Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam ro...Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam roof and floor,and rock burst.Transparency in coal mine geological conditions provides technical support for intelligent coal mining and geological disaster prevention.In this sense,it is of great significance to address the requirements for informatizing coal mine geological conditions,dynamically adjust sensing parameters,and accurately identify disaster characteristics so as to prevent and control coal mine geological disasters.This paper examines the various action fields associated with geological disasters in mining faces and scrutinizes the types and sensing parameters of geological disasters resulting from coal seam mining.On this basis,it summarizes a distributed fiber-optic sensing technology framework for transparent geology in coal mines.Combined with the multi-field monitoring characteristics of the strain field,the temperature field,and the vibration field of distributed optical fiber sensing technology,parameters such as the strain increment ratio,the aquifer temperature gradient,and the acoustic wave amplitude are extracted as eigenvalues for identifying rock breaking,aquifer water level,and water cut range,and a multi-field sensing method is established for identifying the characteristics of mining-induced rock mass disasters.The development direction of transparent geology based on optical fiber sensing technology is proposed in terms of the aspects of sensing optical fiber structure for large deformation monitoring,identification accuracy of optical fiber acoustic signals,multi-parameter monitoring,and early warning methods.展开更多
Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the ar...Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the artificial land is essential,yet challenging.Here,we use an improved fiber-optic monitoring system for its subsurface multi-physical characterization.The system enables us to gather spatiotemporal distribution of various parameters,including strata deformation,temperature,and moisture.Yan’an New District was selected as a case study to conduct refined in-situ monitoring through a 77 m-deep borehole and a 30 m-long trench.Findings reveal that the ground settlement involves both the deformation of the filling loess and the underlying intact loess.Notably,the filling loess exhibits a stronger creep capability compared to underlying intact loess.The deformation along the profile is unevenly distributed,with a positive correlation with soil moisture.Water accumulation has been observed at the interface between the filling loess and the underlying intact loess,leading to a significant deformation.Moreover,the temperature and moisture in the filling loess have reached a new equilibrium state,with their depths influenced by atmospheric conditions measuring at 31 m and 26 m,respectively.The refined investigation allows us to identify critical layers that matter the sustainable development of newly created urban areas,and provide improved insights into the evolution mechanisms of land creation.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Nanoscale confinement environments often affect the transport mechanisms of nanofluids.Understanding the dynamic behavior of molecules in two-dimensional(2D)confined channels is of great importance in the areas of sen...Nanoscale confinement environments often affect the transport mechanisms of nanofluids.Understanding the dynamic behavior of molecules in two-dimensional(2D)confined channels is of great importance in the areas of sensing,catalysis and energy storage.As a popular candidate for a new type of gas sensing material,MXenes have the problem of nonselectivity towards polar gases with slow responses,which severely limits their applications.Here,we report a study on regulating the confinement effect of 2D channels between MXene layers through annealing treatment and ion(Na^(+))intercalation for high-performance ammonia(NH_(3))sensing.Firstly,the annealing treatment accurately modulates the size of the 2D channels to effectively block the entry of large-size gas molecules and improve the selectivity for NH_(3).Ab initio molecular dynamics(AIMD)also confirms that the modulated channel size has a special"nano-pumping effect",which can accelerate the dynamic behavior of NH_(3) molecules in the 2D confined space.Moreover,the intercalation of Na+ions increases the adsorption capacity of NH_(3).Therefore,the"nano-pumping effect"and theintercalation of Na+ions effectively enhance the response speed and sensitivity of MXene to NH_(3),respectively.The experimental results show that the modified Ti_(3)C_(2) exhibits high sensitivity(0.17),rapid response(181 s),excellent selectivity and stability towards NH_(3).展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
