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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 review of the recent progress of fabrication, properties, and structural applications of high-performance and multifunctional cementitious composites with carbon nanofibers, carbon nanotubes and nano graphite platelets is presented.
Abstract: As structural materials, cementitious materials are quasi-brittle and susceptible to cracking, and have no functional properties. Nanotechnology is introduced into cementitious materials to address these issues. Nano materials, especially nano carbon materials (NCMs) were found to be able to improve/modify the mechanical property, durability and functional properties of cementitious materials due to their excellent intrinsic properties and composite effects. Here, this review focuses on the recent progress of fabrication, properties, and structural applications of high-performance and multifunctional cementitious composites with NCMs including carbon nanofibers, carbon nanotubes and nano graphite platelets. The improvement/modification mechanisms of these NCMs to composites are also discussed.

284 citations

Journal ArticleDOI
TL;DR: A facile synthesis strategy for preparing Fe3O4@ZIF-8 magnetic core-shell microspheres has been successfully developed as mentioned in this paper, which involves first pre-treating the magnetic cores with an anionic polyelectrolyte to alter the surface charge of the particles and adsorb Zn2+ cations to initiate nucleation.

284 citations

Journal ArticleDOI
TL;DR: Corncob residue, the main by-product in the furfural industry, is used as a precursor to prepare porous carbon by a simple and direct thermal treatment: one-step activation without pre-carbonization.

284 citations

Proceedings ArticleDOI
01 Jun 2016
TL;DR: The proposed sequential training method for convolutional neural networks to effectively transfer pre-trained deep features for online applications is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance.
Abstract: Due to the limited amount of training samples, finetuning pre-trained deep models online is prone to overfitting. In this paper, we propose a sequential training method for convolutional neural networks (CNNs) to effectively transfer pre-trained deep features for online applications. We regard a CNN as an ensemble with each channel of the output feature map as an individual base learner. Each base learner is trained using different loss criterions to reduce correlation and avoid over-training. To achieve the best ensemble online, all the base learners are sequentially sampled into the ensemble via important sampling. To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features. The proposed online training method is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance. Extensive experiments are conducted on two challenging benchmark data set and demonstrate that our tracking algorithm can outperform state-of-the-art methods with a considerable margin.

284 citations

Journal ArticleDOI
TL;DR: The results indicate that the different biological encoding of numbers may be shaped by visual reading experience during language acquisition and other cultural factors such as mathematics learning strategies and education systems, which cannot be explained completely by the differences in languages per se.
Abstract: The universal use of Arabic numbers in mathematics raises a question whether these digits are processed the same way in people speaking various languages, such as Chinese and English, which reflect differences in Eastern and Western cultures. Using functional MRI, we demonstrated a differential cortical representation of numbers between native Chinese and English speakers. Contrasting to native English speakers, who largely employ a language process that relies on the left perisylvian cortices for mental calculation such as a simple addition task, native Chinese speakers, instead, engage a visuo-premotor association network for the same task. Whereas in both groups the inferior parietal cortex was activated by a task for numerical quantity comparison, functional MRI connectivity analyses revealed a functional distinction between Chinese and English groups among the brain networks involved in the task. Our results further indicate that the different biological encoding of numbers may be shaped by visual reading experience during language acquisition and other cultural factors such as mathematics learning strategies and education systems, which cannot be explained completely by the differences in languages per se.

283 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
2022836
20216,974
20206,457
20196,261
20185,375