760 research outputs found

    Factors Affecting Success of Serial Crowdfunding: From Heuristic and Systematic Perspectives

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    Much of the current crowdfunding literature focuses on revealing determinants of one-time crowdfunding performance. However, the impacts of existing platform cues on serial crowdfunding performance remain largely unexplored. Drawing heuristic-systematic model, this study examines how performance-based heuristics cues and opinion-based systematic cues exert differential impacts on subsequent crowdfunding performance. This paper will fill the research gap in the crowdfunding literature by examining how backers are processing performance-related and opinion-related information when making decisions in serial crowdfunding context

    ์œ ๊ธฐ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์—์„œ ๋ฐœ๊ด‘์ฒด์˜ ๊ทน์„ฑ์ด ์žฌ๊ฒฐํ•ฉ ๊ธฐ์ž‘๊ณผ ์†Œ์ž ์„ฑ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2018. 2. ๊น€์žฅ์ฃผ.์žฌ๊ฒฐํ•ฉ ํ˜„์ƒ์€ ์œ ๊ธฐ ๊ด‘์ „์ž ์†Œ์ž์˜ ๊ตฌ๋™์— ์ง์ ‘์ ์œผ๋กœ ์—ฐ๊ด€๋œ ํ•ต์‹ฌ์ ์ธ ํ˜„์ƒ์ด๋‹ค. ์œ ๊ธฐ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ (OLED)์˜ ๊ฒฝ์šฐ, ์žฌ๊ฒฐํ•ฉ์€ ๊ด‘์ž๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์—‘์‹œํ†ค์„ ํ˜•์„ฑํ•˜์—ฌ ๋ฐœ๊ด‘์— ๊ธฐ์—ฌํ•˜๋ฉฐ, ์œ ๊ธฐ ๊ด‘์ „์ง€ (OPV)์˜ ๊ฒฝ์šฐ ์žฌ๊ฒฐํ•ฉ์€ ๊ด‘์ž์— ์˜ํ•ด ์ƒ์„ฑ๋œ ์—๋„ˆ์ง€๊ฐ€ ์†์‹ค๋˜๊ธฐ ๋•Œ๋ฌธ์— ์ตœ์†Œํ™” ํ•ด์•ผํ•  ํ˜„์ƒ์ด๋‹ค. ์œ ๊ธฐ ๋ฐ˜๋„์ฒด์—์„œ ๊ณ ๋ ค๋˜๋Š” ๋‘ ๊ฐ€์ง€์˜ ์ฃผ์š”ํ•œ ์žฌ๊ฒฐํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์žˆ๋‹ค. ํ•˜๋‚˜๋Š” ์ •๊ณต๊ณผ ์ „์ž ์‚ฌ์ด์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ž‘์ œ๋นˆ ์žฌ๊ฒฐํ•ฉ ํ˜„์ƒ์ด๋ฉฐ, ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ํŠธ๋ž˜ํ•‘ ๋œ ์ „ํ•˜์™€ ๋ฐ˜๋Œ€ ๋ถ€ํ˜ธ์˜ ์ „ํ•˜ ์‚ฌ์ด์— ๋ฐœ์ƒํ•˜๋Š” ํŠธ๋žฉ ๋ณด์กฐ ์žฌ๊ฒฐํ•ฉ ํ˜„์ƒ์ด๋‹ค. ๊ฐ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ์†Œ์ž ์ข…๋ฅ˜์— ๋”ฐ๋ผ ๋‹ค๋ฅด์ง€๋งŒ, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ์›์ธ์„ ํŒŒ์•…ํ•˜๊ณ  ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ์†Œ์ž์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š”๋ฐ ๊ณตํ†ต์ ์œผ๋กœ ์ค‘์š”ํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์—ฐ๊ตฌํ•œ ์—ผ๋ฃŒ ๋„ํ•‘๋œ OLED ์‹œ์Šคํ…œ์—์„œ๋Š”, ์žฌ๊ฒฐํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ๊ตฌ๋™ ์ „์•• ๋ฐ ํšจ์œจ์„ ๊ฒฐ์ •์ง“๋Š” ์ค‘์š”ํ•œ ์š”์†Œ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ค‘์š”์„ฑ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์žฌ๊ฒฐํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ฒฐ์ •ํ•˜๋Š” ์š”์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋งŽ์ด ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. ํŠธ๋žฉ ์‹ฌ๋„์˜ ํšจ๊ณผ๊ฐ€ ๊ทธ ์ค‘ ํ•˜๋‚˜ ์ด์ง€๋งŒ, ๊นŠ์€ ํŠธ๋žฉ ์ค€์œ„๋ฅผ ๊ฐ€์ง€๋Š” ์‹œ์Šคํ…œ์—์„œ๋„ ๋ž‘์ œ๋นˆ ์žฌ๊ฒฐํ•ฉ ํ˜„์ƒ์ด ์ง€๋ฐฐ์ ์ธ ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๋ณด๊ณ  ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ์ด๋ก ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ฒซ๋ฒˆ์งธ๋กœ, ์—‘์‹œํ”Œ๋ ‰์Šค ํ˜•์„ฑ ๊ณต๋™ ํ˜ธ์ŠคํŠธ ์‹œ์Šคํ…œ ๊ตฌ์กฐ์˜ ๊ฐ€์ƒ ์†Œ์ž๋ฅผ ์—ฐ๊ตฌ ํ•˜์˜€๋‹ค. ์ธต๊ฐ„ ์žฅ๋ฒฝ ํšจ๊ณผ, ๋ฐœ๊ด‘์ธต์˜ ์ „ํ•˜ ์ด๋™๋„ ๋ฐ ํŠธ๋žฉ ์‹ฌ๋„์— ๋”ฐ๋ผ ํ‘œ๋™-ํ™•์‚ฐ ๋ชจ๋ธ๋ง์„ ์ด์šฉํ•˜์—ฌ ์†Œ์ž์—์„œ์˜ ์ „ํ•˜ ๋ฐ€๋„, ์žฌ๊ฒฐํ•ฉ ์†๋„ ๋ฐ ๋ž‘์ œ๋นˆ ์žฌ๊ฒฐํ•ฉ ๋น„์œจ์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๋ฐœ๊ด‘์ธต์˜ ์ „ํ•˜์ด๋™๋„๊ฐ€ ๋‚ฎ์•„์งˆ์ˆ˜๋ก, ์ธต๊ฐ„ ์—๋„ˆ์ง€ ์žฅ๋ฒฝ์˜ ํฌ๊ธฐ๊ฐ€ ์ปค์งˆ์ˆ˜๋ก ๋ฐœ๊ด‘์ธต์—์„œ ์ •๊ณต ๋˜๋Š” ์ „์ž ๋” ์ถ•์ ๋œ๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ์œผ๋ฉฐ, ์ด๋กœ ์ธํ•ด ์†Œ์ž์—์„œ์˜ ๋ž‘์ œ๋นˆ ์žฌ๊ฒฐํ•ฉ์˜ ๋น„์œจ์ด ์ฆ๊ฐ€๋œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋ฐํ˜”๋‹ค. ๋˜ํ•œ ํŠธ๋žฉ ์‹ฌ๋„์˜ ๊ฒฝ์šฐ, ํŠธ๋žฉ์„ ๋น ์ ธ๋‚˜๊ฐ€๋Š” ํ˜„์ƒ์— ์˜ํ–ฅ์„ ์ฃผ์–ด ํŠธ๋žฉ๋œ ์ •๊ณต์˜ ๋ฐ€๋„์™€ ๊ด€๋ จ์ด ์žˆ์œผ๋ฉฐ, ํŠธ๋žฉ ์‹ฌ๋„๊ฐ€ 0.3 eV ์ด์ƒ์œผ๋กœ ์ปค์งˆ ๊ฒฝ์šฐ, ์ด ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ํฌ๊ธฐ์— ์ƒ๊ด€์—†์ด ์ผ์ •ํ•ด ์ง„๋‹ค๋Š” ์‚ฌ์‹ค์„ ์•Œ์•˜๋‹ค. ์ด ๊ฒฐ๊ณผ๋ฅผ ์—‘์‹œํ”Œ๋ ‰์Šค ๊ณต๋™ ํ˜ธ์ŠคํŠธ์— ๋Œ€์‘์‹œ์ผœ ๋ณผ ๋•Œ, ์ด๋Ÿฌํ•œ ๊ตฌ์กฐ๊ฐ€ ๋ž‘์ œ๋นˆ ์žฌ๊ฒฐํ•ฉ์ด ์šฐ์„ธํ•œ ์‹œ์Šคํ…œ์„ ๋งŒ๋“ค๊ธฐ์— ์ ํ•ฉํ•œ ํ˜ธ์ŠคํŠธ ๋ผ๋Š” ์‚ฌ์‹ค์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๋„ํŽ€ํŠธ์˜ ์Œ๊ทน์ž ๋ชจ๋ฉ˜ํŠธ๊ฐ€ ์—ผ๋ฃŒ๋กœ ๋„ํ•‘๋œ OLED์˜ ์žฌ๊ฒฐํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์š”์ธ ์ค‘ ํ•˜๋‚˜์ž„์„ ๋ฐํ˜”๋‹ค. ์šฐ์„ , ์ „๋ฅ˜-์ „์•• ๋ฐ ์‹œ๊ฐ„ ์ „๊ณ„ ๋ฐœ๊ด‘ ํŠน์„ฑ์„ ํ†ตํ•˜์—ฌ, 5 Debye ์ด์ƒ์˜ ํฐ ์Œ๊ทน์ž ๋ชจ๋ฉ˜ํŠธ๋ฅผ ๊ฐ€์ง„ ๋™์ข… ๋ฆฌ๊ฐ„๋“œ ๊ตฌ์กฐ์˜ ์ด๋ฆฌ๋“ ๋ฐœ๊ด‘์ฒด๋Š” ํŠธ๋žฉ ๋ณด์กฐ ์žฌ๊ฒฐํ•ฉ์— ์˜ํ•œ ๋ฐœ๊ด‘ ํ˜„์ƒ์„ ๊ฐ€์ง„๋‹ค๋Š” ๊ฒƒ์„, ๊ทธ๋Ÿฌ๋‚˜ 2 Debye ๋ฏธ๋งŒ์˜ ์ž‘์€ ์Œ๊ทน์ž ๋ชจ๋ฉ˜ํŠธ๋ฅผ ๊ฐ–๋Š” ์ด์ข… ๋ฆฌ๊ฐ„๋“œ์˜ ์ด๋ฆฌ๋“ ๋ฐœ๊ด‘์ฒด๋Š” ๋ž‘์ œ๋นˆ ์žฌ๊ฒฐํ•ฉ์— ์˜ํ•œ ๋ฐœ๊ด‘ ํŠน์„ฑ์„ ๊ฐ€์ง„๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ํ‘œ๋™-ํ™•์‚ฐ ๋ชจ๋ธ์—์„œ ์Œ๊ทน์ž ๋ชจ๋ฉ˜ํŠธ์˜ ํŠธ๋žฉ ํšจ๊ณผ๋ฅผ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ, ํŠธ๋žฉ ๊นŠ์ด๊ฐ€ 0.25 eV ๋ณด๋‹ค ํฐ ๊ฒฝ์šฐ ํŠธ๋žฉ ๊นŠ์ด์— ์˜ํ•œ ํšจ๊ณผ๋Š” ๋ฌด์‹œ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์Œ๊ทน์ž ๋ชจ๋ฉ˜ํŠธ๊ฐ€ ์†Œ์ž์˜ ์žฌ๊ฒฐํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ฒฐ์ •์ง“๋Š” ์ค‘์š”ํ•œ ์š”์ธ์ด ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜€๋ƒˆ๋‹ค. ์ด ๋ฐœ๊ฒฌ์€ ํ˜•๊ด‘ ๋˜๋Š” ์—ดํ™œ์„ฑ ์ง€์—ฐ ํ˜•๊ด‘ OLED๋ฅผ ํฌํ•จํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์ข…๋ฅ˜์˜ OLED ์—ฐ๊ตฌ์— ์œ ์šฉํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋œ๋‹ค.1. Introduction 1 1.1 Organic Light Emitting Diodes 1 1.2 Langevin and trap assisted recombination 4 1.3 Device with Exciplex Forming Co-host system 11 1.4 Outline of the thesis 13 2. Electrical Modeling 15 2.1 Introduction 15 2.2 Governing Equations in the model 18 2.3 Implementation of the model 24 3. Factors Affecting the Recombination Mechanisms in Dye-doped Organic Light Emitting diodes 31 3.1 Introduction 31 3.2 Experimental 34 3.3 Result and discussion 37 3.4 Conclusion 46 4. Unveiling the role of Dopant Polarity on the Recombination and Performance of Organic Light Emitting Diodes 47 4.1 Introduction 47 4.2 Experimental 50 4.3 Device Characteristics (J-V-L, transient EL) 51 4.4 Simulation parameters 65 4.5 Langevin recombination against trap-assisted recombination 70 4.6 Effect of disorder due to dipoles 68 4.7 Discussion 75 4.8 Conclusion 79 Appendix MATLAB code of drift-diffusion model 92 Bibliography 124 Abstract 134 CURRICULUM VITAE 137 List of publication 139Docto

