Silicon carbide fibers are considered ideal reinforcing materials for ceramic matrix composites due to their excellent mechanical properties and high-temperature performance.Different types of fibers necessitate indiv...Silicon carbide fibers are considered ideal reinforcing materials for ceramic matrix composites due to their excellent mechanical properties and high-temperature performance.Different types of fibers necessitate individual investigation due to variations in their composition and fabrication processes.This study presents a comprehensive investigation into evolution of the mechanical properties,surface microstructure,and composition of Shicolon-Ⅱ fibers subjected to argon heat treatment at temperatures ranging from 1300℃to 1700℃.The Shicolon-Ⅱ fibers are composed of small-sized β-SiC grains,SiC_(x)O_(y) amorphous phase,and a minor amount of graphite microcrystals.Following treatment in an argon atmosphere at 1300℃,the fibers maintain a monofilament tensile strength of 3.620 GPa,corresponding to a retention of 98.32%.This strength diminishes to 2.875 GPa,equating to a retention of 78.08%,after treatment at 1500℃.The reduction in mechanical properties of the fibers can be ascribed to the decomposition of the amorphous phase and the growth of β-SiC grains.Furthermore,creep resistance is an essential factor influencing the long-term performance of composite materials.After treatment at temperatures above 1400℃,the high-temperature creep resistance of the fibers is significantly enhanced due to growth of β-SiC grains.This study offers valuable theoretical insights into high-temperature applications of second-generation fibers,contributing to an enhanced understanding of their performance under extreme conditions.展开更多
An alternative elliptical and circle air-hole-assisted Al_(0.24)Ga_(0.76)As photonic crystal fiber(PCF)was proposed for generating broadband high-coherence mid-infrared supercontinuum,and the dispersion,effect-ive mod...An alternative elliptical and circle air-hole-assisted Al_(0.24)Ga_(0.76)As photonic crystal fiber(PCF)was proposed for generating broadband high-coherence mid-infrared supercontinuum,and the dispersion,effect-ive mode area and nonlinear coefficient were investigated by using finite element method(FEM),the evolu-tion of optical pulses propagating along the fiber was simulated,and the supercontinuum and the coherence were analyzed and evaluated under different pumping conditions.The results show that a supercontinuum spectrum with a spectral width of 4.852μm can be obtained in the proposed fiber with d_(1)/Λof 0.125,d_(2)/Λof 0.583 and the zero-dispersion wavelength of 3.228μm by pumping with a Gaussian pulse with a peak power of 800 W and a full width at half maximum(FWHM)of 20 fs at wavelength of 3.3μm.When the fiber is pumped by the pulse with the peak power of 2000 W,the FWHM of 80 fs at the wavelength of 4.0μm in the in the anomalous dispersion region,the modulation instability is obviously suppressed,and the high-coher-ence supercontinuum spectrum spanning from 1.1μm to 8.99μm is observed.A part of the pulse energy is transferred to the anomalous dispersion region when pumped at the wavelength of 2.8μm in the normal dis-persion region and a broadband high-coherence supercontinuum spectrum extending from 0.8μm to 9.8μm is generated in the 10 mm proposed fiber.This paper introduces elliptical air holes in the Al_(0.24)Ga_(0.76)As photonic crystal fiber,which enhances flexibility for tailoring the performance of supercontinuum,ultimately achieving the broadest supercontinuum spectrum with the shortest fiber length to date.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
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-...Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.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
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
One-dimensional ensemble dispersion entropy(EDE1D)is an effective nonlinear dynamic analysis method for complexity measurement of time series.However,it is only restricted to assessing the complexity of one-di-mension...One-dimensional ensemble dispersion entropy(EDE1D)is an effective nonlinear dynamic analysis method for complexity measurement of time series.However,it is only restricted to assessing the complexity of one-di-mensional time series(TS1d)with the extracted complexity features only at a single scale.Aiming at these problems,a new nonlinear dynamic analysis method termed two-dimensional composite multi-scale ensemble Gramian dispersion entropy(CMEGDE_(2D))is proposed in this paper.First,the TS_(1D) is transformed into a two-dimensional image(I_(2D))by using Gramian angular fields(GAF)with more internal data structures and geometri features,which preserve the global characteristics and time dependence of vibration signals.Second,the I2D is analyzed at multiple scales through the composite coarse-graining method,which overcomes the limitation of a single scale and provides greater stability compared to traditional coarse-graining methods.Subsequently,a new fault diagnosis method of rolling bearing is proposed based on the proposed CMEGDE_(2D) for fault feature ex-traction and the chicken swarm algorithm optimized support vector machine(CsO-SvM)for fault pattern identification.The simulation signals and two data sets of rolling bearings are utilized to verify the effectiveness of the proposed fault diagnosis method.The results demonstrate that the proposed method has stronger dis-crimination ability,higher fault diagnosis accuracy and better stability than the other compared methods.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
