Institution
Beihang University
Education•Beijing, 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.
Topics: Control theory, Microstructure, Nonlinear system, Artificial neural network, Feature extraction
Papers published on a yearly basis
Papers
More filters
••
TL;DR: This paper proves that a global optimal solution can be found in a convex subset of the original feasible region for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short.
Abstract: In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.
166 citations
••
TL;DR: An overview of the studies on several ice accretion effects on aircraft flight dynamics is presented in this paper, where special attention is paid to the following areas: ways to obtain the aerodynamic data of iced aircraft, flight dynamic modeling and simulation for the aircraft, effects of ice accumulation on aircraft stability and control as well as on flight performance and aircraft icing envelope protection and control adaption.
166 citations
••
TL;DR: A database collecting subjects’ HM in panoramic video sequences is established, and it is found that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions.
Abstract: Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects’ HM in panoramic video sequences. From this database, we find that the HM data are highly consistent across subjects. Furthermore, we find that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Based on our findings, we propose a DRL-based HM prediction (DHP) approach with offline and online versions, called offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to determine potential HM positions at each panoramic frame. Then, a heat map of the potential HM positions, named the HM map, is generated as the output of offline-DHP. In online-DHP, the next HM position of one subject is estimated given the currently observed HM position, which is achieved by developing a DRL algorithm upon the learned offline-DHP model. Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.
166 citations
••
TL;DR: In this paper, the BESIII detector operating at the BEPCII storage ring at center-of-mass energies from 4.009 to 4.420 GeV is observed for the first time with a statistical significance of 6.3 sigma.
Abstract: With data samples collected with the BESIII detector operating at the BEPCII storage ring at center-of-mass energies from 4.009 to 4.420 GeV, the process e(+)e(-) -> gamma X(3872) is observed for the first time with a statistical significance of 6.3 sigma. The measured mass of the X(3872) is (3871.9 +/- 0.7(stat) +/- 0.2(syst)) MeV/c(2), in agreement with previous measurements. Measurements of the product of the cross section sigma[e(+)e(-) -> gamma X(3872)] and the branching fraction B [X(3872) -> pi(+)pi(-)J/psi] at center-of-mass energies 4.009, 4.229, 4.260, and 4.360 GeV are reported. Our measurements are consistent with expectations for the radiative transition process Y(4260) -> gamma X(3872).
166 citations
•
TL;DR: Two strategies for the adaption of Mixup on sentence classification are proposed: one performs interpolation on word embeddings and another on sentence embedDings, and both serve as an effective, domain independent data augmentation approach for sentence classification.
Abstract: Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification. However, how this technique can be applied to and what is its effectiveness on natural language processing (NLP) tasks have not been investigated. In this paper, we propose two strategies for the adaption of Mixup on sentence classification: one performs interpolation on word embeddings and another on sentence embeddings. We conduct experiments to evaluate our methods using several benchmark datasets. Our studies show that such interpolation strategies serve as an effective, domain independent data augmentation approach for sentence classification, and can result in significant accuracy improvement for both CNN and LSTM models.
166 citations
Authors
Showing all 67500 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
H. S. Chen | 179 | 2401 | 178529 |
Alan J. Heeger | 171 | 913 | 147492 |
Lei Jiang | 170 | 2244 | 135205 |
Wei Li | 158 | 1855 | 124748 |
Shu-Hong Yu | 144 | 799 | 70853 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Igor Katkov | 125 | 972 | 71845 |
Tao Zhang | 123 | 2772 | 83866 |
Nicholas A. Kotov | 123 | 574 | 55210 |
Shi Xue Dou | 122 | 2028 | 74031 |
Li Yuan | 121 | 948 | 67074 |
Robert O. Ritchie | 120 | 659 | 54692 |
Haiyan Wang | 119 | 1674 | 86091 |