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    ์ „๊ธฐ๋ฐฉ์‚ฌ ํด๋ฆฌ์•„ํฌ๋ฆด๋กœ๋‹ˆํŠธ๋ฆด ๋‚˜๋…ธ์„ฌ์œ  ๊ธฐ๋ฐ˜ ํŠธ๋žœ์Šค๋“€์„œ ์ œ์กฐ ๋ฐ ํ™”ํ•™/๋ฐ”์ด์˜ค์„ผ์„œ๋กœ์˜ ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต),2020. 2. ์žฅ์ •์‹.In recent decades, there has been tremendous researches for developing one-dimensional (1D) nanomaterials used for sensor transducer, owing to their structural properties such as high aspect ratio and high specific surface area. Among diverse method to fabricate 1D nanomaterials, electrospinning has been widely utilized because of their simple usage and low operating temperature. Additionally, because the manufactured fibers come in a mat form, various applications are possible in itself. Although, multifarious synthesis methods have been studied to prepare 1D nanomaterials via electrospinning, Research into the decoration of metals or metal oxides on the surface of nanofibers and method of carbonize nanofibers to produce flexible and free-standing mats are still lacking. This dissertation proposes the method to prepare diverse electrospun polyacrylonitrile nanofibers (PAN NFs) based composite materials for sensor application by decoration of metal or metal oxide and additional carbon. Firstly, shape controlled palladium nanoflower decorated polypyrrole/PAN NFs (Pd_PPy/PAN NFs) were prepared using electrospinning of PAN solution followed by polypyrrole vapor deposition polymerization (VDP) and electrodeposition of palladium nanoflowers. The shape of palladium was determined by controlling the sulfuric acid concentration in electrolyte during electrodeposition, and applied as a hydrogen peroxide sensor electrode material. Secondly, chemical vapor deposition (CVD) and metal etching were adopted to decorate copper (Cu) derived carbon on carbon nanofiber (CNF). The structure of Cu derived carbon was determined by the type of Cu used, and protrusion shape was produced on CNFs when sphere type Cu was used. Then, platelet derived growth factor (PDGF)-B binding aptamer was immobilized on as prepared materials and applied as PDGF biosensor with high sensitivity and selectivity. Finally, to fabricate manganese dioxide decorated carbon nanofiber (Mn@CNF), potassium permanganate was used as a precursor and chemically reduced by stirring and heat treatment. Using Mn@CNF as a transducer for sensor, the nerve gas agent simulant (DMMP) was detected with ultrasensitive. In addition, the fact that the material produced in this dissertation exhibits electrical resistance and target analyte sensing performance that does not diminish despite of bending, offers the potential for flexible and free-standing substrate for sensor application.์ตœ๊ทผ, ๋†’์€ ์ข…ํšก๋น„ ๋ฐ ๋น„ํ‘œ๋ฉด์ ๊ณผ ๊ฐ™์€ ๊ตฌ์กฐ์  ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ์„ผ์„œ ํŠธ๋žœ์Šค๋“€์„œ ๋ฌผ์งˆ๋กœ 1์ฐจ์› ๋‚˜๋…ธ๋ฌผ์งˆ์„ ์ด์šฉํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•˜๊ฒŒ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. 1D ๋‚˜๋…ธ ๋ฌผ์งˆ์„ ์ œ์กฐํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ• ์ค‘, ์ „๊ธฐ ๋ฐฉ์‚ฌ๋Š” ๊ฐ„๋‹จํ•œ ์‚ฌ์šฉ๋ฒ•๊ณผ ๋‚ฎ์€ ์ž‘๋™ ์˜จ๋„ ๋•Œ๋ฌธ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด์™”๋‹ค. ๋˜ํ•œ, ์ œ์กฐ ๋œ ์„ฌ์œ ๋Š” ๋งคํŠธ ํ˜•ํƒœ๋กœ๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ๊ทธ ์ž์ฒด์ ์œผ๋กœ ๋‹ค์–‘ํ•œ ์‘์šฉ์— ์ ์šฉ์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์„ ์ง€๋‹Œ๋‹ค. ์ „๊ธฐ ๋ฐฉ์‚ฌ๋ฅผ ํ†ตํ•ด 1D ๋‚˜๋…ธ ๋ฌผ์งˆ์„ ์ œ์กฐํ•˜๊ธฐ์œ„ํ•œ ๋‹ค์–‘ํ•œ ํ•ฉ์„ฑ ๋ฐฉ๋ฒ•์ด ์—ฐ๊ตฌ๋˜์–ด ์™”์ง€๋งŒ, ๋‚˜๋…ธ ์„ฌ์œ ์— ๊ธˆ์† ๋˜๋Š” ๊ธˆ์† ์‚ฐํ™”๋ฌผ์˜ ๋„์ž…ํ•ด ๋ณตํ•ฉ๋‚˜๋…ธ์žฌ๋ฃŒ๋ฅผ ๋งŒ๋“ค๊ฑฐ๋‚˜ ๋‚˜๋…ธ์„ฌ์œ ๋ฅผ ํƒ„ํ™” ํ›„ ์œ ์—ฐํ•˜๊ฒŒ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ฏธ์ง„ํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๊ธˆ์†, ๊ธˆ์†์‚ฐํ™”๋ฌผ ๋˜๋Š” ํƒ„์†Œ ์†Œ์žฌ๋ฅผ ์ „๊ธฐ๋ฐฉ์‚ฌ ํด๋ฆฌ์•„ํฌ๋ฆด๋กœ๋‹ˆํŠธ๋ฆด ๋‚˜๋…ธ์„ฌ์œ ์— ๋„์ž…ํ•ด ๋ณตํ•ฉ์žฌ๋ฃŒ๋ฅผ ์ œ์กฐํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๊ธฐ์ˆ ํ•˜์˜€๋‹ค. ์šฐ์„ , ํด๋ฆฌ์•„ํฌ๋ฆด๋กœ๋‹ˆํŠธ๋ฆด ์šฉ์•ก์„ ์ „๊ธฐ ๋ฐฉ์‚ฌํ•ด ์ œ์กฐํ•œ ๋‚˜๋…ธ์„ฌ์œ ์— ๊ธฐ์ƒ ์ฆ์ฐฉ ์ค‘ํ•ฉ์œผ๋กœ ํด๋ฆฌํ”ผ๋กค์„ ์ฝ”ํŒ…ํ•˜์˜€๊ณ , ์ด๋ฅผ ์ž‘๋™์ „๊ทน์œผ๋กœ ํ•˜์—ฌ ํ˜•์ƒ์„ ์ œ์–ดํ•œ ํŒ”๋ผ๋“ ๋‚˜๋…ธ ํ”Œ๋ผ์›Œ๋ฅผ ์ „๊ธฐ๋„๊ธˆ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด ๋„์ž…ํ•˜์˜€๋‹ค. ํŒ”๋ผ๋“์˜ ํ˜•์ƒ์€ ์ „๊ธฐ๋„๊ธˆ ์‹œ ์‚ฌ์šฉํ•˜๋Š” ์ „ํ•ด์งˆ์—์„œ ํ™ฉ์‚ฐ ๋†๋„๋ฅผ ์กฐ์ ˆํ•จ์œผ๋กœ์จ ๊ฒฐ์ •๋˜์—ˆ๋‹ค. ์ด๋ ‡๊ฒŒ ์ œ์กฐํ•œ ๋ฌผ์งˆ์€ ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ์„ผ์„œ ์ „๊ทน ์žฌ๋ฃŒ๋กœ์„œ ์ ์šฉ๋˜์—ˆ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ, ํ™”ํ•™ ๊ธฐ์ƒ ์ฆ์ฐฉ ๋ฐ ๊ธˆ์† ์‹๊ฐ ๋ฐฉ๋ฒ•์€ ํƒ„์†Œ ๋‚˜๋…ธ ์„ฌ์œ  ์ƒ์— ๊ตฌ๋ฆฌ๋ฅผ ์ด์šฉํ•ด ์ œ์กฐํ•œ ํƒ„์†Œ๋ฅผ ๋„์ž…ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ตฌ๋ฆฌ๋ฅผ ์ด์šฉํ•ด ์ œ์กฐํ•œ ํƒ„์†Œ์˜ ๊ตฌ์กฐ๋Š” ์‚ฌ์šฉํ•œ ๊ตฌ๋ฆฌ ํ˜•์ƒ ์ข…๋ฅ˜์— ์˜ํ•ด ๊ฒฐ์ •๋˜์—ˆ๋‹ค. ๊ทธ์ค‘ ๊ตฌ ํ˜•์ƒ ๊ตฌ๋ฆฌ๋ฅผ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ ํƒ„์†Œ๋‚˜๋…ธ ์„ฌ์œ  ์ƒ์— ๋Œ๊ธฐ ํ˜•ํƒœ์˜ ํƒ„์†Œ๋ฅผ ๋„์ž… ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ œ์กฐํ•œ ๋ฌผ์งˆ์— ํ˜ˆ์†ŒํŒ ์œ ๋ž˜ ์„ฑ์žฅ ์ธ์ž (PDGF) ๊ฒฐํ•ฉ ์••ํƒ€๋จธ๋ฅผ ๊ณ ์ •์‹œ์ผœ, ๋†’์€ ๊ฐ๋„ ๋ฐ ์„ ํƒ์„ฑ์„ ๊ฐ–๋Š” ๋ฐ”์ด์˜ค ์„ผ์„œ๋กœ์„œ ์ ์šฉ ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ด์‚ฐํ™”๋ง๊ฐ„์„ ๋„์ž…ํ•œ ํƒ„์†Œ๋‚˜๋…ธ์„ฌ์œ ๋ฅผ ์ œ์กฐํ•˜๊ธฐ ์œ„ํ•ด, ๊ณผ๋ง๊ฐ„์‚ฐ ์นผ๋ฅจ์„ ์ „๊ตฌ์ฒด๋กœ ์‚ฌ์šฉํ•˜๊ณ  ์—ด์ฒ˜๋ฆฌ ๋ฐ ๊ต๋ฐ˜์„ ์ด์šฉํ•ด ํ™”ํ•™์ ์œผ๋กœ ํ™˜์›์‹œ์ผฐ๋‹ค. ์ œ์กฐํ•œ ๋ฌผ์งˆ์€, ์‹ ๊ฒฝ ์œ ๋„์ฒด์ธ ๋””๋ฉ”ํ‹ธ ๋ฉ”ํ‹ธํฌ์Šคํฌ๋„ค์ดํŠธ ๋ถ„์ž ๊ฒ€์ถœ์šฉ ํ™”ํ•™์„ผ์„œ์˜ ํŠธ๋žœ์Šค๋“€์„œ ๋ฌผ์งˆ๋กœ ์ ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์กฐํ•œ ๋ฌผ์งˆ์ด ๊ตฝํž˜์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ €ํ•˜๋˜์ง€ ์•Š๋Š” ์ „๊ธฐ ์ €ํ•ญ ๋ฐ ํƒ€๊ฒŸ ๋ถ„์„๋ฌผ์งˆ ๊ฐ์ง€ ์„ฑ๋Šฅ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค๋Š” ์‚ฌ์‹ค์„ ํ†ตํ•ด ์œ ์—ฐํ•˜๊ณ  ๋…๋ฆฝ๋œ ๊ธฐํŒ ์„ผ์„œ ๋ฌผ์งˆ๋กœ ํ™œ์šฉ ํ•  ๊ฐ€๋Šฅ์„ฑ์„ ์ œ๊ณตํ•˜์˜€๋‹ค.1. Introduction 1 1.1. Background 1 1.1.1. Conducting polymer 1 1.1.1.1. Polypyrrole 3 1.1.1.2. Vapor deposition polymerization (VDP) 8 1.1.2. One-dimensional nanomaterials 10 1.1.2.1. Electrospinning 13 1.1.2.2. Electrospun polymer derived carbon nanomaterials 16 1.1.3. Composite materials 17 1.1.3.1. Noble metal/conducting polymer composite materials 18 1.1.3.2. Metal oxide/carbon composite materials 19 1.1.4. Electrodeposition 20 1.1.5. CVD graphene 22 1.1.6. Sensor application 24 1.1.6.1. Liquid electrolyte gated FET type sensor 26 1.1.6.1.1. Hydrogen peroxide (H2O2) sensor 28 1.1.6.1.2. Platelet-derived growth factor (PDGF) sensor 30 1.1.6.2. Chemiresistive sensor 31 1.1.6.2.1. DMMP gas sensor 33 1.1.6.3. Flexible sensor 34 1.2. Objectives and Outlines 35 1.2.1. Objectives 35 1.2.2. Outlines 35 2. Experimental Details 37 2.1. Flexible Palladium nanoparticle decorated electrospun polypyrrole/polyacrylonitrile nanofibers for hydrogen peroxide coalescing detection 37 2.1.1. Materials 37 2.1.2. Fabrication of Pd_PPy/PAN NFs 37 2.1.3. Electrical measurement of Pd_PPy/PAN NFs based non-enzyme sensor 38 2.1.4. Characterization 39 2.2. Copper derived CVD carbon/electrospun-carbon flexible and free-standing mat for PDGF biosensor 40 2.2.1. Materials 40 2.2.2. Fabrication of CuC/CNF mat 40 2.2.3. Electrical measurement of CuC/CNF mat aptamer sensor 41 2.2.4. Characterization 52 2.3. Mn@CNF flexible and free-standing mat for DMMP gas sensor. 43 2.3.1. Materials 43 2.3.2. Fabrication of Mn@CNF mat 43 2.3.3. Electrical measurement of Mn@CNF mat chemiresistive sensor 44 2.3.4. Characterization 45 3. Results and Discussion 46 3.1. Flexible Palladium nanoparticle decorated electrospun polypyrrole/polyacrylonitrile nanofibers for hydrogen peroxide coalescing detection 46 3.1.1. Fabrication of Pd_PPy/PAN NFs 46 3.1.2. Characterization of Pd_PPy/PAN NFs 54 3.1.3. Electrical properties of the shape controlled Pd_PPy/PAN NFs electrode 61 3.1.4. Real-time response of FET-type H2O2 sensor based on shape-controlled Pd_PPy/PAN NFs electrode 63 3.2. Copper derived CVD carbon/electrospun-carbon flexible and free-standing mat for PDGF biosensor 67 3.2.1 Fabrication of the Cu derived carbon/CNF mat 67 3.2.3. Characterization of the Cu derived carbon/CNF mat. 71 3.2.4. Fabrication of liquid-ion gated FET-type sensor electrode 77 3.2.3. Electrical properties of the CuC/CNF mat based sensor 83 3.2.4. Real-time response of the Apt-FlakeC/CNF and Apt-SP10C/CNF mat based sensor 8+ 3.3. Mn@CNF flexible and free-standing mat for DMMP gas sensor 93 3.3.1. Fabrication of Mn@CNF mat 93 3.3.2. Characterization of Mn@CNF mat 98 3.3.3. Electrical properties and real-time responses of the Mn@CNF mat based sensor to DMMP gas 103 4. Conclusion 114 Reference 117 ๊ตญ๋ฌธ์ดˆ๋ก 151Docto

    ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ๋ฌผ์ฒด์ธ์ง€์—์„œ์˜ ์ธก๋„๋ก ์  ๋ฐฉ๋ฒ•๋ก 