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
The remodeling of axonal connections following injury is an important feature driving functional recovery.The reticulospinal tract is an interesting descending motor tract that contains both excitatory and inhibitory ...The remodeling of axonal connections following injury is an important feature driving functional recovery.The reticulospinal tract is an interesting descending motor tract that contains both excitatory and inhibitory fibers.While the reticulospinal tract has been shown to be particularly prone to axonal growth and plasticity following injuries of the spinal cord,the differential capacities of excitatory and inhibitory fibers for plasticity remain unclear.As adaptive axonal plasticity involves a sophisticated interplay between excitatory and inhibitory input,we investigated in this study the plastic potential of glutamatergic(vGlut2)and GABAergic(vGat)fibers originating from the gigantocellular nucleus and the lateral paragigantocellular nucleus,two nuclei important for locomotor function.Using a combination of viral tracing,chemogenetic silencing,and AI-based kinematic analysis,we investigated plasticity and its impact on functional recovery within the first 3 weeks following injury,a period prone to neuronal remodeling.We demonstrate that,in this time frame,while vGlut2-positive fibers within the gigantocellular and lateral paragigantocellular nuclei rewire significantly following cervical spinal cord injury,vGat-positive fibers are rather unresponsive to injury.We also show that the acute silencing of excitatory axonal fibers which rewire in response to lesions of the spinal cord triggers a worsening of the functional recovery.Using kinematic analysis,we also pinpoint the locomotion features associated with the gigantocellular nucleus or lateral paragigantocellular nucleus during functional recovery.Overall,our study increases the understanding of the role of the gigantocellular and lateral paragigantocellular nuclei during functional recovery following spinal cord injury.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
UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechani...UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechanical properties are unclear.Molecular dynamics simulations are valuable but often limited by computational constraints.Our aim is to simulate higher molecular weights to better represent real UHMWPE fibers.We used Packmol and Polyply methodologies to construct PE systems,with Polyply reproducing more reasonable properties of UHMWPE fibers.Additionally,tensile simulations showed that orientation and crystallinity greatly impact Young's modulus more than molecular weight.Energy decomposition indicated that higher molecular weights lead to covalent bonds that can withstand more energy during stretching,thus increasing breaking strength.Combining simulations with machine learning,we found that orientation has the most significant impact on Young's modulus,contributing 60%,and molecular weight plays the most crucial role in determining the breaking strength,accounting for 65%.This study provides a theoretical basis and guidelines for enhancing UHMWPE's modulus and strength.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
To develop a suitable production process for fiber reinforced investment casting shell mold,three methods were studied:the traditional method(M_(1)),the method of adding fiber into silica sol with mechanical stirring ...To develop a suitable production process for fiber reinforced investment casting shell mold,three methods were studied:the traditional method(M_(1)),the method of adding fiber into silica sol with mechanical stirring and ultrasonic agitation(M_(2)),and the method of adding fiber into slurry with mechanical stirring and ultrasonic agitation for durations of 3,15,30,and 45 min(M_(3)).The bending strength,high-temperature self-load deformation,and thermal conductivity of the shell molds were investigated.The results reveal that the enhancement of fiber dispersion through ultrasonic agitation improves the comprehensive performance of the shell molds.The maximum green bending strength of the shell mold by M_(2) reaches 3.29 MPa,which is 29% higher than that of the shell mold prepared by M_(1).Moreover,the high-temperature self-load deformation of the shell mold is reduced from 0.62% to 0.44%.In addition,simultaneous ultrasonic agitation and mechanical stirring effectively shorten the slurry preparation time while maintaining comparable levels of fiber dispersion.With the process M_(3)-45 min,the fillers are uniformly dispersed in the slurry,and the fired bending strength and the high-temperature self-load deformation reach 6.25 MPa and 0.41%,respectively.Therefore,the proposed ultrasonic agitation route is promising for the fabrication of fiber-reinforced shell molds with excellent fibers dispersion.展开更多
