Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Multiphase lattice Boltzmann simulations for porous media applications : a review

Abstract: Over the last two decades, lattice Boltzmann methods have become an increasingly popular tool to compute the flow in complex geometries such as porous media. In addition to single phase simulations allowing, for example, a precise quantification of the permeability of a porous sample, a number of extensions to the lattice Boltzmann method are available which allow to study multiphase and multicomponent flows on a pore scale level. In this article we give an extensive overview on a number of these diffuse interface models and discuss their advantages and disadvantages. Furthermore, we shortly report on multiphase flows containing solid particles, as well as implementation details and optimization issues.

A Wide-Bandgap Donor Polymer for Highly Efficient Non-fullerene Organic Solar Cells with a Small Voltage Loss

Abstract: To achieve efficient non-fullerene organic solar cells, it is important to reduce the voltage loss from the optical bandgap to the open-circuit voltage of the cell. Here we report a highly efficient non-fullerene organic solar cell with a high open-circuit voltage of 1.08 V and a small voltage loss of 0.55 V. The high performance was enabled by a novel wide-bandgap (2.05 eV) donor polymer paired with a narrow-bandgap (1.63 eV) small-molecular acceptor (SMA). Our morphology characterizations show that both the polymer and the SMA can maintain high crystallinity in the blend film, resulting in crystalline and small domains. As a result, our non-fullerene organic solar cells realize an efficiency of 11.6%, which is the best performance for a non-fullerene organic solar cell with such a small voltage loss.

Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning

Abstract: In this paper, we focus on tackling the problem of automatic semantic annotation of high resolution (HR) optical satellite images, which aims to assign one or several predefined semantic concepts to an image according to its content. The main challenges arise from the difficulty of characterizing complex and ambiguous contents of the satellite images and the high human labor cost caused by preparing a large amount of training examples with high-quality pixel-level labels in fully supervised annotation methods. To address these challenges, we propose a unified annotation framework by combining discriminative high-level feature learning and weakly supervised feature transferring. Specifically, an efficient stacked discriminative sparse autoencoder (SDSAE) is first proposed to learn high-level features on an auxiliary satellite image data set for the land-use classification task. Inspired by the motivation that the encoder of the prelearned SDSAE can be regarded as a generic high-level feature extractor for HR optical satellite images, we then transfer the learned high-level features to semantic annotation. To compensate the difference between the auxiliary data set and the annotation data set, the transferred high-level features are further fine-tuned in a weakly supervised scheme by using the tile-level annotated training data. Finally, the fine-tuning process is formulated as an ultimate optimization problem, which can be solved efficiently with our proposed alternate iterative optimization method. Comprehensive experiments on a publicly available land-use classification data set and an annotation data set demonstrate the superiority of our SDSAE-based high-level feature learning method and the effectiveness of our weakly supervised semantic annotation framework compared with state-of-the-art fully supervised annotation methods.