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.

DeepHuman: 3D Human Reconstruction From a Single Image

Abstract: We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image. To reduce the ambiguities associated with the reconstruction of invisible areas, our method leverages a dense semantic representation generated from SMPL model as an additional input. One key feature of our network is that it fuses different scales of image features into the 3D space through volumetric feature transformation, which helps to recover accurate surface geometry. The surface details are further refined through a normal refinement network, which can be concatenated with the volume generation network using our proposed volumetric normal projection layer. We also contribute THuman, a 3D real-world human model dataset containing approximately 7000 models. The network is trained using training data generated from the dataset. Overall, due to the specific design of our network and the diversity in our dataset, our method enables 3D human model estimation given only a single image and outperforms state-of-the-art approaches.

An Optimal Charging Station Location Model With the Consideration of Electric Vehicle's Driving Range

Abstract: Reasonable charging station positions are critical to prompt the widespread use of electric vehicles (EVs). This paper proposes a bi-level programming model with the consideration of EV’s driving range, for finding the optimal locations of charging stations. In this model, the upper level is to optimize the position of charging stations so as to maximize the path flows that use the charging stations, while the user equilibrium of route choice with the EV’s driving range constraint is formulated in the lower level. In order to find the optimal solution of the model efficiently, we reformulate the proposed model as a single-level mathematical program and further linearize it in designing the heuristic algorithm. The model validity is demonstrated with numerical examples on two test networks. It is shown that the vehicle’s driving range has a great influence on the optimal charging station locations.

Reducing Complexity of HEVC: A Deep Learning Approach

Abstract: High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the preceding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra-and inter-modes, which is based on convolutional neural network (CNN) and long-and short-term memory (LSTM) network. First, we establish a large-scale database including substantial CU partition data for HEVC intra-and inter-modes. This enables deep learning on the CU partition. Second, we represent the CU partition of an entire coding tree unit (CTU) in the form of a hierarchical CU partition map (HCPM). Then, we propose an early-terminated hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the encoding complexity of intra-mode HEVC can be drastically reduced by replacing the brute-force search with ETH-CNN to decide the CU partition. Third, an early-terminated hierarchical LSTM (ETH-LSTM) is proposed to learn the temporal correlation of the CU partition. Then, we combine ETH-LSTM and ETH-CNN to predict the CU partition for reducing the HEVC complexity at inter-mode. Finally, experimental results show that our approach outperforms other state-of-the-art approaches in reducing the HEVC complexity at both intra-and inter-modes.

Remarkable Roles of Cu To Synergistically Optimize Phonon and Carrier Transport in n-Type PbTe-Cu2Te

Abstract: High thermoelectric performance of n-type PbTe is urgently needed to match its p-type counterpart. Here, we show a peak ZT ∼ 1.5 at 723 K and a record high average ZT > 1.0 at 300–873 K realized in n-type PbTe by synergistically suppressing lattice thermal conductivity and enhancing carrier mobility by introducing Cu2Te inclusions. Cu performs several outstanding roles: Cu atoms fill the Pb vacancies and improve carrier mobility, contributing to an unexpectedly high power factor of ∼37 μW cm–1 K–2 at 423 K; Cu atoms filling Pb vacancies and Cu interstitials both induce local disorder and, together with nano- and microscale Cu-rich precipitates and their related strain fields, lead to a very low lattice thermal conductivity of ∼0.38 Wm–1 K–1 in PbTe-5.5%Cu2Te, approaching the theoretical minimum value of ∼0.36 Wm–1 K–1. This work provides an effective strategy to enhance thermoelectric performance by simultaneously improving electrical and thermal transport properties.