Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.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
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
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
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 https://github.com/cym0997/DL-YOLO.展开更多
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.展开更多
The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a mo...The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
The pore structure of shale oil reservoir significantly affects the occurrence and mobility of hydrocarbons.The potential of a new type of alkaline lake shale oil has been demonstrated,but there are few reports on the...The pore structure of shale oil reservoir significantly affects the occurrence and mobility of hydrocarbons.The potential of a new type of alkaline lake shale oil has been demonstrated,but there are few reports on the pore system of alkaline lake shale,which restricts the efficient exploration and development of shale oil.This study investigates the Fengcheng Formation shale in the Mahu sag of the Junggar Basin,employing methods such as low-temperature nitrogecn adsorption(LTNA),mercury intrusion capillary pressure(MICP),and nuclear magnetic resonance(NMR)to quantitatively characterize the multi-scale pore structure and fractal characteristics of shale,while evaluating the applicability of these methods.Based on a comprehensive analysis of material composition,different pore types,and fractal dimensions,the controlling factors for the development of different pore types and their seepage capacity are discussed.The results indicate that inorganic mineral pores are the main development in alkaline lake shale,with the pore morphology being characterized by slit-like and ink-bottle shapes.The multi-scale pore size distribution(PSD)shows that Ⅱ-micropores(10-100 nm)and mesopores(100–1000 nm)are the main contributors to the pore system.The development of Ⅱ-micropores is associated with feldspar and calcareous minerals,the development of Ⅰ-micropores(<10 nm)and mesopores is related to quartz content,while large pores are mainly found in interlayer fissures of clay minerals.The development of Ⅰ-micropores increases the roughness of pore surface and enhances the adsorption capacity of the pores,while the development of Ⅱ-micropores associated with calcareous minerals hinders pore seepage capacity.Mesopores and macropores(>1000 nm)exhibit good flowability.The high content of siliceous minerals plays a positive role in the pore system of alkaline lake shale.The shale with higher fractal dimension Dmin exhibits greater adsorption capacity,which hinders the accumulation of free-state shale oil.Different types of pore space play different roles in the occurrence of shale oil,with free-state shale oil primarily occurring in micro-fractures and inorganic mineral pores,and the pore size is exceeding 10 nm.展开更多
This paper proposes an approach to determing the optimal cluster spacing for volume fracturing in shale oil reservoirs based on three scales,i.e.microscopic capillary displacement,large-scale core imbibition,and macro...This paper proposes an approach to determing the optimal cluster spacing for volume fracturing in shale oil reservoirs based on three scales,i.e.microscopic capillary displacement,large-scale core imbibition,and macroscopic reservoir nuclear magnetic resonance(NMR)logging.Through flow experiments using capillary with different diameters and lengths,and large-scale core counter-current and dynamic imbibition tests,and combing with the NMR logging data of single wells,a graded optimization criterion for cluster spacing is established.The proposed approach was tested in the shale oil reservoir in the seventh member of the Triassic Yanchang Formation(Change 7 Member),the Ordos Basin.The following findings are obtained.First,in the Chang 7 reservoir,oil in pores smaller than 8μm requires a threshold pressure,and for 2-8μm pores,the movable drainage distance ranges from 0.7 m to 4.6 m under a pressure difference of 27 mPa.Second,the large-scale core imbibition tests show a counter-current imbibition distance of only 10 cm,but a dynamic imbibition distance up to 30 cm.Third,in-situ NMR logging results verified that the post-fracturing matrix drainage radius around fractures is 0-4 m,which is consistent with those of capillary flow experiments and large-scale core imbibition tests.The main pore-size range(2-8μm)of the Chang 7 reservoir corresponds to a permeability interval of(0.1-0.4)×10^(-3)μm^(2).Accordingly,a graded optimization criterion for cluster spacing is proposed as follows:for reservoirs with permeability less than 0.20×10^(-3)μm^(2),the cluster spacing should be reduced to smaller than 4.2 m;for reservoirs with permeability of(0.2-0.4)×10^(-3)μm^(2),the cluster spacing should be designed as 4.2-9.2 m.Field application on a pilot platform,where the cluster spacing was reduced to 4.0-6.0 m,yielded an increased initial oil production by approximately 36.6%over a 100-m horizontal reservoir section as compared with untested similar platforms.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
Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on fau...Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.展开更多
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
Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used i...Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.展开更多
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
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supported by the Science and Technology Project of State Grid Corporation of China(5400-202199281A-0-0-00).
文摘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.
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 https://github.com/cym0997/DL-YOLO.