High-performance fiber fabrics and composites experienced transverse compression deformation at ultrahigh strain rates near the impact point when subjected to high-velocity impacts,which significantly affected their b...High-performance fiber fabrics and composites experienced transverse compression deformation at ultrahigh strain rates near the impact point when subjected to high-velocity impacts,which significantly affected their ballistic limits.In this paper,a fiber-scale experimental method for characterizing ultrahigh strain-rate transverse compression behavior was proposed.To begin with,in order to measure the extremely low stress and strain in small specimens,the conventional Hopkinson bar was reduced to the hundred-micron scale,thereby achieving wave impedance matching with single fibers.In addition,tangential and normal laser Doppler velocimetry(LDV)methods were employed to realize non-contact,high-precision,and high-speed axial velocity measurements of micron-scale incident and transmission bars,respectively.Meanwhile,a microscopic observation system was used to facilitate the installation of miniature fiber samples.The experimental setup and procedures were introduced,and the system accuracy was verified through sample-free loading tests based on one-dimensional stress wave propagation theory.Dynamic compression experiments on Graphene-UHMWPE fibers were carried out,followed by post-compression microstructural characterization via scanning electron microscopy(SEM).Results demonstrated that successful mechanical characterization was achieved at strain rates exceeding 105,an order of magnitude higher than the previously reported maximum rates.Furthermore,during the loading process,the fibers underwent uniform compression deformation while exhibiting pronounced strain-rate effects.This method offers a novel approach for dynamic mechanical characterization of microscale single fibers,enabling the development of comprehensive strain-ratedependent material models to guide the design of advanced composites and high-performance fibers.展开更多
This study uses all-atom molecular dynamics simulations to investigate the dislocation propagation, stress transmission, and mechanical properties in poly(p-phenylene terephthalamide) fibers under uniaxial tension. Th...This study uses all-atom molecular dynamics simulations to investigate the dislocation propagation, stress transmission, and mechanical properties in poly(p-phenylene terephthalamide) fibers under uniaxial tension. The results indicate that the dislocation propagates and the stress transfers not only along the fiber axis but also between adjacent molecular chains through hydrogen bonds, demonstrating their influence on the yield behavior. As the degree of polymerization increases, breakage of covalent bonds and interchain slippage contribute to the yield of fibers together. This work provides theoretical guidance for the design and manufacturing of high-performance fibers.展开更多
Self-vibrating systems comprised of active materials have great potential for application in the fields of energy harvesting,actuation,bionic instrumentation,and autonomous robotics.However,it is challenging to obtain...Self-vibrating systems comprised of active materials have great potential for application in the fields of energy harvesting,actuation,bionic instrumentation,and autonomous robotics.However,it is challenging to obtain analytical solutions describing these systems,which hinders analysis and design.In this work,we propose a self-vibrating liquid crystal elastomer(LCE)fiber-spring system exposed to spatially-constant gradient light,and determine analytical solutions for its amplitude and period.First,using a dynamic model of LCE,we obtain the equations governing the self-vibration.Then,we analyze two different motion states and elucidate the mechanism of self-vibration.Subsequently,we derive analytical solutions for the amplitude and frequency using the multi-scale method,and compare the solutions with numerical results.The analytical outcomes are shown to be consistent with the numerical calculations,while taking far less computational time.Our findings reveal the utility of the multi-scale method in describing self-vibration,which may contribute to more efficient and accurate analyses of self-vibrating systems.展开更多
基金National Natural Science Foundation of China(52172108)National Key R&D Program of China(2022YFB3707700)Strategic Priority Research Program of the Chinese Academy of Sciences(XDC0144005)。
文摘Silicon carbide fibers are considered ideal reinforcing materials for ceramic matrix composites due to their excellent mechanical properties and high-temperature performance.Different types of fibers necessitate individual investigation due to variations in their composition and fabrication processes.This study presents a comprehensive investigation into evolution of the mechanical properties,surface microstructure,and composition of Shicolon-Ⅱ fibers subjected to argon heat treatment at temperatures ranging from 1300℃to 1700℃.The Shicolon-Ⅱ fibers are composed of small-sized β-SiC grains,SiC_(x)O_(y) amorphous phase,and a minor amount of graphite microcrystals.Following treatment in an argon atmosphere at 1300℃,the fibers maintain a monofilament tensile strength of 3.620 GPa,corresponding to a retention of 98.32%.This strength diminishes to 2.875 GPa,equating to a retention of 78.08%,after treatment at 1500℃.The reduction in mechanical properties of the fibers can be ascribed to the decomposition of the amorphous phase and the growth of β-SiC grains.Furthermore,creep resistance is an essential factor influencing the long-term performance of composite materials.After treatment at temperatures above 1400℃,the high-temperature creep resistance of the fibers is significantly enhanced due to growth of β-SiC grains.This study offers valuable theoretical insights into high-temperature applications of second-generation fibers,contributing to an enhanced understanding of their performance under extreme conditions.
