Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candida...With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candidates due to their intrinsic safety and low cost,but they suffer from severe interfacial instability at rates exceeding 10 mA cm^(-2),which drastically shortens their cycle life.Inspired by theoretical calculations,triglyme(TGDE)additive with strong electron-donating groups into Zn(OTf)_(2) electrolytes effectively disrupts the hydrogen-bond network among free water molecules,while the weak coordination of TGDE with Zn^(2+)promotes the entry of OTf-into the primary Zn^(2+)solvated sheath,thus decreasing the coordination number of water with Zn^(2+).As such,the hydrogen-bond network and the bulk solvated structure are reconstructed with better stability.Moreover,the strong adsorption of TGDE lying on the Zn(002)surface would induce Zn depositions along(002)together with the reduced exposed surface,further effectively inhibiting side reactions.Likewise,TGDE electrolyte induces the formation of such ZnF_(2)-ZnS dual-layer solid electrolyte interface(SEI)with superior chemical stability and ionic conductivity,thereby regulating Zn^(2+)flux with dendrite-free depositions.Based on this electrolyte,Zn‖Zn cells can be stably cycled for 1300 h at a limit of 10 mA cm^(-2) and 10 mAh cm^(-2).The assembled Zn‖V_(2)O_(5) full cells still maintain 99.9%capacity retention after 1000 cycles at 10 A g^(-1).This work provides a feasible approach for designing aqueous electrolytes to reconstruct the hydrogen-bond network and solvated structure,which can be extended to the applications of high-rate and high-temperature scenarios.展开更多
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data...This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.展开更多
Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to ...Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.展开更多
In this study,an architecture featuring a gradient conductive network structure and three-dimensional dual-continuous network structure is constructed in a carbon nanotubes/cellulose-boron nitride/poly(vinyl alcohol)(...In this study,an architecture featuring a gradient conductive network structure and three-dimensional dual-continuous network structure is constructed in a carbon nanotubes/cellulose-boron nitride/poly(vinyl alcohol)(CNT/cellulose-BN/PVA)composite.Using cellulose aerogel as a template,CNT were incorporated into the cellulose template by vertically impregnating the CNT suspension.Following the impregnation of BN/PVA and high-pressure compression,three-dimensional dual-continuous network structure was successfully constructed in the CNT/cellulose-BN/PVA composite.The comprehensive performance of the composite,including electromagnetic interference(EMI)shielding and Joule heating performance,was investigated.The results indicate that the total EMI shielding effectiveness(SE)for the CNT/cellulose-BN/PVA composite reveals similar values for electromagnetic waves incident from different directions,but totally different shielding mechanisms.For the CNT/cellulose-BN/PVA composite with three impregnation cycles of CNT,the EMI SE values exceeded 39 dB for electromagnetic waves incident from both the high-and low-CNT-content sides.93%of the microwaves were reflected when electromagnetic waves were incident from the high-CNT-content side,while the reflection coefficient decreased to 0.44 for the transverse direction.In addition,the construction of the dual-continuous network structure enabled the composite to exhibit both excellent electrical conductivity and good thermal conductivity simultaneously,endowing the material with good Joule heating performance.CNT/cellulose-BN/PVA composite films have significant potential for application as EMI shielding materials in extremely cold weather.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distin...Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distinguishing the MA contributions of different scale factors and tuning the optimal combined effects remains a formidable challenge. This study employs a synergistic approach combining template protection etching and vacuum annealing to construct a controlled system of micrometer-sized cavities and amorphous carbon matrices in metal-organic framework (MOF) derivatives. The results demonstrate that the spatial effects introduced by the hollow structure enhance dielectric loss but significantly weaken impedance matching. By increasing the proportion of amorphous carbon, the balance between electromagnetic loss and impedance matching can be effectively maintained. Importantly, in a suitable graphitization environment, the presence of oxygen vacancies in amorphous carbon can induce significant polarization to compensate for the reduced conductivity loss due to the absence of sp2 carbon. Through the synergistic effects of morphology and composition, the samples exhibit a broader absorption bandwidth (6.28 GHz) and stronger reflection loss (−61.64 dB) compared to the original MOF. In conclusion, this study aims to elucidate the multiscale impacts of macroscopic micro-nano structure and microscopic defect engineering, providing valuable insights for future research in this field.展开更多
Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based elect...Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.展开更多
Cardiomyopathy is one of the most serious public health threats.The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning.Cardiologists are ...Cardiomyopathy is one of the most serious public health threats.The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning.Cardiologists are often required to draw endocardial and epicardial contours of the left ventricle(LV)manually in routine clinical diagnosis or treatment planning period.This task is time-consuming and error-prone.Therefore,it is necessary to develop a fully automated end-to-end semantic segmentation method on cardiac magnetic resonance(CMR)imaging datasets.However,due to the low image quality and the deformation caused by heartbeat,there is no effective tool for fully automated end-to-end cardiac segmentation task.In this work,we propose a multi-scale segmentation network(MSSN)for left ventricle segmentation.It can effectively learn myocardium and blood pool structure representations from 2D short-axis CMR image slices in a multi-scale way.Specifically,our method employs both parallel and serial of dilated convolution layers with different dilation rates to capture multi-scale semantic features.Moreover,we design graduated up-sampling layers with subpixel layers as the decoder to reconstruct lost spatial information and produce accurate segmentation masks.We validated our method using 164 T1 Mapping CMR images and showed that it outperforms the advanced convolutional neural network(CNN)models.In validation metrics,we archived the Dice Similarity Coefficient(DSC)metric of 78.96%.