Yingbin Li | Searcharxiv

arXiv2024

Helicity-selected near-circularly polarized attosecond pulses generated from mixed He-Ne gases

We present and theoretically demonstrate a method for generating helicity-selected near-circularly polarized attosecond pulses in mixed He-Ne gases using bichromatic counter-rotating circularly polarized (BCCP) fields. High-order harmonics driven by BCCP fields exhibit circular polarization for individual orders in the frequency domain, but adjacent orders have opposite helicities. By utilizing the He-Ne mixture, we select only one helical component of the harmonics, resulting in the generation of highly elliptically polarized attosecond pulses in the time domain. Our analyses based on the quantum-orbit theory and the strong field approximation further clarify that the polarization of attosecond pulses is governed by the interference mechanism of high-order harmonics emitted by He and Ne. This combination of BCCP fields and an atomic mixture which requires no alignment in experiments, significantly simplifies the generation of elliptically polarized harmonics dominated by one helical component, thereby paving the way for an efficient and robust method to generate bright attosecond pulses with large ellipticity.

physics.optics↗

arXiv2019

Recognition Of Surface Defects On Steel Sheet Using Transfer Learning

Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation, where there are few data available to train and confine to a certain scene. Therefore, in this paper, a new approach is proposed which consists of part of pretrained VGG16 as a feature extractor and a new CNN neural network as a classifier to recognize the defect of steel strip surface based on the feature maps created by the feature extractor. Our method achieves an accuracy of 99.1% and 96.0% while the dataset contains 150 images each class and 10 images each class respectively, which is much better than previous methods.

cs.CV↗