Copper manufactured by laser powder bed fusion(LPBF)process typically exhibits poor strength-ductility coordination,and the addition of strengthening phases is an effective way to address this issue.To explore the eff...Copper manufactured by laser powder bed fusion(LPBF)process typically exhibits poor strength-ductility coordination,and the addition of strengthening phases is an effective way to address this issue.To explore the effects of strengthening phases on Cu,Cu-carbon nanotubes(CNTs)composites were prepared using LPBF technique with Cu-CNTs mixed powder as the matrix.The formability,microstructure,mechanical properties,electrical conductivity,and thermal properties were studied.The result shows that the prepared composites have high relative density.The addition of CNTs results in inhomogeneous equiaxed grains at the edges of the molten pool and columnar grains at the center.Compared with pure copper,the overall mechanical properties of the composite are improved:tensile strength increases by 52.8%and elongation increases by 146.4%;the electrical and thermal properties are also enhanced:thermal conductivity increases by 10.8%and electrical conductivity increases by 12.7%.展开更多
Zn's natural degradability and biocompatibility make it a promising candidate for implants,however,its mechanical properties remain insufficient for bone applications.In this study,the performance of Zn was enhanc...Zn's natural degradability and biocompatibility make it a promising candidate for implants,however,its mechanical properties remain insufficient for bone applications.In this study,the performance of Zn was enhanced by developing Zn-Cu alloys via laser powder bed fusion(LPBF).Optimal LPBF parameters for forming stable tracks were achieved by adjusting laser power and scanning speed.Under optimized conditions of 100 W and 100 mm/s,high density(99.58%)Zn-Cu alloys with improved hardness(68.2 HV)and yield strength(160 MPa)were achieved.These improvements are attributed to solid solution strengthening,segregation strengthening,and grain refinement.The Zn-Cu alloys also demonstrated favorable degradation behavior,with a rate of 0.16 mm/year.This degradation is primarily driven by micro-galvanic corrosion between the CuZn 5 phase and Zn matrix,along with refined grains and increased grain boundary density.This work demonstrates a viable strategy for fabricating Zn-based implants with enhanced structural integrity and mechanical performance via LPBF.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5w...A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.展开更多
Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely us...Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely used in fields such as injection molding,die casting,and stamping dies.Adding reinforcing particles into steel is an effective means to improve its performance.Nb/18Ni300 composites were fabricated by LPBF using two kinds of Nb powders with different particle sizes,and their microstructures and properties were studied.The results show that the unmelted Nb particles are uniformly distributed in the 18Ni300 matrix and the grains are refined,which is particularly pronounced with fine Nb particles.In addition,element diffusion occurs between the particles and the matrix.The main phases of the base alloy are α-Fe and a small amount of γ-Fe.With the addition of Nb,part of the α-Fe is transformed into γ-Fe,and unmelted Nb phases appear.The addition of Nb also enhances the hardness and wear resistance of the composites but slightly reduces their tensile properties.After aging treatment,the molten pools and grain boundaries become blurred,grains are further refined,and the interfaces around the particles are thinned.The aging treatment also promotes the formation of reverted austenite.The hardness,ultimate tensile strength,and volumetric wear rate of the base alloy reach 51.9 HRC,1704 MPa,and 17.8×10^(-6) mm^(3)/(N·m),respectively.In contrast,the sample added with fine Nb particles has the highest hardness(56.1 HRC),ultimate tensile strength(1892 MPa)and yield strength(1842 MPa),and the volume wear rate of the sample added with coarse Nb particles is reduced by 90%to 1.7×10^(-6) mm^(3)/(N·m).展开更多
The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-e...The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughn...A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughness poses a challenge for the future development of high-field superconducting magnets in fusion reactors.The yield strength of the International Thermonuclear Experimental Reactor developed for low-temperature structural materials at 4.2K is below 1100MPa,which fails to meet the demand for structural components with yield strengths exceeding 1500MPa at 4.2K in the future fusion reactors.CHSN01(formerly N50H),which is a low-temperature structural material developed in China,exhibits exceptional strength and toughness,thereby making it highly promising for practical applications.Recently,a 30 t jacket measuring approximately 5000m in total length was produced.Its low-temperature mechanical properties were tested using a sampling method to ensure compliance with application requirements.This paper presents the experimental data of the CHSN01 jacket and tests of the physical properties of the material in the temperature range of 4–300 K.The physical properties were unaffected by magnetic field.Furthermore,this paper discusses the feasibility of employing CHSN01 as a cryogenic structural material capable of withstanding high magnetic fields in next-generation fusion reactors.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
