12 research outputs found

    ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•œ ์ด๋ฏธ์ง€ ๋ณต์›

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021.8. ์•ˆ์ค€์˜.Image restoration is an important technology which can be used as a pre-processing step to increase the performances of various vision tasks. Image super-resolution is one of the important task in image restoration which restores a high-resolution (HR) image from low-resolution (LR) observation. The recent progress of deep convolutional neural networks has enabled great success in single image super-resolution (SISR). its performance is also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this dissertation, I propose a new single image super-resolution framework by using neural architecture search (NAS) method. As the performance improves, the network becomes more complex and deeper, so I apply NAS algorithm to find the optimal network while reducing the effort in network design. In detail, the proposed scheme is summarized to three topics: image super-resolution using efficient neural architecture search, multi-branch neural architecture search for lightweight image super-resolution, and neural architecture search for image super-resolution using meta-transfer learning. At first, I expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. I use a hierarchical search strategy to find the best connection with local and global features. In this process, I define a complexity-based-penalty and add it to the reward term of REINFORCE algorithm. Experiments show that my DeCoNASNet outperforms the state-of-the-art lightweight super-resolution networks designed by handcraft methods and existing NAS-based design. I propose a new search space design with multi-branch structure to enlarge the search space for capturing multi-scale features, resulting in better reconstruction on grainy areas. I also adopt parameter sharing scheme in multi-branch network to share their information and reduce the whole network parameter. Experiments show that the proposed method finds an optimal SISR network about twenty times faster than the existing methods, while showing comparable performance in terms of PSNR vs. parameters. Comparison of visual quality validates that the proposed SISR network reconstructs texture areas better than the previous methods because of the enlarged search space to find multi-scale features. Lastly, I apply meta-transfer learning to the NAS procedure for image super-resolution. I train the controller and child network with the meta-learning scheme, which enables the controllers to find promising network for several scale simultaneously. Furthermore, meta-trained child network is reused as the pre-trained parameters for final evaluation phase to improve the final image super-resolution results even better and search-evaluation gap problem is efficiently reduced.์ด๋ฏธ์ง€ ๋ณต์›์€ ๋‹ค์–‘ํ•œ ์˜์ƒ์ฒ˜๋ฆฌ ๋ฌธ์ œ์˜ ์„ฑ๋Šฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ์ „ ์ฒ˜๋ฆฌ ๋‹จ๊ณ„๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ๊ธฐ์ˆ ์ด๋‹ค. ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™”๋Š” ์ด๋ฏธ์ง€ ๋ณต์›๋ฐฉ๋ฒ• ์ค‘ ์ค‘์š”ํ•œ ๋ฌธ์ œ์˜ ํ•˜๋‚˜๋กœ์จ ์ €ํ•ด์ƒ๋„์˜ ์ด๋ฏธ์ง€๋ฅผ ๊ณ ํ•ด์ƒ๋„์˜ ์ด๋ฏธ์ง€๋กœ ๋ณต์›ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ์ตœ๊ทผ์—๋Š” ์ปจ๋ฒŒ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง (CNN)์„ ์‚ฌ์šฉํ•˜๋Š” ๋”ฅ ๋Ÿฌ๋‹(deep learning) ๊ธฐ๋ฐ˜์˜ ๋ฐฉ๋ฒ•๋“ค์ด ๋‹จ์ผ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™” (SISR) ๋ฌธ์ œ๋ฅผ ํ‘ธ๋Š”๋ฐ ๋งŽ์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™” ์„ฑ๋Šฅ์€ CNN์„ ๊นŠ๊ฒŒ ์Œ“๊ฑฐ๋‚˜ ๋ณต์žกํ•œ ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•จ์œผ๋กœ์จ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฃผ์–ด์ง„ ๋ฌธ์ œ์— ๋Œ€ํ•œ ์ตœ์ ์˜ ๊ตฌ์กฐ๋ฅผ ์ฐพ๋Š” ๊ฒƒ์€ ํ•ด๋‹น ๋ถ„์•ผ์˜ ์ „๋ฌธ๊ฐ€๋ผ ํ•  ์ง€๋ผ๋„ ์–ด๋ ต๊ณ  ์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๋Š” ์ž‘์—…์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์‹ ๊ฒฝ๋ง ๊ตฌ์ถ• ์ ˆ์ฐจ๋ฅผ ์ž๋™ํ™”ํ•˜๋Š” ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ ๊ฒ€์ƒ‰ (NAS) ๋ฐฉ๋ฒ•์ด ๋„์ž…๋˜์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ ๊ฒ€์ƒ‰ (NAS) ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋‹จ์ผ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ํฌ๊ฒŒ ์„ธ ๊ฐ€์ง€๋กœ ์š”์•ฝ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ํšจ์œจ์ ์ธ ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰๊ธฐ๋ฒ•(ENAS)์„ ์ด์šฉํ•œ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™”, ๋ณ‘๋ ฌ ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™”, ๋ฉ”ํƒ€ ์ „์†ก ํ•™์Šต์„ ์ด์šฉํ•˜๋Š” ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰๊ธฐ๋ฒ•์„ ํ†ตํ•œ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™” ์ด๋‹ค. ์šฐ์„ , ์šฐ๋ฆฌ๋Š” ์ฃผ๋กœ ์˜์ƒ ๋ถ„๋ฅ˜์— ์“ฐ์ด๋˜ ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰ ๊ธฐ๋ฒ•์„ ์˜์ƒ ๊ณ ํ•ด์ƒ๋„ํ™”์— ์ ์šฉํ•˜์˜€์œผ๋ฉฐ, DeCoNASNet์ด๋ผ ๋ช…๋ช…๋œ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ณ„์ธต์  ๊ฒ€์ƒ‰ ์ „๋žต์„ ์‚ฌ์šฉํ•˜์—ฌ ์ง€์—ญ/์ „์—ญ ํ”ผ์ณ(feature) ํ•ฉ๋ณ‘์„ ์œ„ํ•œ ์ตœ์ƒ์˜ ์—ฐ๊ฒฐ ๋ฐฉ๋ฒ•์„ ๊ฒ€์ƒ‰ํ•˜์˜€๋‹ค. ์ด ๊ณผ์ •์—์„œ ํ•„์š” ๋ณ€์ˆ˜๊ฐ€ ์ ์œผ๋ฉด์„œ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋‚ผ ์ˆ˜ ์žˆ๋„๋ก ๋ณต์žก์„ฑ ๊ธฐ๋ฐ˜ ํŽ˜๋„ํ‹ฐ (complexity-based penalty) ๋ฅผ ์ •์˜ํ•˜๊ณ  ์ด๋ฅผ REINFORCE ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ณด์ƒ ์‹ ํ˜ธ์— ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ DeCoNASNet์€ ๊ธฐ์กด์˜ ์‚ฌ๋žŒ์ด ์ง์ ‘ ์„ค๊ณ„ํ•œ ์‹ ๊ฒฝ๋ง๊ณผ ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰ ๊ธฐ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ค๊ณ„๋œ ์ตœ๊ทผ์˜ ๊ณ ํ•ด์ƒ๋„ํ™” ๊ตฌ์กฐ์˜ ์„ฑ๋Šฅ์„ ๋Šฅ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋˜ํ•œ ์—ฌ๋Ÿฌ ํฌ๊ธฐ์˜ ํ”ผ์ณ(feature)๋ฅผ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•ด ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰ ๊ธฐ๋ฒ•์˜ ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์„ ํ™•๋Œ€ํ•˜์—ฌ ๋ณ‘๋ ฌ ์‹ ๊ฒฝ๋ง์„ ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋•Œ, ๋ณ‘๋ ฌ์‹ ๊ฒฝ๋ง์˜ ๊ฐ ์œ„์น˜์—์„œ ๋งค๊ฐœ ๋ณ€์ˆ˜๋ฅผ ๊ณต์œ ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ ๋ณ‘๋ ฌ์‹ ๊ฒฝ๋ง์˜ ๊ฐ ๊ตฌ์กฐ๋ผ๋ฆฌ ์ •๋ณด๋ฅผ ๊ณต์œ ํ•˜๊ณ  ์ „์ฒด ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•˜๋Š”๋ฐ ํ•„์š”ํ•œ ๋งค๊ฐœ ๋ณ€์ˆ˜๋ฅผ ์ค„์ด๋„๋ก ํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋งค๊ฐœ ๋ณ€์ˆ˜ ํฌ๊ธฐ ๋Œ€๋น„ ์„ฑ๋Šฅ์ด ์ข‹์€ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ํ™•์žฅ๋œ ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์—์„œ ์—ฌ๋Ÿฌ ํฌ๊ธฐ์˜ ํ”ผ์ณ (feature)๋ฅผ ํ•™์Šตํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์ด์ „ ๋ฐฉ๋ฒ•๋ณด๋‹ค ๋ณต์žกํ•œ ์˜์—ญ์„ ๋” ์ž˜ ๋ณต์›ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ฉ”ํƒ€ ์ „์†ก ํ•™์Šต(meta-transfer learning)์„ ์‹ ๊ฒฝ๋ง ๊ฒ€์ƒ‰์— ์ ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ํฌ๊ธฐ์˜ ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฉ”ํƒ€ ์ „์†ก ํ•™์Šต ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ์ œ์–ด๊ธฐ๊ฐ€ ์—ฌ๋Ÿฌ ํฌ๊ธฐ์˜ ์ข‹์€ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ๋™์‹œ์— ์ฐพ์„ ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ฉ”ํƒ€ ํ›ˆ๋ จ๋œ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋Š” ์ตœ์ข… ์„ฑ๋Šฅ ํ‰๊ฐ€ ์‹œ ํ•™์Šต์˜ ์‹œ์ž‘์ ์œผ๋กœ ์žฌ์‚ฌ์šฉ ๋˜์–ด ์ตœ์ข… ์ด๋ฏธ์ง€ ๊ณ ํ•ด์ƒ๋„ํ™” ์„ฑ๋Šฅ์„ ๋”์šฑ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ํšจ๊ณผ์ ์œผ๋กœ ๊ฒ€์ƒ‰-ํ‰๊ฐ€ ๊ดด๋ฆฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๋‹ค.1 INTRODUCTION 1 1.1 contribution 3 1.2 contents 4 2 Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS 5 2.1 Introduction 5 2.2 Proposed Method 9 2.2.1 Overall structure of DeCoNASNet 9 2.2.2 Constructing the DNB 11 2.2.3 Constructing controller for the DeCoNASNet 13 2.2.4 Training DeCoNAS and complexity-based penalty 13 2.3 Experimental results 15 2.3.1 Settings 15 2.3.2 Results 16 2.3.3 Ablation study 21 2.4 Summary 22 3 Multi-Branch Neural Architecture Search for Lightweight Image Super-resolution 23 3.1 Introduction 23 3.2 Related Work 26 3.2.1 Single image super-resolution 26 3.2.2 Neural architecture search 27 3.2.3 Image super-resolution with neural architecture search 29 3.3 Method 32 3.3.1 Overview of the Proposed MBNAS 32 3.3.2 Controller and complexity-based penalty 33 3.3.3 MBNASNet 35 3.3.4 Multi-scale block with partially shared Nodes 37 3.3.5 MBNAS 38 3.4 datasets and experiments 39 3.4.1 Settings 39 3.4.2 Experiments on single image super-resolution (SISR) 41 3.5 Discussion 48 3.5.1 Effect of the complexity-based penalty to the performance of controller 49 3.5.2 Effect of multi-branch structure and partial parameter sharing scheme 50 3.5.3 Effect of gradient flow control weights and complexity-based penalty coefficient 51 3.6 Summary 52 4 Meta-transfer learning for simultaneous search of various scale image super-resolution 54 4.1 Introduction 54 4.2 Related Work 56 4.2.1 Single image super-resolution 56 4.2.2 Neural architecture search 57 4.2.3 Image super-resolution with neural architecture search 58 4.2.4 Meta-learning 59 4.3 Method 59 4.3.1 Meta-learning 60 4.3.2 Meta-transfer learning 62 4.3.3 Transfer-learning 63 4.4 datasets and experiments 63 4.4.1 Settings 63 4.4.2 Experiments on single image super-resolution(SISR) 64 4.5 Summary 66 5 Conclusion 69 Abstract (In Korean) 80๋ฐ•

