Strontium titanate(SrTiO_(3))is a thermoelectric material with large Seebeck coefficient that has potential applications in high-temperature power generators.To simultaneously achieve a low thermal conductivity and hi...Strontium titanate(SrTiO_(3))is a thermoelectric material with large Seebeck coefficient that has potential applications in high-temperature power generators.To simultaneously achieve a low thermal conductivity and high electrical conductivity,polycrystalline SrTiO_(3)with a multi-scale architecture was designed by the co-doping with lanthanum,cerium,and niobium.High-quality nano-powders were synthesized via a hydrothermal method.Nano-inclusions and a nano/micro-sized second phase precipitated during sintering to form mosaic crystal-like and epitaxial-like structures,which decreased the thermal conductivity.Substituting trivalent Ce and/or La with divalent Sr and substituting pentavalent Nb with tetravalent Ti enhanced the electrical conductivity without decreasing the Seebeck coefficient.By optimizing the dopant type and ratio,a low thermal conductivity of 2.77 W·m^(-1)·K^(-1)and high PF of 1.1 mW·m^(-1)·K^(-2)at 1000 K were obtained in the sample co-doped with 5-mol%La,5-mol%Ce,and 5-mol%Nb,which induced a large ZT of 0.38 at 1000 K.展开更多
Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer ...Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.展开更多
The influence of homogenization parameters on element segregation,dendritic structure,and the precipitate evolution in the GH3535-0.08wt%Y alloy was investigated.Additionally,some specific homogenization parameters we...The influence of homogenization parameters on element segregation,dendritic structure,and the precipitate evolution in the GH3535-0.08wt%Y alloy was investigated.Additionally,some specific homogenization parameters were maintained constant throughout the experiments.Results indicate that the heat treatment at 1150℃for 10 h is the optimal homogenization condition.Following this optimal treatment,dendrite structures and element segregation are eliminated.Furthermore,both SiC and Y_(5)Si_(3)precipitates in the as-cast alloy decrease significantly.Conversely,the homogenization at 1188℃induces overheating defects within the alloy.Although SiC and Y_(5)Si_(3)phases also decrease,some large M6C phases can still be observed,adversely affecting subsequent forging processes.展开更多
To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator a...To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator at temperatures of 380-440℃and strain rates of 0.05-1 s^(−1).The Johnson-Cook model,Hensel-Spittel model,strain-compensated Arrhenius model,and critical fracture strain model were established.Results show that through the evaluation of the models using the correlation coefficient(R)and the average absolute relative error,the strain-compensated Arrhenius model can represent the flow behavior of the alloy more accurately.Shear bands are more pronounced in the as-homogenized specimens,whereas dynamic recrystallization is predominantly observed in as-rolled specimens.Fracture morphology analysis reveals that a mixed fracture mechanism is prevalent in the as-homogenized specimen,whereas a ductile fracture mechanism is predominant in the as-rolled specimen.The processing maps indicate that the unstable region is reduced in the as-rolled specimens compared with that in the as-homogenized specimens.The optimal hot working windows for the as-homogenized and as-rolled specimens are determined as 410-440℃/0.14-1 s^(−1)and 380-400℃/0.05-0.29 s^(−1),respectively.展开更多
Dissimilar AZ31B magnesium alloy and DC56D steel were welded via AA1060 aluminum alloy by magnetic pulse welding.The effects of primary and secondary welding processes on the welded interface were comparatively invest...Dissimilar AZ31B magnesium alloy and DC56D steel were welded via AA1060 aluminum alloy by magnetic pulse welding.The effects of primary and secondary welding processes on the welded interface were comparatively investigated.Macroscopic morphology,microstructure,and interfacial structure of the joints were analyzed using scanning electron microscope,energy dispersive spectrometer,and X-ray diffractometer(XRD).The results show that magnetic pulse welding of dissimilar Mg/Fe metals is achieved using an Al interlayer,which acts as a bridge for deformation and diffusion.Specifically,the AZ31B/AA1060 interface exhibits a typical wavy morphology,and a transition zone exists at the joint interface,which may result in an extremely complex microstructure.The microstructure of this transition zone differs from that of AZ31B magnesium and 1060 Al alloys,and it is identified as brittle intermetallic compounds(IMCs)Al_(3)Mg_(2) and Al_(12)Mg_(17).The transition zone is mainly distributed on the Al side,with the maximum thickness of Al-side transition layer reaching approximately 13.53μm.Incomplete melting layers with varying thicknesses are observed at the primary weld interface,while micron-sized hole defects appear in the transition zone of the secondary weld interface.The AA1060/DC56D interface is mainly straight,with only a small number of discontinuous transition zones distributed intermittently along the interface.These transition zones are characterized by the presence of the brittle IMC FeAl_(3),with a maximum thickness of about 4μm.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
