Institution
Harbin Institute of Technology
Education•Harbin, China•
About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.
Topics: Microstructure, Control theory, Ultimate tensile strength, Alloy, Laser
Papers published on a yearly basis
Papers
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TL;DR: This work demonstrates the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology and empirically demonstrates that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance.
Abstract: We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks—a distortion identification network and a quality prediction network—sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.
391 citations
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University of Ljubljana1, Austrian Institute of Technology2, University of Birmingham3, Czech Technical University in Prague4, Parthenope University of Naples5, Panasonic6, Pohang University of Science and Technology7, Linköping University8, Graz University of Technology9, Zhejiang University10, Shanghai Jiao Tong University11, Seoul National University12, Electronics and Telecommunications Research Institute13, University of Coimbra14, Autonomous University of Barcelona15, University of Surrey16, École Polytechnique Fédérale de Lausanne17, Chinese Academy of Sciences18, University of Oxford19, Harbin Institute of Technology20
TL;DR: The evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset are presented, offering a more systematic comparison of the trackers.
Abstract: The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).
391 citations
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TL;DR: A novel hydrothermal-synthesis strategy is presented to achieve simultaneous and synergistic modulation of crystal phase and disorder in partially crystallized 1T-MoSe2 nanosheets to dramatically enhance their HER catalytic activity.
Abstract: MoSe2 is a promising earth-abundant electrocatalyst for the hydrogen-evolution reaction (HER), even though it has received much less attention among the layered dichalcogenide (MX2 ) materials than MoS2 so far Here, a novel hydrothermal-synthesis strategy is presented to achieve simultaneous and synergistic modulation of crystal phase and disorder in partially crystallized 1T-MoSe2 nanosheets to dramatically enhance their HER catalytic activity Careful structural characterization and defect characterization using positron annihilation lifetime spectroscopy correlated with electrochemical measurements show that the formation of the 1T phase under a large excess of the NaBH4 reductant during synthesis can effectively improve the intrinsic activity and conductivity, and the disordered structure from a lower reaction temperature can provide abundant unsaturated defects as active sites Such synergistic effects lead to superior HER catalytic activity with an overpotential of 152 mV versus reversible hydrogen electrode (RHE) for the electrocatalytic current density of j = -10 mA cm-2 , and a Tafel slope of 52 mV dec-1 This work paves a new pathway for improving the catalytic activity of MoSe2 and generally MX2 -based electrocatalysts via a synergistic modulation strategy
390 citations
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TL;DR: A simple and efficient hybrid attribute reduction algorithm based on a generalized fuzzy-rough model based on fuzzy relations is introduced and the technique of variable precision fuzzy inclusion in computing decision positive region can get the optimal classification performance.
390 citations
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TL;DR: In this article, the effect of carbon black support corrosion on the stability of Pt/C catalyst was investigated by cyclic voltammograms and X-ray photoelectron spectroscopy (XPS).
390 citations
Authors
Showing all 89023 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jiaguo Yu | 178 | 730 | 113300 |
Lei Jiang | 170 | 2244 | 135205 |
Gang Chen | 167 | 3372 | 149819 |
Xiang Zhang | 154 | 1733 | 117576 |
Hui-Ming Cheng | 147 | 880 | 111921 |
Yi Yang | 143 | 2456 | 92268 |
Bruce E. Logan | 140 | 591 | 77351 |
Bin Liu | 138 | 2181 | 87085 |
Peng Shi | 137 | 1371 | 65195 |
Hui Li | 135 | 2982 | 105903 |
Lei Zhang | 135 | 2240 | 99365 |
Jie Liu | 131 | 1531 | 68891 |
Lei Zhang | 130 | 2312 | 86950 |
Zhen Li | 127 | 1712 | 71351 |
Kurunthachalam Kannan | 126 | 820 | 59886 |