Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distr...Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.展开更多
In this paper,we investigate the spectrum sensing performance of a distributed satellite clusters(DSC)under perturbation,aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak sign...In this paper,we investigate the spectrum sensing performance of a distributed satellite clusters(DSC)under perturbation,aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals.Specifically,we propose a cooperative beamforming(BF)algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation.Then,a constrained optimization problem with maximizing the signal to interference plus noise ratio(SINR)is modeled to obtain the BF weight vectors,and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector.Finally,we derive the closedform expression of the detection probability for the considered DSC over Shadowed-Rician fading channels.Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.展开更多
A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion mo...A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.展开更多
This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy model...This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.展开更多
With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and com...With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.展开更多
A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into ke...A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS sampling.The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis predictions.For the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s request.To fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge features.Finally,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI.Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.展开更多
Distributed fiber sensors based on forward stimulated Brillouin scattering(F-SBS)have attracted special attention because of their capability to detect the acoustic impedance of liquid material outside fiber.However,t...Distributed fiber sensors based on forward stimulated Brillouin scattering(F-SBS)have attracted special attention because of their capability to detect the acoustic impedance of liquid material outside fiber.However,the reported results were based on the extraction of a 1st-order local spectrum,causing the sensing distance to be restricted by pump depletion.Here,a novel post-processing technique was proposed for distributed acoustic impedance sensing by extracting the 2nd-order local spectrum,which is beneficial for improving the sensing signal-to-noise ratio(SNR)significantly,since its pulse energy penetrates into the fiber more deeply.As a proof-of-concept,distributed acoustic impedance sensing along~1630 m fiber under moderate spatial resolution of~20 m was demonstrated.展开更多
Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fibe...Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fiber sensing technology can effectively capture the process of fracture initiation and propagation,yet the evaluation method for the initiation and propagation of bedding fractures remains immature.This study integrates a distributed optical fiber sensing device based on optical frequency domain reflectometry(OFDR)with a large-scale true tri-axial fracturing physical simulation apparatus to conduct real-time monitoring experiments on shale samples from the Lianggaoshan Formation in the Sichuan Basin,where bedding is well-developed.The experimental results demonstrate that two bedding fractures in the shale sample initiated and propagated.The evolution characteristics of fiber-optic strain in a horizontal adjacent well,induced by the initiation and propagation of bedding fractures,are characterized by the appearance of a tensile strain convergence zone in the middle of the optical fiber,flanked by two compressive strain convergence zones.The initiation and propagation of the distal bedding fracture causes the fiber-optic strain in the horizontal adjacent well to superimpose,with the asymmetric propagation of the bedding fracture leading to an asymmetric tensile strain convergence zone in the optical fiber.Utilizing a finite element method coupled with a cohesive element approach,a forward model of fiber-optic strain in the horizontal adjacent well induced by the initiation and propagation of hydraulic fracturing bedding fractures was constructed.Numerical simulation analyses were conducted to evaluate the evolution of fiber-optic strain in the horizontal adjacent well,confirming the correctness of the observed evolution characteristics.The presence of a"wedge-shaped"tensile strain convergence zone in the fiber-optic strain waterfall plot,accompanied by two compressive strain convergence zones,indicates the initiation and propagation of bedding fractures during the fracturing process.These findings provide valuable insights for interpreting distributed fiber-optic data in shale fracturing field applications.展开更多