基金supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
基金National Key Research&Development Program of China,No.2021YFC3201201Ningxia Key Research and Development Program(Special Talents),No.2023BSB03021+1 种基金Natural Science Foundation of Ningxia,No.2023AAC05014University First-Class Discipline Construction Project of Ningxia,No.NXYLXK2021A03。
文摘The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
基金financially supported by the National Natural Science Foundation of China(Nos.42272137,42202160)AAPG Foundation Grants-in-Aid and the Strategic Cooperation Technology Projecti of CNPC and CUPB(No.ZLZX2020-01-05)。
文摘The pore structure of shale oil reservoir significantly affects the occurrence and mobility of hydrocarbons.The potential of a new type of alkaline lake shale oil has been demonstrated,but there are few reports on the pore system of alkaline lake shale,which restricts the efficient exploration and development of shale oil.This study investigates the Fengcheng Formation shale in the Mahu sag of the Junggar Basin,employing methods such as low-temperature nitrogecn adsorption(LTNA),mercury intrusion capillary pressure(MICP),and nuclear magnetic resonance(NMR)to quantitatively characterize the multi-scale pore structure and fractal characteristics of shale,while evaluating the applicability of these methods.Based on a comprehensive analysis of material composition,different pore types,and fractal dimensions,the controlling factors for the development of different pore types and their seepage capacity are discussed.The results indicate that inorganic mineral pores are the main development in alkaline lake shale,with the pore morphology being characterized by slit-like and ink-bottle shapes.The multi-scale pore size distribution(PSD)shows that Ⅱ-micropores(10-100 nm)and mesopores(100–1000 nm)are the main contributors to the pore system.The development of Ⅱ-micropores is associated with feldspar and calcareous minerals,the development of Ⅰ-micropores(<10 nm)and mesopores is related to quartz content,while large pores are mainly found in interlayer fissures of clay minerals.The development of Ⅰ-micropores increases the roughness of pore surface and enhances the adsorption capacity of the pores,while the development of Ⅱ-micropores associated with calcareous minerals hinders pore seepage capacity.Mesopores and macropores(>1000 nm)exhibit good flowability.The high content of siliceous minerals plays a positive role in the pore system of alkaline lake shale.The shale with higher fractal dimension Dmin exhibits greater adsorption capacity,which hinders the accumulation of free-state shale oil.Different types of pore space play different roles in the occurrence of shale oil,with free-state shale oil primarily occurring in micro-fractures and inorganic mineral pores,and the pore size is exceeding 10 nm.
基金Supported by the China National Oil and Gas Major Project(2025ZD1404800)PetroChina Science and Technology Major Project(2023ZZ15YJ03)CNPC Changqing Oilfield Company Major Special Project(2023DZZ04)。
文摘This paper proposes an approach to determing the optimal cluster spacing for volume fracturing in shale oil reservoirs based on three scales,i.e.microscopic capillary displacement,large-scale core imbibition,and macroscopic reservoir nuclear magnetic resonance(NMR)logging.Through flow experiments using capillary with different diameters and lengths,and large-scale core counter-current and dynamic imbibition tests,and combing with the NMR logging data of single wells,a graded optimization criterion for cluster spacing is established.The proposed approach was tested in the shale oil reservoir in the seventh member of the Triassic Yanchang Formation(Change 7 Member),the Ordos Basin.The following findings are obtained.First,in the Chang 7 reservoir,oil in pores smaller than 8μm requires a threshold pressure,and for 2-8μm pores,the movable drainage distance ranges from 0.7 m to 4.6 m under a pressure difference of 27 mPa.Second,the large-scale core imbibition tests show a counter-current imbibition distance of only 10 cm,but a dynamic imbibition distance up to 30 cm.Third,in-situ NMR logging results verified that the post-fracturing matrix drainage radius around fractures is 0-4 m,which is consistent with those of capillary flow experiments and large-scale core imbibition tests.The main pore-size range(2-8μm)of the Chang 7 reservoir corresponds to a permeability interval of(0.1-0.4)×10^(-3)μm^(2).Accordingly,a graded optimization criterion for cluster spacing is proposed as follows:for reservoirs with permeability less than 0.20×10^(-3)μm^(2),the cluster spacing should be reduced to smaller than 4.2 m;for reservoirs with permeability of(0.2-0.4)×10^(-3)μm^(2),the cluster spacing should be designed as 4.2-9.2 m.Field application on a pilot platform,where the cluster spacing was reduced to 4.0-6.0 m,yielded an increased initial oil production by approximately 36.6%over a 100-m horizontal reservoir section as compared with untested similar platforms.
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.
文摘Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.
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funded by the Directorate General of Research and Development,Ministry of Higher Education,Science and Technology of the Republic of Indonesia,with grant number 2.6.63/UN32.14.1/LT/2025.
文摘Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.
