High-sensitivity sensors represent a critical frontier in modern sensing technology,driving innovations across fields such as biomedical monitoring,precision instrumentation,environmental detection,and indus-trial aut...High-sensitivity sensors represent a critical frontier in modern sensing technology,driving innovations across fields such as biomedical monitoring,precision instrumentation,environmental detection,and indus-trial automation.As demands for accuracy,miniaturization,and reliability continue to grow,developing novel sensor architectures and functional materials has become essential to achieving enhanced performance under extreme or complex conditions.展开更多
This paper presents a comprehensive analysis of the photometric system of the University of Chinese Academy of Sciences 70 cm Telescope located at the Yan-qi Lake campus of the University of Chinese Academy of Science...This paper presents a comprehensive analysis of the photometric system of the University of Chinese Academy of Sciences 70 cm Telescope located at the Yan-qi Lake campus of the University of Chinese Academy of Sciences.We evaluated the linearity,bias stability,and dark current of the camera.Utilizing the Johnson-Cousins Blue-Visible-Red-Infrared filter system and an Andor DZ936 charge-coupled device camera,we conducted extensive observations of Landolt standard stars to determine the color terms,atmospheric extinction coefficients,photometric zero-points,and the sky background brightness.The results indicate that this telescope demonstrates excellent performance in photometric calibration and good system performance overall,meeting the requirements for limited scientific research and teaching purposes.展开更多
针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题,以傅里叶-贝塞尔级数展开(Fourier-Bessel series expansion,FBSE)为基础,提出了一种将FBSE和基于能量的尺度空间经验小波变换(energy scale space empirica...针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题,以傅里叶-贝塞尔级数展开(Fourier-Bessel series expansion,FBSE)为基础,提出了一种将FBSE和基于能量的尺度空间经验小波变换(energy scale space empirical wavelet transform,ESEWT)相结合的齿轮振动信号降噪方法,即FBSE-ESEWT。首先,将采集到的齿轮振动信号利用FBSE技术获得其频谱,以替代传统的傅里叶谱,接着凭借能量尺度空间划分法对获取的FBSE频谱进行自适应分割和筛选,以精确定位有效频带的边界点。随后通过构建小波滤波器组得到信号分量并进行重构,以减小噪声和冗余信息干扰;然后,为捕捉到更全面的特征信息将处理后的信号进行广义S变换得到时频图,输入2D卷积神经网络进行故障诊断验证算法可行性。通过对Simulink仿真信号和实际采集信号进行实验,结果表明,相对于原始经验小波变换(EWT)、经验模态分解(EMD)等方法,FBSE-ESEWT具有更好的降噪效果,信噪比提高了13.96 dB,诊断准确率高达98.03%。展开更多
文摘High-sensitivity sensors represent a critical frontier in modern sensing technology,driving innovations across fields such as biomedical monitoring,precision instrumentation,environmental detection,and indus-trial automation.As demands for accuracy,miniaturization,and reliability continue to grow,developing novel sensor architectures and functional materials has become essential to achieving enhanced performance under extreme or complex conditions.
基金supported by National Key R&D Program of China(2023YFA1609700)Research and Education Integration Funding。
文摘This paper presents a comprehensive analysis of the photometric system of the University of Chinese Academy of Sciences 70 cm Telescope located at the Yan-qi Lake campus of the University of Chinese Academy of Sciences.We evaluated the linearity,bias stability,and dark current of the camera.Utilizing the Johnson-Cousins Blue-Visible-Red-Infrared filter system and an Andor DZ936 charge-coupled device camera,we conducted extensive observations of Landolt standard stars to determine the color terms,atmospheric extinction coefficients,photometric zero-points,and the sky background brightness.The results indicate that this telescope demonstrates excellent performance in photometric calibration and good system performance overall,meeting the requirements for limited scientific research and teaching purposes.
文摘针对齿轮故障诊断中采集到的振动信号常伴有噪声干扰且故障特征难以提取的问题,以傅里叶-贝塞尔级数展开(Fourier-Bessel series expansion,FBSE)为基础,提出了一种将FBSE和基于能量的尺度空间经验小波变换(energy scale space empirical wavelet transform,ESEWT)相结合的齿轮振动信号降噪方法,即FBSE-ESEWT。首先,将采集到的齿轮振动信号利用FBSE技术获得其频谱,以替代传统的傅里叶谱,接着凭借能量尺度空间划分法对获取的FBSE频谱进行自适应分割和筛选,以精确定位有效频带的边界点。随后通过构建小波滤波器组得到信号分量并进行重构,以减小噪声和冗余信息干扰;然后,为捕捉到更全面的特征信息将处理后的信号进行广义S变换得到时频图,输入2D卷积神经网络进行故障诊断验证算法可行性。通过对Simulink仿真信号和实际采集信号进行实验,结果表明,相对于原始经验小波变换(EWT)、经验模态分解(EMD)等方法,FBSE-ESEWT具有更好的降噪效果,信噪比提高了13.96 dB,诊断准确率高达98.03%。