This paper reviews the work on huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings developed at the National Engineering Laboratory for Fiber Optic Sensing Technology （NEL-FOST）, Wuhan ...This paper reviews the work on huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings developed at the National Engineering Laboratory for Fiber Optic Sensing Technology （NEL-FOST）, Wuhan University of Technology, China. A versatile drawing tower grating sensor network based on ultra-weak fiber Bragg gratings （FBGs） is firstly proposed and demonstrated. The sensing network is interrogated with time- and wavelength-division multiplexing method, which is very promising for the large-scale sensing network.展开更多
Accurate and real-time detection of hydrogen(H_(2))is essential for ensuring energy security.Fiber-optic H_(2) sensors are gaining attention for their integration and remote sensing capabilities.However,they face chal...Accurate and real-time detection of hydrogen(H_(2))is essential for ensuring energy security.Fiber-optic H_(2) sensors are gaining attention for their integration and remote sensing capabilities.However,they face challenges,including complex fabrication processes and limited response times.Here,we propose a fiber-optic H_(2) sensing tip based on Tamm plasmon polariton(TPP)resonance,consisting of a multilayer metal/dielectric Bragg reflector deposited directly on the fiber end facet,simplifying the fabrication process.The fiber-optic TPP(FOTPP)tip exhibits both TPP and multiple Fabry-Perot(FP)resonances simultaneously,with the TPP employed for highly sensitive H_(2) detection.Compared to FP resonance,TPP exhibits more than twice the sensitivity under the same structural dimension without cavity geometry deformation.The excellent performance is attributed to alterations in phase-matching conditions,driven by changes in penetration depth of TPP.Furthermore,the FP mode is utilized to achieve an efficient photothermal effect to catalyze the reaction between H_(2) and the FOTPP structure.Consequently,the response and recovery speeds of the FOTPP tip under resonance-enhanced photothermal assistance are improved by 6.5 and 2.1 times,respectively.Our work offers a novel strategy for developing TPP-integrated fiber-optic tips,refines the theoretical framework of photothermal-assisted detection systems,and provides clear experimental evidence.展开更多
Defects in cast-in-situ piles have an adverse impact on load transfer at the pile‒soil interface and pile bearing capacity. In recent years, thermal integrity profiling (TIP) has been developed to measure temperature ...Defects in cast-in-situ piles have an adverse impact on load transfer at the pile‒soil interface and pile bearing capacity. In recent years, thermal integrity profiling (TIP) has been developed to measure temperature profiles of cast-in-situ piles, enabling the detection of structural defects or anomalies at the early stage of construction. However, using this integrity testing method to evaluate potential defects in cast-in-situ piles requires a comprehensive understanding of the mechanism of hydration heat transfer from piles to surrounding soils. In this study, small-scale model tests were conducted in laboratory to investigate the performance of TIP in detecting pile integrity. Fiber-optic distributed temperature sensing (DTS) technology was used to monitor detailed temperature variations along model piles in sand. Additionally, sensors were installed in sand to measure water content and matric suction. An interpretation method against available DTS-based thermal profiles was proposed to reveal the potential defective regions. It shows that the temperature difference between normal and defective piles is more obvious in wet sand. In addition, there is a critical zone of water migration in sand due to the water absorption behavior of cement and temperature transfer-induced water migration in the early-age concrete setting. These findings could provide important insight into the improvement of the TIP testing method for field applications.展开更多
An optical fiber dual Fabry-Perot interferometric carbon monoxide gas sensor based on PANI/Co3 O4/GO(PCG)sensing membrane coated on the end face of the optical fiber is proposed and fabricated.One end face of photonic...An optical fiber dual Fabry-Perot interferometric carbon monoxide gas sensor based on PANI/Co3 O4/GO(PCG)sensing membrane coated on the end face of the optical fiber is proposed and fabricated.One end face of photonic crystal fiber(PCF)without cut-off wavelength is fused with a single-mode fiber(SMF),and the other end face of the PCF is coated with PCG sensing membrane.The collapsed layer formed during the air hole fusion of PCF is used as the first reflector,the interface between PCF and sensing membrane is used as the second reflector,and the interface between the sensing membrane and the air is used as the third reflector,thus the dual Fabry-Pe rot structure sensor is formed.The results show that the sensor has excellent sensitivity and selectivity to carbon monoxide.With the increasing concentration of carbon monoxide gas in the range of 0-60 ppm,the intensity of interference spectrum decreases.The sensitivity of the sensor is 0.3473 dB m/ppm,and its linearity is good.The response time and recovery time are 68 s and 106 s,respectively.The sensor has the advantages of the compact size,low cost,high sensitivity,strong selectivity and simple structure.It is suitable for the sensing detection of low concentration carbon monoxide gas.展开更多
