Inspired by the microstructures found in animal cilia,we have developed,to our knowledge,a novel microcontact force sensing unit utilizing a fiber Bragg grating(FBG)as the fundamental platform.Employing polydimethylsi...Inspired by the microstructures found in animal cilia,we have developed,to our knowledge,a novel microcontact force sensing unit utilizing a fiber Bragg grating(FBG)as the fundamental platform.Employing polydimethylsiloxane(PDMS)as the biomimetic material,we have fabricated cilia-like structures onto the grating substrate.These structures enable precise detection of micro-contact forces through induced shifts in the gratings’wavelength.This integration leads to a comprehensive micro-sensing system,combining fiber optic Bragg gratings and biomimetic cilia,facilitating precise micro-contact force measurements.Additionally,our system showcases versatile functionalities including Morse code input and Braille recognition,underscoring its multifunctional utility,thus heralding new frontiers in applications such as intelligent AI recognition,assistance for the visually impaired,and meticulous surface analysis.展开更多
Since high efficiency and zero-carbon emission,hydrogen,as a clean energy carrier,is potentially an alternative fuel.Unfortunately,hydrogen is a gas with a high diffusion coefficient,wide explosion limit,and low ignit...Since high efficiency and zero-carbon emission,hydrogen,as a clean energy carrier,is potentially an alternative fuel.Unfortunately,hydrogen is a gas with a high diffusion coefficient,wide explosion limit,and low ignition energy.Thus,to ensure the safe use of hydrogen,accurate and rapid monitoring of hydrogen leakage and abnormal concentration change must be addressed immediately,which is a critical scientific and technical problem.Therefore,we propose an optics-mechanics coupling fiber hydrogen sensor without electricity-related hazard factors.This proposed fiber hydrogen sensor is constructed by combining optics-mechanics coupling,specific adsorption of hydrogen to the surface of palladium(Pd),and Fabry-Pérot(F-P)interference mechanism;the optics-mechanics coupling is aroused by hydrogen-induced stress in the suspended Pd film,which functions as an F-P resonator mirror and a hydrogen-sensitive material.According to this configuration and principle,we achieve efficient and high-selective hydrogen detection at room temperature.This optics-mechanics coupling-based fiber hydrogen sensor is characterized by the high sensitivity(0.397 nm/1%),extensive dynamic range(0.5%–3.5%),8 s response time,and 16 s recovery time.Hence,as an intrinsically safe hydrogen sensor with the high sensitivity and quick response,this optics-mechanics coupling-based fiber hydrogen sensor can be widely used in the hydrogen energy industry chain for rapid and high-performance hydrogen detection.展开更多
Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.Th...Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.展开更多
A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relax...A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.展开更多
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
The forward model of optical fiber strain induced by fractures,together with the associated model resolution matrix,is used to demonstrate the interpretability of fracture parameters once the fracture intersects the f...The forward model of optical fiber strain induced by fractures,together with the associated model resolution matrix,is used to demonstrate the interpretability of fracture parameters once the fracture intersects the fiber.A regularized inversion framework for fracture parameters is established to evaluate the influence of measured data quality on the accuracy of iterative regularized inversion.An interpretation approach for both fracture width and height is proposed,and the synthetic forward data with measurement error and field examples are employed to validate the accuracy of the simultaneous inversion of fracture width and height.The results indicate that,after the fracture contacts the fiber,the strain response is strongly sensitive only to the fracture parameters at the intersection location,whereas the interpretability of parameters at other locations remains limited.The iterative regularized inversion method effectively suppresses the impact of measurement error and exhibits high computational efficiency,showing clear advantages for inversion applications.When incorporating the first-order regularization with a Neumann boundary constraint on the tip width,the inverted fracture-width distribution becomes highly sensitive to fracture height;thus,combined with a bisection strategy,simultaneous inversion of fracture width and height can be achieved.Examination using the model resolution matrix,noisy synthetic data,and field data confirms that the iterative regularized inversion model for fracture width and height provides high interpretive accuracy and can be applied to the calculation and analysis of fracture width,fracture height,net pressure and other parameters.展开更多
Optical phase transfer via fiber optics is the most effective method for optical frequency standard comparison on the scale below thousands of kilometers.However,the monotonic phase discrimination range of conventiona...Optical phase transfer via fiber optics is the most effective method for optical frequency standard comparison on the scale below thousands of kilometers.However,the monotonic phase discrimination range of conventional optical phase-locked loops is limited,and link delays restrict the control bandwidth,which makes it a challenge to achieve a continuously reliable optical link.This paper presents an event-timing-based phase detection method that overcomes the monotonic phase discrimination range limitation of conventional phase-locked loops through dual-edge timestamp recording,achieving an optical phase measurement resolution on the order of 10 attoseconds.With such a technique,we established a 7-segment-cascaded optical link over 1402km of commercial fiber while sharing dense wavelength division multiplexing(DWDM)channels with live telecom traffic.The system maintained continuous operation for 11.7 days without phase cycle slips despite encountering 15 km aerial fiber noise up to 21000 rad^(2)·Hz^(−1)·km^(−1)at 1 Hz.Relative instabilities of the link are 3.7×10^(−15)at 1 s and 3.9×10^(−20)at 100000 s.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
