The Tianlian 1-03 satellite, the third geosynchronous data relay satellite of China, was successfully launched into space on a LM-3C launch vehicle from the Xichang Satellite Launch Center at 23:43 Beijing time on Jul...The Tianlian 1-03 satellite, the third geosynchronous data relay satellite of China, was successfully launched into space on a LM-3C launch vehicle from the Xichang Satellite Launch Center at 23:43 Beijing time on July 25. Twenty-six minutes after the liftoff, the satellite展开更多
Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon...Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.展开更多
Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual infor...Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.展开更多
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ...Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.展开更多
Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implica...Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implications for battery safety,operational reliability,and overall performance.Current SOC estimation techniques often demonstrate limited accuracy,particularly when confronted with complex operational scenarios and wide temperature variations,where their generalization capacity and dynamic adaptation prove insufficient.To address these shortcomings,this work presents a PSO-TCN-Transformer network model for SOC estimation.This research uses the Particle Swarm Optimization(PSO)method to automatically configure the architectural parameters of the Temporal Convolutional Network(TCN)and Transformer components.This automated optimization enhances the model’s ability to represent the dynamically evolving nature of SOC.Additionally,this integrated framework significantly increases the model’s capacity to capture SOC dynamics in complex operational scenarios.During training and evaluation using a comprehensive dataset that covers complex operating conditions and a broad temperature spanning from−20℃ to 40℃,the proposed model achieves a root mean square error(RMSE)of less than 0.6%,a maximum absolute error(MAXE)below 4.0%,and a coefficient of determination(R^(2))of 99.99%.Additional comparative experiments on data from an energy storage company further verify the model’s superior performance,with an RMSE of 1.18%and an MAXE of 1.95%.The implications of this work extend to the development of optimization strategies and hybrid architectures,providing insights that can be adapted for state estimation across a range of complex dynamic systems.展开更多
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such ...Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments.展开更多
Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in wate...Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems(WDSs).While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior,most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified.To bridge this gap,this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization.First,a multi-task learning framework based on Graph Attention Networks(GAT)is employed to jointly detect both the location and magnitude of multiple leakages by explicitly incorporating hydraulic responses and network topology into the learning process.The model’s detection robustness is evaluated across networks with contrasting looped,branched,and hybrid topologies to examine how structural characteristics influence detection accuracy under multievent conditions.Second,the study develops a restoration-planning module that constructs a two-objective decision space combining restoration cost and segment vulnerability,where the latter accounts for disruption potential arising from hydraulic importance and local service connectivity.Non-dominated sorting is used to derive Pareto-optimal restoration sequences,enabling explicit quantification of the trade-offs between operational cost and service disruption.This provides decision-makers with a ranked set of restoration orders that reflect both hydraulic impact and functional risk,rather than relying on heuristics or cost-only criteria.Notably,the proposed framework separates offline training from online inference,requiring only a single forward pass for real-time decision-making without the need for iterative hydraulic simulations.Results demonstrate that topology strongly governs both detection performance and the structure of optimal repair sequences,underscoring the importance of integrating network-aware learning with multi-criteria restoration evaluation.展开更多
Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this pa...Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this paper,we propose a Semantic-Guided Parallax Attention Stereo Matching Network(SGPASMnet)that can be trained in unsupervised manner,building upon the Parallax Attention Stereo Matching Network(PASMnet).Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets,facilitating robust training across diverse scene-specific datasets and enhancing generalization.SGPASMnet incorporates two novel components:a Cross-Scale Feature Interaction(CSFI)block and semantic feature augmentation using a pre-trained semantic segmentation model,SegFormer,seamlessly embedded into the parallax attention mechanism.The CSFI block enables effective fusion ofmulti-scale features,integrating coarse and fine details to enhance disparity estimation accuracy.Semantic features,extracted by SegFormer,enrich the parallax attention mechanism by providing high-level scene context,significantly improving performance in ambiguous regions.Our model unifies these enhancements within a cohesive architecture,comprising semantic feature extraction,an hourglass network,a semantic-guided cascaded parallax attentionmodule,outputmodule,and a disparity refinement network.Evaluations on the KITTI2015 dataset demonstrate that our unsupervised method achieves a lower error rate compared to the original PASMnet,highlighting the effectiveness of our enhancements in handling complex scenes.By harnessing unsupervised learning without ground truth disparity needed,SGPASMnet offers a scalable and robust solution for accurate stereo matching,with superior generalization across varied real-world applications.展开更多
