Inorganic perovskite quantum dots CsPbX3 (X = Cl, Br, and I) has recently received extensive attention as a new promising class of X‐ray scintillators. However, relatively low light yield (LY) of CsPbX3 and strong optical scattering of the thick opaque scintillator film restrict their practical applications for high‐resolution X‐ray microscopic imaging. Here, the Ce3+ ion doped CsPbBr3 nanocrystals (NCs) with enhanced LY and stability are obtained and then the ultrathin (30 µm) and transparent scintillator films with high density are prepared by a suction filtration method. The small amount Ce3+ dopant greatly enhances the LY of CsPbBr3 NCs (about 33 000 photons per MeV), which is much higher than that of bare CsPbBr3 NCs. Moreover, the scintillator films made by these NCs with high density realize a high spatial resolution of 862 nm thanks to its thin and transparent feature, which is so far a record resolution for perovskite scintillator‐based X‐ray microscopic imaging. This strategy not only provides a simple way to increase the resolution down to nanoscale but also extends the application of as‐prepared CsPbBr3 scintillator for high resolution X‐ray microscopic imaging.

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The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.

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mechanical and electronic engineering, Shandong University of science and technology.I have presided over or participated in more than 30 research projects and published more than 50 academic papers, of which 30 were searched by SCI and EI, and applied for more than 60 national patents, ofwhich more than 20 were invention patents.