Multicore fiber(MCF)which contains more than one core in a single fiber cladding has attracted ever increasing attention for application in optical sensing systems owing to its unique capability of independent light t...Multicore fiber(MCF)which contains more than one core in a single fiber cladding has attracted ever increasing attention for application in optical sensing systems owing to its unique capability of independent light transmission in multiple spatial channels.Different from the situation in standard single mode fiber(SMF),the fiber bending gives rise to tangential strain in off-center cores,and this unique feature has been employed for directional bending and shape sensing,where strain measurement is achieved by using either fiber Bragg gratings(FBGs),optical frequency-domain reflectometry(OFDR)or Brillouin distributed sensing technique.On the other hand,the parallel spatial cores enable space-division multiplexed(SDM)system configuration that allows for the multiplexing of multiple distributed sensing techniques.As a result,multi-parameter sensing or performance enhanced sensing can be achieved by using MCF.In this paper,we review the research progress in MCF based distributed fiber sensors.Brief introductions of MCF and the multiplexing/de-multiplexing methods are presented.The bending sensitivity of off-center cores is analyzed.Curvature and shape sensing,as well as various SDM distributed sensing using MCF are summarized,and the working principles of diverse MCF sensors are discussed.Finally,we present the challenges and prospects of MCF for distributed sensing applications.展开更多
A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtaine...A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtained. With the assistance of the nonlinear mirror, we demonstrate that the spatial resolution of the sensor is improved from 3 m to 1 m, and the temperature accuracy is improved from ±0.6℃ to ±0.2℃. The theoretical analysis and the experimental data are in good agreement.展开更多
Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study,...Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory(FRI) data, and SPOT Vegetation(SPOT-VGT) normalized difference vegetation index(NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China(with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg(1 Pg = 10g), with an average of 77.2 Mg ha, during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fineresolution biomass map.展开更多
Probability of detection(POD)graphics allow for a change from qualitative to quantitative assessment for every damage detection system,and as such it is a main request for conventional non-destructive testing(NDT)tech...Probability of detection(POD)graphics allow for a change from qualitative to quantitative assessment for every damage detection system,and as such it is a main request for conventional non-destructive testing(NDT)techniques.Its availability can greatly help towards the industrialization of the corresponding Structural health monitoring(SHM)system.But having in mind that for SHM systems the sensors are at fixed positions,and the location of a potential damage would change its detectability.Consequently robust simulation tools are required to obtain the model assisted probability of detection(MAPOD)which is needed to validate the SHM system.This tool may also help for the optimization of the sensor distribution,and finally will allow a probabilistic risk management.INDEUS,simulation of ultrasonic waves SHM system,was a main milestone in this direction.This article deals with the simulation tools for a strain based SHM system,using fiber optic sensors(FOS).FOS are essentially strain/temperature sensors,either with multi-point or with distributed sensing.The simulation tool includes the finite element model(FEM)for the original and damaged structure,and algorithms to compare the strain data at the pre-established sensors locations,and from this comparison to extract information about damage occurrence and location.The study has been applied to the structure of an all-composite unmanned aircraft vehicle(UAV)now under construction,designed at Universidad Politecnica de Madrid for the inspection of electrical utilities networks.Distributed sensing optical fibers were internally bonded at the fuselage and wing.Routine inspection is planned to be done with the aircraft at the test bench by imposing known loads.From the acquired strain data,damage occurrence may be calculated as slight deviations from the baselines.This is a fast inspection procedure without requiring trained specialists,and it would allow for detection of hidden damages.Simulation indicates that stringer partial debondings are detected before they become critical,while small delaminations as those produced by barely visible impact damages would require a prohibited number of sensing lines.These simulation tools may easily be applied to any other complex structure,just by changing the FEM models.From these results it is shown how a fiber optic based SHM system may be used as a reliable damage detection procedure.展开更多
