823 research outputs found
Factors Affecting Success of Serial Crowdfunding: From Heuristic and Systematic Perspectives
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
์ ๊ธฐ๋ฐ๊ด๋ค์ด์ค๋์์ ๋ฐ๊ด์ฒด์ ๊ทน์ฑ์ด ์ฌ๊ฒฐํฉ ๊ธฐ์๊ณผ ์์ ์ฑ๋ฅ์ ๋ฏธ์น๋ ์ํฅ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ฌ๋ฃ๊ณตํ๋ถ, 2018. 2. ๊น์ฅ์ฃผ.์ฌ๊ฒฐํฉ ํ์์ ์ ๊ธฐ ๊ด์ ์ ์์์ ๊ตฌ๋์ ์ง์ ์ ์ผ๋ก ์ฐ๊ด๋ ํต์ฌ์ ์ธ ํ์์ด๋ค. ์ ๊ธฐ๋ฐ๊ด๋ค์ด์ค๋ (OLED)์ ๊ฒฝ์ฐ, ์ฌ๊ฒฐํฉ์ ๊ด์๋ฅผ ์์ฑํ๋ ์์ํค์ ํ์ฑํ์ฌ ๋ฐ๊ด์ ๊ธฐ์ฌํ๋ฉฐ, ์ ๊ธฐ ๊ด์ ์ง (OPV)์ ๊ฒฝ์ฐ ์ฌ๊ฒฐํฉ์ ๊ด์์ ์ํด ์์ฑ๋ ์๋์ง๊ฐ ์์ค๋๊ธฐ ๋๋ฌธ์ ์ต์ํ ํด์ผํ ํ์์ด๋ค. ์ ๊ธฐ ๋ฐ๋์ฒด์์ ๊ณ ๋ ค๋๋ ๋ ๊ฐ์ง์ ์ฃผ์ํ ์ฌ๊ฒฐํฉ ๋ฉ์ปค๋์ฆ์ด ์๋ค. ํ๋๋ ์ ๊ณต๊ณผ ์ ์ ์ฌ์ด์์ ๋ฐ์ํ๋ ๋์ ๋น ์ฌ๊ฒฐํฉ ํ์์ด๋ฉฐ, ๋ค๋ฅธ ํ๋๋ ํธ๋ํ ๋ ์ ํ์ ๋ฐ๋ ๋ถํธ์ ์ ํ ์ฌ์ด์ ๋ฐ์ํ๋ ํธ๋ฉ ๋ณด์กฐ ์ฌ๊ฒฐํฉ ํ์์ด๋ค. ๊ฐ ๋ฉ์ปค๋์ฆ์ด ๋ฏธ์น๋ ์ํฅ์ ์์ ์ข
๋ฅ์ ๋ฐ๋ผ ๋ค๋ฅด์ง๋ง, ์ด๋ฌํ ํ์์ ์์ธ์ ํ์
ํ๊ณ ์ดํดํ๋ ๊ฒ์ ์์์ ์ฑ๋ฅ์ ํฅ์์ํค๋๋ฐ ๊ณตํต์ ์ผ๋ก ์ค์ํ๋ค. ์ด ๋
ผ๋ฌธ์์ ์ฐ๊ตฌํ ์ผ๋ฃ ๋ํ๋ OLED ์์คํ
์์๋, ์ฌ๊ฒฐํฉ ๋ฉ์ปค๋์ฆ์ ๊ตฌ๋ ์ ์ ๋ฐ ํจ์จ์ ๊ฒฐ์ ์ง๋ ์ค์ํ ์์์ด๋ค. ์ด๋ฌํ ์ค์์ฑ์๋ ๋ถ๊ตฌํ๊ณ ์ฌ๊ฒฐํฉ ๋ฉ์ปค๋์ฆ์ ๊ฒฐ์ ํ๋ ์์ธ์ ๋ํ ์ฐ๊ตฌ๋ ๋ง์ด ์ด๋ฃจ์ด์ง์ง ์์๋ค. ํธ๋ฉ ์ฌ๋์ ํจ๊ณผ๊ฐ ๊ทธ ์ค ํ๋ ์ด์ง๋ง, ๊น์ ํธ๋ฉ ์ค์๋ฅผ ๊ฐ์ง๋ ์์คํ
์์๋ ๋์ ๋น ์ฌ๊ฒฐํฉ ํ์์ด ์ง๋ฐฐ์ ์ธ ์์คํ
์ ๊ตฌ์ถํ๋ ๊ฒ์ด ๊ฐ๋ฅํ๋ค๊ณ ๋ณด๊ณ ๋์์ผ๋ฉฐ, ์ด๋ฌํ ํ์์ ์ค๋ช
ํ ์ ์๋ ์ด๋ก ์ ๋ํ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค.
