17 research outputs found

    ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ๋ณดํ–‰ ๋™์ž‘ ์˜ˆ์ธก๊ณผ ๋ณดํ–‰ ๊ธฐ๋ฐ˜ ๊ฐœ์ธ ์‹๋ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ๋ฐ•์ข…์šฐ.This thesis is concerned with human gait motion learning and prediction, focusing on differences in gait motion between individuals to address the problems of personalized gait pattern prediction and gait based biometric identification. We first present an algorithm that predicts individual gait patterns from a human's anthropometric characteristics, e.g., age, height, weight, gender, and other related anthropometric data. We adopt the Gaussian process dynamical model~(GPDM) framework to address the high dimensionality of human gait motions, and to learn a common stochastic dynamics model for gaits. We also utilize Gaussian process regression~(GPR) to learn a mapping from the space of body features to the motion parameters in the GPDM framework. Using our framework, an entire cycle of individualized gait motions at arbitrary walking speeds can be predicted from body feature data. We also propose a gait-based identification algorithm based on deep neural networks. To allow for new subjects whose information is not included in the original database, we develop a novel autoencoder architecture that is designed to extract features efficiently from the measured gait motions. We use the reconstruction error of the trained autoencoder as a similarity measure between the learned motion and the input, for which user-specified thresholds can be used for the identification. Our proposed method is able to identify both subjects in the database as well as unknown subjects, only using the observed gait motion. Furthermore, it is possible to update the database incrementally for newly given gait motions. Finally, we propose an algorithm used to predict a subject's age, height, weight, and gender from the subject's gait motion. We construct a neural network architecture for this purpose by modifying the previous autoencoder structure used for the identification, and devise an efficient learning strategy utilizing transfer learning to reduce the prediction error. To validate proposed algorithms, we collect gait motions and anthropometric data from more than a hundred subjects. For our gait motion prediction algorithm, an individualized gait pattern is generated for observed anthropometric data and at arbitrary walking speeds. The algorithm also shows 30% less gait pattern prediction errors compared to a frame-by-frame statistical regression method. In the case of gait identification, our algorithm shows identification accuracy of 98.5-100%, outperforming the results reported in recent studies.1 Introduction 1 1.1 Research Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Contributions of This Thesis . . . . . . . . . . . . . . . . . . . . . . 7 1.2.1 Gait Pattern Prediction from Individual Physical Features . 7 1.2.2 Gait Pattern-Based Human Identification . . . . . . . . . . . 9 1.2.3 Gait Pattern-Based Physical Feature and Gender Prediction 10 1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Preliminaries 13 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Human Motion Modeling with Nonlinear Dimensionality Reduction 13 2.2.1 Principal Component Analysis . . . . . . . . . . . . . . . . . 14 2.2.2 Geometrically-Inspired Algorithms . . . . . . . . . . . . . . . 16 2.2.3 Gaussian Process-Based Models . . . . . . . . . . . . . . . . 17 2.3 Deep Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.1 Feedforward Network . . . . . . . . . . . . . . . . . . . . . . 29 2.3.2 Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.3 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . 31 2.3.4 Optional Elements . . . . . . . . . . . . . . . . . . . . . . . . 34 3 Gait Pattern Prediction from Individual Physical Features 39 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2.1 Acquisition and Preprocessing of Human Gait Motion . . . 43 3.2.2 Gaussian Process Learning of Gait Motion . . . . . . . . . . 45 3.2.3 Generation of Individualized Gait Motion . . . . . . . . . . . 50 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Determination of the Dimension of the Latent Space . . . . 52 3.3.2 Gait Patterns at Intermediate Walking Speed . . . . . . . . 53 3.3.3 Prediction of the Initial Poses . . . . . . . . . . . . . . . . . 55 3.3.4 Generation of the Individualized Gait Pattern . . . . . . . . 56 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 Gait Pattern-Based Human Identification 63 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.2.1 Gait Identification for Known Classes . . . . . . . . . . . . . 66 4.2.2 Gait Identification Including Unknown Classes . . . . . . . . 68 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.3.1 Gait Identification with Known Subjects . . . . . . . . . . . 71 4.3.2 Gait Identification with Known and Unknown Subjects . . . 75 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5 Gait Pattern-Based Physical Feature and Gender Prediction 81 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.2.1 Transfer Learning Scheme for Feature Estimation . . . . . . 83 5.2.2 Experiments and Results . . . . . . . . . . . . . . . . . . . . 85 5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6 Conclusion 93 Bibliography 97 Abstract 115Docto