A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium,and is based on Rayleigh backscattering in an optical fiber.The sensor is widely used in ...A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium,and is based on Rayleigh backscattering in an optical fiber.The sensor is widely used in the safety monitoring of oil and gas pipelines,the classification of weak acoustic signals,defense,seismic prospecting,and other fields.In the field of seismic prospecting,distributed optical-fiber acoustic sensing(DAS)will gradually replace the use of the traditional geophone.The present paper mainly expounds the recent application of DAS,and summarizes recent research achievements of DAS in resource exploration,intrusion monitoring,pattern recognition,and other fields and various DAS system structures.It is found that the high-sensitivity and long-distance sensing capabilities of DAS play a role in the extensive monitoring applications of DAS in engineering.The future application and development of DAS technology are examined,with the hope of promoting the wider application of the DAS technology,which benefits engineering and society.展开更多
Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS response...Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS responses associated with near-wellbore hydraulic fracture properties is still unclear.To address this knowledge gap,we use coupled wellbore-reservoir-geomechanics simulations to study measured strain-change behavior and infer hydraulic fracture characteristics.The crossflow among fractures is captured through explicit modeling of the transient wellbore flow.In addition,local grid refinement is applied to accurately capture strain changes along the fiber.A Base Case model was designed with four fractures of varying properties,simulating strain change signals when the production well is shut-in for 10 d after 240 d of production and reopened for 2 d.Strain-pressure plots for different fracture clusters were used to gain insights into inferring fracture properties using DSS data.When comparing the model with and without the wellbore,distinct strain change signals were observed,emphasizing the importance of incorporating the wellbore in FO-DSS modeling.The effects of fracture spacing and matrix permeability on strain change signals were thoroughly investigated.The results of our numerical study can improve the understanding of the relation between DSS signals and fracture hydraulic properties,thus maximizing the value of the dataset for fracture diagnostics and characterization.展开更多
Based on advantages of technology of distributive fiber-optic temperature sensing and specific to its applications in monitoring mine conflagration, the corresponding Processes such as connection arrangement, signal t...Based on advantages of technology of distributive fiber-optic temperature sensing and specific to its applications in monitoring mine conflagration, the corresponding Processes such as connection arrangement, signal transmission and monitoring were illustrated. As applied in Sitai Coal Mine of Datong Coal Mine Group Co., this method is effective and accurate and could provide reliable gist for monitoring spontaneous combustion in gob area of mines.展开更多
Wearable thermoelectric devices hold significant promise in the realm of self-powered wearable electron-ics,offering applications in energy harvesting,movement tracking,and health monitoring.Nevertheless,developing th...Wearable thermoelectric devices hold significant promise in the realm of self-powered wearable electron-ics,offering applications in energy harvesting,movement tracking,and health monitoring.Nevertheless,developing thermoelectric devices with exceptional flexibility,enduring thermoelectric stability,multi-functional sensing,and comfortable wear remains a challenge.In this work,a stretchable MXene-based thermoelectric fabric is designed to accurately discern temperature and strain stimuli.This is achieved by constructing an adhesive polydopamine(PDA)layer on the nylon fabric surface,which facilitates the subsequent MXene attachment through hydrogen bonding.This fusion results in MXene-based thermo-electric fabric that excels in both temperature sensing and strain sensing.The resultant MXene-based thermoelectric fabric exhibits outstanding temperature detection capability and cyclic stability,while also delivering excellent sensitivity,rapid responsiveness(60 ms),and remarkable durability in strain sens-ing(3200 cycles).Moreover,when affixed to a mask,this MXene-based thermoelectric fabric utilizes the temperature difference between the body and the environment to harness body heat,converting it into electrical energy and accurately discerning the body’s respiratory rate.In addition,the MXene-based ther-moelectric fabric can monitor the state of the body’s joint through its own deformation.Furthermore,it possesses the capability to convert solar energy into heat.These findings indicate that MXene-based ther-moelectric fabric holds great promise for applications in power generation,motion tracking,and health monitoring.展开更多