In current research,Li_(2)O-Al_(2)O_(3)-SiO_(2)glass-ceramics were prepared by conventional meltquenching and subsequent heat treatment method.The effect of Al_(2)O_(3)content on microstructures,thermal properties,cry...In current research,Li_(2)O-Al_(2)O_(3)-SiO_(2)glass-ceramics were prepared by conventional meltquenching and subsequent heat treatment method.The effect of Al_(2)O_(3)content on microstructures,thermal properties,crystallization behaviours and mechanical properties were investigated.FTIR,Raman spectroscopy and nuclear magnetic resonance spectroscopy microstructure analysis showed that the silico-oxygen network was damaged,while the increase of[AlO_(4)]content repaired the glass network,and finally made the glass network have better connectivity,with the decrease of SiO_(2).The thermal analysis confirmed the increasing glass transition and crystallization temperatures from growing Al_(2)O_(3)content.In addition,different crystal phases can be precipitated in the glass matrix,such as LiAlSi_(4)O_(10),Li_(2)Si_(2)O_(5) in glass with low Al_(2)O_(3)content,the combination of Li_xAl_xSi_(1-x)O_(2),LiAlSi_(3)O_(8),Li_(2)SiO_(3)in glass with intermediate Al_(2)O_(3)content,and the combination of LiAlSi_(2)O_(6),SiO_(2)in glass with high Al_(2)O_(3)content.The hardness of as-prepared glass gradually increases with the increase of the Al_(2)O_(3)content.The Vickers hardness of the glass-ceramics is highly dependent on the Al_(2)O_(3)content in the glass and the heat treatment temperatures,reaching a maximum of 10.11 GPa.Scanning electron microscope images show that the crystals change from spherical to massive and finally to sheet.The change of glass structure,crystal phase and morphology is the main reason for the different mechanical properties.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
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
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
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos...The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.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
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
Cryogenic rolling impacts on microstructure and mechanical properties of spray-formed 7055(SF-7055)Al alloy were investigated.Results show that with the increase of the reduction from 20%to 80%,the grain of cryogenic ...Cryogenic rolling impacts on microstructure and mechanical properties of spray-formed 7055(SF-7055)Al alloy were investigated.Results show that with the increase of the reduction from 20%to 80%,the grain of cryogenic rolled SF-7055 Al alloy is elongated to form a fiber texture.Numerous proliferating dislocations in the microstructure accumulate into dislocation walls and cells,and eventually form subgrains.These subgrain boundaries divide the original grain,thereby reducing the grain size.Under severe deformation conditions,they even enable the formation of nanograins.Meanwhile,the Cu-rich precipitates in the matrix are also broken and refined under the action of large rolling stress.In the process of cryogenic rolling,the tensile strength and hardness of SF-7055 Al alloy gradually increase,while the plasticity decreases.Moreover,the fracture morphology of cryogenic rolled SF-7055 Al alloy gradually transforms to the ductile and quasi-cleavage hybrid fracture characteristics with increased reduction.展开更多
Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subje...Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.展开更多
The microstructure of high Nb-TiAl alloys was optimized by the addition of a small amount of Ta elements to further improve their properties.A series of Ti46Al1.5Cr8Nb-xTa(x=0.2,0.4,0.6,0.8,1.0,at.%)alloys were prepar...The microstructure of high Nb-TiAl alloys was optimized by the addition of a small amount of Ta elements to further improve their properties.A series of Ti46Al1.5Cr8Nb-xTa(x=0.2,0.4,0.6,0.8,1.0,at.%)alloys were prepared by vacuum arc melting.The microstructure,mechanical properties,and related influencing mechanisms were systematically investigated.The results indicate that the solidification microstructure of the Ti46Al1.5Cr8Nb-xTa alloys comprises theγ-TiAl phase,α_(2)-Ti_(3)Al phase,and B2 phase.As the Ta content increases from 0.2 at.%to 1.0 at.%,the content ofα_(2)phase and B2 phase increases,while theγphase content decreases.Among them,the B2 phase shows the most pronounced change,being significantly refined,with its content increasing from 12.49%to 21.91%.In addition,the average size of the lamellar colony decreases from 160.65 to 94.44μm.The addition of the Ta element shifts the solidification path toward lower aluminum concentrations,leading to changes in phase content.The tantalum-induced increase in the B2 phase and enhanced supercooling at the solidification front provide the basis for lamellar colony refinement.Compressive testing at room temperature reveals that the Ti46 Al1.5 Cr8 Nb0.4 Ta alloy exhibits optimal compressive properties,achieving a compressive strength of 2,434 MPa and a compressive strain of 33.1%.The improvement of its properties is attributed to a combination of lamellar colony refinement,solid solution strengthening resulting from the incorporation of Ta element,and a reduction in the c/a of theγphase.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51702168 and 51532006).
