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.

EEMD method and WNN for fault diagnosis of locomotive roller bearings

Abstract: Research highlights? EEMD and WNN are combined to propose an automated fault diagnosis method. ? Features are extracted from the sensitive IMF of EEMD in this method. ? The features are fed into WNN to identify the bearing health conditions. ? The method can identify the fault severities and the compound faults. The ensemble empirical mode decomposition (EEMD) can overcome the mode mixing problem of the empirical mode decomposition (EMD) and therefore provide more precise decomposition results. Wavelet neural network (WNN) possesses the advantages of both wavelet transform and artificial neural networks. This paper combines the merits of EEMD and WNN to propose an automated and effective fault diagnosis method of locomotive roller bearings. First, the vibration signals captured from the locomotive roller bearings are preprocessed by EEMD method and intrinsic mode functions (IMFs) are produced. Second, a kurtosis based method is presented and used to select the sensitive IMF. Third, time- and frequency-domain features are extracted from the sensitive IMF, its frequency spectrum and its envelope spectrum. Finally, these features are fed into WNN to identify the bearing health conditions. The diagnosis results show that the proposed method enables the identification of the single faults in the bearings and at the same time the recognition of the fault severities and the compound faults.

Fine-tuning of the chemical structure of photoactive materials for highly efficient organic photovoltaics

Abstract: The performance of organic photovoltaics is largely dependent on the balance of short-circuit current density (JSC) and open-circuit voltage (VOC). For instance, the reduction of the active materials’ optical bandgap, which increases the JSC, would inevitably lead to a concomitant reduction in VOC. Here, we demonstrate that careful tuning of the chemical structure of photoactive materials can enhance both JSC and VOC simultaneously. Non-fullerene organic photovoltaics based on a well-matched materials combination exhibit a certified high power conversion efficiency of 12.25% on a device area of 1 cm2. By combining Fourier-transform photocurrent spectroscopy and electroluminescence, we show the existence of a low but non-negligible charge transfer state as the possible origin of VOC loss. This study highlights that the reduction of the bandgap to improve the efficiency requires a careful materials design to minimize non-radiative VOC losses. Materials design rules play a key role in enabling high performance in organic photovoltaics. Here the authors achieve 12.25% efficiency on 1 cm2 non-fullerene solar cells by tuning the side chains’ branching point and the fluorine substitutions in donor and acceptor materials.

Group Consistent Similarity Learning via Deep CRF for Person Re-identification

Abstract: Person re-identification benefits greatly from deep neural networks (DNN) to learn accurate similarity metrics and robust feature embeddings. However, most of the current methods impose only local constraints for similarity learning. In this paper, we incorporate constraints on large image groups by combining the CRF with deep neural networks. The proposed method aims to learn the "local similarity" metrics for image pairs while taking into account the dependencies from all the images in a group, forming "group similarities". Our method involves multiple images to model the relationships among the local and global similarities in a unified CRF during training, while combines multi-scale local similarities as the predicted similarity in testing. We adopt an approximate inference scheme for estimating the group similarity, enabling end-to-end training. Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities. The overall results outperform those by state-of-the-arts with considerable margins on three widely-used benchmarks.

Energy-Efficient Transmission Design in Non-orthogonal Multiple Access

Abstract: Non-orthogonal multiple access (NOMA) is considered as a promising technology for improving the spectral efficiency in fifth-generation systems. In this correspondence, we study the benefit of NOMA in enhancing energy efficiency (EE) for a multiuser downlink transmission, wherein the EE is defined as the ratio of the achievable sum rate of the users to the total power consumption. Our goal is to maximize EE subject to a minimum required data rate for each user, which leads to a nonconvex fractional programming problem. To solve it, we first establish the feasible range of the transmitting power that is able to support each user's data rate requirement. Then, we propose an EE-optimal power allocation strategy that maximizes EE. Our numerical results show that NOMA has superior EE performance in comparison with conventional orthogonal multiple access.