With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and ...With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon...This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
Thermoplastic polyurethane(TPU)consists of a hardsegment and a soft segment,where the former affords mechanical strength and thermalstability,while the latter provides a possibility of good ionic conductivity by promo...Thermoplastic polyurethane(TPU)consists of a hardsegment and a soft segment,where the former affords mechanical strength and thermalstability,while the latter provides a possibility of good ionic conductivity by promoting dissociation of ions from the lithium salt.Thus,TPU attracts a wide interest recently as a promising polymer electrolyte for solid-state lithium batteries.However,the relatively low ionic conductivity of TPU still restricts its actual applications due to the aggregation of polymer chains,which greatly reduces the dissociation of lithium salts.Herein,a strategy to address this challenge was adopted by in situ polymerization poly(ethylene glycol diacrylate)(PEGDA)in fully dispersed TPU.Hence a stretchable solid-state electrolyte(denoted as TELL and the contrast sample was denoted as TLL)with high ionic conductivity of 7.18×10^(-4) S/cm was obtained at room temperature.The Li^(+)transference number is 0.85 in Li|TELL|Li cell and can stably undergo charge-discharge cycles for 1400 h at a current density of 0.1 mA/cm^(2),while the contrast sample is short-circuited after 634 h of cycling.The LiFePO_(4)|TELL|Li cell achieves a capacity retention of 78.93%after 200 cycles at 2 C.The LiFePO_(4)|TLL| Li cellonly gains the capacity retention of 51.9%after 50 cyclesat the same current density.So,the method adopted here may provide a new approach to realize a flexible solid-state electrolyte with high ion-conductivity.展开更多
With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer def...With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects,this study proposes a lightweight model for wafer defect detection called LightWMNet.First,using a hierarchical attention Encoder-Decoder architecture,the features of wafer defect pattern(WDP)are channel recalibrated to generate high-resolution fine-grained features and low-resolution coarse-grained features.Secondly,the backbone network incorporates two novel attention modules—feedforward spatial attention(FFSa)and feedforward channel attention(FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels.These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead.Finally,the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model.Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy(PA),intersection over union(IoU)and Dice coefficient of the proposed network reach 98.26%,94.83%and 97.22%,respectively.Without significantly increasing the computational complexity and size of the model,compared with the existing state-of-the-art(SOTA)model,the classification accuracy of lightWMNet in single defect,three mixed defects and four mixed defects is improved by 0.5%,0.25%and 0.89%respectively.Furthermore,we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories.The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.展开更多
Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication ...Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors.The dynamic load altering attack(DLAA)is a typical attack that can destabilize the power system,causing the grid frequency to deviate fromits nominal value.Therefore,in this paper,we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation(NPR)to mitigate the impact.To begin with,the dynamic LFC model is constructed by highlighting the importance of the network parameter.Then,we model the DLAA and analyze its impact on LFC using the theory of second-order dynamic systems.Finally,we model the NPR and prove its effect in mitigating the DLAA.Besides,we construct a least-effort NPR considering its infrastructure cost and aim to reduce the operation cost.Finally,we carry out extensive simulations to demonstrate the impact of the DLAA and evaluate the mitigation performance of NPR.The proposed cost-benefit NPR approach can not only mitigate the impact of DLAA with 100%and also save 41.18$/MWh in terms of the operation cost.展开更多
Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods ofte...Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods often overlook the multi-scale nature of bird targets,limiting their ability to capture rich contextual information across different scales.To address this,we propose a cross-dimensional attention network(CDA-Net)for bird detection that integrates spatial and channel information to improve species recognition.The proposed CDA-Net partitions feature maps into multiple channel wise sub-features.Spatial and channel attention are applied to each subfeature,and the resulting features are fused using the Hadamard product.The fused features are then forwarded to the detection head to generate the final detection results.This approach effectively captures and integrates information across spatial and channel dimensions.Experiments on our self-constructed Nanhai Wetland Waterbird Dataset and the public CUB-200-2011 dataset yield precision scores of 91.32%and 81.99%,respectively,outperforming existing methods.Our approach effectively handles scale variation in bird detection and provides a valuable tool for advancing automated wetland waterbird monitoring.展开更多