The rotatory optics element in the tensor dielectric coefficient matrix is an important para-(meter) for analyzing and calculating a rotatory optical fiber by electromagnetic theory. But the mea-(surement) of rotatory...The rotatory optics element in the tensor dielectric coefficient matrix is an important para-(meter) for analyzing and calculating a rotatory optical fiber by electromagnetic theory. But the mea-(surement) of rotatory optics element is difficult for the rotatory optical fiber. A simple principle and method for measuring rotatory optics element are put forward in this paper. Firstly by using electromagnetic theory it was demonstrated that the rotatory optics element has a simple linear relation with the rotatory angle, and then the rotatory optics element has a simple linear relation with the magnetic field strength (or bias current in the helix coil) . Secondly a measurement system for the rotatory optics element in the rotatory optical fiber was designed. Using the measurement system the rotatory element can be obtained by measuring the bias current simply.展开更多
Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology...Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.展开更多
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contr...Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.展开更多
The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)...The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.展开更多
A temperature and acoustic impedance simultaneous sensor based on forward stimulated Brillouin scattering(FSBS)in highly nonlinear fiber(HNLF)with high sensitivity and high accuracy is proposed and demonstrated in thi...A temperature and acoustic impedance simultaneous sensor based on forward stimulated Brillouin scattering(FSBS)in highly nonlinear fiber(HNLF)with high sensitivity and high accuracy is proposed and demonstrated in this paper.High-order acoustic modes(HOAMs)are used to achieve individual or simultaneous measurement of the two parameters.Transverse acoustic waves(TAWs)involved in the FSBS process can efficiently sense the mechanical or environmental changes outside the fiber cladding,which will be reflected in a linear shift of the acoustic resonance frequency.By analyzing the frequencies of specific scattering peaks,the temperature and acoustic impedance outside the fiber cladding can be obtained simultaneously.The highest measured temperature and acoustic impedance sensitivities are 184.93 k Hz/℃and444.56 k Hz/MRayl,and the measurement accuracies are 0.09℃and 0.009 MRayl,respectively,which are both at desirable levels.We believe this work can provide potential application solutions for sensing fields involving temperature or acoustic impedance measurements.展开更多
Fiber optic temperature sensors stand out in a variety of applications due to their small size,chemical resistance,and resistance to electromagnetic interference.The traditional optical fiber temperature sensor direct...Fiber optic temperature sensors stand out in a variety of applications due to their small size,chemical resistance,and resistance to electromagnetic interference.The traditional optical fiber temperature sensor directly places the sensing structure in the temperature to be measured,and uses the thermo-optical effect and thermal expansion effect of the SiO_(2)material that constitutes the sensing structure to achieve measurement,while the thermo-optical coefficient and thermal expansion coefficient of SiO_(2) are very small,which limits the high sensitivity response characteristics of the optical fiber temperature sensing structure.In order to solve the problem of low sensitivity of traditional optical fiber temperature sensors,a Mach-Zehnder interferometric temperature sensor with a liquid-encapsulated tapered microfiber is developed.The sensor converts the temperature change into a change in the refractive index of the liquid material and thus realizes the measurement of temperature.In the range of 25~50℃,as the temperature increases,the wavelength of the transmission spectrum shifts towards shorter wavelengths.Experimental results show that the sensitivity of the liquid encapsulated microfiber interferometric temperature sensor can reach-57.91 nk·nm^(-1).This sensor has great potential for applications in marine environmental monitoring,biomedical diagnosis,and aerospace.展开更多
Optogenetic has been widely applied in various pathogenesis investigations of neuropathic diseases since its accurate and targeted regulation of neuronal activity.However,due to the mismatch between the soft tissues a...Optogenetic has been widely applied in various pathogenesis investigations of neuropathic diseases since its accurate and targeted regulation of neuronal activity.However,due to the mismatch between the soft tissues and the optical waveguide,the long-term neural regulation within soft tissue(such as brain and spinal cord)by implantable optical fibers is a large challenge.Herein,we designed a modulus selfadaptive hydrogel optical fiber(MSHOF)with tunable mechanical properties(Young’modulus was tunable in the range of 0.32-10.56MPa)and low light attenuation(0.12-0.21 dB/cm,472nm laser light),which adapts to light transmission under soft tissues.These advantages of MSHOF can ensure the effectiveness of optogenetic stimulation meanwhile safeguarding the safety of the brain/materials interaction interface.In addition,this work provides more design possibilities of MSHOF for photogenetic stimuli and has significant application prospects in photomedical therapy.展开更多
