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
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
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
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
Stimuli-responsive two-dimensional (2D) covalent organic frameworks (COFs) with precise structures and permanent porosity have been employed as platforms for sensors. The slight change of backbones inside frameworks l...Stimuli-responsive two-dimensional (2D) covalent organic frameworks (COFs) with precise structures and permanent porosity have been employed as platforms for sensors. The slight change of backbones inside frameworks leads to different electronic states by external stimuli, such as solvent, pH, and water. Herein, we introduced an alkynyl-based building block (ETBA) with high planarity to synthesize two imine-based alkynyl-COFs (ETBA-TAPE-COF and ETBA-PYTA-COF) with high yield, good crystallinity, and chemical stability. Due to the presence of acetylene bonds, ETBA-TAPE-COF does not adopt the completely overlapping AA stacking mode. Slight interlayer displacement occurs along the parallel direction relative to the acetylene linkages, which facilitates lower configurational energy. Additionally, the introduction of pyrene group contributes to high π-electron mobility of ETBA-PYTA-COF. The interactions between electron-withdrawing group (ETBA) and electron-donating group (PYTA) during the processes of protonation and intramolecular charge transfer (ICT) endow ETBA-PYTA-COF with excellent acidochromic and solvatochromic properties, respectively. Based on this, a fluorescence sensor is successfully established, which can be used for rapid response to trace amounts of water in organic solvents. In contrast, ETBA-TAPE-COF does not exhibit these photophysical properties due to its higher HOMO–LUMO gap compared to ETBA-PYTA-COF. This work proposes a new strategy for designing and preparing COFs with unique photophysical properties without introducing additional functional groups.展开更多
Ionogels have demonstrated substantial applications in smart wearable systems,soft robotics,and biomedical engineering due to the exceptional ionic conductivity and optical transparency.However,achieving ionogels with...Ionogels have demonstrated substantial applications in smart wearable systems,soft robotics,and biomedical engineering due to the exceptional ionic conductivity and optical transparency.However,achieving ionogels with desirable mechanical properties,environmental stability,and multi-mode sensing remains challenging.Here,we propose a simple strategy for the fabrication of multifunctional silk fabric-based ionogels(BSFIGs).The resulting fabric ionogels exhibits superior mechanical properties,with high tensile strength(11.3 MPa)and work of fracture(2.53 MJ/m^(3)).And its work of fracture still has 1.42 MJ/m^(3)as the notch increased to 50%,indicating its crack growth insensitivity.These ionogels can be used as sensors for strain,temperature,and tactile multimode sensing,demonstrating a gauge factor of 1.19 and a temperature coefficient of resistance of3.17/℃^(-1).Furthermore,these ionogels can be used for the detection of different roughness and as touch screens.The ionogels also exhibit exceptional optical transmittance and environmental stability even at80℃.Our scalable fabrication process broadens the application potential of these multifunctional ionogels in diverse fields,from smart systems to extreme environments.展开更多
Chloroform and other volatile organic pollutants have garnered widespread attention from the public and researchers,because of their potential harm to the respiratory system,nervous system,skin,and eyes.However,resear...Chloroform and other volatile organic pollutants have garnered widespread attention from the public and researchers,because of their potential harm to the respiratory system,nervous system,skin,and eyes.However,research on chloroform vapor sensing is still in its early stages,primarily due to the lack of specific recognition motif.Here we report a mesoporous photonic crystal sensor incorporating carbon dots-based nanoreceptor(HMSS@CDs-PCs)for enhanced chloroform sensing.The colloidal PC packed with hollow mesoporous silica spheres provides an interconnected ordered macro-meso-hierarchical porous structure,ideal for rapid gas sensing utilizing the photonic bandgap shift as the readout signal.The as-synthesized CDs with pyridinic-N-oxide functional groups adsorbed in the hollow mesoporous silica spheres are found to not only serve as the chloroform adsorption sites,but also a molecular glue that prevents crack formation in the colloidal PC.The sensitivity of HMSS@CDs-PCs sensor is 0.79 nm ppm^(-1)and an impressively low limit of detection is 3.22 ppm,which are the best reported values in fast-response chloroform vapor sensor without multi-signal assistance.The positive response time is 7.5 s and the negative response time 9 s.Furthermore,relatively stable sensing can be maintained within a relative humidity of 20%-85%RH and temperature of 25-55℃.This study demonstrates that HMSS@CDs-PCs sensors have practical application potential in indoor and outdoor chloroform vapor detection.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