Orthogonal Frequency Division Multiplexing(OFDM)enables efficient Dynamic Spectrum Access(DSA)but suffers from high sidelobe that causes excessive out-of-band(OOB)emissions and expose the system to spectrum-layer cybe...Orthogonal Frequency Division Multiplexing(OFDM)enables efficient Dynamic Spectrum Access(DSA)but suffers from high sidelobe that causes excessive out-of-band(OOB)emissions and expose the system to spectrum-layer cyberattacks such as man-in-the-middle(MITM),eavesdropping,and primary user emulation(PUE)attacks.To address both spectral leakage and its security implications,this paper introduces a secure and intelligent hybrid optimization strategy that combinesan Eigenspace-based Generalized Sidelobe Canceller(ES-GSC)with a Genetic Algorithm(GA),to derive optimally weighted cancellation carriers.The proposed method jointly suppresses sidelobes and reinforces resistance to leakage-based attacks.MATLAB Simulation demonstrate considerable reductions in OOB emissions and higher resilience against spectrum-layer threats compared with existing techniques.展开更多
The effective channeling of fluid flow by fractures is a liability for enhanced oil recovery(EOR)methods like CO_(2) flooding or CO_(2) storage.Developing a distributed fracture model to understand the heterogeneity o...The effective channeling of fluid flow by fractures is a liability for enhanced oil recovery(EOR)methods like CO_(2) flooding or CO_(2) storage.Developing a distributed fracture model to understand the heterogeneity of the fracture network is essential in characterizing tight and low-permeability reservoirs.In the Ordos Basin,the Chang 8-1-2 layer of the Yanchang Formation is a typical tight and low permeability reservoir in the JH17 wellblock.The strong heterogeneity of distributed fractures,differing fracture scales and fracture types make it difficult to effectively characterize the fracture distribution within the Chang 8-1-2 layer.In this paper,multi-source and multi-attribute methods are used to integrate data into a neural network at different scales,and fuzzy logic control is used to judge the correlation of various attributes.The results suggest that attribute correlation between coherence and fracture indication is the best,followed by correlations with fault distance,north–south slope,and north–south curvature.Advantageous attributes from the target area are used to train the neural network,and the fracture density model and discrete fracture network(DFN)model are built at different scales.This method can be used to effectively predict the distribution characteristics of fractures in the study area.And any learning done by the neural network from this case study can be applied to fracture network modeling for reservoirs of the same type.展开更多
文摘The Tianlian 1-03 satellite, the third geosynchronous data relay satellite of China, was successfully launched into space on a LM-3C launch vehicle from the Xichang Satellite Launch Center at 23:43 Beijing time on July 25. Twenty-six minutes after the liftoff, the satellite
基金Supported by the National key research and development program in the 14th five year plan 2021YFA1200700)the National Natural Science Foundation of China(62535018,62431025,62561160113)the Natural Science Foundation of Shanghai(23ZR1473400).
文摘Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.
文摘Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01296).
文摘Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.
基金funded in part by the Doctoral Scientific Research Foundation of Beijing University of Civil Engineering and Architecture under Grant ZF15054in part by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802in part by the BUCEA Post Graduate Innovation Project under Grant PG2024095.
文摘Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implications for battery safety,operational reliability,and overall performance.Current SOC estimation techniques often demonstrate limited accuracy,particularly when confronted with complex operational scenarios and wide temperature variations,where their generalization capacity and dynamic adaptation prove insufficient.To address these shortcomings,this work presents a PSO-TCN-Transformer network model for SOC estimation.This research uses the Particle Swarm Optimization(PSO)method to automatically configure the architectural parameters of the Temporal Convolutional Network(TCN)and Transformer components.This automated optimization enhances the model’s ability to represent the dynamically evolving nature of SOC.Additionally,this integrated framework significantly increases the model’s capacity to capture SOC dynamics in complex operational scenarios.During training and evaluation using a comprehensive dataset that covers complex operating conditions and a broad temperature spanning from−20℃ to 40℃,the proposed model achieves a root mean square error(RMSE)of less than 0.6%,a maximum absolute error(MAXE)below 4.0%,and a coefficient of determination(R^(2))of 99.99%.Additional comparative experiments on data from an energy storage company further verify the model’s superior performance,with an RMSE of 1.18%and an MAXE of 1.95%.The implications of this work extend to the development of optimization strategies and hybrid architectures,providing insights that can be adapted for state estimation across a range of complex dynamic systems.
基金supported by the High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
基金funded by Project of Sichuan Provincial Department of Science and Technology under 2025JDKP0150the Fundamental Research Funds for the Central Universities under 25CAFUC03093.
文摘Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments.
