Ultrasonic neuromodulation has gained recognition as a promising therapeutic approach.A miniature transducer capable of generating suitable-strength and broadband ultrasound is of great significance for achieving high...Ultrasonic neuromodulation has gained recognition as a promising therapeutic approach.A miniature transducer capable of generating suitable-strength and broadband ultrasound is of great significance for achieving high spatial precision ultrasonic neural stimulation.However,the ultrasound transducer with the above integrated is yet to be challenged.Here,we developed a fiber-optic photoacoustic emitter(FPE)with a diameter of 200μm,featuring controllable sound intensity and a broadband response(−6 dB bandwidth:162%).The device integrates MXene(Ti_(3)C_(2)Tx),known for its exceptional photothermal properties,and polydimethylsiloxane,which offers a high thermal expansion coefficient.This FPE,exhibiting high spatial precision(lateral:163.3μm,axial:207μm),is capable of selectively activating neurons in targeted regions.Using the TetTagging method to selectively express a cfos-promoter-inducible mCHERRY gene within the medial prefrontal cortex(mPFC),we found that photoacoustic stimulation significantly and temporarily activated the neurons.In vivo fiber photometry demonstrated that photoacoustic stimulation induced substantial calcium transients in mPFC neurons.Furthermore,we confirmed that photoacoustic stimulation of the mPFC using FPE markedly alleviates acute social defeat stress-induced emotional stress in mice.This work demonstrates the potential of FPEs for clinical applications,with a particular focus on modulating neural activity to regulate emotions.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
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
Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fibe...Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fiber sensing technology can effectively capture the process of fracture initiation and propagation,yet the evaluation method for the initiation and propagation of bedding fractures remains immature.This study integrates a distributed optical fiber sensing device based on optical frequency domain reflectometry(OFDR)with a large-scale true tri-axial fracturing physical simulation apparatus to conduct real-time monitoring experiments on shale samples from the Lianggaoshan Formation in the Sichuan Basin,where bedding is well-developed.The experimental results demonstrate that two bedding fractures in the shale sample initiated and propagated.The evolution characteristics of fiber-optic strain in a horizontal adjacent well,induced by the initiation and propagation of bedding fractures,are characterized by the appearance of a tensile strain convergence zone in the middle of the optical fiber,flanked by two compressive strain convergence zones.The initiation and propagation of the distal bedding fracture causes the fiber-optic strain in the horizontal adjacent well to superimpose,with the asymmetric propagation of the bedding fracture leading to an asymmetric tensile strain convergence zone in the optical fiber.Utilizing a finite element method coupled with a cohesive element approach,a forward model of fiber-optic strain in the horizontal adjacent well induced by the initiation and propagation of hydraulic fracturing bedding fractures was constructed.Numerical simulation analyses were conducted to evaluate the evolution of fiber-optic strain in the horizontal adjacent well,confirming the correctness of the observed evolution characteristics.The presence of a"wedge-shaped"tensile strain convergence zone in the fiber-optic strain waterfall plot,accompanied by two compressive strain convergence zones,indicates the initiation and propagation of bedding fractures during the fracturing process.These findings provide valuable insights for interpreting distributed fiber-optic data in shale fracturing field applications.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
In order to improve the multiplexing capability of the optical sensors based on the lower interferential optic fiber sensing technology and the white light fiber-optic Mach-Zehnder interferometer,reflective ladder top...In order to improve the multiplexing capability of the optical sensors based on the lower interferential optic fiber sensing technology and the white light fiber-optic Mach-Zehnder interferometer,reflective ladder topology network ( RLT) with tailored formula was proposed. The topology network consists of 6 rungs sensing elements linked by 5 couplers. Two cases with different choices of couplers were contrasted: one is equal coupling ratio,and the other is tailored coupling ratio. Through the simulation of these two cases,the detailed multiplexing capability was analyzed,and accordingly the experiments were also carried out. The simulation results showed that,the tailored formula enhances the multiplexing capability of the structure. In the first case, the maximum number of sensors which can be multiplexed is 8,and in the other case is 12 fiber optic sensors. The experimental results have a good agreement with numerical simulation results. Thus,it is considered expedient to incorporate RLT into large-scale building,grounds,bridges,dams,tunnels,highways and perimeter security.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