We propose a high-refractive-index(RI)sensor based on a no-core fiber(NCF)with a waist-enlarged fusion-taper(WEFT)structure,achieving high measurement accuracy with the assistance of the gated recurrent unit(GRU)neura...We propose a high-refractive-index(RI)sensor based on a no-core fiber(NCF)with a waist-enlarged fusion-taper(WEFT)structure,achieving high measurement accuracy with the assistance of the gated recurrent unit(GRU)neural network.This sensor integrates the NCF in series with single-mode fibers,forming the WEFT structure through arc discharge using a fiber fusion splicer to construct a modal interferometer.In the experiment,the proposed sensor has been used for high RI(ranging from 1.4330 to 1.4505)measurement.Due to the high RI being close to that of the optical fiber,traditional spectral interference dip demodulation produces nonlinear responses,increasing the measurement error in sensing.The GRU neural network algorithm is employed to train and test the recorded spectral samples,and the experimental results indicate that the coefficient of determination for this neural network model reaches 99.93%,with a mean squared error of 2.24×10-8(RIU).This deep learning model can be widely applied to similar fiber sensing applications and demonstrates significant potential for intelligent sensing within optical networks.展开更多
In this paper,a double-effect DNN-based Digital Back-Propagation(DBP)scheme is proposed and studied to achieve the Integrated Communication and Sensing(ICS)ability,which can not only realize nonlinear damage mitigatio...In this paper,a double-effect DNN-based Digital Back-Propagation(DBP)scheme is proposed and studied to achieve the Integrated Communication and Sensing(ICS)ability,which can not only realize nonlinear damage mitigation but also monitor the optical power and dispersion profile over multi-span links.The link status information can be extracted by the characteristics of the learned optical fiber parameters without any other measuring instruments.The efficiency and feasibility of this method have been investigated in different fiber link conditions,including various launch power,transmission distance,and the location and the amount of the abnormal losses.A good monitoring performance can be obtained while the launch optical power is 2 dBm which does not affect the normal operation of the optical communication system and the step size of DBP is 20 km which can provide a better distance resolution.This scheme successfully detects the location of single or multiple optical attenuators in long-distance multi-span fiber links,including different abnormal losses of 2 dB,4 dB,and 6 dB in 360 km and serval combinations of abnormal losses of(1 dB,5 dB),(3 dB,3 dB),(5 dB,1 dB)in 360 km and 760 km.Meanwhile,the transfer relationship of the estimated coefficient values with different step sizes is further investigated to reduce the complexity of the fiber nonlinear damage compensation.These results provide an attractive approach for precisely sensing the optical fiber link status information and making correct strategies timely to ensure optical communication system operations.展开更多
Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality c...Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality characteristics of SPOF limit their integration with traditional fiber optic sensors.This article introduces for the first time a flexible fiber optic vibration sensor based on laser interference technology,which can be applied to vibration measurement under high stretch conditions.This sensor utilizes elastic optical fibers made of polydimethylsiloxane(PDMS)as sensing elements,combined with phase generating carrier technology,to achieve vibration measurement at 50−260 Hz within the stretch range of 0−42%.展开更多
To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measureme...To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.展开更多
基金Fundamental Research Funds for the Central Universities(3072024XX2502)Basic Research Support Program for Excellent Young Teachers in Heilongjiang Province(YQJH2023313)。
文摘Inspired by the microstructures found in animal cilia,we have developed,to our knowledge,a novel microcontact force sensing unit utilizing a fiber Bragg grating(FBG)as the fundamental platform.Employing polydimethylsiloxane(PDMS)as the biomimetic material,we have fabricated cilia-like structures onto the grating substrate.These structures enable precise detection of micro-contact forces through induced shifts in the gratings’wavelength.This integration leads to a comprehensive micro-sensing system,combining fiber optic Bragg gratings and biomimetic cilia,facilitating precise micro-contact force measurements.Additionally,our system showcases versatile functionalities including Morse code input and Braille recognition,underscoring its multifunctional utility,thus heralding new frontiers in applications such as intelligent AI recognition,assistance for the visually impaired,and meticulous surface analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.62275039,62171076,and 61727816)Fundamental Research Funds for the Central Universities,China(Grant No.DUT24MS022)State Key Laboratory of Advanced Optical Communication Systems and Networks,China(Grant No.2022GZKF001).
文摘Since high efficiency and zero-carbon emission,hydrogen,as a clean energy carrier,is potentially an alternative fuel.Unfortunately,hydrogen is a gas with a high diffusion coefficient,wide explosion limit,and low ignition energy.Thus,to ensure the safe use of hydrogen,accurate and rapid monitoring of hydrogen leakage and abnormal concentration change must be addressed immediately,which is a critical scientific and technical problem.Therefore,we propose an optics-mechanics coupling fiber hydrogen sensor without electricity-related hazard factors.This proposed fiber hydrogen sensor is constructed by combining optics-mechanics coupling,specific adsorption of hydrogen to the surface of palladium(Pd),and Fabry-Pérot(F-P)interference mechanism;the optics-mechanics coupling is aroused by hydrogen-induced stress in the suspended Pd film,which functions as an F-P resonator mirror and a hydrogen-sensitive material.According to this configuration and principle,we achieve efficient and high-selective hydrogen detection at room temperature.This optics-mechanics coupling-based fiber hydrogen sensor is characterized by the high sensitivity(0.397 nm/1%),extensive dynamic range(0.5%–3.5%),8 s response time,and 16 s recovery time.Hence,as an intrinsically safe hydrogen sensor with the high sensitivity and quick response,this optics-mechanics coupling-based fiber hydrogen sensor can be widely used in the hydrogen energy industry chain for rapid and high-performance hydrogen detection.
文摘Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.
基金support of her postdoctoral research at the GFZ Helmholtz Centre for Geosciences.P.Pan acknowledges the financial support of the National Natural Science Foundation of China(Grant No.52339001)H.Hofmann and Y.Ji acknowledge the financial support of the Helmholtz Association's Initiative and Networking Fund for the Helmholtz Young Investigator Group ARES(contract number VH-NG-1516).
文摘A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金Supported by the Ministry of Education U40 Program(ZYGXONJSKYCXNLZCXM-E19)National Natural Science Foundation of China(52574078)。
文摘The forward model of optical fiber strain induced by fractures,together with the associated model resolution matrix,is used to demonstrate the interpretability of fracture parameters once the fracture intersects the fiber.A regularized inversion framework for fracture parameters is established to evaluate the influence of measured data quality on the accuracy of iterative regularized inversion.An interpretation approach for both fracture width and height is proposed,and the synthetic forward data with measurement error and field examples are employed to validate the accuracy of the simultaneous inversion of fracture width and height.The results indicate that,after the fracture contacts the fiber,the strain response is strongly sensitive only to the fracture parameters at the intersection location,whereas the interpretability of parameters at other locations remains limited.The iterative regularized inversion method effectively suppresses the impact of measurement error and exhibits high computational efficiency,showing clear advantages for inversion applications.When incorporating the first-order regularization with a Neumann boundary constraint on the tip width,the inverted fracture-width distribution becomes highly sensitive to fracture height;thus,combined with a bisection strategy,simultaneous inversion of fracture width and height can be achieved.Examination using the model resolution matrix,noisy synthetic data,and field data confirms that the iterative regularized inversion model for fracture width and height provides high interpretive accuracy and can be applied to the calculation and analysis of fracture width,fracture height,net pressure and other parameters.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFC2200103)the Shandong Provincial Natural Science Foundation(Grant Nos.ZR2022LLZ006 and ZR2022LLZ011)+1 种基金the Innovation Program for Quantum Science and Technology(Grant Nos.2021ZD0300904 and 2021ZD0300903)the Key R&D Plan of Shandong Province(Grant No.2023CXPT105)。