An ultrasonic sensitivity-improved fiber-optic Fabry-Perot interferometer （FPI） is proposed and employed for ultra- sonic imaging of seismic physical models （SPMs）. The FPI comprises a flexible ultra-thin gold fil...An ultrasonic sensitivity-improved fiber-optic Fabry-Perot interferometer （FPI） is proposed and employed for ultra- sonic imaging of seismic physical models （SPMs）. The FPI comprises a flexible ultra-thin gold film and the end face of a graded-index multimode fiber （MMF）, both of which are enclosed in a ceramic tube. The MMF in a specified length can collimate the diverged light beam and compensate for the light loss inside the air cavity, leading to an increased spectral fringe visibility and thus a steeper spectral slope. By using the spectral sideband filtering technique, the collimated FP1 shows an improved ultrasonic response. Moreover, two-dimensional images of two SPMs are achieved in air by recon- structing the pulse-echo signals through using the time-of-flight approach. The proposed sensor with easy fabrication and compact size can be a good candidate for high-sensitivity and high-precision nondestructive testing of SPMs.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
Sixth Generation(6G)mobile communication networks will involve sensing as a new function,with the overwhelming trend of Integrated Sensing And Communications(ISAC).Although expanding the serving range of the networks,...Sixth Generation(6G)mobile communication networks will involve sensing as a new function,with the overwhelming trend of Integrated Sensing And Communications(ISAC).Although expanding the serving range of the networks,there exists performance trade-offbetween communication and sensing,in that they have competitions on the physical resources.Different resource allocation schemes will result in different sensing and communication performance,thus influencing the system’s overall performance.Therefore,how to model the system’s overall performance,and how to optimize it are key issues for ISAC.Relying on the large-scale deployment of the networks,cooperative ISAC has the advantages of wider coverage,more robust performance and good compatibility of multiple monostatic and multistatic sensing,compared to the non-cooperative ISAC.How to capture the performance gain of cooperation is a key issue for cooperative ISAC.To address the aforementioned vital problems,in this paper,we analyze the sensing accuracy gain,propose a unified ISAC performance evaluation framework and design several optimization methods in cooperative ISAC systems.The cooperative sensing accuracy gain is theoretically analyzed via Cramér Rao lower bound.The unified ISAC performance evaluation model is established by converting the communication mutual information to the effective minimum mean squared error.To optimize the unified ISAC performance,we design the optimization algorithms considering three factors:base stations’working modes,power allocation schemes and waveform design.Through simulations,we show the performance gain of the cooperative ISAC system and the effectiveness of the proposed optimization methods.展开更多
Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires a...Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:42130706。
文摘Coal mining induces changes in the nature of rock and soil bodies,as well as hydrogeological conditions,which can easily trigger the occurrence of geological disasters such as water inrush,movement of the coal seam roof and floor,and rock burst.Transparency in coal mine geological conditions provides technical support for intelligent coal mining and geological disaster prevention.In this sense,it is of great significance to address the requirements for informatizing coal mine geological conditions,dynamically adjust sensing parameters,and accurately identify disaster characteristics so as to prevent and control coal mine geological disasters.This paper examines the various action fields associated with geological disasters in mining faces and scrutinizes the types and sensing parameters of geological disasters resulting from coal seam mining.On this basis,it summarizes a distributed fiber-optic sensing technology framework for transparent geology in coal mines.Combined with the multi-field monitoring characteristics of the strain field,the temperature field,and the vibration field of distributed optical fiber sensing technology,parameters such as the strain increment ratio,the aquifer temperature gradient,and the acoustic wave amplitude are extracted as eigenvalues for identifying rock breaking,aquifer water level,and water cut range,and a multi-field sensing method is established for identifying the characteristics of mining-induced rock mass disasters.The development direction of transparent geology based on optical fiber sensing technology is proposed in terms of the aspects of sensing optical fiber structure for large deformation monitoring,identification accuracy of optical fiber acoustic signals,multi-parameter monitoring,and early warning methods.