文摘An alternative elliptical and circle air-hole-assisted Al_(0.24)Ga_(0.76)As photonic crystal fiber(PCF)was proposed for generating broadband high-coherence mid-infrared supercontinuum,and the dispersion,effect-ive mode area and nonlinear coefficient were investigated by using finite element method(FEM),the evolu-tion of optical pulses propagating along the fiber was simulated,and the supercontinuum and the coherence were analyzed and evaluated under different pumping conditions.The results show that a supercontinuum spectrum with a spectral width of 4.852μm can be obtained in the proposed fiber with d_(1)/Λof 0.125,d_(2)/Λof 0.583 and the zero-dispersion wavelength of 3.228μm by pumping with a Gaussian pulse with a peak power of 800 W and a full width at half maximum(FWHM)of 20 fs at wavelength of 3.3μm.When the fiber is pumped by the pulse with the peak power of 2000 W,the FWHM of 80 fs at the wavelength of 4.0μm in the in the anomalous dispersion region,the modulation instability is obviously suppressed,and the high-coher-ence supercontinuum spectrum spanning from 1.1μm to 8.99μm is observed.A part of the pulse energy is transferred to the anomalous dispersion region when pumped at the wavelength of 2.8μm in the normal dis-persion region and a broadband high-coherence supercontinuum spectrum extending from 0.8μm to 9.8μm is generated in the 10 mm proposed fiber.This paper introduces elliptical air holes in the Al_(0.24)Ga_(0.76)As photonic crystal fiber,which enhances flexibility for tailoring the performance of supercontinuum,ultimately achieving the broadest supercontinuum spectrum with the shortest fiber length to date.
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supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.
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.
基金Supported by the National Natural Science Foundation of China(Grant No.51975004)the Outstanding Youth Fund of Universities in Anhui Province of China(Grant No.2022AH020032).
文摘One-dimensional ensemble dispersion entropy(EDE1D)is an effective nonlinear dynamic analysis method for complexity measurement of time series.However,it is only restricted to assessing the complexity of one-di-mensional time series(TS1d)with the extracted complexity features only at a single scale.Aiming at these problems,a new nonlinear dynamic analysis method termed two-dimensional composite multi-scale ensemble Gramian dispersion entropy(CMEGDE_(2D))is proposed in this paper.First,the TS_(1D) is transformed into a two-dimensional image(I_(2D))by using Gramian angular fields(GAF)with more internal data structures and geometri features,which preserve the global characteristics and time dependence of vibration signals.Second,the I2D is analyzed at multiple scales through the composite coarse-graining method,which overcomes the limitation of a single scale and provides greater stability compared to traditional coarse-graining methods.Subsequently,a new fault diagnosis method of rolling bearing is proposed based on the proposed CMEGDE_(2D) for fault feature ex-traction and the chicken swarm algorithm optimized support vector machine(CsO-SvM)for fault pattern identification.The simulation signals and two data sets of rolling bearings are utilized to verify the effectiveness of the proposed fault diagnosis method.The results demonstrate that the proposed method has stronger dis-crimination ability,higher fault diagnosis accuracy and better stability than the other compared methods.
