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.

Short-term breakdown and long-term failure in nanodielectrics: a review

Abstract: Nanodielectrics, which are concentrated in polymer matrix incorporating nanofillers, have received considerable attention due to their potential benefits as dielectrics. In this paper, short-term breakdown and long-term failure properties of nanodielectrics have been reviewed. The characteristics of polymer matrix, types of nanoparticle and its content, and waveforms of the applied voltage are fully evaluated. In order to effectively comment on the published experimental data, a ratio k has been proposed to compare the electric properties of the nanodielectrics with the matrix and assess the effect for nanoparticles doping. There is evidence that the short-term breakdown properties of nanodielectrics show a strong dependence on the applied voltage waveforms. The polarity and the cohesive energy density (CED) of polymer matrix have a dramatic influence on the properties of nanodielectrics. Nanoparticle doped composites show a positive effect on the long-term failure properties, such as ageing resistance and partial discharge (PD) properties of nanocomposites are superior than microcomposites and the matrix. The larger the dielectric constant and CED of the matrix become, the more significant improvements in long-term performance appear. Based on the reported experimental results, we also present our understandings and propose some suggestions for further work.

An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition

Abstract: Video semantic recognition usually suffers from the curse of dimensionality and the absence of enough high-quality labeled instances, thus semisupervised feature selection gains increasing attentions for its efficiency and comprehensibility. Most of the previous methods assume that videos with close distance (neighbors) have similar labels and characterize the intrinsic local structure through a predetermined graph of both labeled and unlabeled data. However, besides the parameter tuning problem underlying the construction of the graph, the affinity measurement in the original feature space usually suffers from the curse of dimensionality. Additionally, the predetermined graph separates itself from the procedure of feature selection, which might lead to downgraded performance for video semantic recognition. In this paper, we exploit a novel semisupervised feature selection method from a new perspective. The primary assumption underlying our model is that the instances with similar labels should have a larger probability of being neighbors. Instead of using a predetermined similarity graph, we incorporate the exploration of the local structure into the procedure of joint feature selection so as to learn the optimal graph simultaneously. Moreover, an adaptive loss function is exploited to measure the label fitness, which significantly enhances model’s robustness to videos with a small or substantial loss. We propose an efficient alternating optimization algorithm to solve the proposed challenging problem, together with analyses on its convergence and computational complexity in theory. Finally, extensive experimental results on benchmark datasets illustrate the effectiveness and superiority of the proposed approach on video semantic recognition related tasks.