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Approximate analytical solution in slow-fast system based on modified multi-scale method 认领 引用 被引量：5
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作者 Xianghong LI Jianhua TANG +1 位作者 Yanli WANG Yongjun SHEN 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2020年第4期605-622,共18页
A simple,yet accurate modi?ed multi-scale method(MMSM)for an approximately analytical solution in nonlinear oscillators with two time scales under forced harmonic excitation is proposed.This method depends on the clas... A simple,yet accurate modi?ed multi-scale method(MMSM)for an approximately analytical solution in nonlinear oscillators with two time scales under forced harmonic excitation is proposed.This method depends on the classical multi-scale method(MSM)and the method of variation of parameters.Assuming that the forced excitation is a constant,one could easily obtain the approximate analytical solution of the simpli?ed system based on the traditional MSM.Then,this solution for the oscillator under forced harmonic excitation could be established after replacing the harmonic excitation by the constant excitation.To certify the correctness and precision of the proposed analytical method,the van der Pol system with two scales subject to slowly periodic excitation is investigated;this system presents rich dynamical phenomena such as spiking(SP),spiking-quiescence(SP-QS),and quiescence(QS)responses.The approximate analytical expressions of the three types of responses are given by the MMSM,and it can be found that the precision of the new analytical method is higher than that of the classical MSM and better than that of the harmonic balance method(HBM).The results obtained by the present method are considerably better than those obtained by traditional methods,quantitatively and qualitatively,particularly when the excitation frequency is far less than the natural frequency of the system. 展开更多
关键词 modified multi-scale method(MMSM) slow-fast system multi-scale method(MSM) van der Pol system
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The Multi-scale Method for Solving Nonlinear Time Space Fractional Partial Differential Equations 认领 引用
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作者 Hossein Aminikhah Mahdieh Tahmasebi Mahmoud Mohammadi Roozbahani 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第1期299-306,共8页
In this paper, we present a new algorithm to solve a kind of nonlinear time space-fractional partial differential equations on a finite domain. The method is based on B-spline wavelets approximations, some of these fu... In this paper, we present a new algorithm to solve a kind of nonlinear time space-fractional partial differential equations on a finite domain. The method is based on B-spline wavelets approximations, some of these functions are reshaped to satisfy on boundary conditions exactly. The Adams fractional method is used to reduce the problem to a system of equations. By multiscale method this system is divided into some smaller systems which have less computations. We get an approximated solution which is more accurate on some subdomains by combining the solutions of these systems. Illustrative examples are included to demonstrate the validity and applicability of our proposed technique, also the stability of the method is discussed. 展开更多
关键词 Adams fractional method B-spline wavelets multi-scale method nonlinear fractional partial differential equations
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Synchronization Stability Analysis of Multi-VSC Grid-connected System via Multi-scale Method 认领 引用
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作者 Meng Huang Yangjian Ling +3 位作者 Han Yan Xikun Fu Xiaoming Zha Herbert Ho-Ching Iu 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2026年第1期282-293,共12页
In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this pap... In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this paper,these influences are investigated from the perspective of the time domain.First,a novel time-domain model of the multi-VSC system is obtained by using a multi-scale method.On this basis,a stability criterion is proposed to assess the synchronization stability of the system.Then,the accuracy of the time-domain model and its stability criterion in various conditions are discussed.Moreover,the negative impact of the interaction on the system is quantified.Finally,the above theoretical analysis is also verified in the controller hardware-in-the-loop(CHIL)experiments. 展开更多
关键词 Multi-scale method multi-VSC phase-locked loops synchronization stability time-domain model
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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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Research on Camouflage Target Detection Method Based on Edge Guidance and Multi-Scale Feature Fusion 认领 引用
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作者 Tianze Yu Jianxun Zhang Hongji Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1676-1697,共22页
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 Fβby 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 Fβ.OnCAMO and CHAMELEON,it obtains 4.5-point increases in Fβ,respectively.These consistent gains substantiate the superiority and robustness of EGMFNet. 展开更多
关键词 Camouflaged object detection multi-scale feature fusion edge-guided image segmentation
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Multi-Scale Supervised Dual-Layer Generative Adversarial Network: A Method for Region Restoration of LCM Images Degraded by Exposure Issues 认领 引用
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作者 Longhu Huang Sheng Zheng 《Computers, Materials & Continua》 SCIE EI 2026年第9期1075-1099,共25页
