The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ...The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.展开更多
The limited penetration of photons in biological tissue restricts the deep-tissue detection and imaging application.The micro-scale spatially offset Raman spectroscopy(micro-SORS)with an optical fiber probe,colleting ...The limited penetration of photons in biological tissue restricts the deep-tissue detection and imaging application.The micro-scale spatially offset Raman spectroscopy(micro-SORS)with an optical fiber probe,colleting photons from deeper regions by offsetting the position of laser excitation from the collection optics in a range of hundreds of microns,shows great potential to be integrated with endoscopy for inside-body noninvasive detection by circumventing this restric-tion,particularly with the combination of surface-enhanced Raman spectroscopy(SERS).However,a detailed tissue penetration study of micro-SORS in combination with SERS is still lacking.Herein,we compared the signal decay of enhanced Raman nanotags through the tissue phantom of agarose gel and the biological tissue of porcine muscle in the near-infrared(NIR)region using a portable Raman spectrometer with a micro-SORS probe(2.1 mm in diameter)and a conventional hand-held probe(9.7mm in diameter).Two kinds of Raman nanotags were prepared from gold nanorods decorated with the nonresonant(4-nitrobenzenethiol)or resonant Raman reporter molecules(IR-780 iodide).The SERS measurements show that the penetration depths of two Raman nanotags are both over 2 cm in agarose gel and 3 mm in porcine muscle.The depth could be improved to over 4 cm in agarose gel and 5 mm in porcine tissue when using the micro-SORS system.This demonstrates the superiority of optical-fiber micro-SORS system over the conventional Raman detection for the detection of nanotags in deeper layers in the turbid medium and biological tissue,offering the possibility of combining the micro-SORS technique with SERS for noninvasive in vivo endoscopy-integrated clinical application.展开更多
As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering du...As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo e Xtreme(MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant No.52178100).
文摘The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.
基金This work was financially supported by National Natural Science Foundation of China(Nos.81871401 and 81901786)China Postdoctoral Science Foundation(Nos.2018M640395 and 2019T120343)+3 种基金the Science and Technology Commission of Shanghai Municipality(No.19441905300)Innovation Research Plan supported by Shanghai Municipal Education Commission(No.ZXWF082101)Shanghai Jiao Tong University(Nos.YG2017MS54 and YG2019QNA28)the Shanghai Key Laboratory of Gynecologic Oncology.
文摘The limited penetration of photons in biological tissue restricts the deep-tissue detection and imaging application.The micro-scale spatially offset Raman spectroscopy(micro-SORS)with an optical fiber probe,colleting photons from deeper regions by offsetting the position of laser excitation from the collection optics in a range of hundreds of microns,shows great potential to be integrated with endoscopy for inside-body noninvasive detection by circumventing this restric-tion,particularly with the combination of surface-enhanced Raman spectroscopy(SERS).However,a detailed tissue penetration study of micro-SORS in combination with SERS is still lacking.Herein,we compared the signal decay of enhanced Raman nanotags through the tissue phantom of agarose gel and the biological tissue of porcine muscle in the near-infrared(NIR)region using a portable Raman spectrometer with a micro-SORS probe(2.1 mm in diameter)and a conventional hand-held probe(9.7mm in diameter).Two kinds of Raman nanotags were prepared from gold nanorods decorated with the nonresonant(4-nitrobenzenethiol)or resonant Raman reporter molecules(IR-780 iodide).The SERS measurements show that the penetration depths of two Raman nanotags are both over 2 cm in agarose gel and 3 mm in porcine muscle.The depth could be improved to over 4 cm in agarose gel and 5 mm in porcine tissue when using the micro-SORS system.This demonstrates the superiority of optical-fiber micro-SORS system over the conventional Raman detection for the detection of nanotags in deeper layers in the turbid medium and biological tissue,offering the possibility of combining the micro-SORS technique with SERS for noninvasive in vivo endoscopy-integrated clinical application.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61911530695)the Key Research and Development Project of Shaanxi Province, China (Grant No. 2023-YBSF-671)。
文摘As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo e Xtreme(MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.
