Institution
National University of Defense Technology
Education•Changsha, China•
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.
Topics: Computer science, Radar, Laser, Synthetic aperture radar, Fiber laser
Papers published on a yearly basis
Papers
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TL;DR: In this article, a 2D finite-difference time-domain (FDTD) method was used to calculate the induced voltages on overhead horizontal conductors without employing the Cooray-Rubinstein formula.
Abstract: In this paper, the lightning-generated electromagnetic fields over lossy ground produced by lightning strikes either to flat ground or to a tall tower are calculated using the 2-D finite-difference time-domain (FDTD) method. The resultant horizontal and vertical electric fields are used as forcing functions in the discretized Agrawal electromagnetic coupling equations for the calculation of induced voltages on overhead horizontal conductors without employing the Cooray-Rubinstein formula. Comparison of the results with those obtained using the 3-D FDTD method and with experimental data found in the literature is used to test the validity of the examined method. The approach employed here generally provides sufficient accuracy while allowing significant reduction in computation time and storage requirements as compared to the 3-D FDTD method. From the analysis carried out in this paper, induced voltages appear to be strongly dependent on ground conductivity, somewhat influenced by return-stroke speed, and essentially independent of return-stroke model [transmission-line (TL), modified transmission line with linear current decay with height (MTLL), or modified transmission line with exponential current decaywith height (MTLE)].
89 citations
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TL;DR: In this paper, two new features from differential response signal are proposed to classify different types of defects combined with rising time, one is called as crossing time; the other is differential time to peak.
Abstract: Pulsed eddy current (PEC) testing is a new emerging and effective electromagnetic non-destructive testing (NDT) technique. The main purpose of this study is to identify surface defects and sub-surface defects using features-based rectangular pulsed eddy current sensor. The further study of PEC rectangular sensor proposed in author's previous work has been made to classify the different types of defects in specimen. In different directions of sensor scanning, peak waves of pick-up coil are studied. We find that when sensor is on different position against the defect, peak waves of response signals present the same shape in direction of magnetic induction flux, while present different shapes in direction of exciting current. Experiment results have shown that the different classes of defects can be identified and classified effectively by selecting the rising time as the time domain feature in both directions. For improving the performance of defect classification, two new features from differential response signal are proposed to classify different types of defects combined with rising time. One is called as crossing time; the other is differential time to peak. The blind test is carried out and the results show that the new features are effective to classify the defects.
89 citations
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TL;DR: A novel face representation approach (GLL) which fuses Gabor filter, Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) to fully explore the blur invariant property and the information in the spatial domain and among different scales and orientations is proposed.
89 citations
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10 Aug 2015TL;DR: This paper is the first to propose a SDN-based routing framework for efficiently message propagation in VANET and demonstrates, through the simulation results, that the proposed framework significantly outperforms the related protocols in terms of both delivery delay time and routing overhead.
Abstract: Vehicular Ad hoc Network (VANET) is an intermittently connected mobile network in which message propagation is quite challenging. Conventional routing protocols proposed for VANET are usually in greedy or optimum fashion. Geographical forwarding only uses local information to make the routing decision which may lead to long packet delay, while link-based forwarding has better performance but requires much more overheads. To disseminate message efficiently in VANET, a routing protocol which has both short delivery delay time and low routing overhead is required. In this paper, we proposed a SDN-based routing framework for efficiently message propagation in VANET. Software-Defined Networking (SDN) is an emerging technology that decouples the control plane from the data forwarding plane in switches and collects all the control planes into a central controller. In SDN-based routing framework, the central controller gathers network information from switches and computes optimal routing paths for switches based on the global network information. Since switches don’t need to exchange routing information with each other, the routing overhead is much lower. This paper is the first to propose a SDN-based routing framework for efficiently message propagation in VANET. A new algorithm is developed to find the global optimal route from the source to the destination in VANET with dynamic network density. We demonstrate, through the simulation results, that our proposed framework significantly outperforms the related protocols in terms of both delivery delay time and routing overhead.
89 citations
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TL;DR: A dynamic core interests mapping (DCIM) algorithm, which jointly considers the users' social network structures and users' article content to identify users over platforms and is significantly more effective than baseline methods such as FNN and MAG.
89 citations
Authors
Showing all 39659 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rui Zhang | 151 | 2625 | 107917 |
Jian Li | 133 | 2863 | 87131 |
Chi Lin | 125 | 1313 | 102710 |
Wei Xu | 103 | 1492 | 49624 |
Lei Liu | 98 | 2041 | 51163 |
Xiang Li | 97 | 1472 | 42301 |
Chang Liu | 97 | 1099 | 39573 |
Jian Huang | 97 | 1189 | 40362 |
Tao Wang | 97 | 2720 | 55280 |
Wei Liu | 96 | 1538 | 42459 |
Jian Chen | 96 | 1718 | 52917 |
Wei Wang | 95 | 3544 | 59660 |
Peng Li | 95 | 1548 | 45198 |
Jianhong Wu | 93 | 726 | 36427 |
Jianhua Zhang | 92 | 415 | 28085 |