基金supported by National Natural Science Foundation of China(Grant Nos.4203070 and 41977217)the Key Research&Development Program of Shaanxi Province(Grant No.2020ZDLSF06-03).
文摘Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the artificial land is essential,yet challenging.Here,we use an improved fiber-optic monitoring system for its subsurface multi-physical characterization.The system enables us to gather spatiotemporal distribution of various parameters,including strata deformation,temperature,and moisture.Yan’an New District was selected as a case study to conduct refined in-situ monitoring through a 77 m-deep borehole and a 30 m-long trench.Findings reveal that the ground settlement involves both the deformation of the filling loess and the underlying intact loess.Notably,the filling loess exhibits a stronger creep capability compared to underlying intact loess.The deformation along the profile is unevenly distributed,with a positive correlation with soil moisture.Water accumulation has been observed at the interface between the filling loess and the underlying intact loess,leading to a significant deformation.Moreover,the temperature and moisture in the filling loess have reached a new equilibrium state,with their depths influenced by atmospheric conditions measuring at 31 m and 26 m,respectively.The refined investigation allows us to identify critical layers that matter the sustainable development of newly created urban areas,and provide improved insights into the evolution mechanisms of land creation.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金supported by the National Natural Science Foundation of China(Nos.52422505 and 12274124)the Innovative Research Group Project of the National Natural Science Foundation of China(No.52321002).
文摘Nanoscale confinement environments often affect the transport mechanisms of nanofluids.Understanding the dynamic behavior of molecules in two-dimensional(2D)confined channels is of great importance in the areas of sensing,catalysis and energy storage.As a popular candidate for a new type of gas sensing material,MXenes have the problem of nonselectivity towards polar gases with slow responses,which severely limits their applications.Here,we report a study on regulating the confinement effect of 2D channels between MXene layers through annealing treatment and ion(Na^(+))intercalation for high-performance ammonia(NH_(3))sensing.Firstly,the annealing treatment accurately modulates the size of the 2D channels to effectively block the entry of large-size gas molecules and improve the selectivity for NH_(3).Ab initio molecular dynamics(AIMD)also confirms that the modulated channel size has a special"nano-pumping effect",which can accelerate the dynamic behavior of NH_(3) molecules in the 2D confined space.Moreover,the intercalation of Na+ions increases the adsorption capacity of NH_(3).Therefore,the"nano-pumping effect"and theintercalation of Na+ions effectively enhance the response speed and sensitivity of MXene to NH_(3),respectively.The experimental results show that the modified Ti_(3)C_(2) exhibits high sensitivity(0.17),rapid response(181 s),excellent selectivity and stability towards NH_(3).
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金This work was supported by the Major Program of the National Natural Science Foundation of China, NSFC （Grant No. 61290311） and the Natural Science Foundation of Hubei Province, China （No. 2014CFB269）.
文摘This paper reviews the work on huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings developed at the National Engineering Laboratory for Fiber Optic Sensing Technology （NEL-FOST）, Wuhan University of Technology, China. A versatile drawing tower grating sensor network based on ultra-weak fiber Bragg gratings （FBGs） is firstly proposed and demonstrated. The sensing network is interrogated with time- and wavelength-division multiplexing method, which is very promising for the large-scale sensing network.