文摘Strontium titanate(SrTiO_(3))is a thermoelectric material with large Seebeck coefficient that has potential applications in high-temperature power generators.To simultaneously achieve a low thermal conductivity and high electrical conductivity,polycrystalline SrTiO_(3)with a multi-scale architecture was designed by the co-doping with lanthanum,cerium,and niobium.High-quality nano-powders were synthesized via a hydrothermal method.Nano-inclusions and a nano/micro-sized second phase precipitated during sintering to form mosaic crystal-like and epitaxial-like structures,which decreased the thermal conductivity.Substituting trivalent Ce and/or La with divalent Sr and substituting pentavalent Nb with tetravalent Ti enhanced the electrical conductivity without decreasing the Seebeck coefficient.By optimizing the dopant type and ratio,a low thermal conductivity of 2.77 W·m^(-1)·K^(-1)and high PF of 1.1 mW·m^(-1)·K^(-2)at 1000 K were obtained in the sample co-doped with 5-mol%La,5-mol%Ce,and 5-mol%Nb,which induced a large ZT of 0.38 at 1000 K.
基金Opening Foundation of Key Laboratory of Explosive Energy Utilization and Control,Anhui Province(BP20240104)Graduate Innovation Program of China University of Mining and Technology(2024WLJCRCZL049)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2701)。
文摘Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.
基金National Natural Science Foundation of China(51801227,52071331)。
文摘The influence of homogenization parameters on element segregation,dendritic structure,and the precipitate evolution in the GH3535-0.08wt%Y alloy was investigated.Additionally,some specific homogenization parameters were maintained constant throughout the experiments.Results indicate that the heat treatment at 1150℃for 10 h is the optimal homogenization condition.Following this optimal treatment,dendrite structures and element segregation are eliminated.Furthermore,both SiC and Y_(5)Si_(3)precipitates in the as-cast alloy decrease significantly.Conversely,the homogenization at 1188℃induces overheating defects within the alloy.Although SiC and Y_(5)Si_(3)phases also decrease,some large M6C phases can still be observed,adversely affecting subsequent forging processes.
文摘To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator at temperatures of 380-440℃and strain rates of 0.05-1 s^(−1).The Johnson-Cook model,Hensel-Spittel model,strain-compensated Arrhenius model,and critical fracture strain model were established.Results show that through the evaluation of the models using the correlation coefficient(R)and the average absolute relative error,the strain-compensated Arrhenius model can represent the flow behavior of the alloy more accurately.Shear bands are more pronounced in the as-homogenized specimens,whereas dynamic recrystallization is predominantly observed in as-rolled specimens.Fracture morphology analysis reveals that a mixed fracture mechanism is prevalent in the as-homogenized specimen,whereas a ductile fracture mechanism is predominant in the as-rolled specimen.The processing maps indicate that the unstable region is reduced in the as-rolled specimens compared with that in the as-homogenized specimens.The optimal hot working windows for the as-homogenized and as-rolled specimens are determined as 410-440℃/0.14-1 s^(−1)and 380-400℃/0.05-0.29 s^(−1),respectively.
文摘Dissimilar AZ31B magnesium alloy and DC56D steel were welded via AA1060 aluminum alloy by magnetic pulse welding.The effects of primary and secondary welding processes on the welded interface were comparatively investigated.Macroscopic morphology,microstructure,and interfacial structure of the joints were analyzed using scanning electron microscope,energy dispersive spectrometer,and X-ray diffractometer(XRD).The results show that magnetic pulse welding of dissimilar Mg/Fe metals is achieved using an Al interlayer,which acts as a bridge for deformation and diffusion.Specifically,the AZ31B/AA1060 interface exhibits a typical wavy morphology,and a transition zone exists at the joint interface,which may result in an extremely complex microstructure.The microstructure of this transition zone differs from that of AZ31B magnesium and 1060 Al alloys,and it is identified as brittle intermetallic compounds(IMCs)Al_(3)Mg_(2) and Al_(12)Mg_(17).The transition zone is mainly distributed on the Al side,with the maximum thickness of Al-side transition layer reaching approximately 13.53μm.Incomplete melting layers with varying thicknesses are observed at the primary weld interface,while micron-sized hole defects appear in the transition zone of the secondary weld interface.The AA1060/DC56D interface is mainly straight,with only a small number of discontinuous transition zones distributed intermittently along the interface.These transition zones are characterized by the presence of the brittle IMC FeAl_(3),with a maximum thickness of about 4μm.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金Funded 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In current research,Li_(2)O-Al_(2)O_(3)-SiO_(2)glass-ceramics were prepared by conventional meltquenching and subsequent heat treatment method.The effect of Al_(2)O_(3)content on microstructures,thermal properties,crystallization behaviours and mechanical properties were investigated.FTIR,Raman spectroscopy and nuclear magnetic resonance spectroscopy microstructure analysis showed that the silico-oxygen network was damaged,while the increase of[AlO_(4)]content repaired the glass network,and finally made the glass network have better connectivity,with the decrease of SiO_(2).The thermal analysis confirmed the increasing glass transition and crystallization temperatures from growing Al_(2)O_(3)content.In addition,different crystal phases can be precipitated in the glass matrix,such as LiAlSi_(4)O_(10),Li_(2)Si_(2)O_(5) in glass with low Al_(2)O_(3)content,the combination of Li_xAl_xSi_(1-x)O_(2),LiAlSi_(3)O_(8),Li_(2)SiO_(3)in glass with intermediate Al_(2)O_(3)content,and the combination of LiAlSi_(2)O_(6),SiO_(2)in glass with high Al_(2)O_(3)content.The hardness of as-prepared glass gradually increases with the increase of the Al_(2)O_(3)content.The Vickers hardness of the glass-ceramics is highly dependent on the Al_(2)O_(3)content in the glass and the heat treatment temperatures,reaching a maximum of 10.11 GPa.Scanning electron microscope images show that the crystals change from spherical to massive and finally to sheet.The change of glass structure,crystal phase and morphology is the main reason for the different mechanical properties.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
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.