At present, the demand for perimeter security system is in-creasing greatly, especially for such system based on distribut-ed optical fiber sensing. This paper proposes a perimeter se-curity monitoring system based on...At present, the demand for perimeter security system is in-creasing greatly, especially for such system based on distribut-ed optical fiber sensing. This paper proposes a perimeter se-curity monitoring system based on phase-sensitive coherentoptical time domain reflectometry(Ф-COTDR) with the practi-cal pattern recognition function. We use fast Fourier trans-form(FFT) to exact features from intrusion events and a multi-class classification algorithm derived from support vector ma-chine(SVM) to work as a pattern recognition technique. Fivedifferent types of events are classified by using a classifica-tion algorithm based on SVM through a three-dimensional fea-ture vector. Moreover, the identification results of the patternrecognition system show that an identification accurate rate of92.62% on average can be achieved.展开更多
A laser sensing system based on beat frequency demodulation is proposed. The sensor uses a single-longitudinal-mode distributed Bragg reflector （DBR） fiber laser as a sensing element. This laser sensor has great mul...A laser sensing system based on beat frequency demodulation is proposed. The sensor uses a single-longitudinal-mode distributed Bragg reflector （DBR） fiber laser as a sensing element. This laser sensor has great multiplexing capability due to its wide free spectral range. Wavelength-division-multiplex （WDM） and frequency-division-multiplex （FDM） techniques are studied. The sensing system has high sensitivity and multiplexing channels.展开更多
Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,ru...Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.展开更多
An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal d...An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.展开更多
基金the National Basic Research Program of China，the National Natural Science Foundation of China，the open research fund of National Mobile Communications Research Laboratory,Southeast University，the Postdoctoral Science Foundation of Jiangsu Province，the University Natural Science Research Program of Jiangsu Province，the Basic Research Program of Jiangsu Province (Natural Science Foundation)
文摘Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.
基金partially supported by the National Science Foundation of China (No.91738201,U21A20450 and 62171234)the Jiangsu Province Basic Research Project (No. BK20192002)the postgraduate research & practice innovation program of jiangsu province under Grant KYCX20_0708
文摘In this paper,we investigate the spectrum sensing performance of a distributed satellite clusters(DSC)under perturbation,aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals.Specifically,we propose a cooperative beamforming(BF)algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation.Then,a constrained optimization problem with maximizing the signal to interference plus noise ratio(SINR)is modeled to obtain the BF weight vectors,and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector.Finally,we derive the closedform expression of the detection probability for the considered DSC over Shadowed-Rician fading channels.Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.
基金Supported by the National Natural Science Foundation of China(61077022)
文摘A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
基金This work has been supported by the National Key R&D Program of China(Grant No.2018YFE0207600)EPSRC Research Grant(EP/K033700/1,EP/K033166/1)+2 种基金the Natural Science Foundation of China(61671046,61911530216,U1834210)the Beijing Natural Science Foundation(4182050)the FWO(Grants G0A2617N and G093817N).
文摘This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.
文摘With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.
基金supported by the Graduate Student Research Innovation Project of Jiangsu Province China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province China under Grant No. 12KJB510019the Nanjing University of Posts and Telecommunications Natural Science Foundation under Grant No. NY212015the Technology Research Program of Hubei Provincial Department of Education under Grant No. D20121408
文摘A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS sampling.The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis predictions.For the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s request.To fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge features.Finally,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI.Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.
基金Project supported by the Sichuan Science and Technology Program(Grant No.2019YJ0530)Scientific Research Fund of Sichuan Provincial Education Department,China(Grant No.18ZA0401)the National Natural Science Foundation of China(Grant No.61205079).
文摘Distributed fiber sensors based on forward stimulated Brillouin scattering(F-SBS)have attracted special attention because of their capability to detect the acoustic impedance of liquid material outside fiber.However,the reported results were based on the extraction of a 1st-order local spectrum,causing the sensing distance to be restricted by pump depletion.Here,a novel post-processing technique was proposed for distributed acoustic impedance sensing by extracting the 2nd-order local spectrum,which is beneficial for improving the sensing signal-to-noise ratio(SNR)significantly,since its pulse energy penetrates into the fiber more deeply.As a proof-of-concept,distributed acoustic impedance sensing along~1630 m fiber under moderate spatial resolution of~20 m was demonstrated.