    ์‹œ๊ณต๊ฐ„ ๋ฐ˜์ „ ๋Œ€์นญ์ด ์žˆ๋Š” ๊ณ„์˜ ๋  ์œ„์ƒ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต),2020. 2. ์–‘๋ฒ”์ •.We study topological phases in systems with spacetime inversion symmetry ISTI_{\text{ST}}. ISTI_{\text{ST}} is an anti-unitary symmetry which is local in momentum space and satisfies IST2=1I_{\text{ST}}^2=1 such as PTPT in 2D and 3D without spin-orbit coupling and C2TC_{2}T in 2D with or without spin-orbit coupling where PP, TT, C2C_2 indicate inversion, time-reversal, and two-fold rotation symmetries, respectively. Under ISTI_{\text{ST}}, the Hamiltonian and the periodic part of the Bloch wave function can be constrained to be real-valued, which makes the Berry curvature and the Chern number to vanish. In this class of systems, gapped band structures of real wave functions can be topologically distinguished by Stiefel-Whitney numbers instead. The first and second Stiefel-Whitney numbers w1w_1 and w2w_2, respectively, are the corresponding invariants in 1D and 2D, which are equivalent to the quantized Berry phase and the Z2Z_2 monopole charge, respectively. We first describe the topological phases characterized by the first Stiefel-Whitney number, including 1D topological insulators with quantized charge polarization, 2D Dirac semimetals, and 3D nodal line semimetals. Next, we show how the second Stiefel-Whitney class characterizes the 3D nodal line semimetals carrying a Z2Z_{2} monopole charge. In particular, we explain how the second Stiefel-Whitney number w2w_2, the Z2Z_{2} monopole charge, and the linking number between nodal lines are related. Then, we study the properties of 2D and 3D topological insulators characterized by the nontrivial second Stiefel Whitney class. After this exposure to our general theory, we explain the reformulated Nielsen-Ninomiya theorem in two dimensions as an interesting application. We derive all these results by assuming only ISTI_{\text{ST}} symmetry. When PP or C2C_2 symmetry is present in addition to ISTI_{\text{ST}} symmetry in the absence of spin-orbit coupling, we show that the second Stiefel-Whitney number can be calculated efficiently using the parity eigenvalues of PP or C2C_2. The relation between the parity eigenvalues and the second Stiefel-Whitney number is applied to the study of odd-parity topological superconductivity in spin-polarized systems.์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์‹œ๊ณต๊ฐ„ ๋ฐ˜์ „๋Œ€์นญ์ด ์žˆ๋Š” ๊ณ„์—์„œ์˜ ์œ„์ƒ์ ์ธ ์ƒ์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ ์‹œ๊ณต๊ฐ„ ๋ฐ˜์ „ ISTI_{\text{ST}} ์€ ์šด๋™๋Ÿ‰์„ ๋ฐ”๊พธ์ง€ ์•Š๋Š” ๋ฐ˜(anti)์œ ๋‹ˆํ„ฐ๋ฆฌ ๋Œ€์นญ ์—ฐ์‚ฐ์ž์ด๋ฉด์„œ IST2=1I_{\text{ST}}^2=1์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋งํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ISTI_{\text{ST}}๋Š” ์Šคํ•€๊ถค๋„๊ฒฐํ•ฉ์ด 3์ฐจ์› ๋ฌผ์งˆ์—์„œ ๊ณต๊ฐ„ ๋ฐ˜์ „ PP์™€ ์‹œ๊ฐ„ ๋ฐ˜์ „ TT์˜ ์กฐํ•ฉ์ธ PTPT ํ˜น์€ ์Šคํ•€๊ถค๋„๊ฒฐํ•ฉ์˜ ์œ ๋ฌด์™€ ์ƒ๊ด€์—†์ด 2์ฐจ์› ๋ฌผ์งˆ์—์„œ ์ˆ˜์ง ์ถ•์œผ๋กœ 180๋„ ํšŒ์ „ C2C_2 ์™€ ์‹œ๊ฐ„ ๋ฐ˜์ „ TT ์˜ ๊ฒฐํ•ฉ์ธ C2TC_2T๊ฐ€ ์žˆ๋‹ค. ์‹œ๊ณต๊ฐ„ ๋ฐ˜์ „ ๋Œ€์นญ์€ ์šด๋™๋Ÿ‰ ๊ณต๊ฐ„ ๋‚ด์—์„œ ํ•ด๋ฐ€ํ† ๋‹ˆ์•ˆ๊ณผ ๋ธ”๋กœํ ํŒŒ๋™ํ•จ์ˆ˜์— ์‹ค์ˆ˜ ์กฐ๊ฑด์„ ์ฃผ๊ณ , ๋”ฐ๋ผ์„œ ๋ฒ ๋ฆฌ ๊ณก๋ฅ ๊ณผ ์ฒœ ์ˆซ์ž๊ฐ€ ํ•ญ์ƒ 0์ด ๋œ๋‹ค. ๋”ฐ๋ผ ์‹ค์ˆ˜ ํŒŒ๋™ํ•จ์ˆ˜์˜ ์œ„์ƒ์ ์ธ ์„ฑ์งˆ์€ ์ฒœ ์ˆซ์ž ๋Œ€์‹ ์— ๋‹ค๋ฅธ ์œ„์ƒ ๋ถˆ๋ณ€๋Ÿ‰์œผ๋กœ ๊ธฐ์ˆ ๋˜์–ด์•ผ ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹ค์ˆ˜ ํŒŒ๋™ํ•จ์ˆ˜์˜ ์œ„์ƒ์ ์ธ ์„ฑ์งˆ์ด ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž๋ผ๋Š” ์œ„์ƒ๋ถˆ๋ณ€๋Ÿ‰์œผ๋กœ ๊ธฐ์ˆ ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ๊ณ , ์ด ๋ถˆ๋ณ€๋Ÿ‰์˜ ์ผ๋ฐ˜์ ์ธ ์„ฑ์งˆ๊ณผ ๋ฌผ๋ฆฌ์ ์ธ ์˜๋ฏธ์— ๋Œ€ํ•ด ์„ค๋ช…ํ•œ๋‹ค. ์ œ 1 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž์™€ ์ œ 2 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž๋Š” 1์ฐจ์›๊ณผ 2์ฐจ์› ์œ„์ƒ ๋ถˆ๋ณ€๋Ÿ‰์œผ๋กœ ์–‘์žํ™”๋œ ๋ฒ ๋ฆฌ ์œ„์ƒ๊ณผ Z2Z_2 ํ™€๊ทน ์ „ํ•˜์— ๋Œ€์‘๋œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋จผ์ € ์ œ 1 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž๋กœ ์„ค๋ช…๋˜๋Š” ์œ„์ƒ์ ์ธ ์ƒ์— ๋Œ€ํ•ด์„œ ๋‹ค๋ฃฌ๋‹ค. 1์ฐจ์›์—์„œ ์–‘์žํ™”๋œ ์ „๊ธฐ ํŽธ๊ทน์„ ๊ฐ€์ง€๋Š” ๋ถ€๋„์ฒด, 2์ฐจ์› ๋””๋ฝ ์ค€๊ธˆ์†๊ณผ 3์ฐจ์› ๋งˆ๋”” ์„  ์ค€๊ธˆ์†์ด ์ด์— ํ•ด๋‹น๋œ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ œ 2 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž๊ฐ€ 3์ฐจ์› ๋งˆ๋”” ์„ ์ด Z2Z_2 ํ™€๊ทน ์ „ํ•˜๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์„ ์–ด๋–ป๊ฒŒ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ์ง€ ์–˜๊ธฐํ•œ๋‹ค. ํŠนํžˆ ์ œ 2 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž, Z2Z_2 ํ™€๊ทน ์ „ํ•˜, ๊ทธ๋ฆฌ๊ณ  ๋งˆ๋”” ์„ ๋“ค์˜ ์—ฐ๊ฒฐ ์ˆ˜ ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•ด์„œ ์„ค๋ช…ํ•œ๋‹ค. ๊ทธ ๋‹ค์Œ ์ œ 2 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž๋กœ ์„ค๋ช…๋˜๋Š” 2์ฐจ์›๊ณผ 3์ฐจ์› ์œ„์ƒ ๋ถ€๋„์ฒด์— ๊ด€ํ•ด์„œ ๋‹ค๋ฃฌ๋‹ค. ์ผ๋ฐ˜์ ์ธ ์ด๋ก ์— ๋Œ€ํ•œ ์„ค๋ช…์„ ๋งˆ์นœ ๋‹ค์Œ, 2์ฐจ์›์—์„œ ๋‹์Šจ๊ณผ ๋‹ˆ๋…ธ๋ฏธ์•ผ์˜ ์ •๋ฆฌ์˜ ์žฌ์ •๋ฆฝ์„ ์šฐ๋ฆฌ ์ด๋ก ์˜ ์žฌ๋ฏธ์žˆ๋Š” ์‘์šฉ์œผ๋กœ์„œ ์„ค๋ช…ํ•œ๋‹ค. ์ด ๋ชจ๋“  ์ด๋ก ์ ์ธ ๋ถ„์„๋“ค์€ ์‹œ๊ณต๊ฐ„ ๋ฐ˜์ „ ๋Œ€์นญ๋งŒ์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์Šคํ•€๊ถค๋„๊ฒฐํ•ฉ์ด ์—†๋Š” ๊ฒฝ์šฐ์— ์‹œ๊ฐ„ ๋ฐ˜์ „ ๋Œ€์นญ๊ณผ ๊ณต๊ฐ„ ๋ฐ˜์ „ ๋Œ€์นญ์ด ๊ฐ๊ฐ ์กด์žฌํ•˜๋ฉด ๊ณต๊ฐ„ ๋ฐ˜์ „ ์—ฐ์‚ฐ์ž์˜ ๊ณ ์œ ๊ฐ’์„ ์ด์šฉํ•ด์„œ ์ œ 2 ์Šˆํ‹ฐํŽ -ํœ˜ํŠธ๋‹ˆ ์ˆซ์ž๋ฅผ ๊ฐ„๋‹จํ•˜๊ฒŒ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ด€๊ณ„์™€ ์ด๋ฏธ ์•Œ๋ ค์ ธ ์žˆ๋Š” ๊ฒฐ๊ณผ๋“ค์„ ์กฐํ•ฉํ•ด์„œ ์Šคํ•€์ด ์ •๋ ฌ๋˜์–ด ์žˆ๋Š” ๊ณ„์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ™€๋ฐ˜์ „์„ฑ์„ ๊ฐ€์ง€๋Š” ์œ„์ƒ ์ดˆ์ „๋„์˜ ์—ฐ๊ตฌ์— ์ ์šฉํ•ด๋ณธ๋‹ค.Chapter 1 Introduction 1 Chapter 2 Stiefel-Whitney classes 8 2.1 The first Stiefel-Whitney class 8 2.2 The second Stiefel-Whitney class 11 Chapter 3 First Stiefel-Whitney class and topological phases 15 3.1 1D topological insulator: SSH model in a real basis 15 3.2 2D Dirac semimetal 18 3.3 3D nodal line semimetals 21 Chapter 4 Second Stiefel-Whitney class and 3D nodal line semimetals with monopole charge 22 4.1 Second Stiefel-Whitney number and Z2 monopole charge of nodal lines 22 4.2 Whitney sum formula and linking of nodal lines 27 4.3 Computation of w2 by using Wilson loop method 32 4.4 Candidate Materials 36 Chapter 5 Stiefel-Whitney insulators in 2D and 3D 41 5.1 Second Stiefel-Whitney number on a torus 41 5.2 Second Stiefel-Whitney number when Nocc = 2: Euler class, fragile topology, and corner charges 44 5.3 Topological phase transition mediated by monopole nodal line, and 3D weak Stiefel-Whitney insulator 47 5.4 3D strong Stiefel-Whitney insulator and quantized magnetoelectric response 48 Chapter 6 Reformulation of the Nielsen-Ninomiya Theorem in 2D 55 6.1 Band topology of nearly flat bands in twisted bilayer graphene 58 6.1.1 A four-band lattice model 59 6.1.2 Band topology of lower two bands 59 6.2 Failure of Nielsen-Ninomiya Theorem due to the Euler class 61 6.2.1 Two-dimensional Nielsen-Ninomiya theorem 61 6.2.2 Winding number and the Euler class 62 Chapter 7 Inversion Parity Formulae 65 7.1 The first Stiefel-Whitney class from parity 66 7.2 The second Stiefel-Whitney class from parity 68 7.2.1 Two occupied bands 68 7.2.2 General occupied bands 72 7.2.3 Z2 monopole charge 74 Chapter 8 Topological Superconductivity 76 8.1 Symmetry and nodal structures 78 8.2 Nodal structure of TSC and parity formula 80 8.3 Generalized parity formula for second-order TSC in 2D 82 8.4 Higher-order TSCs in 3D and further generalization 84 8.5 Lattice model 85 8.6 Discussions 87 Chapter 9 Discussion 90 Chapter A Reality condition from spacetime inversion symmetry 93 Chapter B Alternative formulation of Stiefel-Whitney numbers using homotopy theory 95 B.1 Homotopy groups of the sewing matrix 96 B.2 The first homotopy class 98 B.3 The second homotopy class 99 B.4 Some properties of homotopy groups 104 B.4.1 Equivalence between real and smooth gauges 105 B.5 Wilson loop method 107 Chapter C Parity indices of odd-parity superconductors 109 Bibliography 111 ์ดˆ๋ก 130Docto

    Influence of early functional load on bone formation around hydroxylapatite-coated IMZ implants in dogs

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    ์น˜์˜ํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ์กฐ๊ธฐ ๊ธฐ๋Šฅ์  ํ•˜์ค‘์ด ์ž„ํ”Œ๋ž€ํŠธ ์ฃผ์œ„ ๊ณจ์กฐ์ง์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด HA plasma spray coated IMZ์ž„ํ”Œ๋ž€ํŠธ๋ฅผ ์„ฑ๊ฒฌ์˜ ํ•˜์•… ์–‘์ชฝ ์†Œ๊ตฌ์น˜ ๋ถ€์œ„์— ์ด์‹ํ•˜์—ฌ, 6์ฃผ์— ๊ธฐ๋Šฅ์„ ํ•˜๋„๋ก ํ•œ ๊ฒƒ์„ โ… ๊ตฐ, 9์ฃผ์— ๊ธฐ๋Šฅ์„ ํ•˜๋„๋ก ํ•œ ๊ฒƒ์„ โ…ก๊ตฐ, 12์ฃผ์— ๊ธฐ๋Šฅ์„ ํ•˜๋„๋ก ํ•œ ๊ฒƒ์„ โ…ข๊ตฐ, ๊ทธ๋ฆฌ๊ณ  16์ฃผ ๋™์•ˆ ๊ธฐ๋Šฅ์„ ํ•˜์ง€ ์•Š์€ ๊ฒƒ์„ ๋Œ€์กฐ๊ตฐ์œผ๋กœ ํ•˜์—ฌ, ์ž„ํ”Œ๋ž€ํŠธ ์ฃผ์œ„ ๊ณจ์กฐ์ง์„ ํ™ฉํ•™ ํ˜„๋ฏธ๊ฒฝ, ํ˜•๊ด‘ ํ˜„๋ฏธ๊ฒฝ, ํŽธ๊ด‘ ํ˜„๋ฏธ๊ฒฝ ์œผ๋กœ ๊ด€์ฐฐํ•˜๊ณ  ํ˜•ํƒœ๊ณ„์ธกํ•™์  ๋ถ„์„๊ณผ EPMA๋ถ„์„์„ ํ†ตํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. 1. ์ž„ํ”Œ๋ž€ํŠธ๋ฅผ ์ด์‹ํ•œ ๋Œ€์กฐ๊ตฐ, ์‹คํ—˜ โ… ๊ตฐ, ์‹คํ—˜ โ…ก๊ตฐ, ์‹คํ—˜ โ…ข๊ตฐ ๋ชจ๋‘ ์ƒํ”ผ ์กฐ์ง์˜ ํ•˜๋ฐฉ ์ด์ฃผ์—†์ด ๊ณจ์œ ์ฐฉ์ด ๊ด€์ฐฐ๋˜์—ˆ๊ณ  ์ผ๋ถ€์—์„œ๋Š” ๊ณจ์ˆ˜์กฐ์ง๊ณผ ์ ‘์ด‰ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. 2. ์ž„ํ”Œ๋ž€ํŠธ ์ด์‹ํ›„ 6์ฃผ์— ์กฐ๊ธฐํ•˜์ค‘์„ ๊ฐ€ํ•œ ์‹คํ—˜ โ… ๊ตฐ์€ ํ•˜์ค‘์„ ๊ฐ€ํ•˜์ง€ ์•Š์€ ๋Œ€์กฐ๊ตฐ์— ๋น„ํ•ด ๊ณจํ˜•์„ฑ ๋ฐ ๊ณจ๊ฐœํ˜•์ด ๋นจ๋ฆฌ ์ผ์–ด๋‚ฌ์œผ๋‚˜, ํ˜‘์ธก ๋ณ€์—ฐ๋ถ€์—์„œ ๊ณจํก์ˆ˜ ์–‘์ƒ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. 3. HA๊ฐ€ ํ‹ฐํƒ€๋Š„ ๊ธˆ์†์œผ๋กœ๋ถ€ํ„ฐ ํƒˆ๋ฝ๋˜์–ด ๋Œ€์‹์„ธํฌ์— ์˜ํ•œ HA์˜ ํƒ์‹์ž‘์šฉ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. 