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
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
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
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
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
The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibratio...The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.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
We proposed a fiber optic high temperature sensor based on the Mach-Zehnder interference(MZI)structure,which is composed of two lengths of multi-mode fibers(MMFs),a length of few-mode fiber(FMF)and two sections of sin...We proposed a fiber optic high temperature sensor based on the Mach-Zehnder interference(MZI)structure,which is composed of two lengths of multi-mode fibers(MMFs),a length of few-mode fiber(FMF)and two sections of single-mode fibers(SMFs).Firstly,the two sections of MMFs were spliced with two sections of SMFs.Then,the MMFs were fused to two ends of FMF to form a symmetrically structured fiber-optic MZI structure.In this structure,the MMF served as the optical mode field coupling element,and the cladding and core of the FMF are the interference arm and the reference arm of the MZI structure,respectively.We investigated the sensor's response characteristics of the temperature and strain.The experimental results indicate that the sensor is sensitive to temperature variation,and the temperature response sensitivity is up to 61.4 pm/℃ in the range of 40-250℃,while the sensor has weak strain sensitivity,its strain sensitivity is only-0.72 pm/μe in the strain range of 0-1400μe.Moreover,the sensor has good stability and repeatability.In brief,the proposed fiber optic high temperature sensor has good properties,such as high sensitivity,compact structure,good stability and repeatability,which can be used for monitoring the temperature of submerged oil electric pump units under oil wells.展开更多
To achieve a low-complexity nonlinearity compensation(NLC)in high-symbol-rate(HSR)systems,we propose a modified weighted digital backpropagation(M-W-DBP)by jointly shifting the calculated position of nonlinear phase n...To achieve a low-complexity nonlinearity compensation(NLC)in high-symbol-rate(HSR)systems,we propose a modified weighted digital backpropagation(M-W-DBP)by jointly shifting the calculated position of nonlinear phase noise and considering the correlation of neighboring symbols in the NLC section of DBP.Based on this model,with the aid of neural network optimization,a learned version of M-W-DBP(M-W-LDBP)is also proposed and explored.Furthermore,enough technical details are revealed for the first time,including the principle of our proposed M-W-DBP and M-W-LDBP,the training process,and the complexity analysis of different DBPclass NLC algorithms.Evaluated numerically with QPSK,16QAM,and PS-64QAM modulation formats,1-step-per-span(1-StPS)M-W-DBP/LDBP achieves up to 1.29/1.49 dB and 0.63/0.74 dB signal-to-noise ratio improvement compared to chromatic dispersion compensation(CDC)in 90-GBaud and 128-GBaud 1000-km single-channel transmission systems,respectively.Moreover,1-StPS M-W-DBP/LDBP provides a more powerful NLC ability than 2-StPS LDBP but only needs about 60%of the complexity.The effectiveness of the proposed M-W-DBP and M-W-LDBP in the presence of laser phase noise is also verified and the necessity of using the learned version of M-WDBP is also discussed.This work is a comprehensive study of M-W-DBP/LDBP and other DBP-class NLC algorithms in HSR systems.展开更多
The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed ...The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed site investigations and long-term monitoring of subsurface geological conditions.Understanding the temporal and spatial distributions of underground multi-field information is vital for successful engineering construction and effective utilization of urban underground space.In this study,a fiber optic nerve system(FONS)was utilized in the Tianfu New Area,Sichuan Province,China,to obtain comprehensive subsurface multi-physical information,including geological deformation,temperature,and surface hydrological data.The FONS incorporates three advanced fiber optic sensing techniques,i.e.fiber Bragg grating(FBG),Brillouin optical time domain reflectometry(BOTDR),and Raman optical time domain reflectometry(ROTDR).Fully-and quasi-distributed strain/temperature sensing cables have been installed in nine monitoring boreholes,covering various geological features such as plains,terraces,and areas within active fault zones.The field monitoring results confirm the feasibility of employing FONS for geological investigations within urban development zones,offering a valuable reference for future applications of this cost-effective technology in geohazard mitigation.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
With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal c...With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.展开更多
The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system ...The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.展开更多
Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has seve...Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision FOGs.The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under vibration.In this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward.Particularly,the loop gain is extracted out by adding a gain-monitoring wave.By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation depth.The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 Hz.This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.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
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.
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.
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(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.
基金Supported by the Scientific Research and Technology Development Project of Petrochina Southwest Oil and Gas Field Company(20230307-02)。
文摘The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.
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.