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2019. 8. ๋ฐ•์ข…์šฐ.This thesis is concerned with the object detection problem in computer vision, which aims to detect instances of semantic objects in images. The object types covered in this research range from those in two-dimensional images and in three-dimensional coordinates of real-world space, to curve-shaped objects like traffic lane markings. We present novel formulations for object detection problems based on measure theory and information geometry. Since our research starts from the deep learning framework in which a large-sized dataset is used to train a model, convolutional neural networks are utilized as function approximators for the representations. We present a positive measure that allows the object detection problem to be interpreted from the view of measure theory. Using a measure that indicates the number of objects, the corresponding density function represents a sample of labeled data for the object detection problem. Accordingly, we introduce a framework where the object detection problem is considered not as a problem of finding bounding boxes, but as a density estimation problem. The information geometric structure of the function space provides an invariant Riemannian metric, which helps to formulate a coordinate invariant divergence for two density functions. The measure-theoretic framework can also be applied to three-dimensional object detection problems. We use RGB-D sensor measurements, which appends the depth map channel to the conventional visual input of RGB channels. The definition of the measure and the density is simply extended to three-dimensional space, while the construction of the density representation requires a sophisticated parameterization method. We present an algorithm to construct density functions parameterized by the Gaussian mixture model. This method exploits the perspective projection transformation so that the three-dimensional density is derived from the image plane. We also address the detection of curve-shaped objects using spline parameterizations. The main target of this research is to detect traffic lanes from road scene images. We propose a traffic lane detection framework using deep neural networks based on B-spline representations of traffic lanes. Unlike previous approaches that depend on pixel-wise segmentation methods, our approach represents traffic lanes with spline curves, which are inherently more natural for representing traffic lanes. Experimental results compared against other state-of-the-art methods demonstrate the performance advantages of our method with respect to accuracy and efficiency.๋ณธ ๋…ผ๋ฌธ์€ ์ปดํ“จํ„ฐ ๋น„์ „ ๋ถ„์•ผ์˜ ๋ฌผ์ฒด์ธ์ง€๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๊ณ  ์žˆ์œผ๋ฉฐ, ๋น„์ „ ์‹œ์Šคํ…œ์˜ ์ž…๋ ฅ์œผ๋กœ๋ถ€ํ„ฐ ์˜๋ฏธ ์žˆ๋Š” ๋ฌผ์ฒด๋ฅผ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฃจ๋Š” ๋ฌผ์ฒด์˜ ์œ ํ˜•์€ 2 ์ฐจ์› ์ด๋ฏธ์ง€์˜ ๋ฌผ์ฒด, 3 ์ฐจ์› ๊ณต๊ฐ„์ƒ์˜ ๋ฌผ์ฒด, ๊ทธ๋ฆฌ๊ณ  ์ฐจ์„ ๊ณผ ๊ฐ™์€ ๊ณก์„  ํ˜• ๋ฌผ์ฒด๋ฅผ ํฌํ•จํ•œ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ๋Š” ์ธก๋„๋ก ๊ณผ ์ •๋ณด๊ธฐํ•˜ํ•™์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ ๋ฌผ์ฒด ์ธ์ง€ ๋ฌธ์ œ์— ์ ํ•ฉํ•œ ํ‘œํ˜„๋ฐฉ์‹์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€๋Ÿ‰์„ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜๋Š” ๋”ฅ๋Ÿฌ๋‹ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ๋ถ€ํ„ฐ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์˜€๊ธฐ์—, ์ด์— ์ ํ•ฉํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์ด ์ถฉ๋ถ„ํžˆ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฌผ์ฒด์ธ์ง€๋ฌธ์ œ๋ฅผ ํ•ด์„ํ•˜๊ธฐ์— ์ ํ•ฉํ•œ positive measure๋ฅผ ์ธก๋„๋ก ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ ์ œ์‹œํ•œ๋‹ค. ๋ฌผ์ฒด ์ˆ˜๋ฅผ ์ง€์นญํ•˜๋Š” ์ธก๋„๋ฅผ ์ด์šฉํ•˜์—ฌ, ์ด์— ๋Œ€์‘ํ•˜๋Š” ๋ฐ€๋„ํ•จ์ˆ˜๋ฅผ ํ†ตํ•ด ํ•™์Šต๋ฐ์ดํ„ฐ์˜ ์ƒ˜ํ”Œ์„ ํ‘œํ˜„ํ•œ๋‹ค. ์ฆ‰, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์กด์˜ bounding box๋ฅผ ์ฐพ๋Š” ๋ฌธ์ œ๋กœ ์ธ์‹๋˜์—ˆ๋˜ ๋ฌผ์ฒด์ธ์ง€๋ฌธ์ œ๋ฅผ ๋ฐ€๋„์ถ”์ •๋ฌธ์ œ๋กœ ์žฌํ•ด์„ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋˜ํ•œ ํ•จ์ˆ˜๊ณต๊ฐ„์˜ ์ •๋ณด๊ธฐํ•˜ํ•™์  ๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ์ œ์‹œ๋˜๋Š” Riemannian metrix์„ ์ด์šฉํ•˜์—ฌ, ๋‘ ๋ฐ€๋„ํ•จ์ˆ˜๊ฐ„์˜ divergence๋ฅด ์ขŒํ‘œ์— ๋…๋ฆฝ์ ์ธ ํ˜•ํƒœ๋กœ ์œ ๋„ํ•œ๋‹ค. ์•ž์„  ์ธก๋„๋ก  ๊ธฐ๋ฐ˜์˜ ๋ฐฉ๋ฒ•๋ก ์€ 3 ์ฐจ์› ๋ฌผ์ฒด์ธ์ง€๋ฌธ์ œ์—๋„ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊นŠ์ด ๋งต ์ฑ„๋„์„ ๊ธฐ์กด์˜ RGB ์ฑ„๋„์— ์ถ”๊ฐ€ํ•œ RGB-D ์„ผ์„œ ์ธก์ •๊ฐ’์„ ์ด์šฉํ•œ๋‹ค. 2 ์ฐจ์›์—์„œ ์ •์˜๋œ ์ธก๋„์™€ ๋ฐ€๋„๋ฅผ 3 ์ฐจ์›์œผ๋กœ ํ™•์žฅํ•˜๋Š” ๊ฒƒ์€ ๋‹จ์ˆœํ•œ ๋ฐ˜๋ฉด, 3 ์ฐจ์› ์ƒ์˜ ๋ฐ€๋„ํ•จ์ˆ˜๋ฅผ ๋งค๊ฐœ ๋ณ€์ˆ˜ํ™” ํ•˜์—ฌ ํ‘œํ˜„ํ•˜๋Š” ์ผ์€ ์„ธ์‹ฌํ•œ ์ฃผ์˜๋ฅผ ์š”๊ตฌํ•œ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐ€์šฐ์‹œ์•ˆ ํ˜ผํ•ฉ๋ชจ๋ธ์„ ์ด์šฉํ•œ ๋งค๊ฐœ ๋ณ€์ˆ˜ํ™”๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฐ€๋„ํ•จ์ˆ˜๋ฅผ ๊ตฌ์„ฑํ•œ๋‹ค. ์ด ๋ฐฉ์‹์€ ์›๊ทผ ํˆฌ์˜ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ, 3 ์ฐจ์›์˜ ๋ฐ€๋„ํ•จ์ˆ˜๊ฐ€ ์ด๋ฏธ์ง€ ํ‰๋ฉด์œผ๋กœ๋ถ€ํ„ฐ ์œ ๋„๋˜๋Š” ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋˜ํ•œ ์Šคํ”Œ๋ผ์ธ ๋งค๊ฐœ ๋ณ€์ˆ˜ํ™”๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ณก์„  ํ˜•ํƒœ์˜ ๊ฐ์ฒด ๊ฐ์ง€ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ์ฃผ ๋ชฉ์ ์€ ๋„๋กœ ์žฅ๋ฉด ์ด๋ฏธ์ง€์—์„œ ๊ตํ†ต ์ฐจ์„ ์„ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ตํ†ต ์ฐจ์„ ์„ B-spline์œผ๋กœ ํ‘œํ˜„ํ•œ ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•˜๋Š” ๊ตํ†ต ์ฐจ์„  ํƒ์ง€ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ํ”ฝ์…€ ๋‹จ์œ„์˜ ๋ถ„ํ•  ๋ฐฉ๋ฒ•์— ์˜์กดํ•˜๋Š” ์ด์ „ ์ ‘๊ทผ ๋ฐฉ์‹๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ๋ณธ ๋…ผ๋ฌธ์˜ ์ ‘๊ทผ ๋ฐฉ์‹์€ ๊ตํ†ต ์ฐจ์„ ์„ ํ‘œ์‹œํ•˜๊ธฐ์— ๋ณธ์งˆ์ ์œผ๋กœ ๋” ์ž์—ฐ์Šค๋Ÿฌ์šด ์Šคํ”Œ๋ผ์ธ ๊ณก์„ ์„ ์ด์šฉํ•œ๋‹ค. ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค๊ณผ ๋น„๊ต ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•˜์—ฌ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ฒฝ์Ÿ๋ ฅ ์žˆ๋Š” ์„ฑ๋Šฅ์„ ๋ณด์ผ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ณ„์‚ฐ์ƒ์œผ๋กœ๋„ ํšจ์œจ์ ์ž„์„ ์ž…์ฆํ•œ๋‹ค.1 Introduction 1 1.1 Contributions of This Thesis 4 1.1.1 Measure-Theoretic Representation for Object Detection 4 1.1.2 Object Detection in Three-Dimensional Space 5 1.1.3 Spline Representation for Traffic Lane Detection 6 2 Preliminaries 9 2.1 Introduction 9 2.2 Measure Theory 10 2.3 Invariant Geometry and f-Divergence 14 2.4 Deep Neural Network 16 2.4.1 Feedforward Network 17 2.4.2 Convolutional Layer 19 2.5 B-Spline 21 3 Measure-Theoretic Representation for Object Detection 23 3.1 Introduction 23 3.2 Object Counting Measure Definition 25 3.3 Implementation to Object Detection 29 3.3.1 Representation of Images Including Objects Based on Object Counting Measure 30 3.3.2 Neural Network Prediction 32 3.3.3 Objective Function for Neural Network Training 35 3.4 Image Augmentation with Affine Transformation 37 3.5 Experiments 39 3.5.1 Object Detection in Aerial Images 39 3.5.2 Comparison with Conventional Objective Function 44 4 Object Detection in Three-Dimensional Space 49 4.1 Introduction 49 4.2 Neural Network Parameterization 51 4.3 Image Augmentation for Neural Network Training 56 4.4 Experiments 57 5 Spline Representation for Traffic Lane Detection 63 5.1 Introduction 63 5.2 BSplineNet 64 5.2.1 Spline Curve Representation 64 5.2.2 Objective Function for Training 66 5.2.3 Neural Network Structure 67 5.2.4 Inference 69 5.3 Experiments 70 5.3.1 Training Scheme 70 5.3.2 TuSimple Dataset 70 5.3.3 CULane Dataset 74 5.4 Measure-Based Approach for Lane Detection 79 5.4.1 Object Counting Density Function Representation 79 5.4.2 Objective Function 81 5.4.3 Experimental Results 83 6 Conclusion 87 A Appendix 91 A.1 Proof of Proposition 3.1 91 A.2 Proof of Proposition 3.2 93Docto

    The study of a process of formation of Jinsasi in the early Joseon Dynasty

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ต์œกํ•™๊ณผ, 2015. 2. ์šฐ์šฉ์ œ.์ด ์—ฐ๊ตฌ๋Š” ๊ณ ๋ ค ๋ง๋ถ€ํ„ฐ ์กฐ์„  ์ดˆ๊ธฐ์— ์ด๋ฅด๋Š” ์‹œ๊ธฐ์— ์ง„์‚ฌ์‹œ ์‹œํ–‰ ์—ฌ๋ถ€๋ฅผ ๋‘๊ณ  ๋ฒŒ์–ด์ง„ ๋…ผ์Ÿ์„ ์‚ดํŽด๋ด„์œผ๋กœ์จ, ์กฐ์„ ์—์„œ ์ง„์‚ฌ์‹œ๊ฐ€ ์‹œํ–‰๋œ ์ด์œ ๋ฅผ ์•Œ์•„๋ณด๋Š” ๋ฐ ๋ชฉ์ ์„ ๋‘์—ˆ๋‹ค. ๋ณธ๊ณ ์—์„œ๋Š” ์ง„์‚ฌ์‹œ์˜ ์‹œํ–‰์ด ๋‹น์‹œ ํ•™๊ต์ œ๋„๋ฅผ ํ™œ์„ฑํ™”์‹œํ‚ค๊ณ  ๋”๋ถˆ์–ด ํ•™๊ต์™€ ๊ณผ๊ฑฐ์˜ ์—ฐ๊ณ„์„ฑ์„ ๊ฐ•ํ™”ํ•˜๋ ค ํ•œ ์กฐ์„  ๊ต์œก์ •์ฑ…์˜ ๊ธฐ๋ณธ์ ์ธ ๋ฐฉํ–ฅ์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•ด์„œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฌธ์ œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ณ ๋ ค ๋ง๊ธฐ ๊ตญ์ž๊ฐ์‹œ์˜ ๋ณ€ํ™”๊ณผ์ •์„ ์‚ดํŽด๋ณด๊ณ  ๊ทธ ์˜๋ฏธ๋ฅผ ๊ณ ์ฐฐํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ์กฐ์„  ์ดˆ๊ธฐ ์ง„์‚ฌ์‹œ ์‹œํ–‰์— ๊ด€ํ•œ ๋…ผ์˜๊ณผ์ •์„ ์‚ดํŽด๋ณด๋Š” ๊ฒƒ์ด๋‹ค. ๊ณ ๋ ค ๊ตญ์ž๊ฐ์‹œ(ๅœ‹ๅญ็›ฃ่ฉฆ)๋Š” ๋•์ข… ์ฆ‰์œ„๋…„(1016)๋ถ€ํ„ฐ ์‹œ์ž‘๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ ๋ชฉ์ ์€ ์ž…๊ฒฉ์ž๋“ค์—๊ฒŒ ๊ตญ์ž๊ฐ์— ์ž…ํ•™ํ•  ์ž๊ฒฉ์„ ์ฃผ๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ณ ์‹œ๊ณผ๋ชฉ์€ ์‹œ๋ถ€(่ฉฉ่ณฆ)์™€ 10์šด์‹œ(ๅ้Ÿป่ฉฉ)๊ฐ€ ์ค‘์‹ฌ์ด์—ˆ๋‹ค. ๊ตญ์ž๊ฐ์‹œ๋Š” ๊ณ ๋ ค ๋ง๊ธฐ์— ๋“ค์–ด์˜ค๋ฉด์„œ ํ์ง€์™€ ๋ณต์„ค์„ ๊ฑฐ๋“ญํ•œ๋‹ค. ์ด๋Š” ๋‹จ์ˆœํžˆ ๊ตญ์ž๊ฐ์‹œ์—๋งŒ ๊ตญํ•œ๋˜๋Š” ๋ณ€ํ™”๊ฐ€ ์•„๋‹ˆ์—ˆ๊ณ  ๊ตญ์ž๊ฐ์˜ ์œ„์ƒ ๋ฐ ๊ต์œก๊ณผ์ •์˜ ๋ณ€ํ™”, ๊ทธ๋ฆฌ๊ณ  ๊ณผ๊ฑฐ์ œ๋„์™€ ํ•™๊ต์ œ๋„์˜ ์—ฐ๊ณ„ ๊ฐ•ํ™”์™€ ๊ด€๋ จ๋˜์–ด ์žˆ๋‹ค. ๊ตญ์ž๊ฐ์€ ์„ฑ๊ท ๊ด€์œผ๋กœ ๋ณ€ํ•˜๋ฉด์„œ ์ข€ ๋” ์œ ํ•™๊ต์œก์— ์น˜์ค‘๋œ ์„ฑ๊ฒฉ์„ ๊ฐ€์ง„ ํ•™๊ต๋กœ ๋ณ€ํ™”ํ–ˆ๊ณ  ์ด๋Š” ๊ตญ์ž๊ฐ์‹œ์˜ ๊ณ ์‹œ๊ณผ๋ชฉ ๋ณ€ํ™”์™€ ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์Šน๋ณด์‹œ(้™ž่ฃœ่ฉฆ)์˜ ์œ„์ƒ ๋ณ€ํ™”๋„ ๊ตญ์ž๊ฐ์‹œ์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ์Šน๋ณด์‹œ๋Š” ๋ณธ๋ž˜ ๊ตญ์ž๊ฐ์‹œ๋ฅผ ํ†ตํ•ด ์„ ๋ฐœํ•œ ๊ตญ์ž๊ฐ์˜ ํ•™์ƒ ์ˆ˜์— ๊ฒฐ์›์ด ์žˆ์„ ๋•Œ, ๋ณด์ถฉํ•ด์„œ ์„ ๋ฐœํ•˜๋Š” ์‹œํ—˜์˜ ์„ฑ๊ฒฉ์ด ๊ฐ•ํ–ˆ๋‹ค. ๊ณ ์‹œ๊ณผ๋ชฉ์€ ๊ฒฝ์„œ์˜ ์˜(็พฉ)๊ฐ€ ์ค‘์‹ฌ์ด์—ˆ๋Š”๋ฐ, ๊ณต๋ฏผ์™• ๋Œ€๋ถ€ํ„ฐ ์˜์˜(็–‘็พฉ)๋ฅผ ์‹œํ—˜ํ–ˆ๊ณ , ์ž…๊ฒฉ์ž ์ˆ˜๋ฅผ ํฌ๊ฒŒ ๋Š˜๋ฆฌ๊ฒŒ ๋œ๋‹ค. ์ด๋•Œ๋ถ€ํ„ฐ ์Šน๋ณด์‹œ๋Š” ๊ธฐ์กด ๊ตญ์ž๊ฐ์‹œ์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ์‹œํ—˜์œผ๋กœ ์œ„์ƒ์ด ์˜ฌ๋ผ๊ฐ€๊ฒŒ ๋˜์—ˆ๋‹ค. ์šฐ์™• ๋Œ€ ๊ตญ์ž๊ฐ์‹œ๊ฐ€ ๋‹ค์‹œ ๋ถ€ํ™œํ–ˆ์ง€๋งŒ ๊ตญ์ž๊ฐ์‹œ๋Š” ์ตœ๊ณ ํ•™๋ถ€์˜ ์ž…ํ•™์ž๊ฒฉ์‹œํ—˜์œผ๋กœ์„œ์˜ ๊ทธ ์œ„์ƒ์ด ์ ์  ์•ฝํ™”๋˜์—ˆ๋‹ค. ๊ตญ์ž๊ฐ์‹œ๋Š” ์กฐ์„ ์ด ๊ฑด๊ตญํ•˜๋ฉด์„œ ํ์ง€๋˜์—ˆ๋Š”๋ฐ, ๋‹ค์‹œ ์‹œํ–‰ํ•˜์ž๋Š” ๋…ผ์˜๊ฐ€ ์ œ๊ธฐ๋œ ๋•Œ๋Š” ์„ธ์ข… ๋Œ€์— ๋“ค์–ด์„œ๋ฉด์„œ๋ถ€ํ„ฐ์ด๋‹ค. ์„ธ์ข… 10๋…„์—(1428) ์„ฑ๊ท ์‚ฌ์„ฑ ์ •๊ณค๊ณผ 13๋…„(1431) ์ค‘๋ถ€๊ต์ˆ˜๊ด€ ์ •์ข…๋ณธ์€ ์ƒ์›์‹œ ์ž…๊ฒฉ์ž ์ˆ˜๊ฐ€ ์‘์‹œ์ž์— ๋น„ํ•ด ๋„ˆ๋ฌด ์ ์€ ๊ฒƒ์„ ๋ฌธ์ œ๋กœ ์ œ๊ธฐํ•˜๋ฉด์„œ, ์ง„์‚ฌ์‹œ๋ฅผ ๋‹ค์‹œ ์‹œํ–‰ํ•˜์ž๊ณ  ๊ฑด์˜ํ•œ๋‹ค. ์„ธ์ข… 17๋…„(1435), ์ง‘ํ˜„์ „ ๋Œ€์ œํ•™ ์ด๋งน๊ท ์€ ๋‹น์‹œ๊นŒ์ง€ ์ง„ํ–‰๋œ ๋…ผ์˜๋ฅผ ๋ฐ˜์˜ํ•˜์—ฌ ๏ฝข์‹œํ•™ํฅํ•™์กฐ๊ฑด(่ฉฉๅญธ่ˆˆๅญธๆขไปถ)๏ฝฃ์„ ์˜ฌ๋ฆฐ๋‹ค. ์ด๋งน๊ท ์€ ์ง„์‚ฌ์‹œ์˜ ์‹œํ–‰๊ณผ ๋™์‹œ์— ๋ฌธ๊ณผ ์ค‘์žฅ์˜ ๊ณ ์‹œ๊ณผ๋ชฉ์— ๋…ผ(๏ฅ) ๋Œ€์‹  ์‹œ(่ฉฉ)๋ฅผ ํฌํ•จ์‹œํ‚ฌ ๊ฒƒ์„ ์ฃผ์žฅํ•˜์˜€์œผ๋ฉฐ, ์ง„์‚ฌ์‹œ์˜ ์‘์‹œ ์—ฐ๋ น์„ 25์„ธ๊นŒ์ง€๋กœ ํ•œ์ •ํ•˜์˜€๋‹ค. ์ด๋Š” ์ง„์‚ฌ์‹œ๊ฐ€ ๊ด€๋ฃŒ๋ฅผ ์„ ๋ฐœํ•˜๋Š” ์‹œํ—˜์ด๋ผ๊ธฐ๋ณด๋‹ค๋Š” ์žฅํ•™์— ๋ชฉ์ ์„ ๋‘” ์‹œํ—˜์ด์—ˆ๋‹ค๋Š” ์ ์„ ๋ถ„๋ช…ํžˆ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ง„์‚ฌ์‹œ์˜ ์‹œํ–‰์€ ํ–ฅ๊ต ๊ต๊ด€์„ ๋ณด์ถฉํ•˜๊ธฐ ์œ„ํ•œ ์„ฑ๊ฒฉ๋„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํ–ฅ๊ต์˜ ์ˆ˜๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๋Š˜์–ด๋‚˜๋ฉด์„œ ํ–ฅ๊ต๊ต๊ด€์˜ ์ˆ˜๊ธ‰๋ฌธ์ œ๊ฐ€ ์ œ๊ธฐ๋˜์—ˆ๋Š”๋ฐ, ์†Œ๊ณผ ์ž…๊ฒฉ์ž์—๊ฒŒ ๊ต๋„(ๆ•ŽๅฐŽ)์˜ ์ž๊ฒฉ์„ ์ฃผ์–ด ํ–ฅ๊ต ๊ต๊ด€์„ ์ถฉ์›ํ•˜๋ ค ํ–ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์†Œ๊ณผ ์ž…๊ฒฉ์ž ์ˆ˜๋ฅผ ๋Š˜๋ฆฌ๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์ง„์‚ฌ์‹œ๋ฅผ ์‹œํ–‰ํ•˜๋Š” ์•ˆ์ด ์ œ๊ธฐ๋˜์—ˆ๋˜ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ง„์‚ฌ์‹œ๋Š” ์„ธ์ข… 26๋…„์— ๋‹ค์‹œ ํ์ง€๋œ๋‹ค. ๊ธฐ๋ก์„ ํ†ตํ•ด ์•Œ ์ˆ˜ ์žˆ๋Š” ํ์ง€์‚ฌ์œ ๋กœ๋Š” ๋‘ ๊ฐ€์ง€๋ฅผ ๋“ค ์ˆ˜ ์žˆ๋‹ค. ๋จผ์ €, ์„ธ์ข… 25๋…„์— ์ œ์ •๋œ ๏ฝข๋ฌธ๊ณผ๊ฐ•๊ฒฝ์ •์‹(ๆ–‡็ง‘่ฌ›็ถ“็จ‹ๅผ)๏ฝฃ์ด๋‹ค. ๏ฝข๋ฌธ๊ณผ๊ฐ•๊ฒฝ์ •์‹๏ฝฃ์„ ํ†ตํ•ด ๋ฌธ๊ณผ ์ดˆ์žฅ์ด ๊ฐ•๊ฒฝ(่ฌ›็ถ“)์œผ๋กœ ๋ณ€ํ™”ํ•˜์˜€๋Š”๋ฐ, ์ด๋Š” ๋ฌธ๊ณผ์˜ ์‹œํ—˜๊ธฐ๊ฐ„์ด ๊ธธ์–ด์ง€๋Š” ์ƒํ™ฉ์„ ์ดˆ๋ž˜ํ–ˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ•œ ํ•ด์— ์ง„์‚ฌ์‹œ, ์ƒ์›์‹œ, ๋ฌธ๊ณผ๋ฅผ ๋ชจ๋‘ ์น˜๋ฅด๋ฉด ์‹œํ—˜๊ธฐ๊ฐ„์ด ์ง€๋‚˜์น˜๊ฒŒ ๊ธธ์–ด์ง€๋Š” ์ ์ด ์ง€์ ๋˜์–ด ์‘์‹œ์ž๋“ค์—๊ฒŒ ํฐ ๋ถˆํŽธ์„ ์ฃผ๊ฒŒ ๋œ๋‹ค๊ณ  ์ง€์ ๋˜์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ๋Š” ์„ธ์ข… 26๋…„์— ์ƒ์›์‹œ ๊ณ ์‚ฌ์žฅ์—์„œ ๋Œ€๋ฆฌ์‘์‹œ์‚ฌ๊ฑด์ด ๋ฐœ๊ฐ๋˜์—ˆ๋‹ค. ๋‹น์‹œ ๋…ผ์˜๋ฅผ ๋ณด๋ฉด, ์ง„์‚ฌ์‹œ ๊ณ ์‚ฌ์žฅ์—์„œ๋„ 25์„ธ๋ฅผ ๋„˜๊ธด ์‚ฌ๋žŒ์ด ์ง„์‚ฌ์‹œ ์‹œํ—˜์žฅ์— ๋‚˜์ด๋ฅผ ์†์ด๊ณ  ์‘์‹œํ•˜๋Š” ๊ฒฝ์šฐ๋ฅผ ์ œ๋Œ€๋กœ ์ ๋ฐœํ•ด ๋‚ด๊ธฐ๊ฐ€ ํ˜„์‹ค์ ์œผ๋กœ ํž˜๋“ค๋‹ค๋Š” ํ๋‹จ์ด ์ง€์ ๋˜์—ˆ๋‹ค. ์ง„์‚ฌ์‹œ๋Š” ๋‹จ์ข… ๋Œ€ ๋‹ค์‹œ ์‹œํ–‰๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง„์‚ฌ์‹œ ์‹œํ–‰๊ทœ์ •์€ ์ด์ „ ๋ฌธ์ข… ๋Œ€๏ฝข์ง„์‚ฌ์‹œ์ทจ์กฐ๊ฑด(้€ฒๅฃซ่ฉฆๅ–ๆขไปถ)๏ฝฃ์œผ๋กœ ํ™•๋ฆฝ๋˜์—ˆ๋Š”๋ฐ, ๏ฝข์ง„์‚ฌ์‹œ์ทจ์กฐ๊ฑด๏ฝฃ์€ ์ด์ „ ์„ธ์ข… ๋Œ€ ๏ฝข์‹œํ•™ํฅํ•™์กฐ๊ฑด๏ฝฃ๊ณผ ๋น„๊ตํ•˜๋ฉด ๋ช‡ ๊ฐ€์ง€ ๋ณ€ํ™”ํ•œ ์ ์ด ๋ณด์ธ๋‹ค. ๋จผ์ €, ์‘์‹œ์ž๋“ค์ด ์˜› ์ž‘ํ’ˆ๋“ค์„ ๋ฒ ๊ปด ์“ฐ๋Š” ๊ฒƒ์„ ๋ง‰๊ธฐ ์œ„ํ•ด 10์šด์‹œ(ๅ้Ÿป่ฉฉ) ๋Œ€์‹  ๊ณ ๋ถ€ ํ•œ ํŽธ, ๊ณ ์‹œ์™€ ์œจ์‹œ ์ค‘์—์„œ ํ•œ ํŽธ์”ฉ ์ง“๋Š” ๋ฐฉ์‹์œผ๋กœ ๋ฐ”๊พธ์—ˆ๋‹ค. ์ด๋Š” ์ง„์‚ฌ์‹œ์˜ ๋‚œ์ด๋„๋ฅผ ์ข…์ „๋ณด๋‹ค ์–ด๋ ต๊ฒŒ ํ•ด์„œ ์‹œํ—˜์˜ ์œ„์ƒ์„ ๋†’์ด๋ ค ํ•œ ์กฐ์น˜๋กœ ๋ณด์ธ๋‹ค. ๋˜ํ•œ, 25์„ธ ์ดํ•˜๋งŒ์ด ์‘์‹œํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ ์—ฐํ•œ๋ฒ•์„ ์—†์• ๊ณ , ๋ชจ๋“  ์‘์‹œ์ž๋“ค์—๊ฒŒ ์ƒ์›์‹œ์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๏ฝขํ•™๋ก€๊ฐ•(ๅญธ็ฆฎ่ฌ›)๏ฝฃ์„ ์น˜๋ฅด๋„๋ก ํ•˜์˜€๋‹ค. ์ด๋Š” ์ง„์‚ฌ์‹œ์˜ ์ง€์œ„๋ฅผ ์ƒ์›์‹œ์™€ ๋™๋“ฑํ•˜๊ฒŒ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋™์‹œ์—, ์ง„์‚ฌ์‹œ ์‘์‹œ์ž๋ผ ํ• ์ง€๋ผ๋„ ๊ฐ€์žฅ ๊ธฐ๋ณธ์ ์ธ ๊ฒฝ์„œ์˜ ์ดํ•ด๋„๋ฅผ ํ•„์ˆ˜์ ์œผ๋กœ ์š”๊ตฌํ•œ ๊ฒƒ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฆ‰, ๏ฝข์ง„์‚ฌ์‹œ์ทจ์กฐ๊ฑด๏ฝฃ์€ ์ง„์‚ฌ์‹œ๋ฅผ ์„ธ์ข… ๋Œ€์™€๋Š” ๋‹ฌ๋ฆฌ ์ƒ์›์‹œ์™€ ๊ฐ™์€ ์ง€์œ„๋ฅผ ๊ฐ€์ง„ ์‹œํ—˜์œผ๋กœ ๊ทœ์ •ํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” ์†Œ๊ณผ๊ฐ€ ์ƒ์›์‹œ์™€ ์ง„์‚ฌ์‹œ๋กœ ์ •๋น„๋˜์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ณ ๋ ค ๋ง์—์„œ ์กฐ์„  ์ดˆ๊ธฐ์— ์ด๋ฅด๋Š” ์ง„์‚ฌ์‹œ ์„ฑ๋ฆฝ ๊ณผ์ •์˜ ์˜์˜๋ฅผ ์ •๋ฆฌํ•˜๋ฉด, ์ง„์‚ฌ์‹œ์˜ ์‹œํ–‰์€ ๋‹น์‹œ ์กฐ์„ ์˜ ํ•™๊ต๊ต์œก์„ ํ™œ์„ฑํ™”์‹œํ‚ค๋ ค๋Š” ์˜๋„๊ฐ€ ๊ฐ•ํ–ˆ๋‹ค๋Š” ์ ์ด๋‹ค. ์ง„์‚ฌ์‹œ๋Š” ๋ฌธ๊ณผ์˜ ์˜ˆ๋น„๊ณ ์‚ฌ๋ณด๋‹ค๋Š” ์˜คํžˆ๋ ค ํ–ฅ๊ต์™€ ์„ฑ๊ท ๊ด€ ์‚ฌ์ด์—์„œ ํ•™๊ต์ œ๋„๋ฅผ ํ™œ์„ฑํ™”์‹œํ‚ค๋ ค๋Š” ํ•™๊ต์ง„ํฅ์ฑ…์— ๋” ๊ฐ€๊นŒ์šด ์‹œํ—˜์ด์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ง„์‚ฌ์‹œ์˜ ๊ณ ์‹œ๊ณผ๋ชฉ์ด ์‹œ์™€ ๋ถ€๋ผ๊ณ  ํ•ด์„œ ์‘์‹œ์ž๋“ค์—๊ฒŒ ๋ฌธ์˜ˆ(ๆ–‡่—)์  ๋Šฅ๋ ฅ๋งŒ์„ ์š”๊ตฌํ•œ ๊ฒƒ์€ ์•„๋‹ˆ์—ˆ๋‹ค. ์กฐ์„ ์˜ ์ง„์‚ฌ์‹œ์—์„œ ์‹œํ—˜ํ•œ ๊ณ ๋ถ€์™€ ๊ณ ์‹œ๋Š” ์—„๊ฒฉํ•œ ๊ฒฉ์‹์„ ์ง€ํ‚ค๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๊ธฐ๋Š” ํ•˜์˜€์ง€๋งŒ, ๊ฒฝ์„œ์˜ ์ดํ•ด์— ๊ธฐ์ดˆํ•œ ์šฉ์‚ฌ(็”จไบ‹)์˜ ์ ์ ˆํ•œ ๊ตฌ์‚ฌ๊ฐ€ ๋” ์ค‘์š”ํ•˜์˜€๋‹ค. ์ฆ‰, ์กฐ์„ ์˜ ์ง„์‚ฌ์‹œ๋Š” ์‘์‹œ์ž์˜ ๋ฌธ์˜ˆ์  ๋Šฅ๋ ฅ๊ณผ ๊ฒฝ์„œ์— ๋Œ€ํ•œ ์ดํ•ด์™€ ํ™œ์šฉ ๋Šฅ๋ ฅ์„ ๋™์‹œ์— ์•Œ์•„๋ณด๋ ค๋Š” ๋ชฉ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค.โ… . ์„œ๋ก  1 1. ๋ฌธ์ œ์ œ๊ธฐ 1 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  5 โ…ก. ์กฐ์„ ์กฐ ์ง„์‚ฌ์‹œ์˜ ๊ธฐ์› 13 1. ๊ตญ์ž๊ฐ์‹œ์˜ ์‹œํ–‰๊ณผ ์„ฑ๊ฒฉ 13 2. ๊ณ ๋ ค ๋ง, ๊ตญ์ž๊ฐ์‹œ์˜ ๋ณ€ํ™” 18 โ…ข. ์กฐ์„  ์ดˆ ์ง„์‚ฌ์‹œ ์‹œํ–‰ ๋…ผ์˜์™€ ์ง„์‚ฌ์‹œ์˜ ์„ฑ๋ฆฝ 30 1. ํƒœ์กฐ-ํƒœ์ข… ์—ฐ๊ฐ„์˜ ์ง„์‚ฌ์‹œ : ์ƒ์›์‹œ๋กœ์˜ ์ผ์›ํ™” 31 1) ํƒœ์กฐ-์ •์ข… ์—ฐ๊ฐ„ 31 2) ํƒœ์ข… ์—ฐ๊ฐ„ 34 2. ์„ธ์ข… ์—ฐ๊ฐ„์˜ ์ง„์‚ฌ์‹œ ์‹œํ–‰ ๋…ผ์˜ 41 3. ๋ฌธ์ข… ์—ฐ๊ฐ„ : ์ง„์‚ฌ์‹œ ์ œ๋„์˜ ํ™•๋ฆฝ 66 โ…ฃ. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  72 ์ฐธ๊ณ ๋ฌธํ—Œ 79 Abstract 83Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ ์น˜๊ณผ๋ณด์ฒ ํ•™์ „๊ณต,2001.Maste

    ์ด์ฒญ์ค€ ๋ฌธํ•™์˜ ์–ธ์–ด ์˜์‹ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์–ด๊ตญ๋ฌธํ•™๊ณผ, 2015. 2. ๋ฐฉ๋ฏผํ˜ธ.๋ณธ๊ณ ๋Š” ์ด์ฒญ์ค€(1939-2008)์˜ ๋ฌธํ•™ ์–ธ์–ด์™€ ๊ธ€์“ฐ๊ธฐ์˜ ํŠน์ง•์„ ์ž‘๊ฐ€ ์˜์‹๊ณผ์˜ ๊ด€๋ จ์„ฑ ์•„๋ž˜์„œ ๊ทœ๋ช…ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ์ด์ฒญ์ค€์˜ ์–ธ์–ด ์ธ์‹๊ณผ ์–ธ์–ด ๊ฐ๊ฐ์ด ๊ทธ๊ฐ€ ์‚ด์•˜๋˜ ์‹œ๋Œ€์˜ ํ˜„์‹ค๊ณผ ์กฐ์‘ํ•˜๋Š” ์–‘์ƒ ๋˜ํ•œ ๋ฉด๋ฐ€ํ•˜๊ฒŒ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋Š” ๊ทธ๋™์•ˆ ์ด์ฒญ์ค€์„ ํฌํ•จํ•œ 4.19์„ธ๋Œ€ ๋ฌธ์ธ๋“ค์ด ๊ทธ๋“ค์˜ ์–ธ์–ด ์˜์‹๊ณผ ์–ธ์–ด ์‚ฌ์šฉ์„ ๊ธฐ์กด ์„ธ๋Œ€๋“ค๊ณผ ๊ตฌ๋ณ„๋˜๋Š” ์ž์‹ ๋“ค๋งŒ์˜ ๋…์ž์  ์ง€์ ์œผ๋กœ ์ „๋žตํ™”ํ•˜์˜€์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ด๋“ค์ด ์ „๋ฉดํ™”ํ•œ ์ƒˆ๋กœ์šด ์–ธ์–ด ์˜์‹์˜ ์‹ค์ฒด์™€ ์†Œ์œ„ ํ•œ๊ธ€ ์„ธ๋Œ€๊ฐ€ ๊ฐ€์ง€๋Š” ์–ธ์–ด ์‚ฌ์šฉ์— ๋Œ€ํ•œ ์ •๋ฐ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ถฉ๋ถ„ํžˆ ์ˆ˜ํ–‰๋˜์ง€ ๋ชปํ–ˆ๋‹ค๋Š” ๋ฌธ์ œ์˜์‹์—์„œ ๋น„๋กฏํ•œ ๊ฒƒ์ด๋‹ค. ์ด์ฒญ์ค€์€ ์–ธ์–ด๊ฐ€ ๋ฌด์—‡๋ณด๋‹ค ์ฃผ์ฒด ์‚ฌ์ด๋ฅผ ๋งค๊ฐœํ•˜๋Š” ๋งค์ฒด๋ผ๋Š” ์‚ฌ์‹ค์„ ๊นŠ๊ฒŒ ์ธ์ง€ํ•˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ, ์–ธ์–ด์˜ ์ง€์‹œ์  ์„ฑ๊ฒฉ๋ณด๋‹ค, ๊ทธ ์ˆ˜ํ–‰์  ์„ฑ๊ฒฉ์— ์ฃผ๋ชฉํ•จ๊ณผ ๋™์‹œ์—, ์–ธ์–ด์˜ ์œ ๋™์ ์ด๊ณ  ๊ด€๊ณ„ ์ง€ํ–ฅ์ ์ธ ํŠน์„ฑ์„ ๊ฐ•์กฐํ–ˆ๋‹ค. ์ด๋Š” ์ƒํ™ฉ์— ๋Œ€ํ•œ ๋‹จ์ •์ , ๋‹จ์ผํ•œ ์„œ์ˆ ์„ ์ง€์–‘ํ•˜๋ฉด์„œ, ๊ฒน์˜ ๊ตฌ์กฐ ์•ˆ์— ๋‹ค์–‘ํ•œ ๋ชฉ์†Œ๋ฆฌ๋“ค์„ ์„œ์ˆ ํ•จ์œผ๋กœ์จ ์–ธ์–ด์˜ ํ•œ๊ณ„๋ฅผ ๋„˜์–ด์„œ๋ ค ํ–ˆ๋˜ ์—ฌ๋Ÿฌ ์‹œ๋„๋“ค๋กœ ๋ฐœํ˜„๋œ๋‹ค. 2์žฅ์—์„œ๋Š” ์ด์ฒญ์ค€์˜ ๋…ํŠนํ•œ ์–ธ์–ด์˜์‹์ด ๋ฐฐํƒœ๋œ ์—ญ์‚ฌ์  ์ƒํ™ฉ๊ณผ ๋งฅ๋ฝ์— ์šฐ์„  ์ฃผ๋ชฉํ•˜์—ฌ, ์ด์ฒญ์ค€์ด ์†ํ–ˆ๋˜, 1960-1980๋…„๋Œ€ ํ•œ๊ตญ์‚ฌํšŒ๋ฅผ ์•„๊ฐ๋ฒค์˜ ์šฉ์–ด๋ฅผ ๋นŒ์–ด ํ•ญ์‹œ์  ์˜ˆ์™ธ์ƒํƒœ๋กœ ๊ทœ์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด ํญ๋ ฅ์ ์ธ ์ •์น˜ ์ƒํ™ฉ์—์„œ ์ฃผ์ฒด๊ฐ€ ์ด์— ๋Œ€์‘ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์•Œ๋ ˆ๊ณ ๋ฆฌ๋ฅผ ์ „๋žต์ ์œผ๋กœ ์„ ํƒํ•˜๊ณ  ์žˆ์—ˆ์Œ์„ ๊ฐ•์กฐํ•˜์˜€๋‹ค. ์ด์ฒญ์ค€ ์ž‘ํ’ˆ์˜ ์ฃผ์š” ๋ชจํ‹ฐํ”„์ธ ์ „์ง“๋ถˆ ๊ฒฝํ—˜์€ ํ•œ๊ตญ ์ „์Ÿ๊ณผ ๊ทธ ์ดํ›„ ํ•œ๊ตญ ์‚ฌํšŒ๋ฅผ ๊ทœ์ •์ง€์—ˆ๋˜ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ ๋Œ€๋ฆฝ์„ ์ƒ๊ธฐ์‹œํ‚ค๋Š” ์›์ดˆ์  ์žฅ๋ฉด์œผ๋กœ ๊ธฐ๋Šฅํ•œ๋‹ค. ๋˜ํ•œ ์ด์ฒญ์ค€ ์ž‘ํ’ˆ ์† ์ธ๋ฌผ๋“ค์€ ๊ฐ€๋ฉด์“ฐ๊ธฐ๋ฅผ ํ†ตํ•ด ์กด์žฌํ•˜๋ฉฐ, ์ž์•„ ๋ง์‹ค์˜ ์š•๋ง์„ ๋“œ๋Ÿฌ๋‚ด๊ณ  ์žˆ์—ˆ๋‹ค. ํ•ญ์‹œ์  ์˜ˆ์™ธ์ƒํƒœ ์•ž์—์„œ ๋งจ์–ผ๊ตด์„ ๋“œ๋Ÿฌ๋‚ผ ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ€๋ฉด์„ ์“ฐ์ง€๋งŒ, ๊ฐ€๋ฉด ์†์—์„œ ํŽธ์•ˆํ•จ์„ ๋Š๋ผ๋‹ค๊ฐ€ ๊ฒฐ๊ตญ ๊ฐ€๋ฉด ์†์œผ๋กœ ์ž์‹ ์„ ๋ฌดํ™”์‹œ์ผœ๋Š” ์ฃผ์ฒด์˜ ๋ชจ์Šต์€, ์ƒ์ง•์ฒด๊ณ„(์‚ฌํšŒ) ์•ˆ์—์„œ ๋˜ ํ•œ ๋ฒˆ์˜ ๊ฑฐ์„ธ๋ฅผ ์ˆ˜์šฉํ•˜๋Š” ๊ฒฐ์ •์ ์ธ ์žฅ๋ฉด์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์Šค์Šค๋กœ์—๊ฒŒ ๋ถ€์—ฌ๋œ ์‚ฌํšŒ์  ์—ญํ• ์„ ๋Š์ž„์—†์ด ๊ฑฐ๋ถ€ํ•˜๋˜ ์ฃผ์ฒด๊ฐ€, ๊ฒฐ๊ตญ ์ž์‹ ์„ ์žƒ์–ด๋ฒ„๋ฆฌ๋Š” ๊ฒƒ์„ ์„ ํƒํ•˜๋Š” ์ž์•„๋ง์‹ค์˜ ์žฅ๋ฉด์€ ์ƒ์ง•๊ณ„๋กœ๋ถ€ํ„ฐ ์Šค์Šค๋กœ๋ฅผ ๋ง๋ช…์‹œํ‚ค๋Š” ์ฃผ์ฒด์˜ ์ •์น˜์  ํŒ๋‹จ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์ฒญ์ค€์€ ์ค‘์ธต๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ์†Œ์„ค ์† ์ฃผ์ฒด๋“ค์ด ์ž์‹ ์˜ ์œ„์น˜๋ฅผ ์ „๋žต์ ์œผ๋กœ ๊ฐ์ถ”๊ณ , ๋‹ค์–‘ํ•œ ๋ชฉ์†Œ๋ฆฌ๊ฐ€ ์ž‘ํ’ˆ์— ๋“œ๋Ÿฌ๋‚  ์ˆ˜ ์žˆ๋„๋ก ํ–ˆ๋Š”๋ฐ, ํŠนํžˆ ์ถ”๋ฆฌ์†Œ์„ค๋“ฑ ํƒ์ƒ‰์˜ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง„ ์žฅ๋ฅด๋ฅผ ํ†ตํ•ด ์ด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๊ตฌํ˜„ํ•ด ๋ƒˆ๋‹ค. ํ•œํŽธ ์–ธ๋กœ๊ฐ€ ๋ง‰ํžŒ ์‹œ๋Œ€, ์ด์ฒญ์ค€์ด ์—ฌ๋Ÿฌ ์ฆ์ƒ๋“ค์„ ํ†ตํ•ด ์‹ ์ฒด ์œ„์— ๋‹ค์‹œ์“ฐ๊ธฐ๋ฅผ ์‹œ๋„ํ•˜๊ณ  ์žˆ์Œ์— ์ฃผ๋ชฉํ–ˆ๋‹ค. ํ•ด๋‹น ์†Œ์„ค๋“ค์€ ๊ทผ๋Œ€์  ๊ทœ์œจํ™”์— ๊ตฌ์„ฑ๋œ ์œก์ฒด์˜ ์ฆ์ƒ์„ ํ†ตํ•ด์„œ, ์—ญ์œผ๋กœ ์–ต์••๋œ ์ •์‹ ๊ณผ ์‚ฌ์œ ๋ฅผ ๋“œ๋Ÿฌ๋‚ธ๋‹ค. ๊ธฐ๊ดดํ•œ ์‹ ์ฒด๋ฅผ ์ง€๋‹Œ ์ธ๋ฌผ๋“ค๊ณผ ํŠน์ •ํ•œ ์ฆ์ƒ์„ ํ˜ธ์†Œํ•˜๋Š” ์ธ๋ฌผ๋“ค์„ ํ†ตํ•ด, ์ด์ฒญ์ค€์€ ํ˜„์‹ค์„ ์žฌํ˜„ํ•˜๋Š” ์–ธ์–ด์˜ ํ•œ๊ณ„๋ฅผ ํ˜•์ƒํ™”ํ•˜๋Š” ๋™์‹œ์—, ์ฃผ์ฒด์™€ ๋ถ„์ ˆํ•˜๋ฉฐ ์ƒˆ๋กœ์šด ํ‹ˆ์ƒˆ๋ฅผ ๋งŒ๋“œ๋Š” ๊ทธ๋กœํ…Œ์Šคํฌํ•œ ์‹ ์ฒด์˜ ์ž์œจ์  ํŠน์ง•์„ ํšจ๊ณผ์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ธ๋‹ค. ์ด ๊ณผ์ •์—์„œ ์ด์ฒญ์ค€์€ ์–ธ์–ด๋ผ๋Š” ์ƒ์ง• ์ฒด๊ณ„๋ฅผ ํ†ตํ•ด, ์‚ฌํšŒ ๊ตฌ์„ฑ์›๋“ค์˜ ์œก์ฒด๋ฅผ ํ†ต์ œํ•˜๊ณ ์ž ํ•˜์˜€๋˜ ๋‹น๋Œ€์˜ ๊ถŒ๋ ฅ๊ณผ ์‚ฌํšŒ, ๊ทธ๋ฆฌ๊ณ  ์ƒ์ง• ์ฒด๊ณ„์— ๋Œ€ํ•œ ๊ทผ๋ณธ์ ์ธ ๋น„ํŒ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด์ฒญ์ค€์€ ๋ฌธํ•™ ์–ธ์–ด๊ฐ€ ๊ฐ–๋Š” ์ˆ˜ํ–‰์„ฑ์˜ ์ฐจ์›์„ ์ž์„œ์ „, ์ž์ „์  ๊ธ€์“ฐ๊ธฐ๋ฅผ ์ง์ ‘ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ์ž‘๊ฐ€๋กœ ๊ตฌ์„ฑ๋˜์–ด๊ฐ€๋Š” ์žฅ๋ฉด์„ ํ†ตํ•ด ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๊ธฐ๋„ ํ•˜๋‹ค. 3์žฅ์—์„œ๋Š” ์ฃผ์ฒด์˜ ์ง„์ˆ ์ด ๋„๋“œ๋ผ์ง€๋Š” ์†Œ์„ค์ด๋‚˜, ์†Œ์„ค ์†์˜ ์žฅ๋ฉด์„ ํ†ตํ•ด์„œ ์–ธ์–ด ์ž์ฒด๊ฐ€ ๊ฐ–๋Š” ๋ณธ์งˆ์  ์˜๋ฏธ๋ฅผ ์˜์‹ฌํ•˜๊ณ  ์งˆ๋ฌธํ•˜๋Š” ์ด์ฒญ์ค€์˜ ์†Œ์„ค๋“ค์„ ์ฃผ๋กœ ๋…ผ์˜ํ•˜์˜€๋‹ค. ์šฐ์„  ๊ฐœ์ธ์˜ ์ง„์ˆ ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋Š” ์‹ ๋ฌธ(่จŠๅ•)์˜ ์žฅ๋ฉด์ด ๋‹ด๊ธด ์ผ๋ จ์˜ ์ถ”๋ฆฌ ๋ฐ ๋ฒ•์ • ์†Œ์„ค๋“ค์„ ํ†ตํ•ด, ์นดํ”„์นด์  ์งˆ๋ฌธ ์•ž์— ๋†“์ธ ์ฃผ์ฒด์˜ ๋ถˆ์•ˆํ•œ ์ง„์ˆ ๊ณผ ๊ทธ ์ง„์ˆ  ์†์—์„œ ์†Œ์„ค ๋ฐ ๊ธ€์“ฐ๊ธฐ์˜ ์˜๋ฏธ๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ์ง€์ ์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ์ด์–ด์„œ ์ž์„œ์ „์˜ ์–‘์‹์— ๊ธฐ๋ฐ˜ํ•œ ์†Œ์„ค์— ์ฃผ๋ชฉํ•˜์—ฌ, ์ด์ฒญ์ค€์˜ ๋ฌธํ•™์—์„œ ๋‚˜ํƒ€๋‚œ ์–ธ์–ด์™€ ํ˜„์‹ค์˜ ๊ธด์žฅ ๊ด€๊ณ„๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ์ž์„œ์ „์€ ํ”ฝ์…˜์ด ์•„๋‹ˆ๋ฉฐ, ์ž์„œ์ „์„ ์“ฐ๋Š” ์ธ๋ฌผ์„ ์žฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋ฏฟ์Œ์€ ์–ธ์–ด๋กœ์„œ ํ˜„์‹ค์„ ์žฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋ฏฟ์Œ๊ณผ ๊ตฌ์กฐ์ ์ธ ์ƒ๋™์„ฑ์„ ๊ฐ–๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ์ž์„œ์ „๊ณผ ๊ด€๋ จ๋œ ๊ธ€์ด์•ผ๋ง๋กœ ์–ธ์–ด์˜ ์žฌํ˜„ ๋ฐ ์ „๋žต์ด ์ฒจ์˜ˆํ•˜๊ฒŒ ๋Œ€๋ฆฝํ•˜๋Š” ์น˜์—ดํ•œ ์žฅ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์ž์„œ์ „๊ณผ ๊ด€๋ จ๋œ ์ด์ฒญ์ค€์˜ ์†Œ์„ค๋“ค์€ ์ž์„œ์ „์˜ ๊ทœ์•ฝ๊ณผ ๋‚œ์ ์„ ์ „๋žต์ ์œผ๋กœ ๊ตฌํ˜„ํ•ด๋‚ด๊ณ  ์žˆ๋‹ค๋Š” ์ ์—์„œ ์ฃผ๋ชฉ์„ ์š”ํ•œ๋‹ค. ์ด์ฒญ์ค€์€ ์‹ค์กดํ–ˆ๋˜ ํŠน์ • ์ธ๋ฌผ์˜ ์—…์ ๊ณผ ๊ณผ๊ฑฐ ํ–‰์ ์„ ์ž์„œ์ „์˜ ํ˜•์‹์œผ๋กœ ๋‹ด์•„๋‚ด๋ฉด์„œ, ๋…์ž์—๊ฒŒ ์ž์„œ์ „์˜ ๊ทœ์•ฝ์— ์˜๊ฑฐํ•œ ๋…ํ•ด๋ฅผ ์š”์ฒญํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์–ด๋Š ์ˆœ๊ฐ„ ์ด์ฒญ์ค€์€ ์ž์„œ์ „์˜ ๊ทœ์•ฝ์„ ์Šค์Šค๋กœ ํŒŒ๊ธฐํ•˜๋ฉฐ ์„œ์‚ฌ์˜ ํ‹ˆ์ƒˆ๋ฅผ ๋น„ํ‹€์–ด ๋ณผ ๊ฒƒ์„ ์š”์ฒญํ•œ๋‹ค. ์ด๋Š” ๋‹จ์ง€ ํƒ€์ธ์˜ ์‚ถ์„ ์žฌํ˜„ํ•˜๋Š” ๊ฒฝ์šฐ์—๋งŒ ๋“œ๋Ÿฌ๋‚˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ์Šค์Šค๋กœ์— ๋Œ€ํ•œ ์ž์ „์  ๊ธ€์“ฐ๊ธฐ์—์„œ๋„ ๋‚˜ํƒ€๋‚œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ 4์žฅ์—์„œ๋Š” ๊ธฐ์กด์˜ ์žฅ๋ฅด์™€ ๋ฌธ์ž์–ธ์–ด์˜ ์™ธ๋ถ€์—์„œ ์–ธ์–ด์˜ ํ•ด๋ฐฉ์  ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜๋Š” ์ด์ฒญ์ค€์˜ ์ž‘ํ’ˆ๋“ค์— ์ฃผ๋ชฉํ–ˆ๋‹ค. ์ด์ฒญ์ค€์€ ํ”ํžˆ ๋Œ€์ค‘์†Œ์„ค์—์„œ ์‚ฌ์šฉ๋˜๋Š” ์ถ”๋ฆฌ์†Œ์„ค์˜ ๊ธฐ๋ฒ•์„ ์†Œ์„ค ์†์— ํญ๋„“๊ฒŒ ๊ตฌํ˜„ํ•ด๋‚ด์—ˆ๋‹ค. ์ด์ฒญ์ค€์€ ์ต์ˆ™ํ•œ ์„œ์‚ฌ์˜ ํ˜•์‹์— ๊ฑฐ๋ฆฌ๋ฅผ ๋‘๋ฉด์„œ, ๊ทผ๋Œ€์˜ ์–ธ์–ด์™€ ๊ทœ๋ฒ”ํ™”๋œ ๋…์„œ ๋ฐ ์„ธ๊ณ„์ธ์‹์ด ๊ฐ€์ง€๋Š” ๋งน์ ์„ ๋น„ํŒํ•˜๊ณ , ์นจ๋ฌต๊ณผ ๊ฐ™์ด ์–ธ์–ด์˜ ์ž‰์—ฌ๋‚˜ ํ‹ˆ์ƒˆ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฐ•์กฐํ–ˆ๋‹ค. ์ด์ฒญ์ค€์˜ ์†Œ์„ค์— ๋‚˜ํƒ€๋‚˜๋Š” ๋…ธ๋ž˜์™€ ์†Œ๋ฆฌ์˜ ์˜๋ฏธ๋Š” ์ด์ฒญ์ค€์ด ์—ฌ๋Ÿฌ ์ž‘ํ’ˆ๋“ค์„ ํ†ตํ•ด ๊ทผ๋Œ€ ์ด์„ฑ์— ๊ธฐ๋ฐ˜์„ ๋‘” ์–ธ์–ด๊ฐ€ ๊ฐ€์ง€๋Š” ํ•œ๊ณ„๋ฅผ ํ˜•์ƒํ™”ํ–ˆ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•  ๋•Œ, ๋น„๋กœ์†Œ ๊ทธ ์˜๋ฏธ๊ฐ€ ๋ช…์ง•ํ•ด์ง„๋‹ค. ์ด์ฒญ์ค€์€ ๋…ธ๋ž˜, ์†Œ๋ฆฌ ํ˜น์€ ์Œ์•… ๋“ฑ ๋‹ค๋ฅธ ์˜์‚ฌ์†Œํ†ต ๋งค์ฒด ๋ฐ ์˜ˆ์ˆ ๊ณผ ์ ‘๋ชฉํ•œ ์–ธ์–ด๋ฅผ ํ†ตํ•ด์„œ, ๊ธฐ์กด์˜ ์–ธ์–ด๊ด€์—์„œ ๋ฒ—์–ด๋‚˜, ์–ธ์–ด๊ฐ€ ๊ฐ€์ง„ ๋‹ค๋ฅธ ์ฐจ์›์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ง€์†์ ์œผ๋กœ ์‹ฌ๋ฌธํ•˜๊ณ  ํƒ๊ตฌํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์š”์ปจ๋Œ€ ์ด์ฒญ์ค€์€ ์–ธ์–ด๊ฐ€ ์ œ ๊ธฐ๋Šฅ์„ ๋ชปํ•˜๋Š” ์‹œ๋Œ€์ธ 1960-1980๋…„๋Œ€ ํ•œ๊ตญ์‚ฌํšŒ์˜ ์‚ฌํšŒ ๋ฐ ์ •์น˜์  ๋งฅ๋ฝ ๋ฐ ๋‹ด๋ก  ์žฅ ์•ˆ์—์„œ ์–ธ์–ด๋ผ๋Š” ์˜์‚ฌ์†Œํ†ต ๋ฐ ์˜ˆ์ˆ ํ‘œํ˜„ ์ˆ˜๋‹จ์ด ๊ฐ€์ง€๋Š” ๊ฐ€๋Šฅ์„ฑ๊ณผ ๊ทธ ์ž„๊ณ„๋ฅผ ์ง€์†์ ์œผ๋กœ ํƒ๋ฌธํ•œ ์ž‘๊ฐ€๋ผ๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๋ถ€์—ฌํ•  ์ˆ˜ ์žˆ๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์‚ฌ ๊ฒ€ํ† ์™€ ๋ฌธ์ œ ์ œ๊ธฐ 1 2. ์—ฐ๊ตฌ์˜ ์‹œ๊ฐ 7 II. ์–ธ์–ด๊ฐ€ ๊ธฐ๋Šฅ์„ ๋ชปํ•˜๋Š” ์‹œ๋Œ€์™€ ์•Œ๋ ˆ๊ณ ๋ฆฌ์˜ ๊ธ€์“ฐ๊ธฐ 29 1. ํ•ญ์‹œ์  ์˜ˆ์™ธ ์ƒํƒœ์™€ ์•Œ๋ ˆ๊ณ ๋ฆฌ 29 1.1 ํ•ญ์‹œ์  ์˜ˆ์™ธ ์ƒํƒœ์™€ ์ „์ง“๋ถˆ ์•ž ๋ถˆ์•ˆํ•œ ๋ฐœํ™”์˜ ์œ„์น˜ 29 1.2 ์ž๊ธฐ ๊ธฐ๋งŒ์œผ๋กœ์„œ์˜ ๋ง๋ช…์˜ ์‹œ๋„์™€ ์‹คํŒจ: ๊ฐ€๋ฉด ์“ฐ๊ธฐ์™€ ์ž์•„ ๋ง์‹ค 43 2. ๊ณต๋™(็ฉบๆดž)์„ ์šฐํšŒํ•˜์—ฌ ํ˜„์‹ค์„ ๋งํ•˜๋Š” ์ทŒ์–ธ(่ด…่จ€)์˜ ๊ฐ€๋Šฅ์„ฑ: ์ฆํ™˜๊ณผ ์šฐํ™” 53 2.1 ์ค‘์ธต๊ตฌ์กฐ๊ฐ€ ๋ณด์—ฌ์ฃผ๋Š” ์ฃผ์ฒด์˜ ์ˆจ๊น€๊ณผ ๋‹ค์„ฑ์  ๋ชฉ์†Œ๋ฆฌ. 53 2.2 ๋ฐœํ™”ํ•˜๋Š” ์‹ ์ฒด์˜ ๋งค๊ฐœ์  ํŠน์„ฑ 64 III. ์ž์„œ์ „ ์“ฐ๊ธฐ๋ฅผ ํ†ตํ•ด ๋“œ๋Ÿฌ๋‚œ ๋ฌธํ•™ ์–ธ์–ด์˜ ์ž„๊ณ„์™€ ์ž‘๊ฐ€์˜์‹ 87 1. ๊ณ ๋ฐฑ์˜ ํ–‰์œ„์™€ ์ˆ˜ํ–‰์„ฑ์˜ ๋ฐœ๊ฒฌ์œผ๋กœ์„œ์˜ ์ž‘๊ฐ€ 87 1.1 ๊ณ ๋ฐฑ์˜ ํ–‰์œ„๋กœ ์“ฐ์—ฌ์ง€๋Š” ์ž‘๊ฐ€์˜ ์ž์„œ์ „ 87 1.2 ์„ ๊ณ ์œ ์˜ˆ์™€ ์†Œ์„ค๊ฐ€์˜ ์ง๋ฌด: ์˜ˆ๊ธฐ์น˜ ๋ชปํ•˜๊ฒŒ ๋„๋‹ฌํ•œ ์‹ฌํŒ๊ณผ ์นดํ”„์นด์  ์งˆ๋ฌธ 103 2. ์ž์„œ์ „ ์“ฐ๊ธฐ์™€ ์ž์„œ์ „ ๋Œ€ํ•„์ด ํ™˜๊ธฐํ•˜๋Š” ์ง„์‹ค์˜ ๋ฌธ์ œ 110 2.1 ์ž์„œ์ „ ์“ฐ๊ธฐ์™€ ์ž์„œ์ „ ๋Œ€ํ•„์ด ํ™˜๊ธฐํ•˜๋Š” ์ง„์‹ค์˜ ์žฌํ˜„ (๋ถˆ)๊ฐ€๋Šฅ์„ฑ 110 2.2 ๋ชจ๋ธ ์†Œ์„ค ๋‹ค์‹œ ์“ฐ๊ธฐ์˜ ์„ฑ์ทจ์™€ ๋‚œ์  124 IV. ์–ธ์–ด์˜ ์‹คํ—˜๊ณผ ์˜์‚ฌ์†Œํ†ต์˜ ๊ฐ€๋Šฅ์„ฑ 141 1. ์†Œ๋ฆฌ์™€ ๋…ธ๋ž˜๊ฐ€ ํ™˜๊ธฐํ•˜๋Š” ๊ทผ๋Œ€ ์–ธ์–ด ์ด์ƒ(ไปฅไธŠ)์˜ ์–ธ์–ด 141 1.1 ๋‚จ๋„ ์‚ฌ๋žŒ ์—ฐ์ž‘์— ๋‚˜ํƒ€๋‚œ ์†Œ๋ฆฌ์˜ ์˜๋ฏธ์™€ ์ž‘๊ฐ€์˜ ์œ„์น˜ 141 1.2 ์ž‰์—ฌ์˜ ์–ธ์–ด ๊ณ ํ–ฅ์˜ ์–ธ์–ด๊ฐ€ ๊ฐ–๋Š” ๊ฐ€๋Šฅ์„ฑ๊ณผ ๋ง์˜ ์œ ํฌ 158 2. ์–ธ์–ด ์™ธ๋ถ€์˜ ์„œ์‚ฌ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์–ธ์–ด์˜ ์—ญ์„ค์  ์กด์žฌ ์–‘์ƒ 162 2.1 ์žฌํ˜„์— ๋„์ „ํ•˜๋Š” ์–ธ์–ด์˜ ์—ญ์„ค์  ์กด์žฌ์–‘์ƒ๊ณผ ์ง„๋ฆฌ์˜ ํ˜„์ „ ๊ฐ€๋Šฅ์„ฑ 162 2.2 ์ˆ˜ํ–‰์  ์–ธ์–ด์˜ ์ •์น˜์  ์˜๋ฏธ์™€ ๋งค์ฒด ๋ณ‘์น˜๋ฅผ ํ†ตํ•œ ์˜์‚ฌ์†Œํ†ต ์‹คํ—˜ 176 V. ๊ฒฐ๋ก  191Docto

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    Involvement of BCRP and P-gp in the Transporter in the Transport of Belotecan and Topotecan

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๊ณต๊ณผ๋Œ€ํ•™์› :์ „๊ธฐ์ปดํ“จํ„ฐ ๊ณตํ•™๋ถ€,2005.Maste

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    Effect of the surface roughness of electrode on the charge injection

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณผํ•™๊ต์œก๊ณผ(๋ฌผ๋ฆฌ์ „๊ณต), 2012. 2. ์ „๋™๋ ฌ.๊ธˆ์† ์ „๊ทน ์œ„์— ์œ ๊ธฐ๋ฌผ ์ฑ„๋„์„ ์ฆ์ฐฉํ•˜์—ฌ ๋งŒ๋“œ๋Š” ๋ฐ”๋‹ฅ ์ „๊ทน ๊ตฌ์กฐ์˜ ์œ ๊ธฐ๋ฌผ ๋ฐ•๋ง‰ ํŠธ๋žœ์ง€์Šคํ„ฐ์—์„œ ์ „๊ทน ํ‘œ๋ฉด์ด ๊ฑฐ์นœ ์ •๋„์— ๋”ฐ๋ผ ์ „ํ•˜ ์ฃผ์ž…์ด ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”์ง€ ์กฐ์‚ฌํ–ˆ๋‹ค. ๊ธˆ ์ „๊ทน์„ ์‹ค๋ฆฌ์ฝ˜ ๊ธฐํŒ์— ์ฆ์ฐฉํ•˜๊ณ , ๊ฐ€์—ดํ•˜์—ฌ ๊ธˆ ์ „๊ทน ํ‘œ๋ฉด์„ ๊ฑฐ์น ๊ฒŒ ๋งŒ๋“ค์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํŽœํ„ฐ์‹ ๊ณผ ์ƒ๋ถ€ ์ „๊ทน์œผ๋กœ ์‚ฌ์šฉํ•  ๊ธˆ ์ „๊ทน์„ ์ฐจ๋ก€๋Œ€๋กœ ์ฆ์ฐฉํ•˜์—ฌ ๊ธˆ ์ „๊ทน/ํŽœํ„ฐ์‹ /๊ธˆ ์ „๊ทน ๊ตฌ์กฐ๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค. ํŽœํ„ฐ์‹  ์ฆ์ฐฉ ์ดˆ๊ธฐ์—๋Š” ๊ฑฐ์นœ ๊ธˆ ์ „๊ทน ์œ„์—์„œ ํŽœํ„ฐ์‹  ์ฆ์ฐฉํ•ต์ด ๋” ๋งŽ์ด ๋ณด์˜€์ง€๋งŒ, ๋ง‰์ด ๋‘๊บผ์›Œ์ง€๋ฉด ๊ฐ€์—ด๋˜์ง€ ์•Š์€ ์ „๊ทน๊ณผ ๊ฐ€์—ด๋กœ ๊ฑฐ์น ์–ด์ง„ ์ „๊ทน์—์„œ ํŽœํ„ฐ์‹  ํ‘œ๋ฉด ๋ชจ์–‘์— ์ฐจ์ด๊ฐ€ ๊ฑฐ์˜ ์—†์—ˆ๋‹ค. ์˜จ๋„๋ฅผ ๋ฐ”๊พธ๋ฉด์„œ ์ธก์ •ํ•œ ์ „๋ฅ˜-์ „์•• ๊ณก์„ ์€ ๋ฐ”๋‹ฅ ์ „๊ทน์˜ ํ‘œ๋ฉด์ด ๊ฑฐ์น ์ˆ˜๋ก ๋ฐ”๋‹ฅ ๊ณ„๋ฉด์˜ ์ „์œ„์žฅ๋ฒฝ์ด ๋†’์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ด ํ˜„์ƒ์€ ๊ธˆ์† ํ‘œ๋ฉด์ด ๊ฑฐ์น ์ˆ˜๋ก ์ผํ•จ์ˆ˜๊ฐ€ ๋‚ฎ์•„์ง€๋ฉฐ ํŽœํ„ฐ์‹ ๊ณผ ๊ฑฐ์นœ ์ „๊ทน ํ‘œ๋ฉด์˜ ๊ฒฝ๊ณ„์— ์ „ํ•˜ ํŠธ๋žฉ์ด ๋” ๋งŽ๊ธฐ ๋•Œ๋ฌธ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.We investigated how the surface roughness of electrode affects the charge injection at the pentacene/Au interface. After depositing Au film on the Si substrate by sputtering, we annealed the sample to control the Au surface roughness. Pentacene and Au top electrode were subsequently deposited to complete the sample. The nucleation density of pentacene was slightly higher on the rougher Au electrode, but surface morphologies of thick pentacene films were similar on both the as-prepared and the roughened Au electrodes. The current-voltage curves obtained from the Au/pentacene/Au structure measured as a function of temperature indicated that the interface barrier was higher for the rougher Au bottom-electrode. We propose that the higher barrier was caused by the lower work function of rougher electrode surface and the higher trap density at the interface.Maste
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