RESUMO
Dielectric capacitors have become an important component in current pulsed power devices and thus have attracted great research interest in recent years. Among all kinds of dielectric materials, the bismuth ferrite (BiFeO3)-based ceramic capacitors show possible applications in dielectric energy storage because of their large polarization. However, the relatively high conductivity badly limits the improvement of electric breakdown strength, thus leading to low energy density. Herein, the perovskite end-member La(Mg2/3Nb1/3)O3 and sintering aid MnO2 were simultaneously introduced into BiFeO3-SrTiO3 solid solutions to improve the relaxation features and electric breakdown strength. Accordingly, a high recoverable energy density of 6.3 J/cm3 and an acceptable efficiency of 74.3% were realized under 450 kV/cm. In addition, the good frequency/thermal stability and superior charge-discharge performances were also realized. This work provides feasible approaches to modify the capacitive energy storage of BiFeO3-based relaxor ferroelectric ceramics.
RESUMO
Owing to the merits of giant power density and ultrafast charge-discharge time, dielectric capacitors including ceramics and films have inspired increasing interest lately. Nevertheless, the energy storage density of dielectric ceramics should be further optimized to cater to the boosting demand for the compact and portable electronic devices. Herein, an ultrahigh recoverable energy storage density Wrec of 13.44 J/cm3 and a high efficiency η of 90.14% are simultaneously realized in BiFeO3-BaTiO3-NaTaO3 relaxor ferroelectric ceramics with high polarization Pmax, reduced remanent polarization Pr, and optimized electric breakdown strength Eb. High Pmax originates from the genes of BiFeO3-based ceramics, and reduced Pr is induced by enhanced relaxor behavior. Particularly, a large Eb is achieved by the synergic contributions from complicated internal and external factors, such as decreased grain size and improved resistivity and electrical homogeneity. Furthermore, the ceramics also exhibit satisfactory frequency, cycling and thermal reliability, and decent charge-discharge property. This work not only indicates that the BiFeO3-based relaxor ferroelectric materials are promising choices for the next-generation electrostatic capacitors but also paves a potential approach to exploit novel high-performance dielectric ceramics.
RESUMO
PURPOSE: Diabetic retinopathy (DR) has become the leading cause of blindness worldwide. In clinical practice, the detection of DR often takes a lot of time and effort for ophthalmologist. It is necessary to develop an automatic assistant diagnosis method based on medical image analysis techniques. METHODS: Firstly, we design a feature enhanced attention module to capture focus lesions and regions. Secondly, we propose a stage sampling strategy to solve the problem of data imbalance on datasets and avoid the CNN ignoring the focus features of samples that account for small parts. Finally, we treat DR detection as a regression task to keep the gradual change characteristics of lesions and output the final classification results through the optimization method on the validation set. RESULTS: Extensive experiments are conducted on open-source datasets. Our methods achieve 0.851 quadratic weighted kappa which outperforms first place in the Kaggle DR detection competition based on the EyePACS dataset and get the accuracy of 0.914 in the referable/non-referable task and 0.913 in the normal/abnormal task based on the Messidor dataset. CONCLUSION: In this paper, we propose three novel automatic DR detection methods based on deep convolutional neural networks. The results illustrate that our methods can obtain comparable performance compared with previous methods and generate visualization pictures with potential lesions for doctors and patients.