基金financial supports from National Key Research and Development Program of China(2023YFB3209500)National Natural Science Foundation of China(NSFC)(12274052 and 62171076)+1 种基金Fundamental Research Funds for the Central Universities(DUT24ZD203)Bolian Research Funds of Dalian Maritime University and Fundamental Research Funds for the Central Universities(3132024605).
文摘Accurate and real-time detection of hydrogen(H_(2))is essential for ensuring energy security.Fiber-optic H_(2) sensors are gaining attention for their integration and remote sensing capabilities.However,they face challenges,including complex fabrication processes and limited response times.Here,we propose a fiber-optic H_(2) sensing tip based on Tamm plasmon polariton(TPP)resonance,consisting of a multilayer metal/dielectric Bragg reflector deposited directly on the fiber end facet,simplifying the fabrication process.The fiber-optic TPP(FOTPP)tip exhibits both TPP and multiple Fabry-Perot(FP)resonances simultaneously,with the TPP employed for highly sensitive H_(2) detection.Compared to FP resonance,TPP exhibits more than twice the sensitivity under the same structural dimension without cavity geometry deformation.The excellent performance is attributed to alterations in phase-matching conditions,driven by changes in penetration depth of TPP.Furthermore,the FP mode is utilized to achieve an efficient photothermal effect to catalyze the reaction between H_(2) and the FOTPP structure.Consequently,the response and recovery speeds of the FOTPP tip under resonance-enhanced photothermal assistance are improved by 6.5 and 2.1 times,respectively.Our work offers a novel strategy for developing TPP-integrated fiber-optic tips,refines the theoretical framework of photothermal-assisted detection systems,and provides clear experimental evidence.
基金The authors grate fully acknowledge the financial support provided by the National Natural Science Foundation of China(Grant Nos.42225702 and 42077235)the Open Research Project Program of the State Key Laboratory of Internet of Things for Smart City(University of Macao),China(Grant No.SKUoTSC(UM)-2021-2023/0RP/GA10/2022).
文摘Defects in cast-in-situ piles have an adverse impact on load transfer at the pile‒soil interface and pile bearing capacity. In recent years, thermal integrity profiling (TIP) has been developed to measure temperature profiles of cast-in-situ piles, enabling the detection of structural defects or anomalies at the early stage of construction. However, using this integrity testing method to evaluate potential defects in cast-in-situ piles requires a comprehensive understanding of the mechanism of hydration heat transfer from piles to surrounding soils. In this study, small-scale model tests were conducted in laboratory to investigate the performance of TIP in detecting pile integrity. Fiber-optic distributed temperature sensing (DTS) technology was used to monitor detailed temperature variations along model piles in sand. Additionally, sensors were installed in sand to measure water content and matric suction. An interpretation method against available DTS-based thermal profiles was proposed to reveal the potential defective regions. It shows that the temperature difference between normal and defective piles is more obvious in wet sand. In addition, there is a critical zone of water migration in sand due to the water absorption behavior of cement and temperature transfer-induced water migration in the early-age concrete setting. These findings could provide important insight into the improvement of the TIP testing method for field applications.