文摘Optical phase transfer via fiber optics is the most effective method for optical frequency standard comparison on the scale below thousands of kilometers.However,the monotonic phase discrimination range of conventional optical phase-locked loops is limited,and link delays restrict the control bandwidth,which makes it a challenge to achieve a continuously reliable optical link.This paper presents an event-timing-based phase detection method that overcomes the monotonic phase discrimination range limitation of conventional phase-locked loops through dual-edge timestamp recording,achieving an optical phase measurement resolution on the order of 10 attoseconds.With such a technique,we established a 7-segment-cascaded optical link over 1402km of commercial fiber while sharing dense wavelength division multiplexing(DWDM)channels with live telecom traffic.The system maintained continuous operation for 11.7 days without phase cycle slips despite encountering 15 km aerial fiber noise up to 21000 rad^(2)·Hz^(−1)·km^(−1)at 1 Hz.Relative instabilities of the link are 3.7×10^(−15)at 1 s and 3.9×10^(−20)at 100000 s.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘The rotatory optics element in the tensor dielectric coefficient matrix is an important para-(meter) for analyzing and calculating a rotatory optical fiber by electromagnetic theory. But the mea-(surement) of rotatory optics element is difficult for the rotatory optical fiber. A simple principle and method for measuring rotatory optics element are put forward in this paper. Firstly by using electromagnetic theory it was demonstrated that the rotatory optics element has a simple linear relation with the rotatory angle, and then the rotatory optics element has a simple linear relation with the magnetic field strength (or bias current in the helix coil) . Secondly a measurement system for the rotatory optics element in the rotatory optical fiber was designed. Using the measurement system the rotatory element can be obtained by measuring the bias current simply.
基金Supported by Key Laboratory of Space Active Optical-Electro Technology of Chinese Academy of Sciences(2021ZDKF4)。
文摘Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
基金support from the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City,University of Macao (Grant No.SKL-IoTSC (UM)-2021-2023/ORPF/A19/2022)the General Research Fund project from Research Grants Council of Hong Kong Special Administrative Region Government of China (Grant No.15214722)the Start-up Fund from The Hong Kong Polytechnic University (Grant No.BD88).
文摘Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.
基金The Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42407250)the Fund from Research Centre for Resources Engineering towards Carbon Neutrality(RCRE)of The Hong Kong Polytechnic University(Grant No.No.1-BBEM)the Fund from Natural Science Foundation of Jiangsu Province(Grant No.BK20241211)。
文摘The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.
文摘A temperature and acoustic impedance simultaneous sensor based on forward stimulated Brillouin scattering(FSBS)in highly nonlinear fiber(HNLF)with high sensitivity and high accuracy is proposed and demonstrated in this paper.High-order acoustic modes(HOAMs)are used to achieve individual or simultaneous measurement of the two parameters.Transverse acoustic waves(TAWs)involved in the FSBS process can efficiently sense the mechanical or environmental changes outside the fiber cladding,which will be reflected in a linear shift of the acoustic resonance frequency.By analyzing the frequencies of specific scattering peaks,the temperature and acoustic impedance outside the fiber cladding can be obtained simultaneously.The highest measured temperature and acoustic impedance sensitivities are 184.93 k Hz/℃and444.56 k Hz/MRayl,and the measurement accuracies are 0.09℃and 0.009 MRayl,respectively,which are both at desirable levels.We believe this work can provide potential application solutions for sensing fields involving temperature or acoustic impedance measurements.
文摘Fiber optic temperature sensors stand out in a variety of applications due to their small size,chemical resistance,and resistance to electromagnetic interference.The traditional optical fiber temperature sensor directly places the sensing structure in the temperature to be measured,and uses the thermo-optical effect and thermal expansion effect of the SiO_(2)material that constitutes the sensing structure to achieve measurement,while the thermo-optical coefficient and thermal expansion coefficient of SiO_(2) are very small,which limits the high sensitivity response characteristics of the optical fiber temperature sensing structure.In order to solve the problem of low sensitivity of traditional optical fiber temperature sensors,a Mach-Zehnder interferometric temperature sensor with a liquid-encapsulated tapered microfiber is developed.The sensor converts the temperature change into a change in the refractive index of the liquid material and thus realizes the measurement of temperature.In the range of 25~50℃,as the temperature increases,the wavelength of the transmission spectrum shifts towards shorter wavelengths.Experimental results show that the sensitivity of the liquid encapsulated microfiber interferometric temperature sensor can reach-57.91 nk·nm^(-1).This sensor has great potential for applications in marine environmental monitoring,biomedical diagnosis,and aerospace.
