Sparse Fast Fourier Transform and its application in intelligent diagnosis system of train rolling bearing

Healthy status monitoring of train bearing online is very meaningful work. But as traditional diagnosis system does, performing Fourier spectrum with the datum from more than 200 bearings in a marshalling train is an enormous challenge. Here a healthy status monitoring system of train rolling bearing based on Sparse Fast Fourier Transform (SFFT) is proposed. The monitoring system consists two sequential parts. First, extract fault features based on SFFT spectrum and other time-domain parameters. According to train bearing working environment, altogether 7 fault features are extracted in this paper. Another part is constructing a classifier based on BP neural network. Experimental results show that the system proposed here achieves gratifying results comparing with traditional fault diagnosis syste

Deep Neural Network Representation of Density Functional Theory Hamiltonian

The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern research of material science. Here we study the crucial problem of representing DFT Hamiltonian for crystalline materials of arbitrary configurations via deep neural network. A general framework is proposed to deal with the infinite dimensionality and covariance transformation of DFT Hamiltonian matrix in virtue of locality and use message passing neural network together with graph representation for deep learning. Our example study on graphene-based systems demonstrates that high accuracy ($\sim$meV) and good transferability can be obtained for DFT Hamiltonian, ensuring accurate predictions of materials properties without DFT. The Deep Hamiltonian method provides a solution to the accuracy-efficiency dilemma of DFT and opens new opportunities to explore large-scale materials and physics.Comment: 5 pages, 4 figure

Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction

ChatGPT has demonstrated impressive performance in various downstream tasks. However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics. We believe this inconsistency arises because the traditional metrics are not well-suited for evaluating generative models. Their overly strict length and phonics constraints may lead to underestimating ChatGPT's correction capabilities. To better evaluate generative models in the CSC task, this paper proposes a new evaluation metric: Eval-GCSC. By incorporating word-level and semantic similarity judgments, it relaxes the stringent length and phonics constraints. Experimental results show that Eval-GCSC closely aligns with human evaluations. Under this metric, ChatGPT's performance is comparable to traditional token-level classification models (TCM), demonstrating its potential as a CSC tool. The source code and scripts can be accessed at https://github.com/ktlKTL/Eval-GCSC

ボールミリング法で改質したβ-TCPセメントの諸特性への粉液比の影響

The authors have developed a β-tricalcium-phosphate (β-TCP) powder modified mechano-chemically through the application of a ball-milling process (mβ-TCP). The resulting powder can be used in a calcium-phosphate-cement (CPC). In this study, the effects of the powder-to-liquid ratio (P/L ratio) on the properties of the CPCs were investigated, and an appropriate P/L ratio that would simultaneously improve injectability and strength was clarified. The mβ-TCP cement mixed at a P/L ratio of 2.5 and set in air exhibited sufficient injectability until 20 min after mixing, and strength similar to or higher than that mixed at a P/L ratio of 2.0 and 2.78. Although the mβ-TCP cements set in vivo and in SBF were found to exhibit a lower strength than those set in air, it did have an appropriate setting time and strength for clinical applications. In conclusion, P/L ratio optimization successfully improved the strength of injectable mβ-TCP cement

Enhanced Intervalley Scattering of Twisted Bilayer Graphene by Periodic AB Stacked Atoms

The electronic properties of twisted bilayer graphene on SiC substrate were studied via combination of transport measurements and scanning tunneling microscopy. We report the observation of enhanced intervalley scattering from one Dirac cone to the other, which contributes to weak localization, of the twisted bilayer graphene by increasing the interlayer coupling strength. Our experiment and analysis demonstrate that the enhanced intervalley scattering is closely related to the periodic AB stacked atoms (the A atom of layer 1 and the B atom of layer 2 that have the same horizontal positions) that break the sublattice degeneracy of graphene locally. We further show that these periodic AB stacked atoms affect intervalley but not intravalley scattering. The result reported here provides an effective way to atomically manipulate the intervalley scattering of graphene.Comment: 4figure

The Origin of the Prompt Emission for Short GRB 170817A: Photosphere Emission or Synchrotron Emission?

The first gravitational-wave event from the merger of a binary neutron star system (GW170817) was detected recently. The associated short gamma-ray burst (GRB 170817A) has a low isotropic luminosity (~1047 erg s−1) and a peak energy E p ~ 145 keV during the initial main emission between −0.3 and 0.4 s. The origin of this short GRB is still under debate, but a plausible interpretation is that it is due to the off-axis emission from a structured jet. We consider two possibilities. First, since the best-fit spectral model for the main pulse of GRB 170817A is a cutoff power law with a hard low-energy photon index ($\alpha =-{0.62}_{-0.54}^{+0.49}$), we consider an off-axis photosphere model. We develop a theory of photosphere emission in a structured jet and find that such a model can reproduce a low-energy photon index that is softer than a blackbody through enhancing high-latitude emission. The model can naturally account for the observed spectrum. The best-fit Lorentz factor along the line of sight is ~20, which demands that there is a significant delay between the merger and jet launching. Alternatively, we consider that the emission is produced via synchrotron radiation in an optically thin region in an expanding jet with decreasing magnetic fields. This model does not require a delay of jet launching but demands a larger bulk Lorentz factor along the line of sight. We perform Markov Chain Monte Carlo fitting to the data within the framework of both models and obtain good fitting results in both cases