A compact and high-resolution fiber-optic refractive index(RI)sensor based on a microwave photonic filter(MPF)is proposed and experimentally validated.The sensing head utilizes a cascaded in-line interferometer fabric...A compact and high-resolution fiber-optic refractive index(RI)sensor based on a microwave photonic filter(MPF)is proposed and experimentally validated.The sensing head utilizes a cascaded in-line interferometer fabricated by an input single-mode fiber(SMF)tapered fusion with no-core fiber-thin-core fiber(TCF)-SMF.The surrounding RI(SRI)can be demodulated by tracing the passband’s central frequency of the MPF,which is constructed by the cascaded in-line interferometer,electro-optic modulator,and a section of dispersion compensation fiber.The sensitivity of the sensor is tailorable through the use of different lengths of TCF.Experimental results reveal that with a 30 mm length of TCF,the sensor achieves a maximum theoretical sensitivity and resolution of-1.403 GHz∕refractive index unit eRIUT and 1.425×10^(-7) RIU,respectively,which is at least 6.3 times higher than what has been reported previously.Furthermore,the sensor exhibits temperature-insensitive characteristics within the range of 25℃-75℃,with a temperatureinduced frequency change of only±1.5 MHz.This value is significantly lower than the frequency change induced by changes in the SRI.The proposed MPF-based cascaded in-line interferometer RI sensor possesses benefits such as easy manufacture,low cost,high resolution,and temperature insensitivity.展开更多
With the rapid development of the Internet of Things(IoT)technology,fiber-optic sensors,as a kind of high-precision and high-sensitivity measurement tool,are increasingly widely used in the field of IoT.This paper out...With the rapid development of the Internet of Things(IoT)technology,fiber-optic sensors,as a kind of high-precision and high-sensitivity measurement tool,are increasingly widely used in the field of IoT.This paper outlines the advantages of fiber-optic sensors over traditional sensors,such as high precision,strong resistance to electromagnetic interference,and long transmission distance.On this basis,the paper discusses the application scenarios of fiber-optic sensors in the Internet of Things,including environmental monitoring,intelligent industry,medical and health care,intelligent transportation,and other fields.It is hoped that this study can provide theoretical support and practical guidance for the further development of fiber-optic sensors in the field of the Internet of Things,as well as promote the innovation and application of IoT.展开更多
Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited...Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.展开更多
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving ...Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving you only look once version 5(YOLOv5) is proposed.By incorporating the lightweight Ghost Net module into the YOLOv5 backbone network,we effectively reduce the model size.The addition of the receptive fields block(RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model.Subsequently,the high-performance lightweight convolution,GSConv,is integrated into the neck structure for further model size compression.Moreover,the baseline model's loss function is substituted with efficient insertion over union(EIoU),accelerating network convergence and enhancing detection precision.Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
基金supported by the National Nature Science Foundation of China(Grant Number:U24A20306,12102140,6227031087,62035006,and 6207030117).
文摘Ultrasonic neuromodulation has gained recognition as a promising therapeutic approach.A miniature transducer capable of generating suitable-strength and broadband ultrasound is of great significance for achieving high spatial precision ultrasonic neural stimulation.However,the ultrasound transducer with the above integrated is yet to be challenged.Here,we developed a fiber-optic photoacoustic emitter(FPE)with a diameter of 200μm,featuring controllable sound intensity and a broadband response(−6 dB bandwidth:162%).The device integrates MXene(Ti_(3)C_(2)Tx),known for its exceptional photothermal properties,and polydimethylsiloxane,which offers a high thermal expansion coefficient.This FPE,exhibiting high spatial precision(lateral:163.3μm,axial:207μm),is capable of selectively activating neurons in targeted regions.Using the TetTagging method to selectively express a cfos-promoter-inducible mCHERRY gene within the medial prefrontal cortex(mPFC),we found that photoacoustic stimulation significantly and temporarily activated the neurons.In vivo fiber photometry demonstrated that photoacoustic stimulation induced substantial calcium transients in mPFC neurons.Furthermore,we confirmed that photoacoustic stimulation of the mPFC using FPE markedly alleviates acute social defeat stress-induced emotional stress in mice.This work demonstrates the potential of FPEs for clinical applications,with a particular focus on modulating neural activity to regulate emotions.