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
The rise in gas leakage incidents underscores the urgent need for advanced gas-sensing platforms with ultra-low concentration detection capability.Sensing gate field effect transistor(FET)gas sensors,renowned for the ...The rise in gas leakage incidents underscores the urgent need for advanced gas-sensing platforms with ultra-low concentration detection capability.Sensing gate field effect transistor(FET)gas sensors,renowned for the gas-induced signal amplification without directly exposing the channel to the ambient environment,play a pivotal role in detecting trace-level hazardous gases with high sensitivity and good stability.In this work,carbon nanotubes are employed as the conducting channel,and yttrium oxide(Y_(2)O_(3))is utilized as the gate dielectric layer.Noble metal Pd is incorporated as a sensing gate for hydrogen(H_(2))detection,leveraging its catalytic properties and unique adsorption capability.The fabricated carbon-based FET gas sensor demonstrates a remarkable detection limit of 20×10^(–9) for H_(2) under an air environment,enabling early warning in case of gas leakage.Moreover,the as-prepared sensor exhibited good selectivity,repeatability,and anti-humidity properties.Further experiments elucidate the interaction between H_(2) and sensing electrode under an air/nitrogen environment,providing insights into the underlying oxygen-assisted recoverable sensing mechanism.It is our aspiration for this research to establish a robust experimental foundation for achieving high performance and highly integrated fabrication of trace gas sensors.展开更多
To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measureme...To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.展开更多
Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic...Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.展开更多
Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their se...Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their service life is essential for the overall functionality of geotechnical infrastructure.Distributed Brillouin sensing(DBS)is increasingly applied in geotechnical projects due to its ability to acquire spatially continuous strain and temperature distributions over distances of up to 150 km using a single optical fibre.However,limited by the complex operations of distributed optic fibre sensing(DFOS)sensors in curved structures,previous reports about exploiting DBS in geotechnical structural health monitoring(SHM)have mostly been focused on flat surfaces.The lack of suitable DFOS installation methods matched to the spatial characteristics of continuous monitoring is one of the major factors that hinder the further application of this technique in curved structures.This review paper starts with a brief introduction of the fundamental working principle of DBS and the inherent limitations of DBS being used on monitoring curved surfaces.Subsequently,the state-of-the-art installation methods of optical fibres in curved structures are reviewed and compared to address the most suitable scenario of each method and their advantages and disadvantages.The installation challenges of optical fibres that can highly affect measurement accuracy are also discussed in the paper.展开更多
This paper studies the sensing base station(SBS)that has great potential to improve the safety of vehicles and pedestrians on roads.SBS can detect the targets on the road with communication signals using the integrate...This paper studies the sensing base station(SBS)that has great potential to improve the safety of vehicles and pedestrians on roads.SBS can detect the targets on the road with communication signals using the integrated sensing and communication(ISAC)technique.Compared with vehicle-mounted radar,SBS has a better sensing field due to its higher deployment position,which can help solve the problem of sensing blind areas.In this paper,key technologies of SBS are studied,including the beamforming algorithm,beam scanning scheme,and interference cancellation algorithm.To transmit and receive ISAC signals simultaneously,a double-coupling antenna array is applied.The free detection beam and directional communication beam are proposed for joint communication and sensing to meet the requirements of beamwidth and pointing directions.The joint timespace-frequency domain division multiple access algorithm is proposed to cancel the interference of SBS,including multiuser interference and duplex interference between sensing and communication.Finally,the sensing and communication performance of SBS under the industrial scientific medical power limitation is analyzed and simulated.Simulation results show that the communication rate of SBS can reach over 100 Mbps and the range of sensing and communication can reach about 500 m.展开更多
