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Multi-scale quantitative study on cemented tailings and waste-rock backfill under different loading rates 认领 引用 被引量：1
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作者 YIN Sheng-hua CHEN Jun-wei +4 位作者 YAN Ze-peng ZENG Jia-lu ZHOU Yun YANG Jian ZHANG Fu-shun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期357-374,共18页
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
关键词 cemented backfill waste rock loading rate multi-scale analysis mercury intrusion porosimetry pore structure micromorphology
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Multi-scale simulation for atomic oxygen erosion in low Earth orbit on polymer matrix by bridging reactive molecular dynamics and finite element analysis 认领 引用 被引量：1
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作者 Jiwon JUNG Jongkyung AN +2 位作者 Seunghwan KWON Byeong-Joo KIM Gun Jin YUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期654-667,共14页
Materials constituting satellites in the Low Earth Orbit(LEO)environment undergo degradation during missions due to harsh conditions such as cyclic temperature variations in high-vacuum,exposure to UV radiation,and co... Materials constituting satellites in the Low Earth Orbit(LEO)environment undergo degradation during missions due to harsh conditions such as cyclic temperature variations in high-vacuum,exposure to UV radiation,and collisions with highly reactive Atomic Oxygens(AO).Especially among those,AO collisions oxidize the surface and induce mass loss by generating volatile gases,leading to component failure.Reactive Force Field(Reax FF)molecular dynamics simulations,capable of describing chemical reactions,have been continuously performed to evaluate the AO erosion resistance of surface materials in LEO.Previous molecular simulation-based studies,however,evaluated AO resistance qualitatively by utilizing constant particle Number,Volume,Energy(NVE)ensemble simulations,where temperatures rise to several thousand kelvins over tens of picoseconds,and such extreme temperature conditions were not directly compatible with physical conditions in LEO.Therefore,we aimed to develop a multi-scale AO erosion analysis bridging thermal Finite Element Analysis(FEA)with mass loss rate determined from the Reax FF MD simulations.The overall thermal analysis was conducted over solar heat flux and surface radiation,while the ABAQUS Umeshmotion and Arbitrary Lagrangian-Eulerian(ALE)algorithm was adopted to analyze the surface recession of the model.The relation between erosion yields in given temperature conditions was calculated using constant particle Number,Volume,Temperature(NVT)ensemble,fitted as the Arrhenius equation form,and implemented to the FEA simulations. 展开更多
关键词 Atomic oxygen Low Earth orbit Multi-scale ReaxFF Surface erosion
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Multi-scale modeling of ultra-thin commercially pure titanium sheet for fuel cell bipolar plates:Plastic anisotropy and distortional strain hardening 认领 引用 被引量：1
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作者 Kyung Mun Min Seonghwan Choi +2 位作者 Xiaohua Hu Jinwoo Lee Hyuk Jong Bong 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第5期1637-1651,共15页
This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for c... This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for characterizing ultra-thin sheets under complex stress states are lacking,a virtual modeling approach was employed.At the grain scale,a crystal plasticity finite element(CPFE)model was constructed to incorporate the relevant slip and twinning systems,enabling prediction of responses under diverse loading conditions.Extending to the continuum scale,the CPFE results,combined with tensile data,were used to calibrate an advanced constitutive model based on the evolutionary Yld2000-2d yield function,capable of capturing anisotropic behavior.Validation against independent limiting dome height tests confirmed the predictive accuracy of the framework.The proposed approach provides a basis for simulating the forming behavior of ultra-thin CP-Ti sheets and supports precise manufacturing of bipolar plates in fuel cell systems. 展开更多
关键词 commercially pure titanium sheet crystal plasticity plastic anisotropy distortional strain hardening multi-scale modeling
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Multi-scale keypoints detection and motion features extraction in dairy cows using ResNet101-ASPP network 认领 引用
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作者 Saisai Wu Shuqing Han +5 位作者 Jing Zhang Guodong Cheng Yali Wang Kai Zhang Mingming Han Jianzhai Wu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第5期2028-2040,共13页
Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,charact... Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,characterized by occlusions and large variations in object scale may result in poor detection results.Therefore,we introduce the atrous spatial pyramid pooling(ASPP) module into the shallow layers network of ResNet101,designed to improve the multi-scale feature extraction capability of the model.The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the parallel atrous convolutional layers to expand the model's receptive field.Furthermore,seven types of motion features,including tracking up,gait symmetry,step height balance,motion speed variability,head swing amplitude,head-neck slope and back curvature are extracted simultaneously by monitoring and tracking the motion trajectory of distinct keypoints.Several of these features represent innovative extraction models and attributes,first proposed in this study.Multiple models are trained and tested on datasets containing 2,385 frames for ablation experiments.The experiments show that,in comparison with the ResNet50,MobileNet_v2_1.0,and EfficientNet-b0backbone networks,the training error and test error of ResNet101 are reduced by 4.04-30.12 pixels and 3.81-28.14 pixels.Therefore,ResNet101 is used as the benchmark for subsequent model improvement by adding the ASPP module.The training error and test error of the ResNet101-ASPP network are reduced by 0.27 and 0.24 pixels,respectively,compared to the benchmark network.The prediction confidence improves by 1.65-2.50% at three different dairy cow object scales.In addition,the keypoints under different occlusion conditions improve considerably,especially for small-scale keypoints,demonstrating the capability of the ASPP module for multi-scale feature extraction.By analyzing the distribution of the seven features and health,mild lameness,and severe lameness in dairy cows,it is shown that all the different features play an important role in distinguishing between different levels of lameness. 展开更多
关键词 dairy cows multi-scale keypoints detection ResNet101-ASPP network motion features
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Response of wind turbine loads to multi-scale turbulent structures:a study based on turbulence signals observed in the field 认领 引用
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作者 Yongfen Chai Yan Wang +2 位作者 Haolin Li Jingjing Zhang Jian Zheng 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期593-609,共17页
Accurately assessing the impact of turbulence structures on load fluctuation is crucial for the long-term stable operation of wind turbines.Based on turbulence signals observed at the Qingtu Lake Observed Array in Chi... Accurately assessing the impact of turbulence structures on load fluctuation is crucial for the long-term stable operation of wind turbines.Based on turbulence signals observed at the Qingtu Lake Observed Array in China,the aerodynamic load responses of the wind turbine to different turbulence scales are quantitatively analyzed in this study.The results indicate that very large-scale motions(VLSMs)are associated with significant load fluctuations due to its low frequency and high energy characteristics,increasing the risk of extreme loads.Large-scale motions coupled with the natural frequency of wind turbines in the medium frequency range,result in resonance phenomena.Small-scale motions,due to their high-frequency rapid vibration characteristics,cause instantaneous oscillations in wind turbine loads.Furthermore,correlation analysis indicates that the flapwise moment and thrust are most sensitive to VLSMs,while the edgewise moment is less affected by the scale characteristics.It is worth noting that this study is the first to explore the modulation effects of different scales of turbulent structures on the amplitude of wind turbine load fluctuation.It was found that turbulent structures exceeding a scale of 3δ have the most significant impact on modulating the load amplitudes,where δ is the boundary layer thickness,which is 99% of the flow velocity outside the boundary layer.These findings contribute to the enhancement of understanding regarding the load response of wind turbines in multi-scale turbulent environments and provide important references for the optimization of wind turbine design and load control. 展开更多
关键词 Multi-scale turbulent structures Wind turbine load response Field observation experiment Wavelet analyze
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Multi-scale modeling:Analysis and design of thermal–mechanical coupling behavior of integrated thermal protection systems 认领 引用
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作者 Yang LIU Haitao ZHAO +3 位作者 Kai LIU Zhongjie ZHAO Min FENG Ji'an CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期370-383,共14页
In this study,an integrated thermal protection system was formed by bonding the Carbon/Carbon(C/C) composite thermal insulation layer and carbon foam thermal insulation tile on an aluminum honeycomb sandwich panel acc... In this study,an integrated thermal protection system was formed by bonding the Carbon/Carbon(C/C) composite thermal insulation layer and carbon foam thermal insulation tile on an aluminum honeycomb sandwich panel according to the functions of each layer of materials,and the thermal–mechanical response was analyzed by experimental tests and numerical simulations.First,infrared lamp facility and arcjet wind tunnel tests were used to check the accuracy of the model and calculate the heat-shielding index.Then,using the aerodynamic heat flow and pressure of the vehicles re-entry process,the temperature field and thermal deformation of the thermal protection system were analyzed according to the thermal–mechanical coupling analysis,and its performance requirements as a vehicles shell were evaluated.Analysis show that the thermomechanical properties of each layer were mismatched due to thermal deformation,resulting in debonding at the interlayer interface,which was also observed in the experiment.In addition,a 1 mm gap in the insulation tile promotes the release of thermal stress and reduces interlayer disbonding.According to the multi-scale model,10 thermal cycles(corresponding to the flight process) were analyzed,and the failure and damage evolution process of C/C composites at the microscopic level were revealed.The results of thermal cycling show that the microscopic damage started from the interfacial debonding of the fiber/matrix and ended with the connection of the pores through crack propagation in the matrix.This study provides a solution for analyzing the thermal–mechanical response of a thermal protection system and a design solution for improving reusability. 展开更多