์ด ๋
ผ๋ฌธ์์๋ ์ฒซ๋ฒ์งธ๋ก, ์์ํ๋ ์ค ํ์ฑ ๊ณต๋ ํธ์คํธ ์์คํ
๊ตฌ์กฐ์ ๊ฐ์ ์์๋ฅผ ์ฐ๊ตฌ ํ์๋ค. ์ธต๊ฐ ์ฅ๋ฒฝ ํจ๊ณผ, ๋ฐ๊ด์ธต์ ์ ํ ์ด๋๋ ๋ฐ ํธ๋ฉ ์ฌ๋์ ๋ฐ๋ผ ํ๋-ํ์ฐ ๋ชจ๋ธ๋ง์ ์ด์ฉํ์ฌ ์์์์์ ์ ํ ๋ฐ๋, ์ฌ๊ฒฐํฉ ์๋ ๋ฐ ๋์ ๋น ์ฌ๊ฒฐํฉ ๋น์จ์ ๊ณ์ฐํ์๋ค. ๊ทธ ๊ฒฐ๊ณผ ๋ฐ๊ด์ธต์ ์ ํ์ด๋๋๊ฐ ๋ฎ์์ง์๋ก, ์ธต๊ฐ ์๋์ง ์ฅ๋ฒฝ์ ํฌ๊ธฐ๊ฐ ์ปค์ง์๋ก ๋ฐ๊ด์ธต์์ ์ ๊ณต ๋๋ ์ ์ ๋ ์ถ์ ๋๋ค๋ ๊ฒฐ๊ณผ๋ฅผ ์ป์์ผ๋ฉฐ, ์ด๋ก ์ธํด ์์์์์ ๋์ ๋น ์ฌ๊ฒฐํฉ์ ๋น์จ์ด ์ฆ๊ฐ๋๋ค๋ ์ฌ์ค์ ๋ฐํ๋ค. ๋ํ ํธ๋ฉ ์ฌ๋์ ๊ฒฝ์ฐ, ํธ๋ฉ์ ๋น ์ ธ๋๊ฐ๋ ํ์์ ์ํฅ์ ์ฃผ์ด ํธ๋ฉ๋ ์ ๊ณต์ ๋ฐ๋์ ๊ด๋ จ์ด ์์ผ๋ฉฐ, ํธ๋ฉ ์ฌ๋๊ฐ 0.3 eV ์ด์์ผ๋ก ์ปค์ง ๊ฒฝ์ฐ, ์ด ํ๋ผ๋ฏธํฐ์ ํฌ๊ธฐ์ ์๊ด์์ด ์ผ์ ํด ์ง๋ค๋ ์ฌ์ค์ ์์๋ค. ์ด ๊ฒฐ๊ณผ๋ฅผ ์์ํ๋ ์ค ๊ณต๋ ํธ์คํธ์ ๋์์์ผ ๋ณผ ๋, ์ด๋ฌํ ๊ตฌ์กฐ๊ฐ ๋์ ๋น ์ฌ๊ฒฐํฉ์ด ์ฐ์ธํ ์์คํ
์ ๋ง๋ค๊ธฐ์ ์ ํฉํ ํธ์คํธ ๋ผ๋ ์ฌ์ค์ ์ ์ ์์๋ค.
