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Institution

Dalian University of Technology

EducationDalian, China
About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a rough set methodology is used to investigate the relationship between organizational attributes, supplier development program involvement attributes, and performance outcomes, focusing on environmental and business dimensions, and the methodology generates decision rules relating the various attributes to the performance outcomes.

433 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: A novel deep fully convolutional network model for accurate salient object detection and an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in the authors' decoder network are proposed.
Abstract: Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing a reformulated dropout (R-dropout) after specific convolutional layers to construct an uncertain ensemble of internal feature units. In addition, we propose an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in our decoder network. The proposed methods can also be applied to other deep convolutional networks. Compared with existing saliency detection methods, the proposed UCF model is able to incorporate uncertainties for more accurate object boundary inference. Extensive experiments demonstrate that our proposed saliency model performs favorably against state-ofthe-art approaches. The uncertain feature learning mechanism as well as the upsampling method can significantly improve performance on other pixel-wise vision tasks.

433 citations

Journal ArticleDOI
TL;DR: In this paper, a series of triphenylamine dyes were designed and synthesized as photosensitizers for the application of organic dye-sensitized solar cells (DSSCs).
Abstract: A series of triphenylamine dyes were designed and synthesized as photosensitizers for the application of organic dye-sensitized solar cells (DSSCs). Different substituted phenylene units, 2,2′;5′,2′′-terthiophene (TT) and dithieno[3,2-b;2′,3′-d]thiophene (DTT) serve as the π-spacers, and the electron acceptors employ the cyanoacrylic acid or rhodanine-3-acetic acid units. Detailed investigation on the relationship between the dye structure, and photophysical, photoelectrochemical properties and performance of DSSCs is described here. By substituting the phenylene group with electron-withdrawing units as π-spacers or replacing the cyanoacrylic acid with rhodanine-3-acetic acid units as electron acceptors, the bathochromic shift of absorption spectra are achieved. The significant differences in the redox potential of these dyes are also influenced by small structure changes. Furthermore, the different dye baths for semiconductor sensitization have a crucial effect on the performance of the DSSCs due to the ...

431 citations

Journal ArticleDOI
TL;DR: Structural health monitoring (SHM) technology has been successfully applied to understand the loads, environment actions, and behaviors of a structure subjected to various actions through solving a problem as discussed by the authors.
Abstract: Structural health monitoring (SHM) technology has been successfully applied to understand the loads, environment actions, and behaviors of a structure subjected to various actions through solving a...

430 citations

Journal ArticleDOI
Mingxing Wu1, Xiao Lin1, Tonghua Wang1, Jieshan Qiu1, Tingli Ma1 
TL;DR: In this paper, nine kinds of carbon materials were introduced into dye-sensitized solar cells (DSCs) system as counter electrodes (CEs), and compared the electrochemical catalytic activity of these carbon materials with Pt for the reduction of triiodide to iodide by measuring cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and Tafel-polarization curve.
Abstract: Nine kinds of carbon materials were introduced into dye-sensitized solar cells (DSCs) system as counter electrodes (CEs). We also compared the electrochemical catalytic activity of these carbon materials with Pt for the reduction of triiodide to iodide by measuring cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and Tafel-polarization curve. The nine kinds of carbon materials in this work included synthesized well-ordered mesoporous carbon (Com), activated carbon (Ca), carbon black (Cb), conductive carbon (Cc), carbon dye (Cd), carbon fiber (Cf), carbon nanotube (Cn), discarded toner of a printer (Cp) and fullerene (C60). All carbon materials showed electrochemical catalytic activity for triiodide reduction in the DSCs system. In particular, the synthesized Com showed excellent electrochemical catalytic activity which can be comparable to the performance of Pt. After optimizing the proportion of TiO2 added into the carbon paste and the spray time of the carbon paste, the DSCs based on these carbon CEs achieved energy conversion efficiencies of 2.8–7.5%. The results demonstrate that carbon material is a promising substitute for the expensive Pt CE for low-cost DSCs.

429 citations


Authors

Showing all 61205 results

NameH-indexPapersCitations
Yang Yang1712644153049
Yury Gogotsi171956144520
Hui Li1352982105903
Michael I. Posner134414104201
Anders Hagfeldt12960079912
Jian Zhou128300791402
Chao Zhang127311984711
Bin Wang126222674364
Chi Lin1251313102710
Tao Zhang123277283866
Bo Wang119290584863
Zhenyu Zhang118116764887
Liang Cheng116177965520
Anthony G. Fane11256540904
Xuelong Li110104446648
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023167
2022838
20216,974
20206,457
20196,261
20185,375