基金the financial support by National Natural Science Foundation of China(No.52334001)。
文摘Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fiber sensing technology can effectively capture the process of fracture initiation and propagation,yet the evaluation method for the initiation and propagation of bedding fractures remains immature.This study integrates a distributed optical fiber sensing device based on optical frequency domain reflectometry(OFDR)with a large-scale true tri-axial fracturing physical simulation apparatus to conduct real-time monitoring experiments on shale samples from the Lianggaoshan Formation in the Sichuan Basin,where bedding is well-developed.The experimental results demonstrate that two bedding fractures in the shale sample initiated and propagated.The evolution characteristics of fiber-optic strain in a horizontal adjacent well,induced by the initiation and propagation of bedding fractures,are characterized by the appearance of a tensile strain convergence zone in the middle of the optical fiber,flanked by two compressive strain convergence zones.The initiation and propagation of the distal bedding fracture causes the fiber-optic strain in the horizontal adjacent well to superimpose,with the asymmetric propagation of the bedding fracture leading to an asymmetric tensile strain convergence zone in the optical fiber.Utilizing a finite element method coupled with a cohesive element approach,a forward model of fiber-optic strain in the horizontal adjacent well induced by the initiation and propagation of hydraulic fracturing bedding fractures was constructed.Numerical simulation analyses were conducted to evaluate the evolution of fiber-optic strain in the horizontal adjacent well,confirming the correctness of the observed evolution characteristics.The presence of a"wedge-shaped"tensile strain convergence zone in the fiber-optic strain waterfall plot,accompanied by two compressive strain convergence zones,indicates the initiation and propagation of bedding fractures during the fracturing process.These findings provide valuable insights for interpreting distributed fiber-optic data in shale fracturing field applications.
文摘Multicore fiber(MCF)which contains more than one core in a single fiber cladding has attracted ever increasing attention for application in optical sensing systems owing to its unique capability of independent light transmission in multiple spatial channels.Different from the situation in standard single mode fiber(SMF),the fiber bending gives rise to tangential strain in off-center cores,and this unique feature has been employed for directional bending and shape sensing,where strain measurement is achieved by using either fiber Bragg gratings(FBGs),optical frequency-domain reflectometry(OFDR)or Brillouin distributed sensing technique.On the other hand,the parallel spatial cores enable space-division multiplexed(SDM)system configuration that allows for the multiplexing of multiple distributed sensing techniques.As a result,multi-parameter sensing or performance enhanced sensing can be achieved by using MCF.In this paper,we review the research progress in MCF based distributed fiber sensors.Brief introductions of MCF and the multiplexing/de-multiplexing methods are presented.The bending sensitivity of off-center cores is analyzed.Curvature and shape sensing,as well as various SDM distributed sensing using MCF are summarized,and the working principles of diverse MCF sensors are discussed.Finally,we present the challenges and prospects of MCF for distributed sensing applications.
基金supported by the National Natural Science Foundation of China under Grant No.60377021partially supported by Program for New Century Excellent Talents in University under Grant No. NCET-07-0152Sichuan Scientific Funds for Young Researchers under Grant No. 08ZQ026-012.
文摘A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtained. With the assistance of the nonlinear mirror, we demonstrate that the spatial resolution of the sensor is improved from 3 m to 1 m, and the temperature accuracy is improved from ±0.6℃ to ±0.2℃. The theoretical analysis and the experimental data are in good agreement.
基金supported by the National Natural Science Foundation of China(No.41401500)the National Key Technologies R&D Program of China(2012BAD22B04)+5 种基金the China Postdoctoral Science Foundation(2015M580629,2016M590679)the Key Scientific Research Projects of Higher Education of Henan Province,China(16A420003,17A420001)Scientific and Technological Innovation Team of Universities in Henan Province,China(18IRTSTHN008)Funds for Fundamental Scientific Research in Colleges in Henan Province,China(NSFRF1630)Innovation Research Team of Henan Polytechnic University,China(B2017-16)the China Coal Industry Association Guidance Program(MTKJ-2015-285)
文摘Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory(FRI) data, and SPOT Vegetation(SPOT-VGT) normalized difference vegetation index(NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China(with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg(1 Pg = 10g), with an average of 77.2 Mg ha, during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fineresolution biomass map.