基金supported by the Scientific Research Program Funded by Shaanxi Provincial Education Department (No.15JK1573)the Postgraduate Innovation and Practice Ability Development Fund of Xi’an Shiyou University (No.YCS21211084)。
文摘We proposed a fiber optic high temperature sensor based on the Mach-Zehnder interference(MZI)structure,which is composed of two lengths of multi-mode fibers(MMFs),a length of few-mode fiber(FMF)and two sections of single-mode fibers(SMFs).Firstly,the two sections of MMFs were spliced with two sections of SMFs.Then,the MMFs were fused to two ends of FMF to form a symmetrically structured fiber-optic MZI structure.In this structure,the MMF served as the optical mode field coupling element,and the cladding and core of the FMF are the interference arm and the reference arm of the MZI structure,respectively.We investigated the sensor's response characteristics of the temperature and strain.The experimental results indicate that the sensor is sensitive to temperature variation,and the temperature response sensitivity is up to 61.4 pm/℃ in the range of 40-250℃,while the sensor has weak strain sensitivity,its strain sensitivity is only-0.72 pm/μe in the strain range of 0-1400μe.Moreover,the sensor has good stability and repeatability.In brief,the proposed fiber optic high temperature sensor has good properties,such as high sensitivity,compact structure,good stability and repeatability,which can be used for monitoring the temperature of submerged oil electric pump units under oil wells.
基金supported in part by National Natural Science Foundation of China(No.62271080)in part by Fund of State Key Laboratory of IPOC(BUPT)(No.IPOC2022ZT06)in part by BUPT Excellent Ph.D Students Foundation(No.CX2022102).
文摘To achieve a low-complexity nonlinearity compensation(NLC)in high-symbol-rate(HSR)systems,we propose a modified weighted digital backpropagation(M-W-DBP)by jointly shifting the calculated position of nonlinear phase noise and considering the correlation of neighboring symbols in the NLC section of DBP.Based on this model,with the aid of neural network optimization,a learned version of M-W-DBP(M-W-LDBP)is also proposed and explored.Furthermore,enough technical details are revealed for the first time,including the principle of our proposed M-W-DBP and M-W-LDBP,the training process,and the complexity analysis of different DBPclass NLC algorithms.Evaluated numerically with QPSK,16QAM,and PS-64QAM modulation formats,1-step-per-span(1-StPS)M-W-DBP/LDBP achieves up to 1.29/1.49 dB and 0.63/0.74 dB signal-to-noise ratio improvement compared to chromatic dispersion compensation(CDC)in 90-GBaud and 128-GBaud 1000-km single-channel transmission systems,respectively.Moreover,1-StPS M-W-DBP/LDBP provides a more powerful NLC ability than 2-StPS LDBP but only needs about 60%of the complexity.The effectiveness of the proposed M-W-DBP and M-W-LDBP in the presence of laser phase noise is also verified and the necessity of using the learned version of M-WDBP is also discussed.This work is a comprehensive study of M-W-DBP/LDBP and other DBP-class NLC algorithms in HSR systems.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.42225702 and 42077235).
文摘The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed site investigations and long-term monitoring of subsurface geological conditions.Understanding the temporal and spatial distributions of underground multi-field information is vital for successful engineering construction and effective utilization of urban underground space.In this study,a fiber optic nerve system(FONS)was utilized in the Tianfu New Area,Sichuan Province,China,to obtain comprehensive subsurface multi-physical information,including geological deformation,temperature,and surface hydrological data.The FONS incorporates three advanced fiber optic sensing techniques,i.e.fiber Bragg grating(FBG),Brillouin optical time domain reflectometry(BOTDR),and Raman optical time domain reflectometry(ROTDR).Fully-and quasi-distributed strain/temperature sensing cables have been installed in nine monitoring boreholes,covering various geological features such as plains,terraces,and areas within active fault zones.The field monitoring results confirm the feasibility of employing FONS for geological investigations within urban development zones,offering a valuable reference for future applications of this cost-effective technology in geohazard mitigation.
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supports from China National Funds for Distinguished Young Scientists(62425505)National Natural Science Foundation of China(U22A20206)+1 种基金the China Postdoctoral Science Foundation(2023M731188)the Fundamental Research Funds for the Central Universities(2024BRA012).
文摘With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20375 and 62075151)the National Key Research and Development Program of China(Grant No.202103021223042).
文摘The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.
基金Fundamental Research Funds for the Central Universities(YWF-23-L-1225)National Natural Science Foundation of China(62201025)Chinese Aeronautical Establishment(2022Z037051001)。
文摘Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision FOGs.The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under vibration.In this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward.Particularly,the loop gain is extracted out by adding a gain-monitoring wave.By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation depth.The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 Hz.This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.
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.