基金supported by the Korea Environmental Industry&Technology Institute(KEITI)through Water Management Program for Drought,funded by Korea Ministry of Environment(MOE)(RS-2023-00231944)supported by the research grant of the Gyeongsang National University in 2023。
文摘Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems(WDSs).While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior,most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified.To bridge this gap,this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization.First,a multi-task learning framework based on Graph Attention Networks(GAT)is employed to jointly detect both the location and magnitude of multiple leakages by explicitly incorporating hydraulic responses and network topology into the learning process.The model’s detection robustness is evaluated across networks with contrasting looped,branched,and hybrid topologies to examine how structural characteristics influence detection accuracy under multievent conditions.Second,the study develops a restoration-planning module that constructs a two-objective decision space combining restoration cost and segment vulnerability,where the latter accounts for disruption potential arising from hydraulic importance and local service connectivity.Non-dominated sorting is used to derive Pareto-optimal restoration sequences,enabling explicit quantification of the trade-offs between operational cost and service disruption.This provides decision-makers with a ranked set of restoration orders that reflect both hydraulic impact and functional risk,rather than relying on heuristics or cost-only criteria.Notably,the proposed framework separates offline training from online inference,requiring only a single forward pass for real-time decision-making without the need for iterative hydraulic simulations.Results demonstrate that topology strongly governs both detection performance and the structure of optimal repair sequences,underscoring the importance of integrating network-aware learning with multi-criteria restoration evaluation.
基金supported by the National Natural Science Foundation of China,No.62301497the Science and Technology Research Program of Henan,No.252102211024the Key Research and Development Program of Henan,No.231111212000.
文摘Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this paper,we propose a Semantic-Guided Parallax Attention Stereo Matching Network(SGPASMnet)that can be trained in unsupervised manner,building upon the Parallax Attention Stereo Matching Network(PASMnet).Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets,facilitating robust training across diverse scene-specific datasets and enhancing generalization.SGPASMnet incorporates two novel components:a Cross-Scale Feature Interaction(CSFI)block and semantic feature augmentation using a pre-trained semantic segmentation model,SegFormer,seamlessly embedded into the parallax attention mechanism.The CSFI block enables effective fusion ofmulti-scale features,integrating coarse and fine details to enhance disparity estimation accuracy.Semantic features,extracted by SegFormer,enrich the parallax attention mechanism by providing high-level scene context,significantly improving performance in ambiguous regions.Our model unifies these enhancements within a cohesive architecture,comprising semantic feature extraction,an hourglass network,a semantic-guided cascaded parallax attentionmodule,outputmodule,and a disparity refinement network.Evaluations on the KITTI2015 dataset demonstrate that our unsupervised method achieves a lower error rate compared to the original PASMnet,highlighting the effectiveness of our enhancements in handling complex scenes.By harnessing unsupervised learning without ground truth disparity needed,SGPASMnet offers a scalable and robust solution for accurate stereo matching,with superior generalization across varied real-world applications.
文摘With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
基金supported by the National Natural Science Foundation of China(No.52308332)the General Scientific Research Project of the Education Department of Zhejiang Province(No.Y202455824).
文摘This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金financially supported by the National Natural Science Foundation of China(Nos.52263010 and 52372188)2023 Introduction of studying abroad talent program,Henan Provincial Key Scientific Research Project of Collegesand Universities(No.23A150038)+1 种基金Key Scientific Research Project of Education Department of Henan Province(No.22A150042)the National students'platform for innovation and entrepreneurship training program(No.201910476010).
文摘Thermoplastic polyurethane(TPU)consists of a hardsegment and a soft segment,where the former affords mechanical strength and thermalstability,while the latter provides a possibility of good ionic conductivity by promoting dissociation of ions from the lithium salt.Thus,TPU attracts a wide interest recently as a promising polymer electrolyte for solid-state lithium batteries.However,the relatively low ionic conductivity of TPU still restricts its actual applications due to the aggregation of polymer chains,which greatly reduces the dissociation of lithium salts.Herein,a strategy to address this challenge was adopted by in situ polymerization poly(ethylene glycol diacrylate)(PEGDA)in fully dispersed TPU.Hence a stretchable solid-state electrolyte(denoted as TELL and the contrast sample was denoted as TLL)with high ionic conductivity of 7.18×10^(-4) S/cm was obtained at room temperature.The Li^(+)transference number is 0.85 in Li|TELL|Li cell and can stably undergo charge-discharge cycles for 1400 h at a current density of 0.1 mA/cm^(2),while the contrast sample is short-circuited after 634 h of cycling.The LiFePO_(4)|TELL|Li cell achieves a capacity retention of 78.93%after 200 cycles at 2 C.The LiFePO_(4)|TLL| Li cellonly gains the capacity retention of 51.9%after 50 cyclesat the same current density.So,the method adopted here may provide a new approach to realize a flexible solid-state electrolyte with high ion-conductivity.
基金supported by the National Natural Science Foundation of China under Grant 61573183.