基金supported by the project TRA2014-58263-C2-2-Rfunded by the National Research program of Spain
文摘Probability of detection(POD)graphics allow for a change from qualitative to quantitative assessment for every damage detection system,and as such it is a main request for conventional non-destructive testing(NDT)techniques.Its availability can greatly help towards the industrialization of the corresponding Structural health monitoring(SHM)system.But having in mind that for SHM systems the sensors are at fixed positions,and the location of a potential damage would change its detectability.Consequently robust simulation tools are required to obtain the model assisted probability of detection(MAPOD)which is needed to validate the SHM system.This tool may also help for the optimization of the sensor distribution,and finally will allow a probabilistic risk management.INDEUS,simulation of ultrasonic waves SHM system,was a main milestone in this direction.This article deals with the simulation tools for a strain based SHM system,using fiber optic sensors(FOS).FOS are essentially strain/temperature sensors,either with multi-point or with distributed sensing.The simulation tool includes the finite element model(FEM)for the original and damaged structure,and algorithms to compare the strain data at the pre-established sensors locations,and from this comparison to extract information about damage occurrence and location.The study has been applied to the structure of an all-composite unmanned aircraft vehicle(UAV)now under construction,designed at Universidad Politecnica de Madrid for the inspection of electrical utilities networks.Distributed sensing optical fibers were internally bonded at the fuselage and wing.Routine inspection is planned to be done with the aircraft at the test bench by imposing known loads.From the acquired strain data,damage occurrence may be calculated as slight deviations from the baselines.This is a fast inspection procedure without requiring trained specialists,and it would allow for detection of hidden damages.Simulation indicates that stringer partial debondings are detected before they become critical,while small delaminations as those produced by barely visible impact damages would require a prohibited number of sensing lines.These simulation tools may easily be applied to any other complex structure,just by changing the FEM models.From these results it is shown how a fiber optic based SHM system may be used as a reliable damage detection procedure.
文摘At present, the demand for perimeter security system is in-creasing greatly, especially for such system based on distribut-ed optical fiber sensing. This paper proposes a perimeter se-curity monitoring system based on phase-sensitive coherentoptical time domain reflectometry(Ф-COTDR) with the practi-cal pattern recognition function. We use fast Fourier trans-form(FFT) to exact features from intrusion events and a multi-class classification algorithm derived from support vector ma-chine(SVM) to work as a pattern recognition technique. Fivedifferent types of events are classified by using a classifica-tion algorithm based on SVM through a three-dimensional fea-ture vector. Moreover, the identification results of the patternrecognition system show that an identification accurate rate of92.62% on average can be achieved.
基金supported by the National 863 Program under Grant No. 2006AA01Z217the National Natural Science Foundation of China under Grant No. 60736039College Science Research Foundation of Tianjin and the Key Laboratory of Optoelectronic Information Technical Science, Ministry of Education of China under Grant No. 2006BA28
文摘A laser sensing system based on beat frequency demodulation is proposed. The sensor uses a single-longitudinal-mode distributed Bragg reflector （DBR） fiber laser as a sensing element. This laser sensor has great multiplexing capability due to its wide free spectral range. Wavelength-division-multiplex （WDM） and frequency-division-multiplex （FDM） techniques are studied. The sensing system has high sensitivity and multiplexing channels.
基金supported by the State Grid Science&Technology Project of China(5400-202224153A-1-1-ZN).
文摘Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.
基金This paper was supported by the National Natural Sci-ence Foundation of China (Grant No. 40371001) and the Youth Foundation of Beijing Normal University
文摘An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.