关键词 Thermal protection system Multi-scale models C/C composites Thermal-mechanical coupling Thermal cycle
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MewCDNet: A Wavelet-Based Multi-Scale Interaction Network for Efficient Remote Sensing Building Change Detection 认领 引用
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作者 Jia Liu Hao Chen +5 位作者 Hang Gu Yushan Pan Haoran Chen Erlin Tian Min Huang Zuhe Li 《Computers, Materials & Continua》 SCIE EI 2026年第1期687-710,共24页
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
关键词 Remote sensing change detection deep learning wavelet transform multi-scale
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Evaluation of the multi-scale variability of ocean bottom pressure in a global ocean general circulation model 认领 引用
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作者 Jiahui Bai Jingwei Xie +7 位作者 Zipeng Yu Jiangfeng Yu Hailong Liu Fan Yang Pengfei Lin Tao Zhang Chunxiang Shi Yun Xiao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第2期87-101,共15页
Ocean bottom pressure(OBP)reflects ocean dynamics,thermodynamics,and Earth’s gravity field,playing a key role in physical oceanography and in reducing aliasing errors in satellite gravimetry.Due to limited observatio... Ocean bottom pressure(OBP)reflects ocean dynamics,thermodynamics,and Earth’s gravity field,playing a key role in physical oceanography and in reducing aliasing errors in satellite gravimetry.Due to limited observations,highfrequency global OBP studies rely on numerical models,which inherently contain uncertainties.This study employs the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics(LASG/IAP)Climate System Ocean Model version 3.0(LICOM3.0)to simulate global OBP from 2002 to 2018,driven by two different atmospheric reanalysis datasets.Validation against in situ observations shows that LICOM3.0 effectively captures sub-seasonal OBP variability(1–30 d)at the available stations,which are located in the Pacific and along the Atlantic coast.Compared to another ocean model,LICOM3.0 reproduces similar OBP patterns for periods longer than 1 d and spatial scales greater than 500 km,demonstrating its capability for large-scale OBP analysis and assessing inter-model uncertainty.However,the model underestimates OBP amplitudes,and exhibits marked discrepancies at sub-daily periods,in marginal seas,and on spatial scales below 500 km with reference data.These issues are consistent across both experiments,indicating that model configuration contributes to the limitations.Potential sources of error are discussed to support future model improvements.Overall,LICOM3.0 can serve as an effective tool for oceanic scientific applications and for de-aliasing in satellite gravimetry applications. 展开更多
关键词 ocean bottom pressure ocean model multi-scale
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A multi-scale fracture prediction method based on improved deep embedded clustering 认领 引用
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作者 Yu-jia Lu Chao Chen +2 位作者 Zhu-jiang Liu Fu-bin Wei Zhe-ge Liu 《Applied Geophysics》 SCIE CSCD 2026年第2期571-590,867,共20页
Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.... Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.However,a single seismic attribute is often used to identify fracture features of a specifi c scale,making it diffi cult to achieve detailed characterization of fractures across multiple scales simultaneously.Multi-attribute fusion algorithms often focus on statistical correlations,lacking in-depth exploration of the spatial topological relationships and intrinsic physical connections among fractures of diff erent scales,resulting in reduced accuracy in complex structural areas.To address this challenge,we propose a multi-scale integrated fracture prediction method based on an improved deep embedded clustering(DEC)framework,using the marine shale reservoir of the Wufeng–Longmaxi Formation in southeastern Sichuan Basin as a case study.Specifically,(1)an improved DEC objective function integrating fracture topology constraints and cluster-balancing mechanisms is developed to enhance the model’s adaptability to complex geological structures;(2)an“expand–then–contract”stacked autoencoder architecture is designed to better capture nonlinear relationships among multi-attribute data and decouple multi-scale fracture features;and(3)an integrated workfl ow from multi-attribute optimization,intelligent fusion clustering to geological interpretation is established,enabling diff erentiated and high-precision characterization of multi-scale fractures.Furthermore,based on the geological characteristics of the study area,we systematically analyze the spatial mapping relationships of the autoencoder’s multi-layer features and elucidate their implicit geophysical signifi cance.This analysis reveals the intrinsic processes through which the proposed model performs fracture attribute optimization,noise separation,and multi-scale feature extraction.Finally,by integrating intelligent fault identifi cation,micro-fracture amplitude variation with azimuth(AVAZ)inversion,and conventional geometric attributes,high-precision spatial characterization of the fracture system is achieved,spanning from large-scale faults to micro-fractures.The prediction results show strong agreement with geological understanding. 展开更多