4. ์ž„ํ”Œ๋ž€ํŠธ์™€ ๊ณจ์กฐ์ง๊ฐ„์˜ ๊ณ„๋ฉด์—์„œ ๊ณจ์ ‘์ด‰๋ฅ  ๋น„๊ต์‹œ ๋Œ€์กฐ๊ตฐ, ์‹คํ—˜ โ… ๊ตฐ, ์‹คํ—˜ โ…ก๊ตฐ, ์‹คํ—˜ โ…ข๊ตฐ ๊ฐ„์— ์œ ์˜์ฐจ๊ฐ€ ์—†์—ˆ๋‹ค. 5. EPMA๋ฅผ ์ด์šฉํ•œ ์ž„ํ”Œ๋ž€ํŠธ ์ฃผ์œ„ ๊ณจ์กฐ์ง์˜ Calcium๊ณผ Phosphorous์˜ ์ •๋Ÿ‰ ๋ถ„์„์—์„œ ์กฐ๊ธฐํ•˜์ค‘์— ๋”ฐ๋ฅธ ๋น„๊ต์‹œ ๋Œ€์กฐ๊ตฐ, ์‹คํ—˜ โ… ๊ตฐ, ์‹คํ—˜ โ…ก๊ตฐ, ์‹คํ—˜ โ…ข๊ตฐ ๊ฐ„์— ์œ ์˜์ฐจ๊ฐ€ ์—†์—ˆ๋‹ค. [์˜๋ฌธ] The purpose of this study was to investigate the influence of early functional load on the bone formation around osseointegrated titanium implants. 15 HA plasma spray coated IMZ implants were placed into the previously extracted site of both mandibular premolar areas of five adult dogs. The implants were divided into four groups : the control group was the implant without abutments during the experiment period : the experimental group โ…  was preloaded by connecting the contoured abutment after 6 weeks of healing : the experimental group โ…ก was preloaded after 9 weeks of healing : the experimental group โ…ข was loaded after 12 weeks of healing : and the mandibular first molar and surrounding tissues were selected for natural tooth group to compare the implant group. All dogs were injected intravenously tetracycline, alizarin red S, and calcein for bone labeling. After the experimental period of 16 weeks, the dogs were sacrificed and longitudinal and horizontal sections of the bone-implant interface were cut and observed using light microscope, polarized microscope, fluorescence microscope, image analizer, and EPMA. The results of this study were as follows : 1. All implant surfaces were well in contact with bone tissue, but some contact with bone marrow tissue was observed. 2. Bone formation and bone remodeling in the group โ…  pre-loaded after 6 weeks of healing appeared earlier than the control group without functional load, but marginal bone resorption of the buccal crestal bone was observed in the group โ… . 3. HA particles separated from titanium body and phagocytosis of these partices by macrophages were observed. 4. The morphemetric analysis showed no significant differences in bone-implant contact rate among 4 groups. 5. EPMA analysis of calcium and phosphorous content showed no significant differences in the bone around implants among 4 groups. The result of this study indicate the possibility of the early functional load to the implant using temporary prosthesis. The insufficient bonding strength in bone-to-implant interface and HA resorption by phagocytosis were observed regardless of functional load. Therefore, more research of HA coating method is required in future.restrictio

    Shear bond strength of porcelain repair resins to nonprecious ceramo - metal alloy

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    ์น˜์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ๊ธˆ์†์˜ ๋…ธ์ถœ์„ ๋™๋ฐ˜ํ•œ ๋„์žฌ์†Œ๋ถ€์ „์žฅ๊ด€์˜ ํŒŒ์ ˆ์‹œ ์ด๋ฅผ ๊ตฌ๊ฐ•๋‚ด์—์„œ ์ˆ˜๋ฆฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ธˆ์†์„ ํ”ผ๊ฐœํ•˜๋ฉด์„œ ๊ธˆ์†๊ณผ ํ™”ํ•™์  ๊ฒฐํ•ฉ์„ ํ•˜๋Š” ๋ ˆ์ง„, ํ™”ํ•™์  ๊ฒฐํ•ฉ์„ ์œ„ํ•œ ๊ธˆ์†ํ‘œ๋ฉด์ฒ˜๋ฆฌ๋ฐฉ๋ฒ•๋“ฑ์— ๋Œ€ํ•œ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด ์ง€๊ณ  ์žˆ๋‹ค. ๊ธˆ์†ํ‘œ๋ฉด์ฒ˜๋ฆฌ๋ฐฉ๋ฒ• ๋ฐ ๋ ˆ์ง„ ์ข…๋ฅ˜ ์— ๋”ฐ๋ฅธ ๋ ˆ์ง„๊ณผ ๊ธˆ์†๊ณผ์˜ ๊ฒฐํ•ฉ๋ ฅ์„ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ธˆ์†ํ‘œ๋ฉด์„ diamond point์˜ ์ฒ˜๋ฆฌ ๋˜๋Š” aluminum oxide๋กœ sandblastํ•œ ํ›„ opaque ๋ ˆ์ง„์ด ํฌํ•จ๋˜์–ด ์žˆ๋Š” ์ˆ˜์ข…์˜ ๋„์žฌ์ˆ˜๋ฆฌ์šฉ ๋ ˆ์ง„(porcelain repair resin)๊ณผ ์ ‘์ฐฉ์„ฑ ๋ ˆ์ง„(adhesive resin)์œผ๋กœ ์ˆ˜๋ณตํ•˜์—ฌ ๋„์žฌ์šฉ ๊ธˆ์†๊ณผ ์˜ ๊ฒฐํ•ฉ๋ ฅ์˜ ์ฐจ์ด๋ฅผ 24์‹œ๊ฐ„ ์ฆ๋ฅ˜์ˆ˜์— ๋ณด๊ด€ํ•œ ํ›„ 24์‹œ๊ฐ„ thermocycling์ฒ˜๋ฆฌํ•œ ๊ตฐ๊ณผ ๋‹ค์‹œ 2๊ฐœ์›”๊ฐ„ 37โ„ƒ์— ์ €์žฅํ•œ ๊ตฐ์„ Universal testing machine(Instron Corp., Canton, mass U.S .A.)์œผ๋กœ ์ „๋‹จ๊ฒฐํ•ฉ๊ฐ•๋„๋ฅผ ์ธก์ •ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. 1. Clearfil, All-bond, Superbond๋Š” aluminum oxide๋กœ sandblastํ•œ ๊ตฐ์ด diamond point๋กœ ์ฒ˜๋ฆฌํ•œ ๊ตฐ๋ณด๋‹ค ๊ฒฐํ•ฉ๋ ฅ์ด ๋†’์•˜์œผ๋‚˜, Panavia์˜ ๊ฒฝ์šฐ๋Š” ์œ ์˜์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. 2. ํ•ญ์€์ˆ˜์กฐ ๋ณด๊ด€์‹œ All - bond, Superbond๋Š” ๊ฒฐํ•ฉ๋ ฅ์ด ๊ฐ์†Œ๋˜์—ˆ์œผ๋‚˜ Cleafil๊ณผ Panavia๋Š” ์œ ์˜์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. 