基金supported by the National Natural Science Foundation of China(No.51574054)the University Innovation Team Building Program of Chongqing(No.CXTDX201601030)+2 种基金Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.KJZD-M201901102)Chongqing Science and Technology Bureau(Nos.cstc2017shmsA20017,cstc2018jcyjAX0294,CSTCCXLJRC 201905)the Innovation Leader Project of Chongqing Science and Technology Bureau(No.CSTCCXLJRC201905)。
文摘An optical fiber dual Fabry-Perot interferometric carbon monoxide gas sensor based on PANI/Co3 O4/GO(PCG)sensing membrane coated on the end face of the optical fiber is proposed and fabricated.One end face of photonic crystal fiber(PCF)without cut-off wavelength is fused with a single-mode fiber(SMF),and the other end face of the PCF is coated with PCG sensing membrane.The collapsed layer formed during the air hole fusion of PCF is used as the first reflector,the interface between PCF and sensing membrane is used as the second reflector,and the interface between the sensing membrane and the air is used as the third reflector,thus the dual Fabry-Pe rot structure sensor is formed.The results show that the sensor has excellent sensitivity and selectivity to carbon monoxide.With the increasing concentration of carbon monoxide gas in the range of 0-60 ppm,the intensity of interference spectrum decreases.The sensitivity of the sensor is 0.3473 dB m/ppm,and its linearity is good.The response time and recovery time are 68 s and 106 s,respectively.The sensor has the advantages of the compact size,low cost,high sensitivity,strong selectivity and simple structure.It is suitable for the sensing detection of low concentration carbon monoxide gas.
基金supported by the Science and Technology Development Plan of Jilin Province(No.20180201036GX)
文摘A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium,and is based on Rayleigh backscattering in an optical fiber.The sensor is widely used in the safety monitoring of oil and gas pipelines,the classification of weak acoustic signals,defense,seismic prospecting,and other fields.In the field of seismic prospecting,distributed optical-fiber acoustic sensing(DAS)will gradually replace the use of the traditional geophone.The present paper mainly expounds the recent application of DAS,and summarizes recent research achievements of DAS in resource exploration,intrusion monitoring,pattern recognition,and other fields and various DAS system structures.It is found that the high-sensitivity and long-distance sensing capabilities of DAS play a role in the extensive monitoring applications of DAS in engineering.The future application and development of DAS technology are examined,with the hope of promoting the wider application of the DAS technology,which benefits engineering and society.
基金funding support from the National Natural Science Foundation of China(Grant No.52204030)Youth Innovation and Technology Support Program for Higher Education Institutions of Shandong Province,China(Grant No.2022KJ070)the National Natural Science Foundation of China Enterprise Innovation and Development Joint Fund Project(Grant No.U19B6003).
文摘Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS responses associated with near-wellbore hydraulic fracture properties is still unclear.To address this knowledge gap,we use coupled wellbore-reservoir-geomechanics simulations to study measured strain-change behavior and infer hydraulic fracture characteristics.The crossflow among fractures is captured through explicit modeling of the transient wellbore flow.In addition,local grid refinement is applied to accurately capture strain changes along the fiber.A Base Case model was designed with four fractures of varying properties,simulating strain change signals when the production well is shut-in for 10 d after 240 d of production and reopened for 2 d.Strain-pressure plots for different fracture clusters were used to gain insights into inferring fracture properties using DSS data.When comparing the model with and without the wellbore,distinct strain change signals were observed,emphasizing the importance of incorporating the wellbore in FO-DSS modeling.The effects of fracture spacing and matrix permeability on strain change signals were thoroughly investigated.The results of our numerical study can improve the understanding of the relation between DSS signals and fracture hydraulic properties,thus maximizing the value of the dataset for fracture diagnostics and characterization.
基金Supported by the National Natural Science Foundation of China （50375026,50375028）
文摘Based on advantages of technology of distributive fiber-optic temperature sensing and specific to its applications in monitoring mine conflagration, the corresponding Processes such as connection arrangement, signal transmission and monitoring were illustrated. As applied in Sitai Coal Mine of Datong Coal Mine Group Co., this method is effective and accurate and could provide reliable gist for monitoring spontaneous combustion in gob area of mines.
基金supported by the National Natural Science Foundation of China(No.21975107)the China Scholarship Council(No.202206790046).
