Dalian University of Technology

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.

Flexible MXene/Carbon Nanotube Composite Paper with High Volumetric Capacitance

Abstract: Dr. M.-Q. Zhao, C. E. Ren, Z. Ling, M. R. Lukatskaya, C. F. Zhang, K. L. Van Aken, Prof. M. W. Barsoum, Prof. Y. Gogotsi Department of Materials Science and Engineering and A. J. Drexel Nanomaterials Institute Drexel University 3141 Chestnut Street , Philadelphia , PA 19104 , USA E-mail: gogotsi@drexel.edu Z. Ling Carbon Research Laboratory Liaoning Key Lab for Energy Materials and Chemical Engineering State Key Lab of Fine Chemicals Dalian University of Technology Dalian 116024 , China C. F. Zhang State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai 200237 , China

Robust object tracking via sparsity-based collaborative model

Abstract: In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.

Visual Tracking with Fully Convolutional Networks

Abstract: We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet. The discoveries motivate the design of our tracking system. It is found that convolutional layers in different levels characterize the target from different perspectives. A top layer encodes more semantic features and serves as a category detector, while a lower layer carries more discriminative information and can better separate the target from distracters with similar appearance. Both layers are jointly used with a switch mechanism during tracking. It is also found that for a tracking target, only a subset of neurons are relevant. A feature map selection method is developed to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Extensive evaluation on the widely used tracking benchmark [36] shows that the proposed tacker outperforms the state-of-the-art significantly.

A molecular ruthenium catalyst with water-oxidation activity comparable to that of photosystem II

Abstract: Across chemical disciplines, an interest in developing artificial water splitting to O(2) and H(2), driven by sunlight, has been motivated by the need for practical and environmentally friendly power generation without the consumption of fossil fuels. The central issue in light-driven water splitting is the efficiency of the water oxidation, which in the best-known catalysts falls short of the desired level by approximately two orders of magnitude. Here, we show that it is possible to close that 'two orders of magnitude' gap with a rationally designed molecular catalyst [Ru(bda)(isoq)(2)] (H(2)bda = 2,2'-bipyridine-6,6'-dicarboxylic acid; isoq = isoquinoline). This speeds up the water oxidation to an unprecedentedly high reaction rate with a turnover frequency of >300 s(-1). This value is, for the first time, moderately comparable with the reaction rate of 100-400 s(-1) of the oxygen-evolving complex of photosystem II in vivo.