Pb0.3Sr0.7TiO3(PST) and Bi1.5Zn0.5Nb0.5Ti1.5O7 (BZNT) thin films were deposited on Pt/Ti/SiO2/Si substrates by Sol-gel and radio frequency (rf) magnetron sputtering, respectively. The dielectric and tunable properties of thin films were investigated as a function of heat treatment processes. It’s found that the heat treatment process at the appropriate temperature and time can be used to obtain a good thin film. The film has the best comprehensive dielectric properties by annealing at 650°C for 45min, the figure of merit (FOM) is 20.1.

Smart grid is developing rapidly with the promotion of artificial intelligence (AI) technology, and online condition assessment of circuit breaker (CB), as one of the most important facility in power system, is increasingly requested. Owing to most of CB faults can be reflected on its coil current (CC), lots of papers research on the relationship between CC data and CB fault. This paper proposed a new method to get distinctive CC feature set, which achieves better data distinction and has more practical physical meaning. The classification experiments based on the new feature set show that the proposed method can quickly and accurately recognize fault, meanwhile point out the malfunctioning device.

Bi1.5Zn0.5Nb0.5Ti1.5O7 (BZNT) thin films were deposited on Pt/Ti/SiO2/Si substrates by radio frequency (rf) magnetron sputtering. In this paper, by studying the phase structure, surface morphology, and dielectric properties of BZNT films, it is found that by increasing the initial temperature in the annealing process, the film formation quality, internal stress and dielectric properties of the film can be improved. and the best performance of the BZNT film is obtained under the annealing process at the initial temperature of 500°C. The tuning amount (Tu), Dielectric loss (Loss) and quality factor (FOM) are: 13.55 percent, 0.00298 and 45.46, respectively.

The reliability of feature matching is required for accurate remote sensing image registration. Outliers reduce the accuracy and effectiveness of feature matching. This paper proposes a novel feature matching method that utilizes the global geometric relationship of feature points in two images to eliminate outliers and preserve inliers. A mathematical model is formulated based on the similarity of the geometric relationship of feature points in the reference image and the sensed image. We also find the optimization solution through analysis and simplification of the mathematical model. The corresponding feature matching algorithm based on outlier removal is proposed according to the optimization solution. The experimental results of several remote sensing images demonstrate that our method can preserve more inliers, remove more outliers and obtain a better registration performance with higher accuracy and robustness than the state-of-the-art methods, such as SIFT, SIFT-RANSAC, SIFT-GTM, SIFT-LPM. INDEX TERMS Remote sensing, image registration, feature matching, outlier removal, mathematical model.