3. ํŒŒ์ ˆ๋ถ€์œ„ ๊ด€์ฐฐ์‹œ Clearfil, All-bond๋Š” ๊ธˆ์†๊ณผ opaque ๋ ˆ์ง„๊ณ„๋ฉด์—์„œ ๋ถ„๋ฆฌ๋˜์—ˆ๋‹ค. 4. ํŒŒ์ ˆ๋ถ€์œ„ ๊ด€์ฐฐ์‹œ Panayia๋Š” ๋ชจ๋‘ Panavia์™€ ์ „์žฅ์šฉ๋ ˆ์ง„๊ณ„๋ฉด์—์„œ ๋ถ„๋ฆฌ๋˜์—ˆ๊ณ , Superbond๋Š” thermocycling ์งํ›„ ์ธก์ •ํ•œ ๊ฒฝ์šฐ Superbond์™€ ์ „์žฅ์šฉ๋ ˆ์ง„๊ณ„๋ฉด์—์„œ ๋ถ„๋ฆฌ๋˜์—ˆ์œผ๋‚˜ ํ•ญ์€์ˆ˜์กฐ๋ณด๊ด€ ํ›„ ์ธก์ •ํ•œ ๊ฒฝ์šฐ๋Š” 40%๊ฐ€ ๊ธˆ์†์ธต๊ณผ ์ ‘์ฐฉ์„ฑ๋ ˆ์ง„๊ณ„๋ฉด์—์„œ ๋ถ„๋ฆฌ๋˜์—ˆ๋‹ค. [์˜๋ฌธ] When the porcelain fused to metal restorations were fractured at the metal interface various techniques and materials for intraoral porcelain repair have been suggested. The purpose of this study was to investigate the effect of metal surface treatment method and water storage on the shear bond strength of four porcelain repair systems. : Clearfil(Kuraray), All-bond(Bisco), Superbond C & B(Sun Medical), Panavia OP(Kuraray). After the metal surfaces of the specimens were sandblasted by aluminum oxide or roughened by diamond point, they were stored in double deionized water(24 Hr,,37โ„ƒ) and thermocy์น˜ing was performed(24 Hr.,1024 cycles), and again half of specimes were stored in water bath(2 Months 37โ„ƒ). Mean shear bond strength and mode of failure were recorded. The results of this study were obtained as follows : 1. Differences were observed between the sandblasted and diamond -treated specimens in Clearfil, All-bond, and Superbond. No statistically significant differences were observed in Panavia. 2. The 2-month storage time significantly affect the bond strength of All - bond and Superbond. No statistically significant differences were observed in Clearfil and Panavia. 3. The failures were observed at the interface between opaque resin and the metal in Clearfil and All-bond. 4. The failures were observed at the interface between opaque resin and veneered resin in Panavia. The failures were observed at the interface between opaque resin and veneered resin in Superbond, but 40% of them were factured at the interface between the metal and opaque resin after 2- month storage time.restrictio

    ๊ณ„๋ฐฐ ๊ทผ ๋ฐœ๋‹ฌ๊ณผ์ •์— ๋”ฐ๋ฅธ proteasome์˜ ํ™œ์„ฑ์˜ ๋ณ€ํ™” ๋ฐ ํ™œ์„ฑ์กฐ์ ˆ์ธ์ž PA28์˜ ์œ ์ „์ž ๊ตฌ์กฐ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ถ„์ž์ƒ๋ฌผํ•™๊ณผ,1997.Docto

    Separation and Characterization of Polymers with Different Chain Structure and Chain End Functionality

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    DoctorPhysical properties of synthetic polymers are greatly influenced by the distributions in their molecular characteristics such as molecular weight, molecular weight distribution (MWD), chemical composition distribution (CCD), chain structure and chain end functionality (CEF). In this disserration, particularly focus was on chain structure and CEF. The chain structure of polymers (linear, branched and cyclic polymer) affect many of its physical properties including glass transition temperature , strength, toughness, solution viscosity, solubility and the size of individual polymer coils in solution. Chain end functionalization of polymers has found many applications, for example, in making telechelic polymer, surface modification, grafting onto approaches and producing multiblock copolymers. However, the inevitable inhomogeneity in the chain structure and CEF in synthetic polymers, precise molecular characterization is necessary. High performance liquid chromatography (HPLC), 1H nuclear laser magnetic resonance (1H NMR), and matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) are the most suitable analytical techniques to separate and characterize the synthetic polymers. In chapter 1, basic principles of HPLC and MALDI-TOF MS for the characterization of synthetic polymers are briefly reviewed. HPLC separation of polymer can be largely divided into three different mods: size exclusion chromatography (SEC), interaction chromatography(IC) and liquid chromatography at the critical condition (LCCC). To predict the retention behavior of polymers in HPLC, the overall understanding of basic separation modes is needed. MALDI-TOF MS is a soft ionization method that enables resolution of individual n-mers of polymers in a mass spectrum distribution. This resolution enables the elucidation of not only mass distribution and repeat unit mass, but also the identity and fidelity of CEF. In chapter 2, the influence of chain structure and CEF of polymers for critical adsorption point (CAP) of liquid chromatography was investigated. To examine the influence of chain structure and chemically different CEF separately, two different linear polymers (Linear PS, 2-arm PS) and a 4-arm star PS were synthesized and studied by normal phase and reversed phase liquid chromatography (NPLC and RPLC). The experimental results were then compared with the computer simulation results to elucidate the effect of chain structure and chemically different CEF on the CAP of linear and branched polymers. It was found that the column temperature at CAP (TCAP), TCAP (Linear PS) = TCAP (2-arm PS) > TCAP (4-arm PS) in both RPLC and NPLC which can be attributed to the variation in