With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the ...With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the academic community.Despite notable advances,existing approaches still face critical challenges in both information modeling and modality fusion.On one hand,many current methods rely heavily on encoders to extract global features from each modality,which limits their ability to capture latent fine-grained emotional cues within modalities.On the other hand,prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to adaptively regulate modality interactions.To address these limitations,we propose a novel framework for MSA,termed Multi-Granularity Guided Fusion(MGGF).The proposed framework consists of three core components:(i)Multi-Granularity Feature Extraction Module,which simultaneously captures both global and local emotional features within each modality,and integrates them to construct richer intra-modal representations;(ii)Cross-ModalGuidance Learning Module(CMGL),which introduces a cross-modal scoring mechanism to quantify the divergence and complementarity betweenmodalities.These scores are then used as guiding signals to enable the fusion strategy to adaptively respond to scenarios of modality agreement or conflict;(iii)Cross-Modal Fusion Module(CMF),which learns the semantic dependencies among modalities and facilitates deep-level emotional feature interaction,thereby enhancing sentiment prediction with complementary information.We evaluate MGGF on two benchmark datasets:MVSA-Single and MVSA-Multiple.Experimental results demonstrate that MGGF outperforms the current state-of-the-art model CLMLF on MVSA-Single by achieving a 2.32% improvement in F1 score.On MVSA-Multiple,it surpasses MGNNS with a 0.26% increase in accuracy.These results substantiate the effectiveness ofMGGFin addressing two major limitations of existing methods—insufficient intra-modal fine-grained sentiment modeling and inadequate cross-modal semantic fusion.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
Magnesium-rare earth(Mg-RE)alloys are pivotal for lightweight applications in aerospace and advanced engineering due to their high specific strength.However,manufacturing large-scale complex components via monolithic ...Magnesium-rare earth(Mg-RE)alloys are pivotal for lightweight applications in aerospace and advanced engineering due to their high specific strength.However,manufacturing large-scale complex components via monolithic casting is challenging owing to defects such as RE oxides and shrinkage porosity,making fusion welding essential for both defect repair and structural joining.This review comprehensively examines recent advances in fusion welding of Mg-RE alloys,with emphasis on the interplay between their unique physicochemical properties and welding metallurgy.Various fusion welding methods suitable for Mg-RE alloys are compared and analyzed.Detailed characterization of joint regions reveals how thermal gradients and cooling rates govern phase evolution,grain morphology,and defect formation.Moreover,welding parameters and heat treatment strategies are systematically discussed for the microstructural configuration,especially for the inherent conflicts between grain coarsening in fusion zone and eutectic dissolution in heat-affected zone.Future research directions are also outlined.By correlating Mg-RE alloy properties with fusion welding processes,this review provides practical insights for designing reliable welded structures in critical applications.展开更多
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva...Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.展开更多
In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although ...In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
基金National Key Research and Development Program of China(2023YFB4606400)Supported by Longmen Laboratory Frontier Exploration Topics(LMQYTSKT003)。
文摘Copper manufactured by laser powder bed fusion(LPBF)process typically exhibits poor strength-ductility coordination,and the addition of strengthening phases is an effective way to address this issue.To explore the effects of strengthening phases on Cu,Cu-carbon nanotubes(CNTs)composites were prepared using LPBF technique with Cu-CNTs mixed powder as the matrix.The formability,microstructure,mechanical properties,electrical conductivity,and thermal properties were studied.The result shows that the prepared composites have high relative density.The addition of CNTs results in inhomogeneous equiaxed grains at the edges of the molten pool and columnar grains at the center.Compared with pure copper,the overall mechanical properties of the composite are improved:tensile strength increases by 52.8%and elongation increases by 146.4%;the electrical and thermal properties are also enhanced:thermal conductivity increases by 10.8%and electrical conductivity increases by 12.7%.
