A phase-sensitive optical time domain reflectometer （φ-OTDR） based on a 120°-phase-difference Michelson in- terferometer is proposed. The Michelson interferometer with arm difference of 4m is used to test the ...A phase-sensitive optical time domain reflectometer （φ-OTDR） based on a 120°-phase-difference Michelson in- terferometer is proposed. The Michelson interferometer with arm difference of 4m is used to test the phase difference between the Rayleigh scattering from two sections of the fiber. A new demodulation method called the inverse transmission matrix demodulation scheme is utilized to demodulate the distributed phase from the backward scattering along the long fiber, The experimental results show that the 120°-phase-difference inter- ferometer φ-OTDR can detect the phase along the 3km fiber, and the acoustic signal within the whole human hearing range of 20 Hz-20 kHz is reproduced accurately and quickly.展开更多
Two time-domain reflectometry (TDR) systems and a new impedance measuring instrument, Thetaprobe,which are based on determination of soil dielectric constant, were used to measure water content of clayeyred soil to er...Two time-domain reflectometry (TDR) systems and a new impedance measuring instrument, Thetaprobe,which are based on determination of soil dielectric constant, were used to measure water content of clayeyred soil to eraluate the accuracy of these instruments. The results indicated that these instruments shouldbe carefUlly re-calibrated before being applied in clayey red soil. With a new calibration curve fed into one ofthe TDR systems tested, nase system, the measured data compared well with tho8e by standard oven-dryingmethod.展开更多
The phase-sensitive time-domain reflectometer [φ-OTDR] has been popularly used for events detection over a long period of time.In this study,the events classification methods based on convolutional neural networks [C...The phase-sensitive time-domain reflectometer [φ-OTDR] has been popularly used for events detection over a long period of time.In this study,the events classification methods based on convolutional neural networks [CNNs] with different features,i.e.,the temporal-spatial features and time-frequency features,are compared and analyzed comprehensively inφ-OTDR.The developed CNNs aim at distinguishing three typical events:wind blowing,knocking,and background noise.The classification accuracy based on temporal-spatial images is higher than that based on time-frequency images[99.49% versus 98.23%).The work here sets a meaningful reference for feature extraction and application in the pattern recognition of φ-OTDR.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos U0934001 and 11076028the Science and Technology Commission of Shanghai Municipality under Grant Nos 11DZ1140202 and 13XD1425400the Pudong New Area Science and Technology Development Fund of China under Grant No PKJ2012-D04
文摘A phase-sensitive optical time domain reflectometer （φ-OTDR） based on a 120°-phase-difference Michelson in- terferometer is proposed. The Michelson interferometer with arm difference of 4m is used to test the phase difference between the Rayleigh scattering from two sections of the fiber. A new demodulation method called the inverse transmission matrix demodulation scheme is utilized to demodulate the distributed phase from the backward scattering along the long fiber, The experimental results show that the 120°-phase-difference inter- ferometer φ-OTDR can detect the phase along the 3km fiber, and the acoustic signal within the whole human hearing range of 20 Hz-20 kHz is reproduced accurately and quickly.
文摘Two time-domain reflectometry (TDR) systems and a new impedance measuring instrument, Thetaprobe,which are based on determination of soil dielectric constant, were used to measure water content of clayeyred soil to eraluate the accuracy of these instruments. The results indicated that these instruments shouldbe carefUlly re-calibrated before being applied in clayey red soil. With a new calibration curve fed into one ofthe TDR systems tested, nase system, the measured data compared well with tho8e by standard oven-dryingmethod.
基金supported by the Fundamental Research Funds for the Central Universities(No.FRF-TP-20-067A1Z)。
文摘The phase-sensitive time-domain reflectometer [φ-OTDR] has been popularly used for events detection over a long period of time.In this study,the events classification methods based on convolutional neural networks [CNNs] with different features,i.e.,the temporal-spatial features and time-frequency features,are compared and analyzed comprehensively inφ-OTDR.The developed CNNs aim at distinguishing three typical events:wind blowing,knocking,and background noise.The classification accuracy based on temporal-spatial images is higher than that based on time-frequency images[99.49% versus 98.23%).The work here sets a meaningful reference for feature extraction and application in the pattern recognition of φ-OTDR.