chain structure. However, the elution times at CAP (tE,CAP) of three polymers are all different: In NPLC, tE,CAP (Linear PS) > tE,CAP (2-arm PS) > tE,CAP (4-arm PS) while in RPLC, tE,CAP (4-arm PS) > tE,CAP (2-arm PS) > tE,CAP (Linear PS). The variation of tE,CAP can be explained by the contribution of the CEF. The computer simulation results are in good agreement with the chromatography experiments results and support the interpretation of experimental data. In chapter 3, living and dead chain of polystyrene synthesized by RAFT polymerization were separated and characterized by HPLC, 1H NMR and MALDI-TOF MS. To achieve full chromatographic resolution of different living and dead chains, a polystyrene with distinctive CEF was prepared in RAFT polymerization by using a specially designed chain-transfer agent (Rโˆ’Sโˆ’(C = S)โˆ’Sโˆ’Z) with polar hydroxyl end groups at both R and Z and a thermal initiator without hydroxyl group. The structures of separated living chains derived from the RAFT agent and initiator were characterized using 1H NMR and MALDI-TOF MS. Molecular-weight distribution (MWD) of the living chains derived from the RAFT agent is close to the Poisson distribution. However, the living chains grown from the initiator have a broader MWD with low molecular weight tailing. As the [initiator]/[RAFT agent] ratio increases, both the amount and the dispersity of the living chains initiated by the initiator fragment increase while the MWD of the living chains initiated by the fragment of the RAFT agent remains unchanged. In chapter 4, various topological polymers such as 4-arm star, tricyclic, eight shaped and polyring are synthesized by ATRP and characterized by SEC, 1H NMR and MALDI-TOF MS. Although hydrodynamic volume of the products with the same molecular weight were somewhat different depending upon their chain structure, but the difference was not large enough to be fully resolved by SEC. As a result, SEC analysis alone is not enough to confirm the chain structure transformation of polymer. To identify the chain structure transformation, the topological polymers were determined by 1H NMR and MALDI-TOF MS.ํ•ฉ์„ฑ ๊ณ ๋ถ„์ž๋Š” ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ, ํ™”ํ•™์กฐ์„ฑ, ์‚ฌ์Šฌ๊ตฌ์กฐ, ๋ง๋‹จ ์ž‘์šฉ๊ธฐ์— ๋”ฐ๋ผ์„œ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ์ด ํฌ๊ฒŒ ๋ณ€ํ•œ๋‹ค. ํŠนํžˆ ๊ณ ๋ถ„์ž์˜ ์‚ฌ์Šฌ๊ตฌ์กฐ (์„ ํ˜•, ๋ถ„์ง€ํ˜•, ๊ณ ๋ฆฌํ˜•)๋Š” ์œ ๋ฆฌ์ „์ด ์˜จ๋„(glass transition temperature), ๊ฐ•๋„(strength), ์ธ์„ฑ(toughness), ์šฉ์•ก์ ๋„, ์šฉํ•ด๋„ ๋ฐ ์‚ฌ์Šฌ์˜ ํฌ๊ธฐ๋ฅผ ๋น„๋กฏํ•œ ๋งŽ์€ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ๊ณ ๋ถ„์ž์˜ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ๋Š” ํ…”๋ฆฌ์ผˆ๋ฆญ ๊ณ ๋ถ„์ž(telicalic polymer), ๊ทธ๋ž˜ํ”„ํŒ… ๊ณ ๋ถ„์ž (grafting polymer), ๋ธ”๋ก ๊ณต์ค‘ํ•ฉ์ฒด(block copolymer) ๋“ฑ ๋‹ค์–‘ํ•œ ๊ณ ๋ถ„์ž ์ค‘ํ•ฉ ๋˜๋Š” ํ‘œ๋ฉด ๊ฐœ์งˆ (surface modification)์— ์‘์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ, ๊ณ ๋ถ„์ž๋Š” ํ•ฉ์„ฑ๊ณผ์ •์—์„œ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ์˜ ์กด์žฌ ์œ ๋ฌด์™€ ์‚ฌ์Šฌ์˜ ๊ตฌ์กฐ๊ฐ€ ๋ถˆ๊ท ์ผํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํ•ฉ์„ฑ ํ›„ ์ •๋ฐ€ ๋ถ„์„๊ณผ ์ •์ œ๊ณผ์ •์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•ฉ์„ฑ ๊ณ ๋ถ„์ž๋ฅผ ๊ณ ์„ฑ๋Šฅ ์•ก์ฒด ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ๋ฒ• (high performance liquid chromatography), 1H NMR ๊ทธ๋ฆฌ๊ณ  ์งˆ๋Ÿ‰ ๋ถ„์„๋ฒ• (MALDI-TOF MS)์„ ์ด์šฉํ•˜์—ฌ ํ•ฉ์„ฑ ๊ณ ๋ถ„์ž๋ฅผ ๋ถ„๋ฆฌํ•˜๊ณ  ๋ถ„์„ํ•˜์˜€๋‹ค. 1์žฅ์—์„œ๋Š” HPLC์™€ MALDI-TOF MS์˜ ์›๋ฆฌ์— ๋Œ€ํ•ด ๊ฐ„๋žตํ•˜๊ฒŒ ์„ค๋ช…ํ•˜์˜€๋‹ค. HPLC๋Š” ๋ถ„๋ฆฌ ์›๋ฆฌ์— ๋”ฐ๋ผ ํฌ๊ธฐ ๋ฐฐ์ œ ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ (Size Exclusion Chromatography, SEC), ์ž„๊ณ„์กฐ๊ฑด ์•ก์ฒด ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ (Liquid Chromatography at Critical Condition, LCCC), ์ƒํ˜ธ์ž‘์šฉ ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ (Interaction chromatography, IC)๋กœ ๋ถ„๋ฅ˜ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ฐ ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ์˜ ๊ฐœ๋… ๋ฐ ๋ถ„๋ฆฌ ์›๋ฆฌ์— ๋Œ€ํ•ด ์„ค๋ช…ํ•˜์˜€๋‹ค. MALDI-TOF MS์˜ ์—ฐ์„ฑ ์ด์˜จํ™”๋ฒ• (soft ionization method)์€ ๊ณ ๋ถ„์ž์˜ n-๋‹จ๋Ÿ‰์ฒด์˜ ๋ถ„ํ•ด๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์—ฌ ์งˆ๋Ÿ‰ ๋ถ„ํฌ ์ŠคํŽ™ํŠธ๋Ÿผ์„ ์–ป์„ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด ๋ถ„ํ•ด๋Šฅ์€ ๋ฐ˜๋ณต ๋‹จ์œ„ ์งˆ๋Ÿ‰๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ๋ฅผ ๋ช…ํ™•ํ•˜๊ฒŒ ํŠน์ •ํ•  ์ˆ˜ ์žˆ๊ธฐ์— ๊ทธ ๊ธฐ๋ณธ ๊ฐœ๋…๊ณผ ์ด๋ก ์— ๋Œ€ํ•ด ์„ค๋ช…ํ•˜์˜€๋‹ค. 2์žฅ์—์„œ๋Š”, HPLC์—์„œ ๊ณ ๋ถ„์ž์˜ ์‚ฌ์Šฌ ๊ตฌ์กฐ์™€ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ์˜ ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด, ์ž„๊ณ„ ํก์ž‘์  (critical adsorption point)์—์„œ ๊ณ ๋ถ„์ž์˜ ๊ฑฐ๋™์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์‚ฌ์Šฌ์˜ ๊ตฌ์กฐ์™€ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ์˜ ์˜ํ–ฅ์„ ๊ฐœ๋ณ„์ ์œผ๋กœ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋ง๋‹จ ์ž‘์šฉ๊ธฐ ๊ฐœ์ˆ˜๊ฐ€ ๋‹ค๋ฅธ ๋‘๊ฐœ์˜ ์„ ํ˜• ๊ณ ๋ถ„์ž (Linear PS, 2-arm PS) ์™€ ๊ตฌ์กฐ๊ฐ€ ๋‹ค๋ฅธ ๋ณ„ํ˜• ๊ณ ๋ถ„์ž (4-arm star PS)๋ฅผ ํ•ฉ์„ฑํ•˜๊ณ , ์ •์ƒ ์•ก์ฒด ํฌ๋กœ๋งˆํ†  ๊ทธ๋ž˜ํ”ผ (normal phase liquid chromatography, NPLC)์™€ ์—ญ์ƒ ์•ก์ฒด ํฌ๋กœ๋งˆํ†  ๊ทธ๋ž˜ํ”ผ (reverse phase liquid chromatography, RPLC)์—์„œ ๊ณ ๋ถ„์ž์˜ ๊ฑฐ๋™์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ์‚ฌ์Šฌ ๊ตฌ์กฐ ๋ฐ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ์˜ ํšจ๊ณผ๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ์‹คํ—˜ ๊ฒฐ๊ณผ์™€ ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. NPLC์™€ RPLC ๋‘˜ ๋‹ค์—์„œ ๊ณ ๋ถ„์ž๋“ค์˜ ์ž„๊ณ„ ํก์ฐฉ์  ์˜จ๋„ (TCAP)๋Š” TCAP(linear PS) = TCAP(2-arm PS) > TCAP(4-arm PS)๋กœ ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ๊ตฌ์กฐ ๋ณ€ํ™”์— ๊ธฐ์ธ ๋œ ๊ฒƒ์ž„์„ ์„ค๋ช… ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณ ๋ถ„์ž๋“ค์˜ ์ž„๊ณ„ ํก์ž‘์  ์šฉ์ถœ ์‹œ๊ฐ„ (tE,CAP)์€ NPLC์—์„œ tE,CAP (linear) > tE,CAP (2-arm PS) > tE,CAP (4-arm PS)์ธ ๋ฐ˜๋ฉด, RPLC์—์„œ๋Š” tE,CAP (4-arm PS) > tE,CAP (2-arm PS) > tE,CAP (linear)๋กœ ๊ณ ๋ถ„์ž๋“ค์˜ ์šฉ์ถœ ์ˆœ์„œ๊ฐ€ ๋’ค ๋ฐ”๋€ ๊ฒฐ๊ณผ๋ฅผ ๊ฐ€์ ธ์™”์œผ๋ฉฐ, ์ด๋Š” ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ ๋ง๋‹จ๊ธฐ์˜ ๊ธฐ์—ฌ์— ์˜ํ•ด ์„ค๋ช… ๋  ์ˆ˜ ์žˆ๋‹ค. ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์‹คํ—˜๊ฒฐ๊ณผ์™€ ์ž˜ ์ผ์น˜ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ•ด์„ํ•˜์˜€๋‹ค. 3์žฅ์—์„œ๋Š” RAFT ์ค‘ํ•ฉ์œผ๋กœ ํ•ฉ์„ฑ๋œ ํด๋ฆฌ์Šคํ‹ฐ๋ Œ(PS)์—์„œ ์‚ด์•„์žˆ๋Š” ์‚ฌ์Šฌ (living chain) ๊ณผ ์ฃฝ์€ ์‚ฌ์Šฌ (dead chain)์„ HPLC, 1H NMR๊ทธ๋ฆฌ๊ณ  MALDI-TOF MS๋ฅผ ํ†ตํ•ด ๋ถ„๋ฆฌ ๋ถ„์„ ํ•˜์˜€๋‹ค. Living chain๊ณผ dead chain์„ ๋ถ„๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๊ทน์„ฑ์˜ OH์ž‘์šฉ๊ธฐ๋ฅผ ์–‘์ชฝ์— ๊ฐ€์ง„ ์‚ฌ์Šฌ ์ „๋‹ฌ์ฒด (chain transfer agent)(Rโˆ’Sโˆ’(C = S)โˆ’Sโˆ’Z)์™€ ๊ทน์„ฑ์˜ ์ž‘์šฉ๊ธฐ๊ฐ€ ์—†๋Š” ์—ด๋ถ„ํ•ด ๊ฐœ์‹œ์ œ (initiator) ๋ฅผ ์ด์šฉํ•˜์—ฌ PS๋ฅผ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ์‚ฌ์Šฌ์˜ ๋ง๋‹จ ์ž‘์šฉ๊ธฐ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ๋ถ„๋ฆฌ๋œ 2์ข…๋ฅ˜์˜ living chain์˜ ๊ตฌ์กฐ๋Š” 1H NMR๊ณผ MALDI-TOF MS๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ์‚ฌ์Šฌ ์ „๋‹ฌ์ฒด๋กœ๋ถ€ํ„ฐ ์„ฑ์žฅํ•œ living chain์˜ ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ๋Š” Poisson distribution๊ณผ ๊ฑฐ์˜ ์ผ์น˜ํ•˜์˜€์œผ๋‚˜, ๊ฐœ์‹œ์ œ๋กœ๋ถ€ํ„ฐ ์„ฑ์žฅํ•œ living chain์€ ์ € ๋ถ„์ž๋Ÿ‰์˜ ํ…Œ์ผ๋ง์„ ๊ฐ–๋Š” ๋ณด๋‹ค ๋„“์€ ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ๋ฅผ ๊ฐ€์กŒ๋‹ค. [๊ฐœ์‹œ์ œ] / [์‚ฌ์Šฌ ์ „๋‹ฌ์ฒด]์˜ ๋น„๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ์„œ ๊ฐœ์‹œ์ œ์— ์˜ํ•ด ์„ฑ์žฅํ•œ living chain์˜ ์–‘ ๋ฐ ๋ถ„ํฌ๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ๋ฐ˜๋ฉด, ์‚ฌ์Šฌ ์ „๋‹ฌ์ฒด๋กœ๋ถ€ํ„ฐ ์„ฑ์žฅํ•œ living chain์€ ๋ถ„์ž๋Ÿ‰ ๋ถ„ํฌ๊ฐ€ ๋ณ€ํ•˜์ง€ ์•Š๊ณ  ์œ ์ง€๋˜์—ˆ๋‹ค. 4์žฅ์—์„œ๋Š” ATRP๋กœ ํ•ฉ์„ฑ๋œ ์œ„์ƒ์ ์ธ ๊ณ ๋ถ„์ž๋“ค (4-arm star, tricyclic, eight shaped and polyring)์„ ํ•ฉ์„ฑํ•˜๊ณ  SEC, 1H NMR๊ทธ๋ฆฌ๊ณ  MALDI-TOF MS๋ฅผ ํ†ตํ•ด ๋ถ„๋ฆฌ ๋ถ„์„ ํ•˜์˜€๋‹ค. ๋™์ผํ•œ ๋ถ„์ž๋Ÿ‰์„ ๊ฐ–๋Š” ๊ณ ๋ถ„์ž๋“ค์˜ ์œ ์ฒด ์—ญํ•™์  ๋ถ€ํ”ผ๋Š” ๊ทธ๋“ค์˜ ์‚ฌ์Šฌ ๊ตฌ์กฐ์— ๋”ฐ๋ผ์„œ ๋‹ค์†Œ ๋‹ค๋ฅด๊ธฐ ๋•Œ๋ฌธ์—, SEC ๋ถ„์„์„ ํ†ตํ•ด์„œ ์‚ฌ์Šฌ์˜ ๊ตฌ์กฐ๊ฐ€ ๋ณ€ํ˜•๋œ ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ SEC ๋ถ„์„๋งŒ์œผ๋กœ๋Š” ๊ณ ๋ถ„์ž์˜ ์‚ฌ์Šฌ ๊ตฌ์กฐ ๋ณ€ํ˜•์„ ํ™•์ธํ•˜๊ธฐ์— ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๊ธฐ์— ์ด๋ฅผ 1H NMR๊ณผ MALDI-TOF MS ํ†ตํ•ด์„œ ์‚ฌ์Šฌ๊ตฌ์กฐ๋ฅผ ๊ทœ๋ช…ํ•˜์˜€๋‹ค

    The Pure Land Buddha-Chanting and the True Nature Chanting Meditation

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    ์ œ1์ฐจ ์ถฉ๋‚จ๋ฏธ๋ž˜์—ฐ๊ตฌํฌ๋Ÿผ(์•ˆ์ค€์˜, ๊ถŒ๋ฉด)

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    ์ œ1์ฃผ์ œ: ๋ฏธ์ƒ๋ฌผ ์—ฐ๋ฃŒ์ „์ง€์˜ ํ˜„ํ™ฉ๊ณผ ๋ฐœ์ „์ „๋ง - ์ž์—ฐ๊ณ„์— ์กด์žฌํ•˜๋Š” ๋‹ค์–‘ํ•œ 1์ฐจ ์—๋„ˆ์ง€์™€ ๊ทธ ๋ณ€ํ™˜๊ธฐ์ˆ  - ํ ๋ฐ”์ด์˜ค๋งค์Šค ์—๋„ˆ์ง€ ์ „ํ™˜ ๊ธฐ์ˆ  - ์œ ๊ธฐ์„ฑ ํ๋ฐ”์ด์˜ค๊ฐ€์Šค๋กœ๋ถ€ํ„ฐ ์ „๊ธฐ๋ฅผ ์ƒ์‚ฐํ•˜๋Š” ๋ฏธ์ƒ๋ฌผ์—ฐ๋ฃŒ์ „์ง€ - ํ•˜ํ์ˆ˜์šฉ lab-scale ๋ฏธ์ƒ๋ฌผ์—ฐ๋ฃŒ์ „์ง€์™€ ์„ฑ๋Šฅ - ๋ฏธ์ƒ๋ฌผ์—ฐ๋ฃŒ์ „์ง€์˜ย ์„ฑ๋Šฅํ–ฅ์ƒ์„ ์œ„ํ•œ ํ˜๊ธฐ์„ฑ๊ณต์ • ๋ณ‘ํ•ฉ ์ „๋žต - ์ถ•์‚ฐํ์ˆ˜ ์•”๋ชจ๋‹ˆ์•„ ํšŒ์ˆ˜๋ฅผ ์œ„ํ•œ ๋ฏธ์ƒ๋ฌผ ์—ฐ๋ฃŒ์ „์ง€ ์‹œ์Šคํ…œ ์ œ2์ฃผ์ œ: ํ•ต์œตํ•ฉ์—๋„ˆ์ง€ ๊ฐœ๋ฐœ, ์–ด๋””๊นŒ์ง€ ์™”๋‚˜? - ์„ธ๊ณ„ ์—๋„ˆ์ง€ ํ™˜๊ฒฝ ๋ณ€ํ™” -๋ฏธ๋ž˜์—๋„ˆ์ง€์›, ํ•ต์œตํ•ฉ์—๋„ˆ์ง€ - ํ•ต์œตํ•ฉ ์—ฐ๊ตฌ์˜ ๋ฏธ๋ž˜ - ๊ฒฐ๋ก  - ์ดํ›„ ์ƒ๋žต- ๋ฏธ์ƒ๋ฌผ ์—ฐ๋ฃŒ์ „์ง€์˜ ํ˜„ํ™ฉ๊ณผ ๋ฐœ์ „์ „๋ง - ํ•ต์œตํ•ฉ์—๋„ˆ์ง€ ๊ฐœ๋ฐœ, ์–ด๋””๊นŒ์ง€ ์™”๋‚˜
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