Organic semiconductor materials have demonstrated extensive potential in the field of gas sensors due to the advantages including designable chemical structure,tunable physical and chemical properties.Through density ...Organic semiconductor materials have demonstrated extensive potential in the field of gas sensors due to the advantages including designable chemical structure,tunable physical and chemical properties.Through density functional theory(DFT)calculations,researchers can investigate gas sensing mechanisms,optimize,and predict the electronic structures and response characteristics of these materials,and thereby identify candidate materials with promising gas sensing applications for targeted design.This review concentrates on three primary applications of DFT technology in the realm of organic semiconductor-based gas sensors:(1)Investigating the sensing mechanisms by analyzing the interactions between gas molecules and sensing materials through DFT,(2)simulating the dynamic responses of gas molecules,which involves the behavior on the sensing interface using DFT combined with other computational methods to explore adsorption and diffusion processes,and(3)exploring and designing sensitive materials by employing DFT for screening and predicting chemical structures,thereby developing new sensing materials with exceptional performance.Furthermore,this review examines current research outcomes and anticipates the extensive application prospects of DFT technology in the domain of organic semiconductor-based gas sensors.These efforts are expected to provide valuable insights for further indepth exploration of DFT applications in sensor technology,thereby fostering significant advancements and innovations in the field.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
Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.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
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
A phenylphenothiazine anchored Tb(Ⅲ)-cyclen complex PTP-Cy-Tb for hypochlorite ion(ClO^(-))detection has been designed and prepared.PTP-Cy-Tb shows a weak Tb-based emission with AIE-characteristics in aqueous solutio...A phenylphenothiazine anchored Tb(Ⅲ)-cyclen complex PTP-Cy-Tb for hypochlorite ion(ClO^(-))detection has been designed and prepared.PTP-Cy-Tb shows a weak Tb-based emission with AIE-characteristics in aqueous solutions.After addition of ClO^(-),the fluorescence of PTP-Cy-Tb gives a large enhancement for oxidization the thioether to sulfoxide group.The detection limit of PTP-Cy-Tb toward ClO^(-)is as low as 8.85 nmol/L.The sensing mechanism was detailedly investigated by time of flight mass spectrometer(TOF-MS),Fourier transform infrared spectroscopy(FT-IR)and density functional theory(DFT)calculation.In addition,PTP-Cy-Tb has been successfully used for on-site and real-time detection of ClO^(-)in real water samples by using the smartphone-based visualization method and test strips.展开更多
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(22172055)the Science Fund for Distinguished Young Scholars of Guangdong Province(2023B1515040026)+1 种基金the Key Area Research and Development Program of Guangdong Province(2023B0101200008)the Natural Science Foundation of Guangdong Province(2022A1515011892).
文摘Stimuli-responsive two-dimensional (2D) covalent organic frameworks (COFs) with precise structures and permanent porosity have been employed as platforms for sensors. The slight change of backbones inside frameworks leads to different electronic states by external stimuli, such as solvent, pH, and water. Herein, we introduced an alkynyl-based building block (ETBA) with high planarity to synthesize two imine-based alkynyl-COFs (ETBA-TAPE-COF and ETBA-PYTA-COF) with high yield, good crystallinity, and chemical stability. Due to the presence of acetylene bonds, ETBA-TAPE-COF does not adopt the completely overlapping AA stacking mode. Slight interlayer displacement occurs along the parallel direction relative to the acetylene linkages, which facilitates lower configurational energy. Additionally, the introduction of pyrene group contributes to high π-electron mobility of ETBA-PYTA-COF. The interactions between electron-withdrawing group (ETBA) and electron-donating group (PYTA) during the processes of protonation and intramolecular charge transfer (ICT) endow ETBA-PYTA-COF with excellent acidochromic and solvatochromic properties, respectively. Based on this, a fluorescence sensor is successfully established, which can be used for rapid response to trace amounts of water in organic solvents. In contrast, ETBA-TAPE-COF does not exhibit these photophysical properties due to its higher HOMO–LUMO gap compared to ETBA-PYTA-COF. This work proposes a new strategy for designing and preparing COFs with unique photophysical properties without introducing additional functional groups.
基金supported by the National Natural Science Foundation of China(No.12302192)the Fundamental Research Funds for the Central Universities(No.SWU-KQ22025)+4 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJQN202300222)Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0241)the Fund for Innovative Research Groups of Natural Science Foundation of Hebei Province(No.A2024202045)Key Technologies and Demonstration Application Research Project for Large-scale Lithium-ion Hybrid Energy Storage Equipment(No.HC23118)Major Basic Research Project of Hebei Province Natural Science Foundation(No.A2023202049).
