Institution
Xidian University
Education•Xi'an, China•
About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: In this article, a piezoelectric-composite slurry with BaTiO3 nanoparticles (100nm) was 3D printed using Mask-Image-Projection-based Stereolithography (MIP-SL) technology.
183 citations
••
TL;DR: A novel HSIC framework, named deep multiscale spatial-spectral feature extraction algorithm, which focuses on learning effective discriminant features for HSIC, and provides the state-of-the-art performance and is much more effective, especially for images with high nonlinear distribution and spatial diversity.
Abstract: Most of the existing spatial-spectral-based hyperspectral image classification (HSIC) methods mainly extract the spatial-spectral information by combining the pixels in a small neighborhood or aggregating the statistical and morphological characteristics. However, those strategies can only generate shallow appearance features with limited representative ability for classes with high interclass similarity and spatial diversity and therefore reduce the classification accuracy. To this end, we present a novel HSIC framework, named deep multiscale spatial-spectral feature extraction algorithm, which focuses on learning effective discriminant features for HSIC. First, the well pretrained deep fully convolutional network based on VGG-verydeep-16 is introduced to excavate the potential deep multiscale spatial structural information in the proposed hyperspectral imaging framework. Then, the spectral feature and the deep multiscale spatial feature are fused by adopting the weighted fusion method. Finally, the fusion feature is put into a generic classifier to obtain the pixelwise classification. Compared with the existing spectral-spatial-based classification techniques, the proposed method provides the state-of-the-art performance and is much more effective, especially for images with high nonlinear distribution and spatial diversity.
183 citations
••
01 Aug 2017TL;DR: Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture.
Abstract: Gesture recognition aims at understanding the ongoing human gestures. In this paper, we present a deep architecture to learn spatiotemporal features for gesture recognition. The deep architecture first learns 2D spatiotemporal feature maps using 3D convolutional neural networks (3DCNN) and bidirectional convolutional long-short-term-memory networks (ConvLSTM). The learnt 2D feature maps can encode the global temporal information and local spatial information simultaneously. Then, 2DCNN is utilized further to learn the higher-level spatiotemporal features from the 2D feature maps for the final gesture recognition. The spatiotemporal correlation information is kept through the whole process of feature learning. This makes the deep architecture an effective spatiotemporal feature learner. Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture.
183 citations
••
TL;DR: Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model.
182 citations
••
01 May 2005TL;DR: A special nonlinear bilevel programming problem is transformed into an equivalent single objective nonlinear programming problem and a new evolutionary algorithm is proposed that can be used to handle nonlinear BLPPs with nondifferentiable leader's objective functions.
Abstract: In this paper, a special nonlinear bilevel programming problem (nonlinear BLPP) is transformed into an equivalent single objective nonlinear programming problem. To solve the equivalent problem effectively, we first construct a specific optimization problem with two objectives. By solving the specific problem, we can decrease the leader's objective value, identify the quality of any feasible solution from infeasible solutions and the quality of two feasible solutions for the equivalent single objective optimization problem, force the infeasible solutions moving toward the feasible region, and improve the feasible solutions gradually. We then propose a new constraint-handling scheme and a specific-design crossover operator. The new constraint-handling scheme can make the individuals satisfy all linear constraints exactly and the nonlinear constraints approximately. The crossover operator can generate high quality potential offspring. Based on the constraint-handling scheme and the crossover operator, we propose a new evolutionary algorithm and prove its global convergence. A distinguishing feature of the algorithm is that it can be used to handle nonlinear BLPPs with nondifferentiable leader's objective functions. Finally, simulations on 31 benchmark problems, 12 of which have nondifferentiable leader's objective functions, are made and the results demonstrate the effectiveness of the proposed algorithm.
182 citations
Authors
Showing all 32362 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Jie Zhang | 178 | 4857 | 221720 |
Bin Wang | 126 | 2226 | 74364 |
Huijun Gao | 121 | 685 | 44399 |
Hong Wang | 110 | 1633 | 51811 |
Jian Zhang | 107 | 3064 | 69715 |
Guozhong Cao | 104 | 694 | 41625 |
Lajos Hanzo | 101 | 2040 | 54380 |
Witold Pedrycz | 101 | 1766 | 58203 |
Lei Liu | 98 | 2041 | 51163 |
Qi Tian | 96 | 1030 | 41010 |
Wei Liu | 96 | 1538 | 42459 |
MengChu Zhou | 96 | 1124 | 36969 |
Chunying Chen | 94 | 508 | 30110 |
Daniel W. C. Ho | 85 | 360 | 21429 |