    1930๋…„๋Œ€ ์ค‘๋ฐ˜ ์†Œ๋ จ ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ์˜ ๋ณ€ํ™”: ๋ฐฑํ•ด-๋ฐœํŠธํ•ด ์‚ฐ์—…๋‹จ์ง€์˜ ์ˆ˜์šฉ์†Œ ์‹ ๋ฌธ์— ๋“œ๋Ÿฌ๋‚œ ์ˆ˜๊ฐ์ž๊ด€(่ง€)์˜ ๋ณ€ํ™”๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์„œ์–‘์‚ฌํ•™๊ณผ, 2016. 2. ํ•œ์ •์ˆ™.๋ณธ๊ณ ๋Š” 1930๋…„๋Œ€ ์ค‘๋ฐ˜ ์†Œ๋ จ์˜ ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ๊ฐ€ ๊ฒช์€ ๋ณ€ํ™”์— ์ฃผ๋ชฉํ•˜๊ณ  ๊ทธ ๋ณ€ํ™”์˜ ์„ฑ๊ฒฉ์„ ๊ทœ๋ช…ํ•˜๋ ค๋Š” ์‹œ๋„์ด๋‹ค. ์†Œ๋ จ์˜ ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ๋Š” 1930๋…„์— ์ถœ๋ฒ”ํ•œ ๊ตด๋ผ๊ทธ(ะ“ะฃะ›ะฐะณ) ์‚ฐํ•˜์˜ ๊ตฌ๊ธˆ๊ธฐ๊ตฌ๋กœ, 1930๋…„๋Œ€๋ฅผ ์ง€๋‚˜๋ฉฐ ๊ธ‰๊ฒฉํžˆ ์„ฑ์žฅํ–ˆ๋‹ค. ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์€ ์Šคํƒˆ๋ฆฐ ์‹œ๋Œ€์— ๋ฒŒ์–ด์ง„ ๋Œ€๊ทœ๋ชจ ์ •์น˜์ ยท์‚ฌํšŒ์  ํƒ„์••์˜ ๋งฅ๋ฝ ์†์—์„œ ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ๋ฅผ ์กฐ๋ช…ํ•˜๋ฉฐ, ์ˆ˜์šฉ์†Œ๊ฐ€ ๋ฌด์—‡๋ณด๋‹ค๋„ ์–ต์••๊ธฐ๊ตฌ ํ˜น์€ ํ˜•๋ฒŒ๊ธฐ๊ตฌ์˜ ๊ธฐ๋Šฅ์„ ํ–ˆ๋‹ค๊ณ  ๊ฐ•์กฐํ–ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๊ด€์ ์€ ์†Œ๋ จ์˜ ์‚ฐ์—…ํ™”๊ฐ€ ์‹ฌํ™”๋˜๋˜ 1930๋…„๋Œ€ ์ค‘๋ฐ˜ ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ๊ฐ€ ๊ฒฝํ—˜ํ•œ ์ค‘์š”ํ•œ ๋ณ€ํ™”๋ฅผ ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š” ํ•œ๊ณ„๋ฅผ ๊ฐ–๋Š”๋‹ค. ๊ฒฝ์ œ์  ์ƒ์‚ฐ์— ์š”๊ตฌ๊ฐ€ ์ „์‚ฌํšŒ์ ์œผ๋กœ ์ฆ๋Œ€ํ•˜๋ฉด์„œ ์ˆ˜์šฉ์†Œ ๋˜ํ•œ ์ด์ „๊ณผ๋Š” ์งˆ์ ์œผ๋กœ ๋‹ค๋ฅธ ๊ทœ๋ชจ์˜ ์ƒ์‚ฐ ํ™œ๋™์„ ์ˆ˜ํ–‰ํ•ด์•ผ ํ–ˆ๋‹ค๋Š” ๊ฒƒ์ด ๊ทธ ๋ณ€ํ™”์˜ ํ•ต์‹ฌ์ด์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ๊ทธ๊ฐ„์˜ ์—ฐ๊ตฌ๊ฐ€ ์ฃผ๋ชฉํ•˜์ง€ ์•Š์•˜๋˜ ์ˆ˜์šฉ์†Œ์˜ ๋‚ด๋ถ€์—์„œ ๋‘๋“œ๋Ÿฌ์กŒ๋‹ค. ์ด์— ๋ณธ๊ณ ๋Š” ์†Œ๋ จ์˜ ๋Œ€ํ‘œ์ ์ธ ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ์ธ ๋ฐฑํ•ด-๋ฐœํŠธํ•ด ์‚ฐ์—…๋‹จ์ง€์—์„œ ๋ฐœ๊ฐ„๋œ ๋‚ด๋ถ€ ์‹ ๋ฌธ๋“ค์„ ๊ฒ€ํ† ํ•˜๋ฉฐ 1935-36๋…„ ์ˆ˜์šฉ์†Œ์˜ ์ผ์ƒ์ด ์ƒ์‚ฐ ํ™œ๋™์„ ์ค‘์‹ฌ์œผ๋กœ ์žฌํŽธ๋œ ๊ณผ์ •์„ ์‚ดํ•€๋‹ค. ํŠนํžˆ ์ˆ˜์šฉ์†Œ ์‹ ๋ฌธ์ด ์ˆ˜๊ฐ์ž๋“ค์„ ์ƒ์‚ฐ์„ฑ ์ฆ์ง„์— ๋งค์ง„ํ•˜๋Š” ๋…ธ๋™์ž๋กœ ๊ทธ๋ ธ์œผ๋ฉฐ ์ด๋“ค์˜ ์ˆ˜๊ฐ์ƒํ™œ์„ ์ผ์ข…์˜ ๊ณต์žฅ์ƒํ™œ๋กœ ๋ฌ˜์‚ฌํ–ˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๊ตฌ์ฒด์ ์œผ๋กœ ๋ถ„์„ํ•œ๋‹ค. ์ด๋Š” ๋‹น๋Œ€์˜ ์ˆ˜์šฉ์†Œ๊ฐ€ ๊ฒฝ์ œ์  ์ƒ์‚ฐ๊ธฐ๊ตฌ๋กœ ๋ณ€๋ชจํ•˜๊ณ  ์žˆ์—ˆ์Œ์„ ๋ณด์—ฌ์ฃผ๋Š” ์ค‘์š”ํ•œ ๋‹จ์„œ์ด๋‹ค. 1935๋…„๋„์— ๋ฐœ๊ฐ„๋œ ์ˆ˜์šฉ์†Œ ์‹ ๋ฌธ์€ ์ˆ˜๊ฐ์ž๋“ค์— ๋Œ€ํ•œ ์žฌ๊ต์œก์„ ๋„๋ชจํ•œ๋‹ค๋Š” ์ด์ „ ์‹œ๊ธฐ์˜ ๊ธฐ์กฐ์™€ ๊ฑฐ๋ฆฌ๋ฅผ ๋‘๋ฉฐ ์ˆ˜๊ฐ์ž๋“ค์˜ ์ƒ์‚ฐ ํ™œ๋™๊ณผ ๊ทธ๋“ค์ด ๋‹ฌ์„ฑํ•œ ์ƒ์‚ฐ์„ฑ์„ ํ•ต์‹ฌ์ ์ธ ์ฃผ์ œ๋กœ ๋‹ค๋ค˜๋‹ค. 1935๋…„ ๋ง์— ์‹œ์ž‘๋œ ์Šคํƒ€ํ•˜๋…ธํ”„์šด๋™์€ ์ด๋Ÿฌํ•œ ๊ฒฝํ–ฅ์„ ๋”์šฑ ๊ฐ•ํ™”ํ–ˆ๋‹ค. ๋ฐฑํ•ด-๋ฐœํŠธํ•ด ์‚ฐ์—…๋‹จ์ง€๋Š” ์Šคํƒ€ํ•˜๋…ธํ”„์šด๋™์— ํŠนํ™”๋œ ์‹ ๋ฌธ์„ ๋ณ„๋„๋กœ ๋ฐœ๊ฐ„ํ•˜๋ฉฐ ์ˆ˜๊ฐ์ž๋“ค์˜ ์ƒ์‚ฐ์„ฑ์ด ์ˆ˜์šฉ์†Œ์˜ ๊ฒฝ์ œ์  ์—ญํ• ๊ณผ ๋ฐ€์ ‘ํžˆ ๊ฒฐ๋ถ€๋˜์–ด ์žˆ์Œ์„ ๊ฐ•์กฐํ–ˆ๋˜ ๊ฒƒ์ด๋‹ค. ์ˆ˜์šฉ์†Œ์˜ ์‹ค์ƒ์„ ๋ฐ˜์˜ํ•˜๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์‚ฌ๋ฃŒ๋“ค์— ๋Œ€ํ•œ ์ ‘๊ทผ์ด ์—ฌ์ „ํžˆ ์–ด๋ ค์šด ์ƒํ™ฉ์—์„œ, ์ˆ˜์šฉ์†Œ ์‹ ๋ฌธ์— ๋“œ๋Ÿฌ๋‚œ ์ˆ˜์šฉ์†Œ ๋‹น๊ตญ์˜ ์ˆ˜๊ฐ์ž๊ด€(่ง€)์€ ์ด์ „๊นŒ์ง€ ๊ฐ„๊ณผ๋˜์—ˆ๋˜ 1930๋…„๋Œ€ ์ค‘์—ฝ ์ˆ˜์šฉ์†Œ์˜ ๋ณ€ํ™”๋ฅผ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋Š” ๋‹จ์ดˆ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๊ฐ•์ œ๋…ธ๋™์ˆ˜์šฉ์†Œ๋Š” ์–ต์••๊ธฐ๊ตฌ์™€ ํ˜•๋ฒŒ๊ธฐ๊ตฌ, ๊ทธ๋ฆฌ๊ณ  ์ƒ์‚ฐ๊ธฐ๊ตฌ์˜ ์˜์—ญ์„ ์˜ค๊ฐ€๋ฉฐ ๋‹น๋Œ€ ์†Œ๋ จ ์‚ฌํšŒ ์ „๋ฐ˜์˜ ๋ณ€ํ™”์— ๋ฐœ๋งž์ถ”์–ด ๋…ํŠนํ•œ ์ž…์ง€๋ฅผ ๊ตฌ์ถ•ํ–ˆ๋˜ ๊ฒƒ์ด๋‹ค. ํŠนํžˆ ์†Œ๋ จ ๊ฒฝ์ œ์˜ ๋ฒˆ์˜๊ธฐ๋ผ๊ณ ๋„ ๋ถˆ๋ ธ๋˜ 1930๋…„๋Œ€ ์ค‘๋ฐ˜, ์ˆ˜๊ฐ์ž๋“ค์„ ๋…ธ๋™์ž๋กœ ๋ฐ”๋ผ๋ณด์•˜๋˜ ์ˆ˜์šฉ์†Œ ๋‚ด๋ถ€์˜ ๋‹ด๋ก ์€ ์†Œ๋ จ ๊ฒฝ์ œ์™€ ์ˆ˜์šฉ์†Œ๊ฐ€ ๋งบ๊ณ  ์žˆ๋˜ ๊ด€๊ณ„๋ฅผ ์ƒˆ๋กœ์ด ์กฐ๋ช…ํ•  ๋งŒํ•œ ์‹ค๋งˆ๋ฆฌ๋ฅผ ์ œ๊ณตํ•œ๋‹ค.This thesis examines the changing nature of Soviet incarceration in the mid-1930s, focusing on the White Sea-Baltic Combine, the corrective labor camp located in Karelia region. An apparatus of the GULag, the administrative agency of the incarceration system in Soviet Russia, corrective labor camps were the representative institution of detention. Historians studying Soviet mass incarceration have pointed out that these camps, which grew rapidly during the bleak years of Stalinist rule in 1930s, served as an instrument of oppression and punishment. Corrective labor camps, they claims, were repressive and corrective in nature. This widely accepted perspective, however, portrays the camps