关键词 Multi-scale fractures Deep embedded clustering Deep learning Autoencoding Marine shale
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Identification of small impact craters in Chang’e-4 landing areas using a new multi-scale fusion crater detection algorithm 认领 引用
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作者 FangChao Liu HuiWen Liu +7 位作者 Li Zhang Jian Chen DiJun Guo Bo Li ChangQing Liu ZongCheng Ling Ying-Bo Lu JunSheng Yao 《Earth and Planetary Physics》 EI CSCD 2026年第1期92-104,共13页
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 of140 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
关键词 impact craters Chang’e-4 landing area multi-scale automatic detection YOLO11 Fusion algorithm
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Efficient Video Emotion Recognition via Multi-Scale Region-Aware Convolution and Temporal Interaction Sampling 认领 引用
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作者 Xiaorui Zhang Chunlin Yuan +1 位作者 Wei Sun Ting Wang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2036-2054,共19页
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
关键词 Multi-scale region-aware convolution temporal interaction sampling video emotion recognition
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M2ATNet: Multi-Scale Multi-Attention Denoising and Feature Fusion Transformer for Low-Light Image Enhancement 认领 引用
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作者 Zhongliang Wei Jianlong An Chang Su 《Computers, Materials & Continua》 SCIE EI 2026年第1期1819-1838,共20页
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
关键词 Low-light image enhancement multi-scale multi-attention transformer
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Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images 认领 引用
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作者 Shuaiyu Zhu Sergey Ablameyko Ji Li 《Computers, Materials & Continua》 SCIE EI 2026年第8期1382-1398,共17页
Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the ... Satellite remote sensing images pose significant challenges for object detection due to their high resolution,complex scenes,and large variations in target scales.To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery,this paper proposes two improvement strategies.Method 1:(a)a Large Separable Kernel Attention(LSKA)mechanism is introduced into the backbone network to enhance feature extraction for small objects;(b)a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion,thereby improving the detection performance of objects at different scales.Method 2:(a)the Gold-YOLO structure is also integrated into the neck network;(b)a MultiSEAMHead detection head is combined to further strengthen the representation and detection capability for small and multi-scale objects.To verify the effectiveness of the proposed improvements,experiments are conducted on the DOTAv1 dataset.The results show that,while maintaining the lightweight advantage of the model,the proposed methods improve detection accuracy(mAP@0.5)by 1.3%and 1.8%,respectively,compared with the baseline YOLOv11n,demonstrating the effectiveness and practical value of the proposed approaches for object detection in remote sensing images. 展开更多
关键词 Remote sensing imagery YOLOv11n multi-scale object detection lightweight deep learning attention mechanism feature fusion
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DL-YOLO:AMulti-Scale Feature Fusion Detection Algorithm for Low-Light Environments 认领 引用
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作者 Yuanmeng Chang Hongmei Liu 《Computers, Materials & Continua》 SCIE EI 2026年第5期1901-1915,共15页
Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posi... Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at http://gffzz188fe103f8f1460asn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/cym0997/DL-YOLO. 展开更多
关键词 Multi-scale feature extraction object detection low-light environments ExDark dataset
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PointNMSA: An Improved PointNeXt Network with Non-Local Multi-Scale Aggregation for 3D Point Cloud Semantic Segmentation 认领 引用
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作者 Aihua Wu Chenlu Huang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1632-1649,共18页
