Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. 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.

Equivalence of digital image correlation criteria for pattern matching.

Abstract: In digital image correlation (DIC), to obtain the displacements of each point of interest, a correlation criterion must be predefined to evaluate the similarity between the reference subset and the target subset. The correlation criterion is of fundamental importance in DIC, and various correlation criteria have been designed and used in literature. However, little research has been carried out to investigate their relations. In this paper, we first provide a comprehensive overview of various correlation criteria used in DIC. Then we focus on three robust and most widely used correlation criteria, i.e., a zero-mean normalized cross-correlation (ZNCC) criterion, a zero-mean normalized sum of squared difference (ZNSSD) criterion, and a parametric sum of squared difference (PSSD(ab)) criterion with two additional unknown parameters, since they are insensitive to the scale and offset changes of the target subset intensity and have been highly recommended for practical use in literature. The three correlation criteria are analyzed to establish their transversal relationships, and the theoretical analyses clearly indicate that the three correlation criteria are actually equivalent, which elegantly unifies these correlation criteria for pattern matching. Finally, the equivalence of these correlation criteria is further validated by numerical simulation and actual experiment.

Valence Band Modification and High Thermoelectric Performance in SnTe Heavily Alloyed with MnTe

Abstract: We demonstrate a high solubility limit of >9 mol% for MnTe alloying in SnTe. The electrical conductivity of SnTe decreases gradually while the Seebeck coefficient increases remarkably with increasing MnTe content, leading to enhanced power factors. The room-temperature Seebeck coefficients of Mn-doped SnTe are significantly higher than those predicted by theoretical Pisarenko plots for pure SnTe, indicating a modified band structure. The high-temperature Hall data of Sn1–xMnxTe show strong temperature dependence, suggestive of a two-valence-band conduction behavior. Moreover, the peak temperature of the Hall plot of Sn1–xMnxTe shifts toward lower temperature as MnTe content is increased, which is clear evidence of decreased energy separation (band convergence) between the two valence bands. The first-principles electronic structure calculations based on density functional theory also support this point. The higher doping fraction (>9%) of Mn in comparison with ∼3% for Cd and Hg in SnTe gives rise to a muc...

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Abstract: Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Pentaquark and Tetraquark states

Abstract: The past seventeen years have witnessed tremendous progress on the experimental and theoretical explorations of the multiquark states. The hidden-charm and hidden-bottom multiquark systems were reviewed extensively in [Phys. Rept. 639 (2016) 1-121]. In this article, we shall update the experimental and theoretical efforts on the hidden heavy flavor multiquark systems in the past three years. Especially the LHCb collaboration not only confirmed the existence of the hidden-charm pentaquarks but also provided strong evidence of the molecular picture. Besides the well-known $XYZ$ and $P_c$ states, we shall discuss more interesting tetraquark and pentaquark systems either with one, two, three or even four heavy quarks. Some very intriguing states include the fully heavy exotic tetraquark states $QQ\bar Q\bar Q$ and doubly heavy tetraquark states $QQ\bar q \bar q$, where $Q$ is a heavy quark. The $QQ\bar Q\bar Q$ states may be produced at LHC while the $QQ\bar q \bar q$ system may be searched for at BelleII and LHCb. Moreover, we shall pay special attention to various theoretical schemes. We shall emphasize the model-independent predictions of various models which are truly/closely related to Quantum Chromodynamics (QCD). There have also accumulated many lattice QCD simulations through multiple channel scattering on the lattice in recent years, which provide deep insights into the underlying structure/dynamics of the $XYZ$ states. In terms of the recent $P_c$ states, the lattice simulations of the charmed baryon and anti-charmed meson scattering are badly needed. We shall also discuss some important states which may be searched for at BESIII, BelleII and LHCb in the coming years.