University of Electronic Science and Technology of China

Sichuan University

Yao Xiao

Jun Li

Renquan Wang

Ying Liu

Hongxing Chen

Yunjiang Rao

Huijuan Wu

Xiangrong Liu

Zheng Wang

Jiping Chen

Haoyu Qiu

Mengjiao Wang

Xinlin Geng

Zonglin Ye

Bo Tang

Qian Xie

Mingru Yang

Disclosed are an anisotropic nanocrystalline rare earth permanent magnet and a preparation method thereof. The rare earth permanent magnet includes an RE-Fe—B matrix phase and a second phase, wherein the RE-Fe—B matrix phase includes main phase REFeB flaky nanocrystallines regularly arranged and an RE-rich phase around main phase grains, the main phase REFeB flaky nanocrystallines having an average grain size in a length direction of 70 nm to 800 nm and an average grain size in a thickness direction of 30 nm to 200 nm; and the second phase includes at least one selected from the group consisting of an M-Cu phase and an M-Cu—O phase, M being at least one selected from the group consisting of Ca and Mg.

Anisotropic nanocrystalline rare earth permanent magnet and preparation method thereof

Disclosed are a method for preparing an anisotropic flake nanocrystalline rare earth permanent magnet material and a rare earth permanent magnet material. In the disclosure, the method includes: step 1, preparation of a precursor flake nanocrystal magnetic powder, where a number of grains of flake nanocrystals inside the precursor flake nanocrystalline magnetic powder accounts for not less than 85% of a total number of grains inside the precursor flake nanocrystalline magnetic powder; step 2, orientation treatment by hot deformation: subjecting the precursor flake nanocrystalline magnetic powder or a green body prepared from the precursor flake nanocrystalline magnetic powder to the hot deformation, such that flake nanocrystals are regularly arranged; and step 3, post-processing for optimizing orientation.

Method for preparing anisotropic flake nanocrystalline rare earth permanent magnet material and rare earth permanent magnet material

The disclosure relates to mixed analog-digital circuits, and more specifically a low-noise millimeter-wave fractional-N frequency synthesizer. It overcomes quantization noise and fractional spurs caused by the limited dynamic range and nonlinearity of time error amplifiers (TA) in traditional phase-locked loop structures based on TA. In addition to the traditional structure, the synthesizer includes a coarse digital-to-time converter (CDTC), a fine digital-to-time converter (FDTC), and DTC non-linearity calibration circuits. By inserting the CDTC and FDTC before and after the TA, respectively, the variance of the input phase difference of the TA can be reduced, thereby improving the TA linearity and suppressing the quantization noise and spur generated by fractional-N operation. Furthermore, by using non-linearity calibration, the non-linearity of DTC and TA can be compensated to avoid large quantization noise and spur while the second order quantization noise reshaping is maintained. Furthermore, a high-gain TA can increase the resolution of the FDTC.

Low-noise millimeter-wave fractional-N frequency synthesizer

A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features. The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.

1D-CNN-based distributed optical fiber sensing signal feature learning and classification method

Growing community of inventors

Sichuan, China

Yao Xiao