Three-dimensional(3D)point cloud semantic segmentation is a core task in indoor scene understanding,providing detailed semantic information about spatial structures and object categories in indoor environments.Althoug... Three-dimensional(3D)point cloud semantic segmentation is a core task in indoor scene understanding,providing detailed semantic information about spatial structures and object categories in indoor environments.Although methods based on deep learning have made steady progress in recent years,accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales.Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context.These issues are further exacerbated by hierarchical downsampling,which often leads to the loss of fine-grained structural information.Moreover,feature interaction restricted to local neighborhoods may limit the capture of non-local semantic dependencies in complex indoor scenes.To address these limitations,we propose PointNMSA(PointNeXt with Non-local Multi-Scale Aggregation),an improved semantic segmentation network built upon the PointNeXt backbone.A Multi-Scale Feature Enhancement(MSFE)module is introduced in the decoding stage to fuse features from different encoding levels,and further refines the fused features to produce more stable multi-scale representations,which preserves geometric details across scales.In addition,a Convolution-Attention Mixing(CA-Mix)module is designed to jointly integrate local spatial structures and non-local contextual dependencies via dual-stream aggregation and multi-dimensional attention fusion,thereby enabling more discriminative feature representations.Experiments on the Stanford Large-Scale 3D Indoor Spaces(S3DIS)benchmark demonstrate the effectiveness of PointNMSA.On the Area 5 test split,PointNMSA achieves a mean intersection over union(mIoU)of 65.10%,outperforming the PointNeXt baseline by 1.59%,while introducing only a modest increase in computational cost(latency from 42.24 to 45.18 ms and parameters from 3.16 to 8.67M).Despite the noticeable growth in parameter count,the increase in inference latency remains relatively limited,indicating a favorable trade-off between segmentation accuracy and computational efficiency.Additional cross-dataset experiments on ScanNet further verify that PointNMSA maintains stable gains under different indoor scene distributions.Such performance gains suggest that PointNMSA provides a more robust and generalizable solution for semantic segmentation in large-scale indoor environments with complex structural layouts. 展开更多
关键词 3D point cloud semantic segmentation indoor scene understanding multi-scale feature aggregation non-local context integration PointNeXt
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Multi-scale nanofiber filter-based TENG for sustainable enhanced PM0.3filtration and self-powered respiratory monitoring 认领 引用 被引量：2
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作者 Mengtong Yi Nan Lu +6 位作者 Yukui Gou Pinmei Yan Hong Liu Xiaoqing Gao Jianying Huang Weilong Cai Yuekun Lai 《Green Energy & Environment》 SCIE EI CAS CSCD 2026年第1期119-130,共12页
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 PM0.3filtration 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
关键词 Multi-scale nanofiber membrane Electrospinning Triboelectric nanogenerators PM0.3filtration Self-powered respiratory monitoring
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Plant mechanics of growth:multi-scale perspective 认领 引用
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作者 Chengyu Zhu Wenyang Liu +3 位作者 Yiqi Mao Junning Chen Qing Li Shujuan Hou 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期366-382,共17页
Force is the sculptor of life.Plant growth is driven by mechanical processes operating across multiple scales,from the cellular to the organ level.This review explores these processes from a multi-scale perspective.It... Force is the sculptor of life.Plant growth is driven by mechanical processes operating across multiple scales,from the cellular to the organ level.This review explores these processes from a multi-scale perspective.It begins with the historical development of mechanics models for plant cell growth,with a particular focus on the classical Lockhart equation.The structure and properties of the cell wall are then scrutinized,emphasizing its regulatory role in cell growth and how its viscosity,elasticity,and plasticity influence cell expansion and morphogenesis.Next,this review investigates mechanical interactions at the cell-tissue interface,focusing on how cellular stress and tissue structural characteristics influence plant growth through cross-scale mechanisms.At the macroscopic scale the mechanical principles governing tissue growth and morphology are analyzed,illustrating how mechanical forces and differential growth shape organ development.Additionally,the recently developed biomechanical morphogenesis approach,based on topology optimization,is explored.By synthesizing plant growth models across different scales,this review enhances our understanding of biomechanics and provides key insights into plant growth.The knowledge gaps identified in this article offer a roadmap for future research in the field. 展开更多
关键词 Plant mechanics Growth Multi-scale Lockhart equation Cell wall extensibility Anisotropy
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Fault diagnosis of rolling bearing based on two-dimensional composite multi-scale ensemble Gramian dispersion entropy 认领 引用
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作者 Wenqing Ding Jinde Zheng +3 位作者 Jianghong Li Haiyang Pan Jian Cheng Jinyu Tong 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期125-144,共20页
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(CMEGDE2D)is proposed in this paper.First,the TS1D is transformed into a two-dimensional image(I2D)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 CMEGDE2D 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
关键词 Composite multi-scale ensemble Gramian dispersion entropy Dispersion entropy Fault diagnosis Rolling bearing Feature extraction
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