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.
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
基金Project(2022YFC2904103)supported by the National Key Research and Development Program of ChinaProjects(52374112,52274108)supported by the National Natural Science Foundation of China+1 种基金Projects(BX20220036,BX20230041)supported by the Postdoctoral Innovation Talents Support Program,ChinaProject(2232080)supported by the Beijing Natural Science Foundation,China。
文摘The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.
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the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金financially and technically supported by the National Key Laboratory Foundation of Science and Technology on Materials under Shock and Impact,Beijing Institute of Technology,China(No.WDZC2024-1)。
文摘Cryogenic rolling impacts on microstructure and mechanical properties of spray-formed 7055(SF-7055)Al alloy were investigated.Results show that with the increase of the reduction from 20%to 80%,the grain of cryogenic rolled SF-7055 Al alloy is elongated to form a fiber texture.Numerous proliferating dislocations in the microstructure accumulate into dislocation walls and cells,and eventually form subgrains.These subgrain boundaries divide the original grain,thereby reducing the grain size.Under severe deformation conditions,they even enable the formation of nanograins.Meanwhile,the Cu-rich precipitates in the matrix are also broken and refined under the action of large rolling stress.In the process of cryogenic rolling,the tensile strength and hardness of SF-7055 Al alloy gradually increase,while the plasticity decreases.Moreover,the fracture morphology of cryogenic rolled SF-7055 Al alloy gradually transforms to the ductile and quasi-cleavage hybrid fracture characteristics with increased reduction.
基金funded by the National Natural Science Foundation of China (52061020).
文摘Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.
基金the financial support by the Major Science and Technology Achievement Transformation Project in Heilongjiang Province(No.ZC2023SH0075)the National Natural Science Foundation of China(Nos.52425401,U2441255,52474377,and 52371015)+1 种基金the Young Elite Scientists Sponsorship·Program by CAST(No.2021QNRC001)the Henan Provincial Key Research and Development&Promotion Special Program(No.251111231400)。
文摘The microstructure of high Nb-TiAl alloys was optimized by the addition of a small amount of Ta elements to further improve their properties.A series of Ti46Al1.5Cr8Nb-xTa(x=0.2,0.4,0.6,0.8,1.0,at.%)alloys were prepared by vacuum arc melting.The microstructure,mechanical properties,and related influencing mechanisms were systematically investigated.The results indicate that the solidification microstructure of the Ti46Al1.5Cr8Nb-xTa alloys comprises theγ-TiAl phase,α_(2)-Ti_(3)Al phase,and B2 phase.As the Ta content increases from 0.2 at.%to 1.0 at.%,the content ofα_(2)phase and B2 phase increases,while theγphase content decreases.Among them,the B2 phase shows the most pronounced change,being significantly refined,with its content increasing from 12.49%to 21.91%.In addition,the average size of the lamellar colony decreases from 160.65 to 94.44μm.The addition of the Ta element shifts the solidification path toward lower aluminum concentrations,leading to changes in phase content.The tantalum-induced increase in the B2 phase and enhanced supercooling at the solidification front provide the basis for lamellar colony refinement.Compressive testing at room temperature reveals that the Ti46 Al1.5 Cr8 Nb0.4 Ta alloy exhibits optimal compressive properties,achieving a compressive strength of 2,434 MPa and a compressive strain of 33.1%.The improvement of its properties is attributed to a combination of lamellar colony refinement,solid solution strengthening resulting from the incorporation of Ta element,and a reduction in the c/a of theγphase.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