基金Projects(52571276,52275395,U24A20120,52475362)supported by the National Natural Science Foundation of ChinaProject(2025JJ30015)supported by the Hunan Provincial Natural Science Foundation,China+3 种基金Project(2023RC3046)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProject(2023YFB4605800)supported by National Key Research and Development Program of ChinaProject(2023CXQD023)supported by the Central South University Innovation-Driven Research Programme,ChinaProject supported by the State Key Laboratory of Precision Manufacturing for Extreme Service Performance,Central South University,China。
文摘Zn's natural degradability and biocompatibility make it a promising candidate for implants,however,its mechanical properties remain insufficient for bone applications.In this study,the performance of Zn was enhanced by developing Zn-Cu alloys via laser powder bed fusion(LPBF).Optimal LPBF parameters for forming stable tracks were achieved by adjusting laser power and scanning speed.Under optimized conditions of 100 W and 100 mm/s,high density(99.58%)Zn-Cu alloys with improved hardness(68.2 HV)and yield strength(160 MPa)were achieved.These improvements are attributed to solid solution strengthening,segregation strengthening,and grain refinement.The Zn-Cu alloys also demonstrated favorable degradation behavior,with a rate of 0.16 mm/year.This degradation is primarily driven by micro-galvanic corrosion between the CuZn 5 phase and Zn matrix,along with refined grains and increased grain boundary density.This work demonstrates a viable strategy for fabricating Zn-based implants with enhanced structural integrity and mechanical performance via LPBF.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+8 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)2024 Scientific Research Project of Shaanxi National Defense Industry Vocational and Technical College(Gfy24-07)Shaanxi Vocational and Technical Education Association 2024 Vocational Education Teaching Reform Research Topic(2024SZX354)National Natural Science Foundation of China(U24A20115)2024 Shaanxi Provincial Education Department Service Local Special Scientific Research Program Project-Industrialization Cultivation Project(24JC005,24JC063)Shaanxi Province“14th Five-Year Plan”Education Science Plan,2024 Project(SGH24Y3181)National Key Research and Development Program of China(2023YFB4606400)Longmen Laboratory Frontier Exploration Topics Project(LMQYTSKT003)。
文摘A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.
基金Key-Area Research and Development Program of Guangdong Province(2023B0909020004)Project of Innovation Research Team in Zhongshan(CXTD2023006)+1 种基金Natural Science Foundation of Guangdong Province(2023A1515011573)Zhongshan Social Welfare Science and Technology Research Project(2024B2022)。
文摘Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely used in fields such as injection molding,die casting,and stamping dies.Adding reinforcing particles into steel is an effective means to improve its performance.Nb/18Ni300 composites were fabricated by LPBF using two kinds of Nb powders with different particle sizes,and their microstructures and properties were studied.The results show that the unmelted Nb particles are uniformly distributed in the 18Ni300 matrix and the grains are refined,which is particularly pronounced with fine Nb particles.In addition,element diffusion occurs between the particles and the matrix.The main phases of the base alloy are α-Fe and a small amount of γ-Fe.With the addition of Nb,part of the α-Fe is transformed into γ-Fe,and unmelted Nb phases appear.The addition of Nb also enhances the hardness and wear resistance of the composites but slightly reduces their tensile properties.After aging treatment,the molten pools and grain boundaries become blurred,grains are further refined,and the interfaces around the particles are thinned.The aging treatment also promotes the formation of reverted austenite.The hardness,ultimate tensile strength,and volumetric wear rate of the base alloy reach 51.9 HRC,1704 MPa,and 17.8×10^(-6) mm^(3)/(N·m),respectively.In contrast,the sample added with fine Nb particles has the highest hardness(56.1 HRC),ultimate tensile strength(1892 MPa)and yield strength(1842 MPa),and the volume wear rate of the sample added with coarse Nb particles is reduced by 90%to 1.7×10^(-6) mm^(3)/(N·m).
基金supported by the National Natural Science Foundation of China(Nos.12405288,12374241,12474484,U2330401,12088101)the Natural Science Foundation of Top Talent of SZTU(No.GDRC202526)。
文摘The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金supported in part by the National Natural Science Foundation of China(No.12305196)Anhui Provincial Natural Science Foundation(No.2308085QA23)+1 种基金Open Fund of Magnetic confinement Fusion Laboratory of Anhui Province(No.2023AMF03003)Science Foundation of Institute of Plasma Physics,Chinese Academy of Sciences(No.DSJJ-2024-10).