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.
基金supported by the Deutsche Forschungsgemeinschaft(DFG),TRR274(Project ID 408885537,Sy Nergy,EXC 2145/ID 390857198,to FMB)。
文摘The remodeling of axonal connections following injury is an important feature driving functional recovery.The reticulospinal tract is an interesting descending motor tract that contains both excitatory and inhibitory fibers.While the reticulospinal tract has been shown to be particularly prone to axonal growth and plasticity following injuries of the spinal cord,the differential capacities of excitatory and inhibitory fibers for plasticity remain unclear.As adaptive axonal plasticity involves a sophisticated interplay between excitatory and inhibitory input,we investigated in this study the plastic potential of glutamatergic(vGlut2)and GABAergic(vGat)fibers originating from the gigantocellular nucleus and the lateral paragigantocellular nucleus,two nuclei important for locomotor function.Using a combination of viral tracing,chemogenetic silencing,and AI-based kinematic analysis,we investigated plasticity and its impact on functional recovery within the first 3 weeks following injury,a period prone to neuronal remodeling.We demonstrate that,in this time frame,while vGlut2-positive fibers within the gigantocellular and lateral paragigantocellular nuclei rewire significantly following cervical spinal cord injury,vGat-positive fibers are rather unresponsive to injury.We also show that the acute silencing of excitatory axonal fibers which rewire in response to lesions of the spinal cord triggers a worsening of the functional recovery.Using kinematic analysis,we also pinpoint the locomotion features associated with the gigantocellular nucleus or lateral paragigantocellular nucleus during functional recovery.Overall,our study increases the understanding of the role of the gigantocellular and lateral paragigantocellular nuclei during functional recovery following spinal cord injury.
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financially supported by the National Natural Science Foundation of China(Nos.52303298 and 52233002)。
文摘UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechanical properties are unclear.Molecular dynamics simulations are valuable but often limited by computational constraints.Our aim is to simulate higher molecular weights to better represent real UHMWPE fibers.We used Packmol and Polyply methodologies to construct PE systems,with Polyply reproducing more reasonable properties of UHMWPE fibers.Additionally,tensile simulations showed that orientation and crystallinity greatly impact Young's modulus more than molecular weight.Energy decomposition indicated that higher molecular weights lead to covalent bonds that can withstand more energy during stretching,thus increasing breaking strength.Combining simulations with machine learning,we found that orientation has the most significant impact on Young's modulus,contributing 60%,and molecular weight plays the most crucial role in determining the breaking strength,accounting for 65%.This study provides a theoretical basis and guidelines for enhancing UHMWPE's modulus and strength.
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supported by the National Natural Science Foundation of China (Grant No. 5186504)the University Science Foundation for Young Science and Technology Talents in Inner Mongolia Autonomous Region of China (Grant No. NJYT22078)+2 种基金the Basic Scientific Research Expenses Program of Universities directly under Inner Mongolia Autonomous Region (Grant No. JY20220059)the Inner Mongolia Autonomous Region ‘Grassland Talent’ project Young Innovative Talent Training Program Level ⅠBasic Research Expenses of Universities directly under the Inner Mongolia Autonomous Region (Grant No. ZTY2023040)。
文摘To develop a suitable production process for fiber reinforced investment casting shell mold,three methods were studied:the traditional method(M_(1)),the method of adding fiber into silica sol with mechanical stirring and ultrasonic agitation(M_(2)),and the method of adding fiber into slurry with mechanical stirring and ultrasonic agitation for durations of 3,15,30,and 45 min(M_(3)).The bending strength,high-temperature self-load deformation,and thermal conductivity of the shell molds were investigated.The results reveal that the enhancement of fiber dispersion through ultrasonic agitation improves the comprehensive performance of the shell molds.The maximum green bending strength of the shell mold by M_(2) reaches 3.29 MPa,which is 29% higher than that of the shell mold prepared by M_(1).Moreover,the high-temperature self-load deformation of the shell mold is reduced from 0.62% to 0.44%.In addition,simultaneous ultrasonic agitation and mechanical stirring effectively shorten the slurry preparation time while maintaining comparable levels of fiber dispersion.With the process M_(3)-45 min,the fillers are uniformly dispersed in the slurry,and the fired bending strength and the high-temperature self-load deformation reach 6.25 MPa and 0.41%,respectively.Therefore,the proposed ultrasonic agitation route is promising for the fabrication of fiber-reinforced shell molds with excellent fibers dispersion.
