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.

Unique ion rectification in hypersaline environment: A high-performance and sustainable power generator system

Abstract: The development of membrane science plays a fundamental role in harvesting osmotic power, which is considered a future clean and renewable energy source. However, the existing designs of the membrane cannot handle the low conversion efficiency and power density. Theory has predicted that the Janus membrane with ionic diode–type current would be the most efficient material. Therefore, rectified ionic transportation in a hypersaline environment (the salt concentration is at least 0.5 M in sea) is highly desired, but it still remains a challenge. Here, we demonstrate a versatile strategy for creating a scale-up Janus three-dimensional (3D) porous membrane–based osmotic power generator system. Janus membranes with tunable surface charge density and porosity were obtained by compounding two kinds of ionomers. Under electric fields or chemical gradients, the Janus membrane has ionic current rectification properties and anion selectivities in a hypersaline environment. Experiments and theoretical calculation demonstrate that abundant surface charge and narrow pore size distribution benefit this unique ionic transport behavior in high salt solution. Thus, the output power density of this membrane-based generator reaches 2.66 W/m2 (mixing seawater and river water) and up to 5.10 W/m2 at a 500-fold salinity gradient (i.e., flowing salt lake into river water). Furthermore, a generator, built by connecting a series of membranes, could power a calculator for 120 hours without obvious current decline, proving the excellent physical and chemical stabilities. Therefore, we believe that this work advances the fundamental understanding of fluid transport and materials design as a paradigm for a high-performance energy conversion generator.

Pairing symmetry of interacting fermions on a twisted bilayer graphene superlattice

Abstract: The pairing symmetry of the Hubbard Hamiltonian on a triangle lattice with a nearly flat low-energy band is studied with the determinant quantum Monte Carlo method (DQMC). We show that the low-temperature phase is insulating at half-filling, even for relatively weak interactions. The natures of the spin and pairing correlations upon doping are determined, and they exhibit an electron-hole asymmetry. Among the pairing symmetries allowed, we demonstrate that the dominating channels are $d$ wave, opening the possibility of condensation into an unconventional ${d}_{{x}^{2}\ensuremath{-}{y}^{2}}+i{d}_{xy}$ phase, which is characterized by an integer topological invariant and gapless edge states. The results are closely related to the correlated insulating phase and unconventional superconductivity discovered recently in twisted bilayer graphene.

End-to-End Hand Mesh Recovery From a Monocular RGB Image

Abstract: In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image. In contrast to existing research on 2D or 3D hand pose estimation from RGB or/and depth image data, HAMR can provide a more expressive and useful mesh representation for monocular hand image understanding. In particular, the mesh representation is achieved by parameterizing a generic 3D hand model with shape and relative 3D joint angles. By utilizing this mesh representation, we can easily compute the 3D joint locations via linear interpolations between the vertexes of the mesh, while obtain the 2D joint locations with a projection of the 3D joints. To this end, a differentiable re-projection loss can be defined in terms of the derived representations and the ground-truth labels, thus making our framework end-to-end trainable. Qualitative experiments show that our framework is capable of recovering appealing 3D hand mesh even in the presence of severe occlusions. Quantitatively, our approach also outperforms the state-of-the-art methods for both 2D and 3D hand pose estimation from a monocular RGB image on several benchmark datasets.

Rare Earth Single-Atom Catalysts for Nitrogen and Carbon Dioxide Reduction.

Abstract: Single-atom catalysts (SACs) have attracted much attention owning to their high catalytic properties. Herein, yttrium and scandium rare earth SACs are successfully synthesized on a carbon support (Y1/NC and Sc1/NC). Different from the well-known M-N4 structure of M-N-C (M = Fe, Co) catalysts, Sc and Y atoms with a large atomic radius tend to be anchored to the large-sized carbon defects through six coordination bonds of nitrogen and carbon. Although Y- and Sc-based nanomaterials are generally inactive to room-temperature electrochemical reactions, Y1/NC and Sc1/NC SACs exhibit catalytic activities to nitrogen reduction reaction and carbon dioxide reduction reaction due to the modulation of the local electronic structure of Y/Sc single atoms by N and C coordination. The catalytic functions of rare earth single atoms not only demonstrate the magical effect of SACs but also promote the application of rare earth catalysts in room-temperature electrochemical reactions.

Depression recognition based on dynamic facial and vocal expression features using partial least square regression

Abstract: Depression is a typical mood disorder, and the persons who are often in this state face the risk in mental and even physical problems. In recent years, there has therefore been increasing attention in machine based depression analysis. In such a low mood, both the facial expression and voice of human beings appear different from the ones in normal states. This paper presents a novel method, which comprehensively models visual and vocal modalities, and automatically predicts the scale of depression. On one hand, Motion History Histogram (MHH) extracts the dynamics from corresponding video and audio data to represent characteristics of subtle changes in facial and vocal expression of depression. On the other hand, for each modality, the Partial Least Square (PLS) regression algorithm is applied to learn the relationship between the dynamic features and depression scales using training data, and then predict the depression scale for an unseen one. Predicted values of visual and vocal clues are further combined at decision level for final decision. The proposed approach is evaluated on the AVEC2013 dataset and experimental results clearly highlight its effectiveness and better performance than baseline results provided by the AVEC2013 challenge organiser.