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes...Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data comp...A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data compression‑reconstruction and structural damage identification.Under the condition where 40% of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R^(2) of 0.916 and normalized root mean square error(NRMSE)of 0.0288.Even under significant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achieving a R^(2) of 0.910 and NRMSE of 0.0253.Forty‑six structural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quantification of the damage severity using the framework exceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5% compared to conventional two‑stage machine learning approaches,demonstrating the efficiency of gradient‑coupled optimization.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
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive...Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on fau...Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.展开更多
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金the financial support provided by the National Natural Science Foundation of China(grant no.22373032)the open research fund of Songshan Lake Materials Laboratory(grant no.2023SLABFK06)。
文摘With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candidates due to their intrinsic safety and low cost,but they suffer from severe interfacial instability at rates exceeding 10 mA cm^(-2),which drastically shortens their cycle life.Inspired by theoretical calculations,triglyme(TGDE)additive with strong electron-donating groups into Zn(OTf)_(2) electrolytes effectively disrupts the hydrogen-bond network among free water molecules,while the weak coordination of TGDE with Zn^(2+)promotes the entry of OTf-into the primary Zn^(2+)solvated sheath,thus decreasing the coordination number of water with Zn^(2+).As such,the hydrogen-bond network and the bulk solvated structure are reconstructed with better stability.Moreover,the strong adsorption of TGDE lying on the Zn(002)surface would induce Zn depositions along(002)together with the reduced exposed surface,further effectively inhibiting side reactions.Likewise,TGDE electrolyte induces the formation of such ZnF_(2)-ZnS dual-layer solid electrolyte interface(SEI)with superior chemical stability and ionic conductivity,thereby regulating Zn^(2+)flux with dendrite-free depositions.Based on this electrolyte,Zn‖Zn cells can be stably cycled for 1300 h at a limit of 10 mA cm^(-2) and 10 mAh cm^(-2).The assembled Zn‖V_(2)O_(5) full cells still maintain 99.9%capacity retention after 1000 cycles at 10 A g^(-1).This work provides a feasible approach for designing aqueous electrolytes to reconstruct the hydrogen-bond network and solvated structure,which can be extended to the applications of high-rate and high-temperature scenarios.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)the China Postdoctoral Science Foundation(No.2022M723689).
文摘This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
基金Project supported by the National Natural Science Foun-dation of China(Grant No.62373197)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(Grant No.23KJB120010)+1 种基金the Industry-University-Research Cooperation Project of Jiangsu Province,China(Grant No.BY20251038)the Cultivation and In-cubation Project of the College of Automation,Nanjing Uni-versity of Posts and Telecommunications.
文摘Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.
基金financially supported by the National Natural Science Foundation of China(No.52103127)the Opening Project of the State Key Laboratory of Polymer Materials Engineering(Sichuan University)(No.sklpme2022-4-10)Shaanxi Provincial Science and Technology Department(No.2025GH-YBXM-042).
文摘In this study,an architecture featuring a gradient conductive network structure and three-dimensional dual-continuous network structure is constructed in a carbon nanotubes/cellulose-boron nitride/poly(vinyl alcohol)(CNT/cellulose-BN/PVA)composite.Using cellulose aerogel as a template,CNT were incorporated into the cellulose template by vertically impregnating the CNT suspension.Following the impregnation of BN/PVA and high-pressure compression,three-dimensional dual-continuous network structure was successfully constructed in the CNT/cellulose-BN/PVA composite.The comprehensive performance of the composite,including electromagnetic interference(EMI)shielding and Joule heating performance,was investigated.The results indicate that the total EMI shielding effectiveness(SE)for the CNT/cellulose-BN/PVA composite reveals similar values for electromagnetic waves incident from different directions,but totally different shielding mechanisms.For the CNT/cellulose-BN/PVA composite with three impregnation cycles of CNT,the EMI SE values exceeded 39 dB for electromagnetic waves incident from both the high-and low-CNT-content sides.93%of the microwaves were reflected when electromagnetic waves were incident from the high-CNT-content side,while the reflection coefficient decreased to 0.44 for the transverse direction.In addition,the construction of the dual-continuous network structure enabled the composite to exhibit both excellent electrical conductivity and good thermal conductivity simultaneously,endowing the material with good Joule heating performance.CNT/cellulose-BN/PVA composite films have significant potential for application as EMI shielding materials in extremely cold weather.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金supported by the National Natural Science Foundation of China(52172091,52172295)Defense Industrial Technology Development Program(JCKY2023605C002)+4 种基金Frontier Leading Technology Basic Research Major Project of Jiangsu Province(SBK2023050110)the National Key Laboratory on Electromagnetic Environmental Effects and Electro-optical Engineering(NO.61422062301)the Opening Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory(ZHD202305)the Opening Project of Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology(ASMA202303)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0371).