基金supported by the National Key Research and Development Program of China(Nos.2021YFA1201302 and 2021YFA1201300)the National Natural Science Foundation of China(Nos.52303033,52173029)+1 种基金Shanghai Sailing Program(No.23YF1400400)the Natural Science Foundation of Shanghai(No.21ZR1400500).
文摘Optogenetic has been widely applied in various pathogenesis investigations of neuropathic diseases since its accurate and targeted regulation of neuronal activity.However,due to the mismatch between the soft tissues and the optical waveguide,the long-term neural regulation within soft tissue(such as brain and spinal cord)by implantable optical fibers is a large challenge.Herein,we designed a modulus selfadaptive hydrogel optical fiber(MSHOF)with tunable mechanical properties(Young’modulus was tunable in the range of 0.32-10.56MPa)and low light attenuation(0.12-0.21 dB/cm,472nm laser light),which adapts to light transmission under soft tissues.These advantages of MSHOF can ensure the effectiveness of optogenetic stimulation meanwhile safeguarding the safety of the brain/materials interaction interface.In addition,this work provides more design possibilities of MSHOF for photogenetic stimuli and has significant application prospects in photomedical therapy.
基金supported by the Aeronautical Science Foun-dation of China(Grant No.2023M026068001).
文摘We propose a high-refractive-index(RI)sensor based on a no-core fiber(NCF)with a waist-enlarged fusion-taper(WEFT)structure,achieving high measurement accuracy with the assistance of the gated recurrent unit(GRU)neural network.This sensor integrates the NCF in series with single-mode fibers,forming the WEFT structure through arc discharge using a fiber fusion splicer to construct a modal interferometer.In the experiment,the proposed sensor has been used for high RI(ranging from 1.4330 to 1.4505)measurement.Due to the high RI being close to that of the optical fiber,traditional spectral interference dip demodulation produces nonlinear responses,increasing the measurement error in sensing.The GRU neural network algorithm is employed to train and test the recorded spectral samples,and the experimental results indicate that the coefficient of determination for this neural network model reaches 99.93%,with a mean squared error of 2.24×10-8(RIU).This deep learning model can be widely applied to similar fiber sensing applications and demonstrates significant potential for intelligent sensing within optical networks.
基金supported by the National Key Research and Development Program of China (2019YFB1803905)the National Natural Science Foundation of China (No.62171022)+2 种基金Beijing Natural Science Foundation (4222009)Guangdong Basic and Applied Basic Research Foundation (2021B1515120057)the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB (No.BK19AF005)。
文摘In this paper,a double-effect DNN-based Digital Back-Propagation(DBP)scheme is proposed and studied to achieve the Integrated Communication and Sensing(ICS)ability,which can not only realize nonlinear damage mitigation but also monitor the optical power and dispersion profile over multi-span links.The link status information can be extracted by the characteristics of the learned optical fiber parameters without any other measuring instruments.The efficiency and feasibility of this method have been investigated in different fiber link conditions,including various launch power,transmission distance,and the location and the amount of the abnormal losses.A good monitoring performance can be obtained while the launch optical power is 2 dBm which does not affect the normal operation of the optical communication system and the step size of DBP is 20 km which can provide a better distance resolution.This scheme successfully detects the location of single or multiple optical attenuators in long-distance multi-span fiber links,including different abnormal losses of 2 dB,4 dB,and 6 dB in 360 km and serval combinations of abnormal losses of(1 dB,5 dB),(3 dB,3 dB),(5 dB,1 dB)in 360 km and 760 km.Meanwhile,the transfer relationship of the estimated coefficient values with different step sizes is further investigated to reduce the complexity of the fiber nonlinear damage compensation.These results provide an attractive approach for precisely sensing the optical fiber link status information and making correct strategies timely to ensure optical communication system operations.
文摘Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality characteristics of SPOF limit their integration with traditional fiber optic sensors.This article introduces for the first time a flexible fiber optic vibration sensor based on laser interference technology,which can be applied to vibration measurement under high stretch conditions.This sensor utilizes elastic optical fibers made of polydimethylsiloxane(PDMS)as sensing elements,combined with phase generating carrier technology,to achieve vibration measurement at 50−260 Hz within the stretch range of 0−42%.
基金supported by the National Natural Science Foundation of China(Nos.61705027,62375031 and 52075131)the Chongqing Science and Technology Commission Basic Research Project(No.CSTC-2020jcyj-msxm0603)the Chongqing Municipal Education Commission Science and Technology Research Program(No.KJQN202000609)。
文摘To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.