as monolithic, unchanging institutions. On the contrary, corrective labor camps evolved over time, particularly in the mid-1930s when they put emphasis more on labor and less on re-education. This new emphasis mirrored a greater socioeconomic change of the mid-1930s. An impulse for industrialization swept the whole country, and its impact was also felt strongly in corrective labor camps despite their marginality in Soviet Russia. In the camps, the industrial impulse was evident in inmates daily works, which demanded inmates to become agents of industrial productivity. To illuminate this change, this thesis scrutinizes the Gulag newspapers published in 1935 and 1936 at the White Sea-Baltic Combine, one of the earliest and best-known concentration camps in the Stalin era. The Gulag newspapers, circulated only within the camp, vividly illustrated inmates foremost as laborers. Articles, editorials, and reader letters written by both officials and inmates betrayed a perspective that highlighted the camp primarily as an economic institution striving for higher labor productivity. Refashioning the camp as a laboratory of productivity was primarily an internal change, but it also drew on resources from the Stakhanovite movement, a widespread social campaign on productivity in the mid-1930s. The Gulag newspapers often lauded the productive inmates as the Stakhanovites and reported in detail about the Stakhanovite movement. In this light, this thesis demonstrates the ways in which corrective labor camps evolved into a unique organization that prioritized prisoners productivity, not re-education.์„œ๋ก  1 โ… . ๋ฐฑํ•ด์šดํ•˜์˜ ๊ฑด์„ค๊ณผ ์ธ๊ฐ„๊ฐœ์กฐ๋ก , 1931-33๋…„ 8 โ…ก. ์ธ๊ฐ„๊ฐœ์กฐ๋ก ์˜ ํ‡ด์กฐ์™€ ์ƒ์‚ฐ์„ฑ๋ก ์˜ ๋Œ€๋‘, 1935๋…„ 13 1. ะ‘ะ‘ะš์˜ ์„ค๋ฆฝ๊ณผ ์ˆ˜์šฉ์†Œ ์‹ ๋ฌธ์˜ ๋ณ€ํ™” 13 2. ํ”ผ๊ต์œก์ž์—์„œ ๋…ธ๋™์ž๋กœ 17 3. ์ˆ˜๊ฐ์ž๋“ค์˜ ๊ต์œก์ž์—์„œ ๋‹น๊ตญ์˜ ๋…ธ๋™์ž๋กœ 20 4. ์žฌ๊ต์œก์—์„œ ์ƒ์‚ฐ์„ฑ์œผ๋กœ 22 โ…ข. ์Šคํƒ€ํ•˜๋…ธํ”„์šด๋™๊ณผ ์ƒ์‚ฐ์„ฑ๋ก ์˜ ๊ฐ•ํ™”, 1936๋…„ 26 1. ะ‘ะ‘ะš์˜ ์Šคํƒ€ํ•˜๋…ธํ”„์šด๋™๊ณผ ์ˆ˜์šฉ์†Œ ์‹ ๋ฌธ์˜ ๋ณ€ํ™” 26 2. ์ˆ˜๊ฐ์ž-์Šคํƒ€ํ•˜๋…ธํ”„๋…ธ๋™์ž์˜ ์—ญํ•  29 3. ์ˆ˜๊ฐ์ž-์Šคํƒ€ํ•˜๋…ธํ”„๋…ธ๋™์ž์˜ ์ง€์œ„ 32 ๊ฒฐ๋ก  36 ์ฐธ๊ณ ๋ฌธํ—Œ 40 Abstract 50Maste

    ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์ด์šฉํ•œ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 8. ์ด๋ณ‘ํ˜ธ.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒˆ๋กœ์šด ๊ด‘ํ•™ ์†Œ์ž๋กœ์จ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์ œ์•ˆํ•˜๊ณ , ์ด๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ๋“ค์— ๋Œ€ํ•˜์—ฌ ๋…ผํ•œ๋‹ค. ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์€ ๋ฐ˜์‚ฌํ˜• ๊ด‘ํ•™ ๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ๋ Œ์ฆˆ์˜ ๊ด‘ํ•™์  ๊ธฐ๋Šฅ์„ ๊ตฌํ˜„ํ•˜๋Š” ๋ฐ˜๊ฑฐ์šธ์ด๋‹ค. ๊ด‘ ๊ฒฐํ•ฉ๊ธฐ๋กœ์จ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์€ see-through ์ด๋ฏธ์ง• ํŠน์„ฑ์„ ๊ฐ€์ง€๋ฉฐ ๊ด‘ํ•™ ์‹œ์Šคํ…œ์˜ ์ •๋ ฌ์— ์žˆ์–ด์„œ ์žฅ์ ์„ ๊ฐ€์ง„๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์‹ค์ œ๋กœ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ์„ธ ๊ฐ€์ง€ ๊ณต์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ๊ฐ์˜ ๊ณต์ • ๋ฐฉ๋ฒ•์€ ์„œ๋กœ ๋‹ค๋ฅธ ์žฅ๋‹จ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‘์šฉ์ฒ˜์— ๋”ฐ๋ผ์„œ ์ ์ ˆํ•œ ๊ณต์ • ๋ฐฉ๋ฒ•์„ ํƒํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฐ ๊ณต์ • ๋ฐฉ๋ฒ•์— ์˜ํ•ด ์›ํ˜•๋“ค์„ ์ œ์ž‘ํ•˜๊ณ  ๊ทธ ํŠน์„ฑ์„ ๋น„๊ต ๋ฐ ๋ถ„์„ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€ ์ธก๋ฉด์—์„œ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์˜ ์œ ์šฉ์„ฑ์„ ์‚ดํŽด๋ณธ๋‹ค. ์ฒซ์งธ๋กœ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ด‘ํ•™ ์†Œ์ž์˜ ๋„์ž…์„ ํ†ตํ•ด์„œ ์ƒˆ๋กœ์šด ๊ฐœ๋…์˜ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฐ˜์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ธก๋ฉด, ๋‘๋ฒˆ์งธ๋กœ๋Š” ๊ธฐ์กด์˜ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด์— ์กด์žฌํ•˜์˜€๋˜ ๋ฌธ์ œ์ ๋“ค์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋“ค์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ธก๋ฉด์„ ์‚ดํŽด๋ณธ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ง‘์  ์˜์ƒ ๋ฐฉ๋ฒ•์— ๊ธฐ๋ฐ˜ํ•œ ๋‹ค์–‘ํ•œ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด๋“ค์„ ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ ์ด์™€ ๊ฐ™์€ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋จผ์ € ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์˜ ๋„์ž…์œผ๋กœ ์ฆ๊ฐ• ํ˜„์‹ค์— ์ ์šฉ ๊ฐ€๋Šฅํ•œ ์ƒˆ๋กœ์šด 2๊ฐ€์ง€์˜ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•  ์ˆ˜ ์žˆ์Œ์„ ์ œ์‹œํ•œ๋‹ค. See-through ๋ฐ ์–‘๋ฐฉํ–ฅ ์ด๋ฏธ์ง• ์ง‘์  ์˜์ƒ ์‹œ์Šคํ…œ์€ ์–ด๋– ํ•œ ๊ธฐ๊ณ„์  ์›€์ง์ž„ ์—†์ด๋„ ์‹œ์Šคํ…œ ์ฃผ๋ณ€์œผ๋กœ ๋ฌด์•ˆ๊ฒฝ์‹ 3์ฐจ์› ์ด๋ฏธ์ง€๋ฅผ ๋ณด์—ฌ์ค„ ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์œผ๋กœ์จ ์ œ์•ˆ๋œ๋‹ค. ์ด๋Š” ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์–ด๋ ˆ์ด๋กœ ์ด์šฉํ•˜์—ฌ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋•Œ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ ์–ด๋ ˆ์ด๋Š” ๊ฒฐ๊ตญ ์˜ค๋ชฉ ๊ฑฐ์šธ ์–ด๋ ˆ์ด์˜ ๊ธฐ๋Šฅ์„ ๊ฐ–๊ฒŒ ๋œ๋‹ค. ํ”„๋กœ์ ์…˜ํ˜• ์ง‘์  ์˜์ƒ์˜ ์ด๋ฏธ์ง• ์›๋ฆฌ์˜ ๋ถ„์„์„ ํ†ตํ•ด 10\%์˜ ๋ฐ˜์‚ฌ์œจ์„ ๊ฐ–๋Š” ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‹ค์„ธ๊ณ„์— 3์ฐจ์› ์˜์ƒ์„ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๊ฒน์ณ ๋ณด์—ฌ์ค„ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์˜ ๋ณผ๋ก ๊ฑฐ์šธ ๋ฐฉํ–ฅ์— ์ง‘์  ๋ถ€์œ ํ˜• ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์›๋ฆฌ๋ฅผ ์ ์šฉํ•˜์—ฌ ์ •ํ™•ํ•œ ์ดˆ์  ์กฐ์ ˆ์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋Š” see-through head mounted display์˜ ๊ฐœ๋…์„ ์ œ์‹œํ•œ๋‹ค. ๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜์˜ ํ”ฝ์…€ ํฌ๊ธฐ๊ฐ€ ์ง‘์  ์˜์ƒ์˜ ์›๊ฑฐ๋ฆฌ ์ด๋ฏธ์ง•์„ ์ œํ•œํ•˜๋Š” ์›์ธ์ž„์„ ๋ฐํžˆ๊ณ , ๋ณผ๋ก ๊ฑฐ์šธ์˜ ์‚ฌ์šฉ์ด ์–ด๋– ํ•œ ๋ฌผ๋ฆฌ์  ๊ฐœ์„  ์—†์ด ํšจ๊ณผ์ ์œผ๋กœ ๋””์Šคํ”Œ๋ ˆ์ด ์žฅ์น˜์˜ ํ”ฝ์…€ ํฌ๊ธฐ๋ฅผ ์ค„์—ฌ ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ง‘์  ์˜์ƒ์˜ ์›๊ฑฐ๋ฆฌ ์ด๋ฏธ์ง•์ด ๊ฐ€๋Šฅํ† ๋ก ํ•จ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์€ ๊ธฐ์กด์˜ ์ง‘์  ์˜์ƒ์— ์•Œ๋ ค์ง„ ๋ฌธ์ œ์˜€๋˜ 2์ฐจ์›/3์ฐจ์› ๋ณ€ํ™˜ ๊ธฐ๋Šฅ์˜ ๊ตฌํ˜„์—๋„ ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ, ํ”„๋กœ์ ์…˜ํ˜• ์ง‘์  ์˜์ƒ์˜ ๊ฒฝ์šฐ, ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค์€ ์Šคํฌ๋ฆฐ ํฌ๊ธฐ์— ๋Œ€์‘ํ•˜๋Š” ํฌ๊ธฐ์˜ ๋Šฅ๋™ ์†Œ์ž๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ง€๊ธˆ๊นŒ์ง€๋Š” 2์ฐจ์›/3์ฐจ์› ๋ณ€ํ™˜์„ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ์ ์ ˆํ•œ ๋ฐฉ๋ฒ•์ด ์กด์žฌํ•˜์ง€ ์•Š์•˜๋‹ค. ๋ฐ˜๋ฉด ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์ ์šฉํ•˜๋ฉด 2์ฐจ์›/3์ฐจ์› ๋ณ€ํ™˜ ๊ธฐ๋Šฅ์„ ์ˆ˜๋™ ์†Œ์ž๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์ ์šฉํ•˜์—ฌ 2์ฐจ์›/3์ฐจ์› ๋ณ€ํ™˜ ๊ธฐ๋Šฅ์„ ๊ตฌํ˜„ํ•œ ํ”„๋กœ์ ์…˜ํ˜• ์ง‘์  ์˜์ƒ ์‹œ์Šคํ…œ์„ ์ตœ์ดˆ๋กœ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์—๋Š” focused mode์˜ ์ง‘์  ์˜์ƒ ๊ตฌ์กฐ์— ๋Œ€ํ•œ 2์ฐจ์›/3์ฐจ์› ๋ณ€ํ™˜ ๊ธฐ๋Šฅ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋‹ค์ˆ˜ ์ด๋ฃจ์–ด์ ธ ์™”์œผ๋‚˜ real/virtual mode ์ง‘์  ์˜์ƒ์— ๋Œ€ํ•ด์„œ๋Š” ์˜ค์ง ํ•œ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋งŒ์ด ์ œ์•ˆ๋œ ๋ฐ” ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์„ ์ด์šฉํ•˜์—ฌ real/virtual mode ์ง‘์  ์˜์ƒ์˜ 2์ฐจ์›/3์ฐจ์› ๋ณ€ํ™˜ ๊ธฐ๋Šฅ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์‹œ์Šคํ…œ์€ ๋˜ํ•œ ๋„“์€ ์‹œ์•ผ๊ฐ๊ณผ ๊นŠ์ด๊ฐ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋‹ค์ธต ์ค‘์‹ฌ ๊นŠ์ด ํ‰๋ฉด ์ƒ์„ฑ์— ์žˆ์–ด ๊ด‘๊ฒฝ๋กœ ๋‹จ์ถ•๊ณผ ๊ฐ™์€ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์žฅ์ ๋“ค์„ ๋ณด์—ฌ์ค€๋‹ค. ๋น„๋ก ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์˜ ์œ ์šฉ์„ฑ์„ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ์— ํ•œ์ •ํ•˜์—ฌ ์‚ดํŽด๋ณด์•˜์œผ๋‚˜, ๊ทธ ์™ธ์˜ ๋‹ค์–‘ํ•œ ๊ด‘ํ•™์  ์‘์šฉ์— ๋Œ€ํ•ด์„œ๋„ ๋ Œ์ฆˆ ๊ธฐ๋Šฅ์˜ ๋ฐ˜๊ฑฐ์šธ์ด ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.This dissertation proposes a new optical component, the half mirror with lens function, and studies about its application to three-dimensional display systems. Half mirror with lens function is a half mirror which provides a lens function by the reflective optic implementation. As an optical combiner, the definition of half mirror with lens function touts the advantageous features of see-through imaging and easy alignment of the optical system. Three viable fabrication methods, each of which has its pros and cons, are proposed to realize the half mirror with lens function. And the comparison of the characteristics of prototypes implemented by the three methods is also given as a reference in determining which one will be the most appropriate for the given application. The usefulness of the half mirror with lens function is studied in two different aspects: one is the source of inspiration in retrieving new concept of the three-dimensional display systemthe other is providing a new way to resolve the existing problems of the conventional three-dimensional display system. The study is conducted by providing various types of three-dimensional display system based on the integral imaging principle. Two new types of three-dimensional display systems, which are inspired by adopting the half mirror with lens function, are presented considering the applications for the augmented reality. See-through and bidirectional integral imaging is a totally new concept that displays an autostereoscopic three-dimensional image, which is based on the integral imaging principle, around a see-through image without any mechanical motion. It is only realizable using the array of half mirrors with lens function which eventually has the surface structure of concave mirror array. Using the analysis of the imaging principle