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.

Three-Bladed Rylene Propellers with Three-Dimensional Network Assembly for Organic Electronics.

Abstract: Two kinds of conjugated C3-symmetric perylene dyes, namely, triperylene hexaimides (TPH) and selenium-annulated triperylene hexaimides (TPH-Se), are efficiently synthesized. Both TPH and TPH-Se have broad and strong absorption in the region 300–600 nm together with suitable LUMO levels of about −3.8 eV. Single-crystal X-ray diffraction studies show that TPH displays an extremely twisted three-bladed propeller configuration and a unique 3D network assembly in which three PBI subunits in one TPH molecule have strong π–π intermolecular interactions with PBI subunits in neighboring molecules. The integration of selenophene to TPH endows TPH-Se with a more distorted propeller configuration and a more compact 3D network assembly due to the Se···O interactions. A single-crystal transistor confirms that both TPH and TPH-Se possess good electron-transport ability. TPH and TPH-Se acceptor-based solar cells show high power conversion efficiency of 8.28% and 9.28%, respectively, which mainly results from the combined...

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

Abstract: The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. The exponential model is one of the most widely used methods for RUL prediction of rolling element bearings. However, two shortcomings exist in the exponential model: 1) the first predicting time (FPT) is selected subjectively; and 2) random errors of the stochastic process decrease the prediction accuracy. To deal with these two shortcomings, an improved exponential model is proposed in this paper. In the improved model, an adaptive FPT selection approach is established based on the $3\sigma$ interval, and particle filtering is utilized to reduce random errors of the stochastic process. In order to demonstrate the effectiveness of the improved model, a simulation and four tests of bearing degradation processes are utilized for the RUL prediction. The results show that the improved model is able to select an appropriate FPT and reduce random errors of the stochastic process. Consequently, it performs better in the RUL prediction of rolling element bearings than the original exponential model.

Multi-Scale Boosted Dehazing Network With Dense Feature Fusion

Abstract: In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles, boosting and error feedback, and we show that they are suitable for the dehazing problem. By incorporating the Strengthen-Operate-Subtract boosting strategy in the decoder of the proposed model, we develop a simple yet effective boosted decoder to progressively restore the haze-free image. To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme. We show that the dense feature fusion module can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features. Extensive evaluations demonstrate that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.