๋ ๋ฒ์งธ๋ก, ๋ํํธ์ ์๊ทน์ ๋ชจ๋ฉํธ๊ฐ ์ผ๋ฃ๋ก ๋ํ๋ OLED์ ์ฌ๊ฒฐํฉ ๋ฉ์ปค๋์ฆ์ ์ํฅ์ ๋ฏธ์น๋ ๊ฐ์ฅ ์ค์ํ ์์ธ ์ค ํ๋์์ ๋ฐํ๋ค. ์ฐ์ , ์ ๋ฅ-์ ์ ๋ฐ ์๊ฐ ์ ๊ณ ๋ฐ๊ด ํน์ฑ์ ํตํ์ฌ, 5 Debye ์ด์์ ํฐ ์๊ทน์ ๋ชจ๋ฉํธ๋ฅผ ๊ฐ์ง ๋์ข
๋ฆฌ๊ฐ๋ ๊ตฌ์กฐ์ ์ด๋ฆฌ๋ ๋ฐ๊ด์ฒด๋ ํธ๋ฉ ๋ณด์กฐ ์ฌ๊ฒฐํฉ์ ์ํ ๋ฐ๊ด ํ์์ ๊ฐ์ง๋ค๋ ๊ฒ์, ๊ทธ๋ฌ๋ 2 Debye ๋ฏธ๋ง์ ์์ ์๊ทน์ ๋ชจ๋ฉํธ๋ฅผ ๊ฐ๋ ์ด์ข
๋ฆฌ๊ฐ๋์ ์ด๋ฆฌ๋ ๋ฐ๊ด์ฒด๋ ๋์ ๋น ์ฌ๊ฒฐํฉ์ ์ํ ๋ฐ๊ด ํน์ฑ์ ๊ฐ์ง๋ค๋ ๊ฒ์ ๋ฐํ๋ค. ๋ํ ์ฐ๋ฆฌ๋ ํ๋-ํ์ฐ ๋ชจ๋ธ์์ ์๊ทน์ ๋ชจ๋ฉํธ์ ํธ๋ฉ ํจ๊ณผ๋ฅผ ์ถ๊ฐ์ ์ผ๋ก ๊ณ ๋ คํ์ฌ, ํธ๋ฉ ๊น์ด๊ฐ 0.25 eV ๋ณด๋ค ํฐ ๊ฒฝ์ฐ ํธ๋ฉ ๊น์ด์ ์ํ ํจ๊ณผ๋ ๋ฌด์ ๊ฐ๋ฅํ๋ฉฐ, ์๊ทน์ ๋ชจ๋ฉํธ๊ฐ ์์์ ์ฌ๊ฒฐํฉ ๋ฉ์ปค๋์ฆ์ ๊ฒฐ์ ์ง๋ ์ค์ํ ์์ธ์ด ๋๋ค๋ ๊ฒ์ ๋ฐํ๋๋ค. ์ด ๋ฐ๊ฒฌ์ ํ๊ด ๋๋ ์ดํ์ฑ ์ง์ฐ ํ๊ด OLED๋ฅผ ํฌํจํ์ฌ ๋ค์ํ ์ข
๋ฅ์ OLED ์ฐ๊ตฌ์ ์ ์ฉํ ๊ฒ์ผ๋ก ์์ธก๋๋ค.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
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
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
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
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
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 times of
the Einstein radius.Comment: 5 pages, 3 figures, 1 tabl
Two-gap and paramagnetic pair-breaking effects on upper critical field of SmFeAsO and SmFeAsOF single crystals
We investigated the temperature dependence of the upper critical field
[] of fluorine-free SmFeAsO and fluorine-doped
SmFeAsOF single crystals by measuring the resistive transition
in low static magnetic fields and in pulsed fields up to 60 T. Both crystals
show that 's along the c axis [] and in an -planar
direction [] exhibit a linear and a sublinear increase,
respectively, with decreasing temperature below the superconducting transition.
'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 and
, respectively.Comment: 21 pages, 8 figure
Combinatorial growth of Si nanoribbons
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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