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中国物理B（英文版）
中国物理B（英文版）

欧阳钟灿

月刊

1674-1056

010-82649026 82649519

100080

北京603信箱

中国物理B（英文版）/Journal Chinese Physics　BCSCDCSTPCD北大核心EISCI
查看更多>>该刊与《物理学报》是中国物理学会主办的物理学英文和中文的综合性国际学术月刊。刊登物理学科领域中，国内外未曾公开发表的具有创新性的科学研究最新成果。内容包括物理学各领域的理论、实验技术及应用。两刊内容不重复。两刊以论文水平高、创新性强，发表速度快的特点，受到国内外物理学工作者的好评和关注。被国际著名的SCI等10种以上检索系统收录。曾多次被评为中国科学院优秀期刊一等奖，1999，2003，2005年荣获第一、第二和第三届国家期刊奖，2001年被国家新闻出版总署评为“中国期刊方阵”中的双高（高知名度、高学术水平）期刊。2001，2002，2003年两刊都评为百种中国杰出期刊。
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    Unveiling the in-plane anisotropic dielectric waveguide modes inα-MoO3 flakes

    廖莹陈佳宁
    630-636页
    查看更多>>摘要：The unique in-plane and out-of-plane anisotropy of α-MoO3 has attracted considerable interest with regard to poten-tial optoelectronic applications.However,most research has focused on the mid-infrared spectrum,leaving its properties and applications in the visible and near-infrared light spectrum less explored.This study advances the understanding of waveguiding properties of α-MoO3 by near-field imaging of the waveguide modes along the[100]and[001]directions ofα-MoO3 flakes at 633 nm and 785 nm.We investigate the effects of flake thickness and documented the modes'dispersion relationships,which is crucial for tailoring the optical responses of α-MoO3 in device applications.Our findings enhance the field of research into α-MoO3,highlighting its utility in fabricating next-generation optoelectronic devices due to its unique optically anisotropic waveguide.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

    赵旭杜雪成熊旭马超...
    637-644页
    查看更多>>摘要：Convolutional neural networks(CNNs)exhibit excellent performance in the areas of image recognition and object detection,which can enhance the intelligence level of spacecraft.However,in aerospace,energetic particles,such as heavy ions,protons,and alpha particles,can induce single event effects(SEEs)that lead CNNs to malfunction and can significantly impact the reliability of a CNN system.In this paper,the MNIST CNN system was constructed based on a 28 nm system-on-chip(SoC),and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system.Various types of soft errors in the CNN system have been detected,and the SEE cross sections have been calculated.Furthermore,the mechanisms behind some soft errors have been explained.This research will provide technical support for the design of radiation-resistant artificial intelligence chips.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Model-driven CT reconstruction algorithm for nano-resolution x-ray phase contrast imaging

    谭雨航蔡学宝杨杰成苏婷...
    645-650页
    查看更多>>摘要：The low-density imaging performance of a zone plate-based nano-resolution hard x-ray computed tomography(CT)system can be significantly improved by incorporating a grating-based Lau interferometer.Due to the diffraction,however,the acquired nano-resolution phase signal may suffer splitting problem,which impedes the direct reconstruction of phase contrast CT(nPCT)images.To overcome,a new model-driven nPCT image reconstruction algorithm is developed in this study.In it,the diffraction procedure is mathematically modeled into a matrix B,from which the projections without signal splitting can be generated invertedly.Furthermore,a penalized weighted least-square model with total variation(PWLS-TV)is employed to denoise these projections,from which nPCT images with high accuracy are directly reconstructed.Numerical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances.Moreover,results also reveal that nPCT images of higher signal-to-noise-ratio(SNR)could be reconstructed from projections having larger splitting distances.In summary,a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer-based hard x-ray nano-resolution phase contrast imaging.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    WT-FCTGN:A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

    廖志芳孙轲刘文龙余志武...
    651-664页
    查看更多>>摘要：Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system.However,the traffic data's complexity and fluctuation,as well as the noise produced during collecting information and summarizing original data of traffic flow,cause large errors in the traffic flow forecasting results.This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN).To eliminate the potential noise and strengthen the potential traffic trend in the data,we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data.The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic,and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow.The performance of the WT-FCTGN model is validated on the public PeMS data set.The experimental results show that the WT-FCTGN model has higher accuracy,and its mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)are obviously lower than those of the other algorithms.The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
      原文链接:
    • NETL
    • NSTL
    • 万方数据