of the projection-type integral imaging, the half mirrors with lens function with 10\% of reflectance were implemented showing natural overlay of three-dimensional image onto the real world scene. See-through head mounted display, which can address a correct accommodation, is also presented with the convex side of the half mirror with lens function utilizing the principle of integral floating. The analysis is given which demonstrates that the pixel pitch of the display device imposes the restriction to the imaging distance of the integrated image. The use of convex mirror effectively reduces the pixel pitch of the display device enabling the farther imaging of integrated image without physical improvement of the system. The half mirror with lens function can also be useful in resolving the long-known problem of integral imaging, implementation of three-dimensional/two-dimensional convertible feature. Especially, there was no adequate solution for the projection-type integral imaging because the conventional methods incorporate the active devices with the size comparable to the screen. The adoption of half mirror with lens function provides the way to implement three-dimensional/two-dimensional convertible feature with the passive optical component. The proposed system is the first proposal to the three-dimensional/two-dimensional convertible projection-type integral imaging. A number of methods have been investigated for the three-dimensional/two-dimensional convertible integral imaging of \textit{focused mode} configuration while only one method exists for the \textit{real/virtual mode} integral imaging. The half mirror with lens function provides the new method to realize the three-dimensional/ two-dimensional convertible feature for the \textit{real/virtual mode} integral imaging. The proposed system also touts various advantageous features such as the wider viewing angle and the shorter optical path difference in creating multiple central depth planes for the depth enhancement. Though the usefulness of the half mirror with lens function is investigated only focusing on the three-dimensional display, it is expected that the half mirror with lens function will also be useful for various optical applications.Abstract i Contents iv List of Figures vii List of Tables xiv Chapter 1 Introduction 1 1.1 Historical review of three-dimensional displays . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Scope and organization . . . . . . . . . . . . . . . . . . . . . . . . 9 Chapter 2 New optical component: half mirror with lens function 12 2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Fabrication methods of the half mirror with lens function . . . . 17 2.3 Characteristic of the half mirror with lens function . . . . . . . . 25 2.4 Summary and discussion . . . . . . . . . . . . . . . . . . . . . . . 33 Chapter 3 See-through three-dimensional displays using half mirror with lens function 35 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 See-through and bi-directional integral imaging using an array of half mirror with lens function . . . . . . . . . . . . . . . . . . 39 3.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.2 Principle of the proposed scheme . . . . . . . . . . . . . . 45 3.2.3 Implementation of the prototype . . . . . . . . . . . . . . 53 3.2.4 Characteristic of the prototype . . . . . . . . . . . . . . . 55 3.2.5 Experimental results . . . . . . . . . . . . . . . . . . . . . 58 3.3 See-through integral floating display system for augmented reality 62 3.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.3.2 Limitations on a long distance imaging by integral imaging 65 3.3.3 Integral floating display using a convex mirror . . . . . . 78 3.3.4 Experimental results . . . . . . . . . . . . . . . . . . . . . 81 3.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . 87 Chapter 4 3D/2D convertible integral imaging systems using half mirror with lens function 89 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.2 3D/2D convertible projection-type integral imaging . . . . . . . . 95 4.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.2.2 Principle of the proposed scheme . . . . . . . . . . . . . . 96 4.2.3 Experimental results . . . . . . . . . . . . . . . . . . . . . 98 4.3 3D/2D convertible integral imaging using dual depth configuration106 4.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.3.2 Principle of the proposed scheme . . . . . . . . . . . . . . 108 4.3.3 Experimental results . . . . . . . . . . . . . . . . . . . . . 112 4.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . 117 Chapter 5 Conclusion 119 Bibliography 122 Appendix 132 ์ดˆ๋ก 133Docto