文摘Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distinguishing the MA contributions of different scale factors and tuning the optimal combined effects remains a formidable challenge. This study employs a synergistic approach combining template protection etching and vacuum annealing to construct a controlled system of micrometer-sized cavities and amorphous carbon matrices in metal-organic framework (MOF) derivatives. The results demonstrate that the spatial effects introduced by the hollow structure enhance dielectric loss but significantly weaken impedance matching. By increasing the proportion of amorphous carbon, the balance between electromagnetic loss and impedance matching can be effectively maintained. Importantly, in a suitable graphitization environment, the presence of oxygen vacancies in amorphous carbon can induce significant polarization to compensate for the reduced conductivity loss due to the absence of sp2 carbon. Through the synergistic effects of morphology and composition, the samples exhibit a broader absorption bandwidth (6.28 GHz) and stronger reflection loss (−61.64 dB) compared to the original MOF. In conclusion, this study aims to elucidate the multiscale impacts of macroscopic micro-nano structure and microscopic defect engineering, providing valuable insights for future research in this field.
基金funded by the Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U21A20143)the National Science Fund for Excellent Young Scholars(52322607)the Excellent Youth Foundation of Heilongjiang Scientific Committee(YQ2022E028)。
文摘Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.
基金This work was supported by the Project of Sichuan Outstanding Young Scientific and Technological Talents(19JCQN0003)the major Project of Education Department in Sichuan(17ZA0063 and 2017JQ0030)+1 种基金in part by the Natural Science Foundation for Young Scientists of CUIT(J201704)the Sichuan Science and Technology Program(2019JDRC0077).
文摘Cardiomyopathy is one of the most serious public health threats.The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning.Cardiologists are often required to draw endocardial and epicardial contours of the left ventricle(LV)manually in routine clinical diagnosis or treatment planning period.This task is time-consuming and error-prone.Therefore,it is necessary to develop a fully automated end-to-end semantic segmentation method on cardiac magnetic resonance(CMR)imaging datasets.However,due to the low image quality and the deformation caused by heartbeat,there is no effective tool for fully automated end-to-end cardiac segmentation task.In this work,we propose a multi-scale segmentation network(MSSN)for left ventricle segmentation.It can effectively learn myocardium and blood pool structure representations from 2D short-axis CMR image slices in a multi-scale way.Specifically,our method employs both parallel and serial of dilated convolution layers with different dilation rates to capture multi-scale semantic features.Moreover,we design graduated up-sampling layers with subpixel layers as the decoder to reconstruct lost spatial information and produce accurate segmentation masks.We validated our method using 164 T1 Mapping CMR images and showed that it outperforms the advanced convolutional neural network(CNN)models.In validation metrics,we archived the Dice Similarity Coefficient(DSC)metric of 78.96%.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金Supported by the National Key Research Program(No.2024-1129-954-112)National Natural Science Foundation of China(No.52372033)Guangxi Science and Technology Major Program(No.AA24263054)。
文摘Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金The National Natural Science Foundation of China(No.52361165658,U24A20169).
文摘A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data compression‑reconstruction and structural damage identification.Under the condition where 40% of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R^(2) of 0.916 and normalized root mean square error(NRMSE)of 0.0288.Even under significant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achieving a R^(2) of 0.910 and NRMSE of 0.0253.Forty‑six structural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quantification of the damage severity using the framework exceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5% compared to conventional two‑stage machine learning approaches,demonstrating the efficiency of gradient‑coupled optimization.
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 National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金Supported by the National Natural Science Foundation of China(U23A20595,52034010,52288101)National Key Research and Development Program of China(2022YFE0203400)+1 种基金Shandong Provincial Natural Science Foundation(ZR2024ZD17)Fundamental Research Funds for the Central Universities(23CX10004A).
文摘Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
文摘Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.