文摘With the increase of semiconductor integration density,in order to cope with the increase of wafer defect complexity and types,especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects,this study proposes a lightweight model for wafer defect detection called LightWMNet.First,using a hierarchical attention Encoder-Decoder architecture,the features of wafer defect pattern(WDP)are channel recalibrated to generate high-resolution fine-grained features and low-resolution coarse-grained features.Secondly,the backbone network incorporates two novel attention modules—feedforward spatial attention(FFSa)and feedforward channel attention(FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels.These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead.Finally,the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model.Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy(PA),intersection over union(IoU)and Dice coefficient of the proposed network reach 98.26%,94.83%and 97.22%,respectively.Without significantly increasing the computational complexity and size of the model,compared with the existing state-of-the-art(SOTA)model,the classification accuracy of lightWMNet in single defect,three mixed defects and four mixed defects is improved by 0.5%,0.25%and 0.89%respectively.Furthermore,we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories.The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘Load frequency control(LFC)is a critical function to balance the power consumption and generation.Thegrid frequency is a crucial indicator for maintaining balance.However,the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors.The dynamic load altering attack(DLAA)is a typical attack that can destabilize the power system,causing the grid frequency to deviate fromits nominal value.Therefore,in this paper,we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation(NPR)to mitigate the impact.To begin with,the dynamic LFC model is constructed by highlighting the importance of the network parameter.Then,we model the DLAA and analyze its impact on LFC using the theory of second-order dynamic systems.Finally,we model the NPR and prove its effect in mitigating the DLAA.Besides,we construct a least-effort NPR considering its infrastructure cost and aim to reduce the operation cost.Finally,we carry out extensive simulations to demonstrate the impact of the DLAA and evaluate the mitigation performance of NPR.The proposed cost-benefit NPR approach can not only mitigate the impact of DLAA with 100%and also save 41.18$/MWh in terms of the operation cost.
基金supported by the National Natural Science Foundation of China(32371874,32401569)supported by Beijing Natural Science Foundation(6244053)。
文摘Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods often overlook the multi-scale nature of bird targets,limiting their ability to capture rich contextual information across different scales.To address this,we propose a cross-dimensional attention network(CDA-Net)for bird detection that integrates spatial and channel information to improve species recognition.The proposed CDA-Net partitions feature maps into multiple channel wise sub-features.Spatial and channel attention are applied to each subfeature,and the resulting features are fused using the Hadamard product.The fused features are then forwarded to the detection head to generate the final detection results.This approach effectively captures and integrates information across spatial and channel dimensions.Experiments on our self-constructed Nanhai Wetland Waterbird Dataset and the public CUB-200-2011 dataset yield precision scores of 91.32%and 81.99%,respectively,outperforming existing methods.Our approach effectively handles scale variation in bird detection and provides a valuable tool for advancing automated wetland waterbird monitoring.
文摘Orthogonal Frequency Division Multiplexing(OFDM)enables efficient Dynamic Spectrum Access(DSA)but suffers from high sidelobe that causes excessive out-of-band(OOB)emissions and expose the system to spectrum-layer cyberattacks such as man-in-the-middle(MITM),eavesdropping,and primary user emulation(PUE)attacks.To address both spectral leakage and its security implications,this paper introduces a secure and intelligent hybrid optimization strategy that combinesan Eigenspace-based Generalized Sidelobe Canceller(ES-GSC)with a Genetic Algorithm(GA),to derive optimally weighted cancellation carriers.The proposed method jointly suppresses sidelobes and reinforces resistance to leakage-based attacks.MATLAB Simulation demonstrate considerable reductions in OOB emissions and higher resilience against spectrum-layer threats compared with existing techniques.
基金supported by the National Science and Technology Project of China(No.2024ZD1004300)。
文摘The effective channeling of fluid flow by fractures is a liability for enhanced oil recovery(EOR)methods like CO_(2) flooding or CO_(2) storage.Developing a distributed fracture model to understand the heterogeneity of the fracture network is essential in characterizing tight and low-permeability reservoirs.In the Ordos Basin,the Chang 8-1-2 layer of the Yanchang Formation is a typical tight and low permeability reservoir in the JH17 wellblock.The strong heterogeneity of distributed fractures,differing fracture scales and fracture types make it difficult to effectively characterize the fracture distribution within the Chang 8-1-2 layer.In this paper,multi-source and multi-attribute methods are used to integrate data into a neural network at different scales,and fuzzy logic control is used to judge the correlation of various attributes.The results suggest that attribute correlation between coherence and fracture indication is the best,followed by correlations with fault distance,north–south slope,and north–south curvature.Advantageous attributes from the target area are used to train the neural network,and the fracture density model and discrete fracture network(DFN)model are built at different scales.This method can be used to effectively predict the distribution characteristics of fractures in the study area.And any learning done by the neural network from this case study can be applied to fracture network modeling for reservoirs of the same type.
