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Institution

Beihang University

EducationBeijing, China
About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigated the relationship between the built environment and travel mode choice behavior by using integrated structural equation model (SEM) and discrete choice model (DCM).
Abstract: Though there is a growing literature on the connection between the built environment and travel behavior, limited efforts have been made to consider the intermediary nature of car ownership and travel distance simultaneously while modeling the relationship between the built environment and travel mode choice behavior. The mediating effects from car ownership and travel distance, as an important piece, are not sufficiently investigated. To fill this gap, in this study the relationships among travel mode choice, car ownership and travel distance were described using a framework of integrated structural equation model (SEM) and discrete choice model (DCM). Drawing on a rich dataset of National Household Travel Survey (NHTS) and numerous built environment measurements in Baltimore metropolitan area, this research applied the integrated SEM and DCM approach to investigate how the built environment affects travel mode choice through influencing car ownership and travel distance. Therefore, the direct and indirect effects of built environment on travel mode choice were revealed. This study hopes to give transportation planners a better understanding on how the built environment influences travel mode choice, and consequently develop effective and targeted countermeasures to reduce car use.

232 citations

Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper provide a comprehensive review of salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction.
Abstract: Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.

232 citations

Journal ArticleDOI
TL;DR: In this paper, a simple, easy-to-implement yet effective high-temperature digital image correlation (DIC) method is established for non-contact full-field deformation measurement at elevated temperatures.
Abstract: A simple, easy-to-implement yet effective high-temperature digital image correlation (DIC) method is established for non-contact full-field deformation measurement at elevated temperatures. The technique employs a bandpass optical filter to eliminate the influence of black-body radiation of high-temperature objects on the intensity of captured images. With the bandpass filter, high-quality digital images of an object at high temperatures up to 1200 °C can be easily acquired and directly compared with the reference image recorded at room temperature using the DIC technique to extract full-field deformation information with high fidelity. To verify the performance of the proposed technique, a chromium-nickel austenite stainless steel sample was heated from room temperature to 1200 °C using an infrared heating device, and the surface images at various temperatures were captured using the bandpass filter imaging system. Afterwards, full-field thermal deformation and coefficient of thermal expansion of the sample were determined using the DIC technique. Experimental results indicate that the proposed high-temperature DIC method is easy to implement and can be applied to practical full-field high-temperature deformation measurement with high accuracy.

232 citations

Journal ArticleDOI
TL;DR: This work proposes an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features and empirically evaluates the method for action recognition in videos shows that it outperforms the state-of-the-art methods.
Abstract: 3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112 $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion , which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).

231 citations

Journal ArticleDOI
TL;DR: A Jacobi-collocation spectral method is developed for Volterra integral equations of second kind with a weakly singular kernel so that the solution of the new equation possesses better regularity and the Jacobi orthogonal polynomial theory can be applied conveniently.
Abstract: In this paper, a Jacobi-collocation spectral method is developed for Volterra integral equations of second kind with a weakly singular kernel. We use some function transformation and variable transformations to change the equation into a new Volterra integral equation deflned on the standard interval (i1;1), so that the solution of the new equation possesses better regularity and the Jacobi orthogonal polynomial theory can be applied conveniently. In order to obtain high order accuracy for the approx- imation, the integral term in the resulting equation is approximated by using Jacobi spectral quadrature rules. The convergence analysis of this novel method is based on the Lebesgue constants corresponding to the Lagrange interpolation polynomials, poly- nomials approximation theory for orthogonal polynomials and the operator theory. The spectral rate of convergence for the proposed method is established in the L 1 -norm and weighted L 2 -norm. Numerical results are presented to demonstrate the efiectiveness of the proposed method.

231 citations


Authors

Showing all 67500 results

NameH-indexPapersCitations
Yi Chen2174342293080
H. S. Chen1792401178529
Alan J. Heeger171913147492
Lei Jiang1702244135205
Wei Li1581855124748
Shu-Hong Yu14479970853
Jian Zhou128300791402
Chao Zhang127311984711
Igor Katkov12597271845
Tao Zhang123277283866
Nicholas A. Kotov12357455210
Shi Xue Dou122202874031
Li Yuan12194867074
Robert O. Ritchie12065954692
Haiyan Wang119167486091
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023205
20221,178
20216,767
20206,916
20197,080