文摘A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughness poses a challenge for the future development of high-field superconducting magnets in fusion reactors.The yield strength of the International Thermonuclear Experimental Reactor developed for low-temperature structural materials at 4.2K is below 1100MPa,which fails to meet the demand for structural components with yield strengths exceeding 1500MPa at 4.2K in the future fusion reactors.CHSN01(formerly N50H),which is a low-temperature structural material developed in China,exhibits exceptional strength and toughness,thereby making it highly promising for practical applications.Recently,a 30 t jacket measuring approximately 5000m in total length was produced.Its low-temperature mechanical properties were tested using a sampling method to ensure compliance with application requirements.This paper presents the experimental data of the CHSN01 jacket and tests of the physical properties of the material in the temperature range of 4–300 K.The physical properties were unaffected by magnetic field.Furthermore,this paper discusses the feasibility of employing CHSN01 as a cryogenic structural material capable of withstanding high magnetic fields in next-generation fusion reactors.
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 in part by the National Key Research and Development Program of China under Grant 2022YFB3102904in part by the National Natural Science Foundation of China under Grant No.U23A20305 and No.62472440.
文摘With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the academic community.Despite notable advances,existing approaches still face critical challenges in both information modeling and modality fusion.On one hand,many current methods rely heavily on encoders to extract global features from each modality,which limits their ability to capture latent fine-grained emotional cues within modalities.On the other hand,prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to adaptively regulate modality interactions.To address these limitations,we propose a novel framework for MSA,termed Multi-Granularity Guided Fusion(MGGF).The proposed framework consists of three core components:(i)Multi-Granularity Feature Extraction Module,which simultaneously captures both global and local emotional features within each modality,and integrates them to construct richer intra-modal representations;(ii)Cross-ModalGuidance Learning Module(CMGL),which introduces a cross-modal scoring mechanism to quantify the divergence and complementarity betweenmodalities.These scores are then used as guiding signals to enable the fusion strategy to adaptively respond to scenarios of modality agreement or conflict;(iii)Cross-Modal Fusion Module(CMF),which learns the semantic dependencies among modalities and facilitates deep-level emotional feature interaction,thereby enhancing sentiment prediction with complementary information.We evaluate MGGF on two benchmark datasets:MVSA-Single and MVSA-Multiple.Experimental results demonstrate that MGGF outperforms the current state-of-the-art model CLMLF on MVSA-Single by achieving a 2.32% improvement in F1 score.On MVSA-Multiple,it surpasses MGNNS with a 0.26% increase in accuracy.These results substantiate the effectiveness ofMGGFin addressing two major limitations of existing methods—insufficient intra-modal fine-grained sentiment modeling and inadequate cross-modal semantic fusion.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
基金supported by the National Natural Science Foundation of China(No.52405394)the Natural Science Foundation of Shanghai(No.24ZR1436500)+2 种基金the Natural Science Foundation of Chongqing(No.CSTB2024NSCQMSX0677)the Science Innovation Foundation of Shanghai Academy of Spaceflight Technology(No.SAST2024-039)the Startup Fund for Young Faculty at SJTU(No.24X010502880).
文摘Magnesium-rare earth(Mg-RE)alloys are pivotal for lightweight applications in aerospace and advanced engineering due to their high specific strength.However,manufacturing large-scale complex components via monolithic casting is challenging owing to defects such as RE oxides and shrinkage porosity,making fusion welding essential for both defect repair and structural joining.This review comprehensively examines recent advances in fusion welding of Mg-RE alloys,with emphasis on the interplay between their unique physicochemical properties and welding metallurgy.Various fusion welding methods suitable for Mg-RE alloys are compared and analyzed.Detailed characterization of joint regions reveals how thermal gradients and cooling rates govern phase evolution,grain morphology,and defect formation.Moreover,welding parameters and heat treatment strategies are systematically discussed for the microstructural configuration,especially for the inherent conflicts between grain coarsening in fusion zone and eutectic dissolution in heat-affected zone.Future research directions are also outlined.By correlating Mg-RE alloy properties with fusion welding processes,this review provides practical insights for designing reliable welded structures in critical applications.
基金supported in part by the National Key Research and Development Program of Chinaunder(Grant 2021YFB3101100)in part by the National Natural Science Foundation of Chinaunder(Grant 42461057),(Grant 62272123),and(Grant 42371470)+1 种基金in part by the Fundamental Research Program of Shanxi Province under(Grant 202303021212164)in part by the Postgraduate Education Innovation Program of Shanxi Province under(Grant 2024KY474).
文摘Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.
基金funded by the National Natural Science Foundation of China under Grant 62306128the Leading Innovation Project of Changzhou Science and Technology Bureau underGrant CQ20230072+2 种基金the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Science and Technology Development Plan Project of Jilin Provinceunder Grant 20240101382JCthe National KeyR esearch and Development Program of China under Grant 2023YFF1105102.
文摘In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