In the field of online automated defect inspection for small-size liquid crystal display modules(LCMs),the accuracy of module loading is crucial for the subsequent lighting inspection.However,due to the physical chara... In the field of online automated defect inspection for small-size liquid crystal display modules(LCMs),the accuracy of module loading is crucial for the subsequent lighting inspection.However,due to the physical characteristics of the module’s flexible ribbon cable,the ribbon often exhibits varying degrees of curling,causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece,resulting in loss of local details and significantly affecting subsequent positioning and loading.To address the problem of local image degradation caused by abnormal exposure,this study proposes a regional image generation method based on a dual-layer generative adversarial network(GAN)with multi-scale supervision.This method first uses a mask localization module to restrict the region for image generation,then employs multi-scale local generation and adversarial learning to produce high-fidelity images in areas with local exposure anomalies,and finally uses a newly added global discriminator to regulate the edges of the generated images,allowing the generated images to smoothly connect with the original images,thereby achieving local image repair.Compared to single-layer GAN models,the repaired overall image achieved a peak signal-to-noise ratio(PSNR)improvement of 3.01%and a structural similarity(SSIM)improvement of 11.7%,while the PSNR of the repaired images in exposure-anomalous regions increased by 49.04%and SSIM increased by 23.18%.In addition,not only does the model produce restored images with excellent visual effects,but through verification by deployment on an actual factory production line,the error between the restored images and normal samples is within 0.08 mm,validating the model’s value in practical industrial applications. 展开更多
关键词 Image restoration multi-scale supervision GAN LCM
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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 被引量：2
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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 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 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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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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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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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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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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MSA-DETR:Multi-scale attention enhanced DETR for object detection in oilfield surveillance 认领 引用
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作者 Qian-Wen Cao Jin-Rong Ma Lai-Bin Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2686-2697,共12页
In modern petroleum engineering,ensuring operational safety at drilling sites is of critical importance.Visual object detection plays a key role inintelligent safety monitoring systems by enabling real-time supervisio... In modern petroleum engineering,ensuring operational safety at drilling sites is of critical importance.Visual object detection plays a key role inintelligent safety monitoring systems by enabling real-time supervision of personnel and equipment.However,safety-critical targets in drilling scenes are often small,partially occluded,and embedded in cluttered environments,leading to decreased detection accuracy and potential safety risks.Existing convolutional neural networks(CNN)-based detectors,although effective in natural scenes,often exhibit limited robustness under such complex industrial conditions.To address these challenges,this paper proposes MSA-DETR,a Transformer-based detection framework designed to enhance multi-scale perception in drilling monitoring scenarios.By improving the ability to capture both global contextualinformation andfine-grained visual cues,the proposed approach enhances sensitivity to safety-relevant objects.Extensive experiments conducted on two realworld drilling monitoring datasets demonstrate that MSA-DETR consistently outperforms state-of-theart detection methods,providing more reliable visual perception for petroleum safety management and accident prevention. 展开更多
关键词 Object detection Petroleum drilling safety Multi-scale perception Drilling engineering Artificial intelligence
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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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Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys 认领 引用
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作者 Xu Qin Qinghang Wang +6 位作者 Xinqian Zhao Shouxin Xia Li Wang Jiabao Long Yuhui Zhang Yanfu Chai Daolun Chen 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期117-123,共7页
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 R2 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
关键词 Magnesium alloys Composition prediction Scanning electron microscope images Multi-scale simplified residual convolutional neural network
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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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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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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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