文摘Ionogels have demonstrated substantial applications in smart wearable systems,soft robotics,and biomedical engineering due to the exceptional ionic conductivity and optical transparency.However,achieving ionogels with desirable mechanical properties,environmental stability,and multi-mode sensing remains challenging.Here,we propose a simple strategy for the fabrication of multifunctional silk fabric-based ionogels(BSFIGs).The resulting fabric ionogels exhibits superior mechanical properties,with high tensile strength(11.3 MPa)and work of fracture(2.53 MJ/m^(3)).And its work of fracture still has 1.42 MJ/m^(3)as the notch increased to 50%,indicating its crack growth insensitivity.These ionogels can be used as sensors for strain,temperature,and tactile multimode sensing,demonstrating a gauge factor of 1.19 and a temperature coefficient of resistance of3.17/℃^(-1).Furthermore,these ionogels can be used for the detection of different roughness and as touch screens.The ionogels also exhibit exceptional optical transmittance and environmental stability even at80℃.Our scalable fabrication process broadens the application potential of these multifunctional ionogels in diverse fields,from smart systems to extreme environments.
基金supported by the National Key Research and Development Program of China(No.2022YFB3205500)National Natural Science Foundation of China(Nos.U23A20360 and 62303192)QL wishes to thank Water Research Australia(WRA 1144/21)for funding support.
文摘Chloroform and other volatile organic pollutants have garnered widespread attention from the public and researchers,because of their potential harm to the respiratory system,nervous system,skin,and eyes.However,research on chloroform vapor sensing is still in its early stages,primarily due to the lack of specific recognition motif.Here we report a mesoporous photonic crystal sensor incorporating carbon dots-based nanoreceptor(HMSS@CDs-PCs)for enhanced chloroform sensing.The colloidal PC packed with hollow mesoporous silica spheres provides an interconnected ordered macro-meso-hierarchical porous structure,ideal for rapid gas sensing utilizing the photonic bandgap shift as the readout signal.The as-synthesized CDs with pyridinic-N-oxide functional groups adsorbed in the hollow mesoporous silica spheres are found to not only serve as the chloroform adsorption sites,but also a molecular glue that prevents crack formation in the colloidal PC.The sensitivity of HMSS@CDs-PCs sensor is 0.79 nm ppm^(-1)and an impressively low limit of detection is 3.22 ppm,which are the best reported values in fast-response chloroform vapor sensor without multi-signal assistance.The positive response time is 7.5 s and the negative response time 9 s.Furthermore,relatively stable sensing can be maintained within a relative humidity of 20%-85%RH and temperature of 25-55℃.This study demonstrates that HMSS@CDs-PCs sensors have practical application potential in indoor and outdoor chloroform vapor detection.
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.
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supported by the National Natural Science Foundation of China(Nos.62071410 and 62101477)Hunan Provincial Natural Science Foundation(No.2021JJ40542).
文摘The rise in gas leakage incidents underscores the urgent need for advanced gas-sensing platforms with ultra-low concentration detection capability.Sensing gate field effect transistor(FET)gas sensors,renowned for the gas-induced signal amplification without directly exposing the channel to the ambient environment,play a pivotal role in detecting trace-level hazardous gases with high sensitivity and good stability.In this work,carbon nanotubes are employed as the conducting channel,and yttrium oxide(Y_(2)O_(3))is utilized as the gate dielectric layer.Noble metal Pd is incorporated as a sensing gate for hydrogen(H_(2))detection,leveraging its catalytic properties and unique adsorption capability.The fabricated carbon-based FET gas sensor demonstrates a remarkable detection limit of 20×10^(–9) for H_(2) under an air environment,enabling early warning in case of gas leakage.Moreover,the as-prepared sensor exhibited good selectivity,repeatability,and anti-humidity properties.Further experiments elucidate the interaction between H_(2) and sensing electrode under an air/nitrogen environment,providing insights into the underlying oxygen-assisted recoverable sensing mechanism.It is our aspiration for this research to establish a robust experimental foundation for achieving high performance and highly integrated fabrication of trace gas sensors.
基金supported by the National Natural Science Foundation of China(Nos.61705027,62375031 and 52075131)the Chongqing Science and Technology Commission Basic Research Project(No.CSTC-2020jcyj-msxm0603)the Chongqing Municipal Education Commission Science and Technology Research Program(No.KJQN202000609)。
文摘To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.
基金supported by the Office of Naval Research(Grant Nos.N00014-16-1-2094 and N00014-24-1-2547)the Lawrence Livermore National Laboratory(Grant No.B622827)the National Science Foundation.Y.-S.J.acknowledges support from KRISS(Grant Nos.25011026 and 25011211).
文摘Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.
基金support provided by Science Foundation Ireland Frontiers for the Future Programme,21/FFP-P/10090.