    A photonic-crystal optical antenna for extremely large local-field enhancement

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    We propose a novel design of an all-dielectric optical antenna based on photonic-band-gap confinement. Specifically, we have engineered the photonic-crystal dipole mode to have broad spectral response (Q ~70) and well-directed vertical-radiation by introducing a plane mirror below the cavity. Considerably large local electric-field intensity enhancement ~4,500 is expected from the proposed design for a normally incident planewave. Furthermore, an analytic model developed based on coupled-mode theory predicts that the electric-field intensity enhancement can easily be over 100,000 by employing reasonably high-Q (~10,000) resonators

    A Deep Learning Based Model for Driving Risk Assessment

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    In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 driversโ€™ driving behavior

    Comparative analysis of multiple classification models to improve PM10 prediction performance

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    With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model

    Limits of Binaries That Can Be Characterized by Gravitational Microlensing

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    Due to the high efficiency of planet detections, current microlensing planet searches focus on high-magnification events. High-magnification events are sensitive to remote binary companions as well and thus a sample of wide-separation binaries are expected to be collected as a byproduct. In this paper, we show that characterizing binaries for a portion of this sample will be difficult due to the degeneracy of the binary-lensing parameters. This degeneracy arises because the perturbation induced by the binary companion is well approximated by the Chang-Refsdal lensing for binaries with separations greater than a certain limit. For binaries composed of equal mass lenses, we find that the lens binarity can be noticed up to the separations of โˆผ60\sim 60 times of the Einstein radius corresponding to the mass of each lens. Among these binaries, however, we find that the lensing parameters can be determined only for a portion of binaries with separations less than โˆผ20\sim 20 times of the Einstein radius.Comment: 5 pages, 3 figures, 1 tabl

    Two-gap and paramagnetic pair-breaking effects on upper critical field of SmFeAsO0.85_{0.85} and SmFeAsO0.8_{0.8}F0.2_{0.2} single crystals

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    We investigated the temperature dependence of the upper critical field [Hc2(T)H_{c2}(T)] of fluorine-free SmFeAsO0.85_{0.85} and fluorine-doped SmFeAsO0.8_{0.8}F0.2_{0.2} single crystals by measuring the resistive transition in low static magnetic fields and in pulsed fields up to 60 T. Both crystals show that Hc2(T)H_{c2}(T)'s along the c axis [Hc2c(T)H_{c2}^c(T)] and in an abab-planar direction [Hc2ab(T)H_{c2}^{ab}(T)] exhibit a linear and a sublinear increase, respectively, with decreasing temperature below the superconducting transition. Hc2(T)H_{c2}(T)'s in both directions deviate from the conventional one-gap Werthamer-Helfand-Hohenberg theoretical prediction at low temperatures. A two-gap nature and the paramagnetic pair-breaking effect are shown to be responsible for the temperature-dependent behavior of Hc2cH_{c2}^c and Hc2abH_{c2}^{ab}, respectively.Comment: 21 pages, 8 figure

    Combinatorial growth of Si nanoribbons

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    Silicon nanoribbons (Si NRs) with a thickness of about 30 nm and a width up to a few micrometers were synthesized. Systematic observations indicate that Si NRs evolve via the following sequences: the growth of basal nanowires assisted with a Pt catalyst by a vapor-liquid-solid (VLS) mechanism, followed by the formation of saw-like edges on the basal nanowires and the planar filling of those edges by a vapor-solid (VS) mechanism. Si NRs have twins along the longitudinal < 110 > growth of the basal nanowires that also extend in < 112 > direction to edge of NRs. These twins appear to drive the lateral growth by a reentrant twin mechanism. These twins also create a mirror-like crystallographic configuration in the anisotropic surface energy state and appear to further drive lateral saw-like edge growth in the < 112 > direction. These outcomes indicate that the Si NRs are grown by a combination of the two mechanisms of a Pt-catalyst-assisted VLS mechanism for longitudinal growth and a twin-assisted VS mechanism for lateral growth
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