基金financial support provided by the National Natural Science Foundation of China(Grant No.12302472)the Science and Technology Support Program of Jiangsu Province(Grant No.BK20230874)+2 种基金the Aeronautical Science Fund(ASF)(Grant No.2023Z057052005)the Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCAS-I-0124G02)the funding received from Jiangsu Hanvo Safety Product Co.,Ltd。
文摘High-performance fiber fabrics and composites experienced transverse compression deformation at ultrahigh strain rates near the impact point when subjected to high-velocity impacts,which significantly affected their ballistic limits.In this paper,a fiber-scale experimental method for characterizing ultrahigh strain-rate transverse compression behavior was proposed.To begin with,in order to measure the extremely low stress and strain in small specimens,the conventional Hopkinson bar was reduced to the hundred-micron scale,thereby achieving wave impedance matching with single fibers.In addition,tangential and normal laser Doppler velocimetry(LDV)methods were employed to realize non-contact,high-precision,and high-speed axial velocity measurements of micron-scale incident and transmission bars,respectively.Meanwhile,a microscopic observation system was used to facilitate the installation of miniature fiber samples.The experimental setup and procedures were introduced,and the system accuracy was verified through sample-free loading tests based on one-dimensional stress wave propagation theory.Dynamic compression experiments on Graphene-UHMWPE fibers were carried out,followed by post-compression microstructural characterization via scanning electron microscopy(SEM).Results demonstrated that successful mechanical characterization was achieved at strain rates exceeding 105,an order of magnitude higher than the previously reported maximum rates.Furthermore,during the loading process,the fibers underwent uniform compression deformation while exhibiting pronounced strain-rate effects.This method offers a novel approach for dynamic mechanical characterization of microscale single fibers,enabling the development of comprehensive strain-ratedependent material models to guide the design of advanced composites and high-performance fibers.
基金financially supported by the National Natural Science Foundation of China(Nos.22473105 and 22341302).
文摘This study uses all-atom molecular dynamics simulations to investigate the dislocation propagation, stress transmission, and mechanical properties in poly(p-phenylene terephthalamide) fibers under uniaxial tension. The results indicate that the dislocation propagates and the stress transfers not only along the fiber axis but also between adjacent molecular chains through hydrogen bonds, demonstrating their influence on the yield behavior. As the degree of polymerization increases, breakage of covalent bonds and interchain slippage contribute to the yield of fibers together. This work provides theoretical guidance for the design and manufacturing of high-performance fibers.
基金supported by the National Natural Science Foundation of China(No.12172001)the University Natural Science Research Project of Anhui Province(No.2022AH020029)+1 种基金the Anhui Provincial Natural Science Foundation(Nos.2208085Y01 and 2008085QA23)the Housing and Urban-Rural Development Science and Technology Project of Anhui Province(No.2023-YF129),China.
文摘Self-vibrating systems comprised of active materials have great potential for application in the fields of energy harvesting,actuation,bionic instrumentation,and autonomous robotics.However,it is challenging to obtain analytical solutions describing these systems,which hinders analysis and design.In this work,we propose a self-vibrating liquid crystal elastomer(LCE)fiber-spring system exposed to spatially-constant gradient light,and determine analytical solutions for its amplitude and period.First,using a dynamic model of LCE,we obtain the equations governing the self-vibration.Then,we analyze two different motion states and elucidate the mechanism of self-vibration.Subsequently,we derive analytical solutions for the amplitude and frequency using the multi-scale method,and compare the solutions with numerical results.The analytical outcomes are shown to be consistent with the numerical calculations,while taking far less computational time.Our findings reveal the utility of the multi-scale method in describing self-vibration,which may contribute to more efficient and accurate analyses of self-vibrating systems.