文摘Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their service life is essential for the overall functionality of geotechnical infrastructure.Distributed Brillouin sensing(DBS)is increasingly applied in geotechnical projects due to its ability to acquire spatially continuous strain and temperature distributions over distances of up to 150 km using a single optical fibre.However,limited by the complex operations of distributed optic fibre sensing(DFOS)sensors in curved structures,previous reports about exploiting DBS in geotechnical structural health monitoring(SHM)have mostly been focused on flat surfaces.The lack of suitable DFOS installation methods matched to the spatial characteristics of continuous monitoring is one of the major factors that hinder the further application of this technique in curved structures.This review paper starts with a brief introduction of the fundamental working principle of DBS and the inherent limitations of DBS being used on monitoring curved surfaces.Subsequently,the state-of-the-art installation methods of optical fibres in curved structures are reviewed and compared to address the most suitable scenario of each method and their advantages and disadvantages.The installation challenges of optical fibres that can highly affect measurement accuracy are also discussed in the paper.
基金supported in part by the National Natural Science Foundation of China under Grant U21B2014,Grant 92267202,and Grant 62271081.
文摘This paper studies the sensing base station(SBS)that has great potential to improve the safety of vehicles and pedestrians on roads.SBS can detect the targets on the road with communication signals using the integrated sensing and communication(ISAC)technique.Compared with vehicle-mounted radar,SBS has a better sensing field due to its higher deployment position,which can help solve the problem of sensing blind areas.In this paper,key technologies of SBS are studied,including the beamforming algorithm,beam scanning scheme,and interference cancellation algorithm.To transmit and receive ISAC signals simultaneously,a double-coupling antenna array is applied.The free detection beam and directional communication beam are proposed for joint communication and sensing to meet the requirements of beamwidth and pointing directions.The joint timespace-frequency domain division multiple access algorithm is proposed to cancel the interference of SBS,including multiuser interference and duplex interference between sensing and communication.Finally,the sensing and communication performance of SBS under the industrial scientific medical power limitation is analyzed and simulated.Simulation results show that the communication rate of SBS can reach over 100 Mbps and the range of sensing and communication can reach about 500 m.
基金supported by National Natural Science Foundation of China(Nos.92263109 and 61904188)the Shanghai Rising-Star Program(No.22QA1410400)。
文摘Organic semiconductor materials have demonstrated extensive potential in the field of gas sensors due to the advantages including designable chemical structure,tunable physical and chemical properties.Through density functional theory(DFT)calculations,researchers can investigate gas sensing mechanisms,optimize,and predict the electronic structures and response characteristics of these materials,and thereby identify candidate materials with promising gas sensing applications for targeted design.This review concentrates on three primary applications of DFT technology in the realm of organic semiconductor-based gas sensors:(1)Investigating the sensing mechanisms by analyzing the interactions between gas molecules and sensing materials through DFT,(2)simulating the dynamic responses of gas molecules,which involves the behavior on the sensing interface using DFT combined with other computational methods to explore adsorption and diffusion processes,and(3)exploring and designing sensitive materials by employing DFT for screening and predicting chemical structures,thereby developing new sensing materials with exceptional performance.Furthermore,this review examines current research outcomes and anticipates the extensive application prospects of DFT technology in the domain of organic semiconductor-based gas sensors.These efforts are expected to provide valuable insights for further indepth exploration of DFT applications in sensor technology,thereby fostering significant advancements and innovations in the field.
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 National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the National Key Research and Development Program of China 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 Nature Science Foundation of China(Nos.22061028 and 22361028)Jiangxi Provincial Natural Science Foundation(No.20224ACB203012)。
文摘A phenylphenothiazine anchored Tb(Ⅲ)-cyclen complex PTP-Cy-Tb for hypochlorite ion(ClO^(-))detection has been designed and prepared.PTP-Cy-Tb shows a weak Tb-based emission with AIE-characteristics in aqueous solutions.After addition of ClO^(-),the fluorescence of PTP-Cy-Tb gives a large enhancement for oxidization the thioether to sulfoxide group.The detection limit of PTP-Cy-Tb toward ClO^(-)is as low as 8.85 nmol/L.The sensing mechanism was detailedly investigated by time of flight mass spectrometer(TOF-MS),Fourier transform infrared spectroscopy(FT-IR)and density functional theory(DFT)calculation.In addition,PTP-Cy-Tb has been successfully used for on-site and real-time detection of ClO^(-)in real water samples by using the smartphone-based visualization method and test strips.