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the financial support by National Natural Science Foundation of China(No.52334001)。
文摘Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fiber sensing technology can effectively capture the process of fracture initiation and propagation,yet the evaluation method for the initiation and propagation of bedding fractures remains immature.This study integrates a distributed optical fiber sensing device based on optical frequency domain reflectometry(OFDR)with a large-scale true tri-axial fracturing physical simulation apparatus to conduct real-time monitoring experiments on shale samples from the Lianggaoshan Formation in the Sichuan Basin,where bedding is well-developed.The experimental results demonstrate that two bedding fractures in the shale sample initiated and propagated.The evolution characteristics of fiber-optic strain in a horizontal adjacent well,induced by the initiation and propagation of bedding fractures,are characterized by the appearance of a tensile strain convergence zone in the middle of the optical fiber,flanked by two compressive strain convergence zones.The initiation and propagation of the distal bedding fracture causes the fiber-optic strain in the horizontal adjacent well to superimpose,with the asymmetric propagation of the bedding fracture leading to an asymmetric tensile strain convergence zone in the optical fiber.Utilizing a finite element method coupled with a cohesive element approach,a forward model of fiber-optic strain in the horizontal adjacent well induced by the initiation and propagation of hydraulic fracturing bedding fractures was constructed.Numerical simulation analyses were conducted to evaluate the evolution of fiber-optic strain in the horizontal adjacent well,confirming the correctness of the observed evolution characteristics.The presence of a"wedge-shaped"tensile strain convergence zone in the fiber-optic strain waterfall plot,accompanied by two compressive strain convergence zones,indicates the initiation and propagation of bedding fractures during the fracturing process.These findings provide valuable insights for interpreting distributed fiber-optic data in shale fracturing field applications.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金Sponsored by the Natural Science Foundation of Heilongjiang Province (Grant No. QC2012C081)the Creative Qualified Scientists and Technicians Foundation of Harbin City (Grant No. RC2012QN001025)the National Natural Science Foundation of China (Grant No. 61107069 and 41174161)
文摘In order to improve the multiplexing capability of the optical sensors based on the lower interferential optic fiber sensing technology and the white light fiber-optic Mach-Zehnder interferometer,reflective ladder topology network ( RLT) with tailored formula was proposed. The topology network consists of 6 rungs sensing elements linked by 5 couplers. Two cases with different choices of couplers were contrasted: one is equal coupling ratio,and the other is tailored coupling ratio. Through the simulation of these two cases,the detailed multiplexing capability was analyzed,and accordingly the experiments were also carried out. The simulation results showed that,the tailored formula enhances the multiplexing capability of the structure. In the first case, the maximum number of sensors which can be multiplexed is 8,and in the other case is 12 fiber optic sensors. The experimental results have a good agreement with numerical simulation results. Thus,it is considered expedient to incorporate RLT into large-scale building,grounds,bridges,dams,tunnels,highways and perimeter security.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the National Natural Science Foundation of China(Grant No.61975167).
文摘A compact and high-resolution fiber-optic refractive index(RI)sensor based on a microwave photonic filter(MPF)is proposed and experimentally validated.The sensing head utilizes a cascaded in-line interferometer fabricated by an input single-mode fiber(SMF)tapered fusion with no-core fiber-thin-core fiber(TCF)-SMF.The surrounding RI(SRI)can be demodulated by tracing the passband’s central frequency of the MPF,which is constructed by the cascaded in-line interferometer,electro-optic modulator,and a section of dispersion compensation fiber.The sensitivity of the sensor is tailorable through the use of different lengths of TCF.Experimental results reveal that with a 30 mm length of TCF,the sensor achieves a maximum theoretical sensitivity and resolution of-1.403 GHz∕refractive index unit eRIUT and 1.425×10^(-7) RIU,respectively,which is at least 6.3 times higher than what has been reported previously.Furthermore,the sensor exhibits temperature-insensitive characteristics within the range of 25℃-75℃,with a temperatureinduced frequency change of only±1.5 MHz.This value is significantly lower than the frequency change induced by changes in the SRI.The proposed MPF-based cascaded in-line interferometer RI sensor possesses benefits such as easy manufacture,low cost,high resolution,and temperature insensitivity.
文摘With the rapid development of the Internet of Things(IoT)technology,fiber-optic sensors,as a kind of high-precision and high-sensitivity measurement tool,are increasingly widely used in the field of IoT.This paper outlines the advantages of fiber-optic sensors over traditional sensors,such as high precision,strong resistance to electromagnetic interference,and long transmission distance.On this basis,the paper discusses the application scenarios of fiber-optic sensors in the Internet of Things,including environmental monitoring,intelligent industry,medical and health care,intelligent transportation,and other fields.It is hoped that this study can provide theoretical support and practical guidance for the further development of fiber-optic sensors in the field of the Internet of Things,as well as promote the innovation and application of IoT.
基金the National Natural Science Foundation of China(Grant No.62172132)Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014)the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).
文摘Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
文摘Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving you only look once version 5(YOLOv5) is proposed.By incorporating the lightweight Ghost Net module into the YOLOv5 backbone network,we effectively reduce the model size.The addition of the receptive fields block(RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model.Subsequently,the high-performance lightweight convolution,GSConv,is integrated into the neck structure for further model size compression.Moreover,the baseline model's loss function is substituted with efficient insertion over union(EIoU),accelerating network convergence and enhancing detection precision.Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