文摘Wearable thermoelectric devices hold significant promise in the realm of self-powered wearable electron-ics,offering applications in energy harvesting,movement tracking,and health monitoring.Nevertheless,developing thermoelectric devices with exceptional flexibility,enduring thermoelectric stability,multi-functional sensing,and comfortable wear remains a challenge.In this work,a stretchable MXene-based thermoelectric fabric is designed to accurately discern temperature and strain stimuli.This is achieved by constructing an adhesive polydopamine(PDA)layer on the nylon fabric surface,which facilitates the subsequent MXene attachment through hydrogen bonding.This fusion results in MXene-based thermo-electric fabric that excels in both temperature sensing and strain sensing.The resultant MXene-based thermoelectric fabric exhibits outstanding temperature detection capability and cyclic stability,while also delivering excellent sensitivity,rapid responsiveness(60 ms),and remarkable durability in strain sens-ing(3200 cycles).Moreover,when affixed to a mask,this MXene-based thermoelectric fabric utilizes the temperature difference between the body and the environment to harness body heat,converting it into electrical energy and accurately discerning the body’s respiratory rate.In addition,the MXene-based ther-moelectric fabric can monitor the state of the body’s joint through its own deformation.Furthermore,it possesses the capability to convert solar energy into heat.These findings indicate that MXene-based ther-moelectric fabric holds great promise for applications in power generation,motion tracking,and health monitoring.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61735014,61327012,and 61275088)the Scientific Research Program Funded by Shaanxi Provincial Education Department,China(Grant No.08JZ58)the Northwest University Graduate Innovation and Creativity Funds,China(Grant No.YZZ17088)
文摘An ultrasonic sensitivity-improved fiber-optic Fabry-Perot interferometer （FPI） is proposed and employed for ultra- sonic imaging of seismic physical models （SPMs）. The FPI comprises a flexible ultra-thin gold film and the end face of a graded-index multimode fiber （MMF）, both of which are enclosed in a ceramic tube. The MMF in a specified length can collimate the diverged light beam and compensate for the light loss inside the air cavity, leading to an increased spectral fringe visibility and thus a steeper spectral slope. By using the spectral sideband filtering technique, the collimated FP1 shows an improved ultrasonic response. Moreover, two-dimensional images of two SPMs are achieved in air by recon- structing the pulse-echo signals through using the time-of-flight approach. The proposed sensor with easy fabrication and compact size can be a good candidate for high-sensitivity and high-precision nondestructive testing of SPMs.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘Sixth Generation(6G)mobile communication networks will involve sensing as a new function,with the overwhelming trend of Integrated Sensing And Communications(ISAC).Although expanding the serving range of the networks,there exists performance trade-offbetween communication and sensing,in that they have competitions on the physical resources.Different resource allocation schemes will result in different sensing and communication performance,thus influencing the system’s overall performance.Therefore,how to model the system’s overall performance,and how to optimize it are key issues for ISAC.Relying on the large-scale deployment of the networks,cooperative ISAC has the advantages of wider coverage,more robust performance and good compatibility of multiple monostatic and multistatic sensing,compared to the non-cooperative ISAC.How to capture the performance gain of cooperation is a key issue for cooperative ISAC.To address the aforementioned vital problems,in this paper,we analyze the sensing accuracy gain,propose a unified ISAC performance evaluation framework and design several optimization methods in cooperative ISAC systems.The cooperative sensing accuracy gain is theoretically analyzed via Cramér Rao lower bound.The unified ISAC performance evaluation model is established by converting the communication mutual information to the effective minimum mean squared error.To optimize the unified ISAC performance,we design the optimization algorithms considering three factors:base stations’working modes,power allocation schemes and waveform design.Through simulations,we show the performance gain of the cooperative ISAC system and the effectiveness of the proposed optimization methods.
基金supported by the National Key Research and Development Program of China under Grant(2024YFE0100400)Taishan Scholars Project Special Funds(tsqn202312035)+2 种基金the open research foundation of State Key Laboratory of Integrated Chips and Systems,the Tianjin Science and Technology Plan Project(No.22JCZDJC00630)the Higher Education Institution Science and Technology Research Project of Hebei Province(No.JZX2024024)Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017).
文摘Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