    (The) Relationship between prehypertension, hypertension and self-recognized osteoarthritis in Korean adults : analysis of KNHANES III('05), IV-1('07) and IV-2('08)

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    ์—ญํ•™๊ฑด๊ฐ•์ฆ์ง„ํ•™๊ณผ/์„์‚ฌ๊ณ ํ˜ˆ์••๊ณผ ๊ณจ๊ด€์ ˆ์—ผ์€ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๋ณตํ•ฉ์ ์ธ ์›์ธ์— ์˜ํ•ด ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๋‹ค์ธ์ž์„ฑ ์งˆํ™˜์œผ๋กœ ๊ณตํ†ต์ ์œผ๋กœ ๋‚˜์ด์™€ ์„ฑ๋ณ„, ๋น„๋งŒ์ด์™ธ์—๋„ ์ƒํ™œ ์Šต๊ด€ ์š”์ธ, ํ™˜๊ฒฝ์  ์š”์ธ, ์œ ์ „์  ์š”์ธ ๋“ฑ์œผ๋กœ๋ถ€ํ„ฐ ๋ณตํ•ฉ์  ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ๊ตญ๋‚ด์—์„œ ํ˜ˆ์••๊ณผ ๊ณจ๊ด€์ ˆ์—ผ์˜ ์—ฐ๊ด€์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ง€ ์•Š์€ ์ƒํ™ฉ์—์„œ ์ด ์—ฐ๊ตฌ๋Š” ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•œ๊ตญ์ธ์˜ ๊ณ ํ˜ˆ์••๊ณผ ๊ณจ๊ด€์ ˆ์—ผ์˜ ์—ฐ๊ด€์„ฑ์„ ์ฐพ์•„๋ณด๊ณ ์ž ํ•˜๋Š”๋ฐ ๊ทธ ์˜์˜๋ฅผ ๊ฐ€์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ์ œ 3๊ธฐ(2005๋…„), ์ œ4๊ธฐ 1์ฐจ๋…„๋„(2007๋…„), 2์ฐจ๋…„๋„(2008๋…„) ์ž๋ฃŒ๋กœ 19์„ธ ์ด์ƒ ์„ฑ์ธ ์ค‘ ํ˜ˆ์••๊ณผ ๊ณจ๊ด€์ ˆ์—ผ ์œ ๋ฌด๊ฐ€ ๋ชจ๋‘ ์กฐ์‚ฌ๋œ ๋‚จ์ž 6,441(42.0%)๋ช…, ์—ฌ์„ฑ 8,881(58.0%)๋กœ ๊ตฌ์„ฑ๋œ 13,522๋ช…์˜ ์ž๋ฃŒ๋ฅผ SAS 9.2์„ ์‚ฌ์šฉํ•˜์—ฌ ๋นˆ๋„๋ถ„์„, ๊ต์ฐจ๋ถ„์„, ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ํ‰๊ท  ์—ฐ๋ น์€ 48.47์„ธ(19~64์„ธ 79.8%, 65์„ธ ์ด์ƒ 20.5%)์˜€๊ณ  ํ‰๊ท  ์ˆ˜์ถ•๊ธฐํ˜ˆ์•• 117.46ยฑ17.62 mmHg, ํ‰๊ท  ์ด์™„๊ธฐ ํ˜ˆ์•• 75.76ยฑ10.80 mmHg์ด์—ˆ์œผ๋ฉฐ ๊ณ ํ˜ˆ์•• ์น˜๋ฃŒ์ œ ๋ณต์šฉ์—ฌ๋ถ€์™€ JNC-7๊ธฐ์ค€์œผ๋กœ ์ •์ƒํ˜ˆ์•• 7,409๋ช…(48.4%), ์ „๊ณ ํ˜ˆ์•• 3,815๋ช…(24.9%), ๊ณ ํ˜ˆ์•• 4,098๋ช…(26.8%), ๊ณจ๊ด€์ ˆ์—ผ์ด ์žˆ๋Š” ์‚ฌ๋žŒ์€ 2,209๋ช…(14.4%)์˜€๋‹ค. ์ „์ฒด ์—ฐ๊ตฌ๋Œ€์ƒ์—์„œ ํ˜ˆ์••๋ถ„๋ฅ˜๋ณ„ ๊ณจ๊ด€์ ˆ์—ผ ์œ ๋ณ‘์œจ์€ ์ •์ƒํ˜ˆ์••์€ 8.3%, ์ „๊ณ ํ˜ˆ์•• 14.4%, ๊ณ ํ˜ˆ์•• 25.6%์˜ ๋ถ„ํฌ์˜€๋‹ค. ์„ฑ, ๋‚˜์ด, ๊ต์œก์ˆ˜์ค€, ์ˆ˜์ž…์ˆ˜์ค€, ํ˜„์žฌ ํก์—ฐ์œ ๋ฌด, ๋น„๋งŒ์„ ๋ณด์ •ํ•œ ๋‹ค์ค‘๋กœ์ง€์Šคํ‹ฑ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ์„ฑ๋ณ„ ์—ฐ๋ น๋ณ„๋กœ ์ƒ์ดํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ ์ „์ฒด์— ๋Œ€ํ•ด์„œ๋Š” ์ •์ƒํ˜ˆ์•• ์‚ฌ๋žŒ์˜ ๊ณจ๊ด€์ ˆ์—ผ ์œ ๋ณ‘์œจ์„ 1๋กœ ํ•˜์˜€์„ ๋•Œ ํ˜ˆ์••์ด ๋†’์€ ๊ตฐ์—์„œ ๊ณจ๊ด€์ ˆ์—ผ์ด ์žˆ์„ ๋น„์ฐจ๋น„๊ฐ€ ๋†’์•˜๊ณ , ์ด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์˜€๋‹ค(์ „๊ณ ํ˜ˆ์••๊ตฐ OR 1.22, 95% CI 1.04-1.42, p = 0.014; ๊ณ ํ˜ˆ์••๊ตฐ OR 1.30, 95% CI 1.13-1.51, p<0.001). ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์—ฌ์„ฑ ํ˜น์€ 19~64์„ธ์—์„œ ์œ ์˜ํ•˜์˜€๋‹ค. ์—ฌ์„ฑ์—์„œ ์ •์ƒํ˜ˆ์••๊ตฐ์— ๋น„ํ•œ ์ „๊ณ ํ˜ˆ์••๊ตฐ ๋ฐ ๊ณ ํ˜ˆ์••๊ตฐ์˜ ๊ณจ๊ด€์ ˆ์—ผ ์œ„ํ—˜์€ ๊ฐ๊ฐ 1.22๋ฐฐ(p=0.033), 1.33๋ฐฐ(p=0.001), 19~64์„ธ ๊ตฐ์—์„œ ์ •์ƒํ˜ˆ์••๊ตฐ์— ๋น„ํ•œ ์ „๊ณ ํ˜ˆ์••๊ตฐ ๋ฐ ๊ณ ํ˜ˆ์••๊ตฐ์˜ ๊ณจ๊ด€์ ˆ์—ผ ์œ„ํ—˜์€ ๊ฐ๊ฐ 1.22๋ฐฐ(p=0.037), 1.37๋ฐฐ(P=0.001)์˜€๋‹ค. 19~64์„ธ ์—ฌ์„ฑ์—์„œ๋„ ์ด๋Ÿฌํ•œ ๋น„์ฐจ๋น„ ์ฆ๊ฐ€๊ฐ€ ๋ณด์˜€๋‹ค(์ „๊ณ ํ˜ˆ์••๊ตฐ OR 1.25, 95% CI 1.00-1.56, P=0.050; ๊ณ ํ˜ˆ์••๊ตฐ OR 1.46, 95% CI 1.17-1.81, p<0.001). ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ํ˜ˆ์••๊ณผ ๊ณจ๊ด€์ ˆ์—ผ์˜ ๊ด€๋ จ์„ฑ์€ ๋‚จ์„ฑ ํ˜น์€ 65์„ธ ์ด์ƒ ๊ตฐ์—์„œ๋Š” ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋Œ€ํ‘œ์„ฑ์ด ์žˆ๋Š” ์ž๋ฃŒ์ธ ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•œ๊ตญ์„ฑ์ธ์˜ ๊ณ ํ˜ˆ์••๊ณผ ๊ณจ๊ด€์ ˆ์—ผ ์œ ๋ณ‘์œจ์˜ ์—ฐ๊ด€์„ฑ์„ ๋ถ„์„ํ•œ ๋‹จ๋ฉด์—ฐ๊ตฌ๋กœ์„œ ์—ฌ์„ฑ ๋˜๋Š” 19-64์„ธ ํ•œ๊ตญ ์„ฑ์ธ์—์„œ ๊ณ ํ˜ˆ์••์€ ๊ด€๋ จ ์š”์ธ์˜ ์˜ํ–ฅ์„ ๋ฐฐ์ œํ•œ ํ›„์—๋„ ๋ณธ์ธ ์ธ์ง€ ๊ณจ๊ด€์ ˆ์—ผ๊ณผ ์œ ์˜ํ•œ ๊ด€๋ จ์„ฑ์„ ๋ณด์˜€๋‹ค. ๋‹ค์ธ์„ฑ์งˆํ™˜์ธ ๋‘ ์งˆ๋ณ‘์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ช…ํ™•ํ•˜๊ฒŒ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์œ ์ „์ , ํ™˜๊ฒฝ์ , ์ƒํ™œ ์Šต๊ด€์  ๋ณ€์ˆ˜ ๋“ฑ์„ ํฌํ•จํ•œ ์‹ฌ์ธต์  ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค.ope

    ๋‹จ๋ถ„์ž ์œ ๊ธฐํƒœ์–‘์ „์ง€ ๊ด‘ํ™œ์„ฑ์ธต์˜ ๋ฐ•๋ง‰ ๊ตฌ์กฐ์™€ ํƒœ์–‘์ „์ง€ ์„ฑ๋Šฅ์˜ ์ƒ๊ด€๊ด€๊ณ„ ์—ฐ๊ตฌ

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    Doctor์œ ๊ธฐํƒœ์–‘์ „์ง€๋Š” ๊ด‘ํ™œ์„ฑ์ธต์— ์œ ๊ธฐ๋ฐ˜๋„์ฒด๋ฅผ ๋„์ž…ํ•˜์—ฌ ๋น„๊ต์  ๊ฐ€๋ณ๊ณ  ์œ ์—ฐํ•˜๋ฉฐ, ์šฉ์•ก ๊ณต์ •์„ ํ†ตํ•œ ์‰ฌ์šด ์ œ์ž‘๊ณผ์ •, ๋‚ฎ์€ ์ œ์กฐ ๋‹จ๊ฐ€๋กœ ์ธํ•ด ์‹ ์žฌ์ƒ์—๋„ˆ์ง€ ์†Œ์ž๋กœ ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ๊ด‘ํ™œ์„ฑ์ธต์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์ „์ž์ฃผ๊ฐœ(electron donor)์™€ ์ „์ž๋ฐ›๊ฐœ(electron acceptor) ๋ฌผ์งˆ์ด ํ˜ผํ•ฉ๋œ ๋ฒŒํฌ ์ด์ข…์ ‘ํ•ฉ(bulk heterojunction, BHJ) ๊ตฌ์กฐ๋ฅผ ๊ฐ–๋Š”๋‹ค. ๋†’์€ ๊ด‘์ „ ๋ณ€ํ™˜ ํšจ์œจ์„ ์–ป๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ™•์žฅ๋œ ๊ณต์•ก๊ธธ์ด, ๋„“์€ ๊ด‘ํก์ˆ˜ ์˜์—ญ, ์ ์ ˆํ•œ ์—๋„ˆ์ง€ ์ค€์œ„, ๋†’์€ ์ „ํ•˜ ์ด๋™๋„ ํŠน์„ฑ์„ ๊ฐ–๋Š” ์ „์ž์ฃผ๊ฐœ, ์ „์ž๋ฐ›๊ฐœ ์žฌ๋ฃŒ์˜ ๊ฐœ๋ฐœ๊ณผ, ํšจ์œจ์ ์ธ ์ „ํ•˜ ๊ตํ™˜์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๋„“์€ ์ „์ž์ฃผ๊ฐœ/์ „์ž๋ฐ›๊ฐœ ๊ณ„๋ฉด, ๋น ๋ฅธ ์ „ํ•˜ ์ด๋™์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๋†’์€ ๊ฒฐ์ •์„ฑ์„ ๊ฐ€์ง„ ๋ฒŒํฌ ์ด์ข…์ ‘ํ•ฉ ๋ชจํด๋กœ์ง€๊ฐ€ ์š”๊ตฌ๋œ๋‹ค. ์ „์ž์ฃผ๊ฐœ ๋˜๋Š” ์ „์ž๋ฐ›๊ฐœ์˜ ๋ถ„์ž๊ตฌ์กฐ๋Š” ๋ฌผ์งˆ์˜ ํŠน์„ฑ๊ณผ ๋ชจํด๋กœ์ง€์— ๋ชจ๋‘ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฏ€๋กœ ํšจ์œจ์ ์ธ ๋ถ„์ž๊ตฌ์กฐ๋ฅผ ๋””์ž์ธํ•˜๊ณ  ๋ถ„์ž๊ตฌ์กฐ์™€ ๋ชจํด๋กœ์ง€, ํƒœ์–‘์ „์ง€ ์†Œ์ž ์„ฑ๋Šฅ ์‚ฌ์ด์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ตœ๊ทผ์—๋Š” ๋‹จ๋ถ„์ž ์œ ๊ธฐ๋ฐ˜๋„์ฒด ์žฌ๋ฃŒ๊ฐ€ ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์ฃผ๋„ํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ๋‹จ๋ถ„์ž ์œ ๊ธฐ๋ฐ˜๋„์ฒด๋Š” ํ•ฉ์„ฑ๊ณผ ์ •์ œ ๊ณผ์ •์ด ์šฉ์ดํ•˜๋ฉฐ ๋ถ„์ž๋Ÿ‰์ด ์ผ์ •ํ•˜์—ฌ ๋ถ„์ž ๊ตฌ์กฐ์™€ ๋ฌผ์„ฑ์˜ ์กฐ์ ˆ์ด ์šฉ์ดํ•˜๋‹ค๋Š” ์žฅ์ ์„ ๊ฐ€์ง€๋ฉฐ, ์ด๋Ÿฌํ•œ ์žฅ์ ์€ ์šฐ์ˆ˜ํ•œ ์ƒ์‚ฐ ์žฌํ˜„์„ฑ๊ณผ ์†Œ์ž ์„ฑ๋Šฅ์˜ ์žฌํ˜„์„ฑ์„ ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ ํ–ฅํ›„ ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ์ƒ์šฉํ™”์—๋„ ์œ ๋ฆฌํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹จ๋ถ„์ž ์œ ๊ธฐ๋ฐ˜๋„์ฒด๋ฅผ ๊ด‘ํ™œ์„ฑ์ธต์— ๋„์ž…ํ•œ ์œ ๊ธฐํƒœ์–‘์ „์ง€์—์„œ ์œ ๊ธฐ๋ฐ˜๋„์ฒด์˜ ๋ถ„์ž๊ตฌ์กฐ๊ฐ€ ๋ชจํด๋กœ์ง€์™€ ์†Œ์ž ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. 1์žฅ์—์„œ๋Š”, ๋ณธ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ๋…ผ์˜์— ์•ž์„œ ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ๊ตฌ์กฐ์™€ ๊ตฌ๋™ ์›๋ฆฌ, ๋ถ„์ž ๊ตฌ์กฐ๊ฐ€ ๊ด‘ํ™œ์„ฑ์ธต์˜ ๋ชจํด๋กœ์ง€์™€ ์†Œ์ž ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ, ๋ชจํด๋กœ์ง€๋ฅผ ์กฐ์ ˆํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ์†Œ๊ฐœํ•˜์˜€๋‹ค. 2์žฅ์—์„œ๋Š” ์•„์„ผ(acene) ๊ธฐ๋ฐ˜์˜ ์‹ ๊ทœ ๋‹จ๋ถ„์ž ์ „์ž์ฃผ๊ฐœ๋ฅผ ๋„์ž…ํ•œ ๋‹จ๋ถ„์ž ํƒœ์–‘์ „์ง€์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ์•„์„ผ์˜ ๊ณต์•ก ๊ธธ์ด์— ๋”ฐ๋ฅธ ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ฐ๊ฐ ํŽ˜๋‹(phenyl), ๋‚˜ํ”„ํƒˆ๋ Œ(naphthalene), ์•ˆํŠธ๋ผ์„ผ(anthracene)์„ ์ฝ”์–ด๋กœ ๊ฐ–๋„๋ก ๋””์ž์ธ๋œ ๋‹จ๋ถ„์ž ์ „์ž์ฃผ๊ฐœ๋Š” ํ’€๋Ÿฌ๋ Œ๊ณ„ ์ „์ž๋ฐ›๊ฐœ์™€ ์šฐ์ˆ˜ํ•œ ํ˜ผํ™”์„ฑ(miscibility)๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์†Œ๋Ÿ‰์˜ ์šฉ๋งค ์ฒจ๊ฐ€์ œ์ธ 1,8-diiodooctane์„ ์ฒจ๊ฐ€ํ•˜์˜€์„ ๋•Œ ๊ฒฐ์ •์„ฑ์ด ํฌ๊ฒŒ ์ฆ๊ฐ€๋˜์–ด ๋†’์€ ์ถฉ์ง„์œจ(fill factor, FF)๊ณผ ๊ด‘์ „ ๋ณ€ํ™˜ ํšจ์œจ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. 3์žฅ์—์„œ๋Š” ๋‚˜ํ”„ํƒˆ๋ Œ ๋‹ค์ด์ด๋ฏธ๋“œ(naphthalene diimide, NDI) ๊ธฐ๋ฐ˜์˜ ์‹ ๊ทœ ๋‹จ๋ถ„์ž ์ „์ž๋ฐ›๊ฐœ์™€ DTS-F ๋‹จ๋ถ„์ž ์ „์ž์ฃผ๊ฐœ๋ฅผ ์ด์šฉํ•œ ์ „๋‹จ๋ถ„์ž ์œ ๊ธฐํƒœ์–‘์ „์ง€(all-small-molecule solar cell)์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” NDI dimer ์‚ฌ์ด์— ์„œ๋กœ ๋‹ค๋ฅธ ฯ€-๊ณต์•ก ๋ง์ปค(ฯ€-conjugated linker)๋ฅผ ๋„์ž…ํ•˜์—ฌ ๋‹จ๋ถ„์ž์˜ ๊ณต์•ก ๊ธธ์ด์™€ ๊ธฐํ•˜๊ตฌ์กฐ๋ฅผ ์กฐ์ ˆํ•˜๊ณ , ๋ชจํด๋กœ์ง€์™€ ์†Œ์ž ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๊ฐ๊ฐ thiophene(T), bithiophene(BT), thiopheneโˆ’vinyleneโˆ’thiophene(TVT) ๋ง์ปค๋ฅผ ๊ฐ€์ง„ NDICN-T, NDICN-BT, NDICN-TVT ๋‹จ๋ถ„์ž๋Š” DTS-F ๋‹จ๋ถ„์ž ์ „์ž์ฃผ๊ฐœ์™€์˜ ๋ฒŒํฌ ์ด์ข…์ ‘ํ•ฉ ๋ฐ•๋ง‰์—์„œ ๋‹ค๋ฅธ ์• ๊ทธ๋ฆฌ๊ฒŒ์ด์…˜ ํŠน์„ฑ๊ณผ ๊ฒฐ์ •์„ฑ์„ ๋ณด์˜€๊ณ , ์ด๋กœ ์ธํ•ด ํƒœ์–‘์ „์ง€ ์†Œ์ž์˜ ์ „ํ•˜ ๊ฑฐ๋™์—์„œ ํฐ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์Šค์นจ๊ฐ ์ž…์‚ฌ ๊ด‘๊ฐ x-์„  ์‚ฐ๋ž€(grazing incidence wide-angle x-ray scattering, GIWAXS) ์‹คํ—˜์œผ๋กœ ๊ฒฐ์ •์„ฑ์„ ๋ถ„์„ ํ–ˆ์„ ๋•Œ, TVT ๋ง์ปค๋ฅผ ๊ฐ–๋Š” NDICN-TVT ๋‹จ๋ถ„์ž๋Š” ๋‹จ์ผ ๋ฐ•๋ง‰์—์„œ์™€ DTS-F์™€์˜ ํ˜ผํ•ฉ ๋ฐ•๋ง‰์—์„œ ๋ชจ๋‘ ๋†’์€ ๊ฒฐ์ •์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๊ณ , ๋‹จ์ผ ๋ฐ•๋ง‰์—์„œ์™€ ํ˜ผํ•ฉ ๋ฐ•๋ง‰์—์„œ์˜ ์ „ํ•˜ ์ด๋™๋„๋ฅผ ์ธก์ •ํ•จ์œผ๋กœ์จ NDICN-TVT์˜ ๋†’์€ ๊ฒฐ์ •์„ฑ์ด ์œ ๊ธฐํƒœ์–‘์ „์ง€ ์†Œ์ž ์•ˆ์—์„œ์˜ ํšจ์œจ์ ์ธ ์ „ํ•˜ ์ด๋™์— ๊ธฐ์—ฌํ•˜์—ฌ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ธ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ฐ˜๋ฉด DTS-F์™€ ๋†’์€ ํ˜ผํ™”์„ฑ์„ ๋ณด์ธ NDICN-BT๋Š” ํ˜ผํ•ฉ ๋ฐ•๋ง‰์—์„œ ๊ฒฐ์ • ๊ตฌ์กฐ ํ˜•์„ฑ์ด ์–ด๋ ค์›Œ ๋‚ฎ์€ ํšจ์œจ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. 4์žฅ์—์„œ๋Š” DTBDT ๊ธฐ๋ฐ˜์˜ ์‹ ๊ทœ ์ „์ž์ฃผ๊ฐœ์™€ O-IDTBR ์ „์ž๋ฐ›๊ฐœ๋ฅผ ๋„์ž…ํ•œ ์ „๋‹จ๋ถ„์ž ์œ ๊ธฐํƒœ์–‘์ „์ง€์—์„œ์˜ ๋ชจํด๋กœ์ง€์™€ ์ „ํ•˜๊ฑฐ๋™์„ ๋ถ„์„ํ•œ ์—ฐ๊ตฌ์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ๊ด‘ํ™œ์„ฑ์ธต ๋ฐ•๋ง‰์—์„œ์˜ ๋‹จ๋ถ„์ž ์œ ๊ธฐ๋ฐ˜๋„์ฒด์˜ ๋ถ„์ž ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ๊ณผ ๊ฒฐ์ •๊ตฌ์กฐ๊ฐ€ ์†Œ์ž ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด DTBDT ๊ธฐ๋ฐ˜ ๋‹จ๋ถ„์ž์˜ ๊ณ์‚ฌ์Šฌ์„ ์กฐ์ ˆํ•˜์˜€๊ณ , ์ „๋‹จ๋ถ„์ž ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ํŠน์„ฑ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์กด์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด์˜จ ํ’€๋Ÿฌ๋ Œ๊ณ„ ์ „์ž๋ฐ›๊ฐœ๋ฅผ ์‚ฌ์šฉํ•œ ์†Œ์ž์™€ ๋น„๊ตํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด DTBDT ๊ธฐ๋ฐ˜ ์ „์ž์ฃผ๊ฐœ๋ฅผ ๋„์ž…ํ•œ ์ „๋‹จ๋ถ„์ž ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ๊ด‘ํ™œ์„ฑ์ธต์—์„œ 100 nm ์ด์ƒ์˜ ํฐ ์ „์ž์ฃผ๊ฐœ ๋˜๋Š” ์ „์ž๋ฐ›๊ฐœ ๋„๋ฉ”์ธ์ด ํ˜•์„ฑ๋˜์–ด ์—‘์‹œํ†ค์ด ์ „์ž์ฃผ๊ฐœ์™€ ์ „์ž๋ฐ›๊ฐœ์˜ ๊ณ„๋ฉด๊นŒ์ง€ ํ™•์‚ฐํ•˜์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์ด ๋น„๊ต์  ๋‚ฎ์€ ํšจ์œจ์˜ ์›์ธ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ „๋‹จ๋ถ„์ž ์œ ๊ธฐํƒœ์–‘์ „์ง€์˜ ๊ด‘ํ™œ์„ฑ์ธต์—์„œ ์ „์ž์ฃผ๊ฐœ์™€ ์ „์ž๋ฐ›๊ฐœ์˜ ์ƒ๋ถ„๋ฆฌ ์ •๋„๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๊ณผ์ œ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค.Organic photovoltaics (OPVs) have been motivated for producing clean and sustainable energy. They can be light in weight, flexible, semitransparent, and cost-effective; and can be fabricated in large area by solution processing. The compatibility with other electronic devices and low fabrication cost of OPVs have encouraged research on OPVs including material syntheses and device engineering. The development of diverse donor and acceptor materials has led to drastic increases in power conversion efficiencies (PCEs); and advances in material design are expected to lead to further improvements. Since the molecular structure strongly influences the bulk heterojunction (BHJ) morphology in active layers, material designs and systematic studies of the relationship between molecular structures and morphologies are exceedingly important. In this study, OPVs incorporating small molecules and their structure-property relationships are discussed. Small molecules have well-defined chemical structures and low batch-to-batch variations; and are free from molecular weight distribution, and chain-end defects. The fewer and simpler synthetic steps of small molecules compared to those of polymers allow diverse material design, and systematic study of structure-property relationships. In chapter 1, the basics and operation physics of OPVs are introduced. The effects of material structures and morphology on OPV characteristics, are also discussed. In chapter 2, OPVs incorporating acene-based small-molecule donors are discussed. A series of new acene-based small molecules are designed; and their photovoltaic characteristics and BHJ morphologies are investigated. Three small-molecule donors, Ph-TTR, Na-TTR, and An-TTR, have phenyl, naphthalene, and anthracene moieties as conjugated cores, respectively. The donors have excellent miscibility with PC71BM acceptor, and the use of 1,8-diiodooctane (DIO) additive gave remarkable increase in crystallinity, thereby increasing PCEs in OPVs. Na-TTR showed the most suitable energy levels and the favorable active layer morphology with high crystallinity, so the Na-WR:PC71BM device exhibited the highest PCE of 6.27% without pre- or post-treatment. In chapter 3, all-small-molecule OPVs with new NDI-based small-molecule acceptors are discussed. A series of NDI-based small molecules was synthesized as nonfullerene acceptors. Three NDI-based small molecules, NDICN-T, NDICN-BT, and NDICN-TVT, were designed with distinct linkers between two NDI units to modulate the conjugation length and geometric structures of the NDI dimers. The photovoltaic devices prepared with NDICN-TVT gave the highest PCE of 3.01%, whereas the DTS-F:NDICN-T and DTS-F:NDICN-BT devices provided PCEs of 1.81% and 0.13%, respectively. Studies of the charge generation properties, charge transfer dynamics, and charge transport properties for understanding the structure-property relationships revealed that DTS-F:NDICN-TVT blend films with well-developed domains and well-ordered crystalline structures performed well, whereas an excessive miscibility between DTS-F and NDICN-BT disrupted the crystallinity of the materials and yielded a poor device performance. In chapter 4, all-small-molecule OPVs with new DTBDT-based donors are realized. To investigate the influence of donor molecule crystallinity on photovoltaic performance in all-small-molecule solar cells, two DTBDT-based small molecules, DTBDT-Rho and DTBDT-S-Rho with different side chains, are designed. The photovoltaic properties of solar cells made of these DTBDT-based donor molecules are systemically studied with the fullerene acceptor PC71BM and the nonfullerene acceptor O-IDTBR to study the aggregation behavior and crystallinity of the donor molecules in both blends. This study reveals that exciton decay loss driven by large-scale phase separation of the DTBDT molecules to be a crucial factor limiting photocurrent generation in the all-small-molecule solar cells incorporating O-IDTBR. These results indicate the modulation of phase separation to be important for improving the photovoltaic performances of all-small-molecule blends. In addition, the enhanced molecular aggregation of DTBDT-S-Rho with the alkylthio side chain leads to higher degrees of phase separation and unfavorable charge transfer, while the enhanced molecular aggregation improves the crystallinity of DTBDT-S-Rho and results in its increased hole mobility
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