1 Inhibitory effect of TH3-125-4 (TH20) on deubiquitinating enzymes, USP15, USP30 117 3.2 Effect of TH3-125-4 (TH20) on ubiquitin chain disassembly 123 3.3 The protective effects of TH3-125-4 (TH20) against CCCP-induced neuronal cell death in parkin knock down SH-SY5Y cells 125 3.4 Inhibitory effect of TH3-125-4 (TH20) on USP30 protein expression in SH-SY5Y cells 129 3.5 Effect of TH3-125-4 (TH20) on CCCP-induced disruption of mitochondrial membrane potential 132 4. Discussion 134 5. Conclusion 137 References 138 Abstract (in Korean) 161Docto

    A hierarchical prognostic model for risk stratification in patients with early breast cancer according to 18 F-fludeoxyglucose uptake and clinicopathological parameters

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    This study was to investigate a hierarchical prognostic model using clinicopathological factors and 18 F-fludeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) for recurrence-free survival (RFS) in patients with early breast cancer who underwent surgery without neoadjuvant chemotherapy. A total of 524 patients with early breast cancer were included. The Cox proportional hazards model was used with clinicopathological variables and maximum standardized uptake value (SUVmax) on PET/CT. After classification and regression tree (CART) modeling, RFS curves were estimated using the Kaplan-Meier method and differences in each risk layer were assessed using the log-rank test. During a median follow-up of 46.2 months, 31 (5.9%) patients experienced recurrence. The CART model identified four risk layers: group 1 (SUVmax โ‰ค6.75 and tumor size โ‰ค2.0 cm); group 2 (SUVmax โ‰ค6.75 and Luminal A [LumA] or TN tumor >2.0 cm); group 3 (SUVmax โ‰ค6.75 and Luminal B [LumB] or human epidermal growth factor receptor 2 [HER2]-enriched] tumor >2.0 cm); group 4 (SUVmax >6.75). Five-year RFS was as follows: 95.9% (group 1), 98% (group 2), 82.8% (group 3), and 85.4% (group 4). Group 3 or group 4 showed worse prognosis than group 1 or group 2 (group 1 vs. group 3: P = 0.040; group 1 vs. group 4: P 6.75) in primary breast cancer was an independent factor for poor RFS. In patients with low SUVmax, LumB or HER2-enriched tumor >2 cm was also prognostic for poor RFS, similar to high SUVmax.ope
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