The homogenized Mg−5.6Gd−0.8Zn(wt.%)alloys were treated with water cooling and furnace cooling to obtain specimens without and with the 14H long-period stacking ordered(LPSO)phase.Subsequently,multi-directional forgin...The homogenized Mg−5.6Gd−0.8Zn(wt.%)alloys were treated with water cooling and furnace cooling to obtain specimens without and with the 14H long-period stacking ordered(LPSO)phase.Subsequently,multi-directional forging(MDF)experiments were carried out.The microstructure and mechanical properties of different regions(the center,middle and edge regions)in the MDFed alloys were systematically investigated,and the effect of LPSO phase on them was discussed.The results show that the alloys in different regions undergo significant grain refinement during the MDF process.Inhomogeneous microstructures with different degrees of dynamic recrystallization(DRX)are formed,resulting in microhardness heterogeneity.The alloy with the LPSO phase has higher microstructure homogeneity,a higher degree of recrystallization,and better comprehensive mechanical properties than the alloy without the LPSO phase.The furnace-cooled alloy after 18 passes of MDF has the best comprehensive mechanical properties,with an ultimate compressive strength of 488 MPa,yield strength of 258 MPa,and fracture strain of 21.2%.DRX behavior is closely related to the LPSO phase and deformation temperature.The kinked LPSO phase can act as a potential nucleation site for DRX grains,while the fragmented LPSO phase promotes DRX nucleation through the particle-stimulated nucleation mechanism.展开更多
This review explores multi-directional functionally graded(MDFG)nanostructures,focusing on their material characteristics,modeling approaches,and mechanical behavior.It starts by classifying different types of functio...This review explores multi-directional functionally graded(MDFG)nanostructures,focusing on their material characteristics,modeling approaches,and mechanical behavior.It starts by classifying different types of functionally graded(FG)materials such as conventional,axial,bi-directional,and tri-directional,and the material distribution models like power-law,exponential,trigonometric,polynomial functions,etc.It also discusses the application of advanced size-dependent theories like Eringen’s nonlocal elasticity,nonlocal strain gradient,modified couple stress,and consistent couple stress theories,which are essential to predict the behavior of structures at small scales.The review covers the mechanical analysis of MDFG nanostructures in nanobeams,nanopipes,nanoplates,and nanoshells and their dynamic and static responses under different loading conditions.The effect of multi-directional material gradation on stiffness,stability and vibration is discussed.Moreover,the review highlights the need for more advanced analytical,semi-analytical,and numerical methods to solve the complex vibration problems ofMDFG nanostructures.It is evident that the continued development of these methods is crucial for the design,optimization,and real-world application of MDFG nanostructures in advanced engineering fields like aerospace,biomedicine,and micro/nanoelectromechanical systems(MEMS/NEMS).This study is a reference for researchers and engineers working in the domain of MDFG nanostructures.展开更多
Edge deployment solutions based on convolutional neural networks(CNNs)have garnered significant attention because of their potential applications.However,traditional CNNs rely on pooling to reduce the feature size,lea...Edge deployment solutions based on convolutional neural networks(CNNs)have garnered significant attention because of their potential applications.However,traditional CNNs rely on pooling to reduce the feature size,leading to substantial information loss and reduced network robustness.Herein,we propose a more robust adaptive pooling network(APN)method implemented using memristor technology.Our method introduces an improved pooling layer that reduces input features to an arbitrary scale without compromising their importance.Different coupling coefficients of the pooling layer are stored as conductance values in arrays.We validate the proposed APN on generic datasets,demonstrating significant performance improvements over previously reported CNN architectures.Additionally,we evaluate the APN on a CAPTCHA recognition task with perturbations to assess network robustness.The results show that the APN achieves 92.6% accuracy in 4-digit CAPTCHA recognition and exhibits higher robustness.This brief presents a highly robust and novel scheme for edge computing using memristor technology.展开更多
The complex grain fragmentation mechanisms of coarse grains in titanium alloys under multi-directional forging(MDF)directly influence the optimization and control of primary hot working processes.This study conducted ...The complex grain fragmentation mechanisms of coarse grains in titanium alloys under multi-directional forging(MDF)directly influence the optimization and control of primary hot working processes.This study conducted MDF experiments onβ-phase as-cast Ti-6554 alloy and simulated non-uniform deformation during cyclic multi-directional compression through macro-and micro-deformation modeling.The results revealed that friction and surface cooling caused low strain and tensile stress concentration at billet edges,leading to mixed grain structures.In contrast,high strain and triaxial compressive stress at billet centers facilitated uniform grain refinement.After 14 compressions and 4 intermediate reheating processes,coarse grains of the billet were refined from 2-5 mm to 0.25-0.50 mm,achieving uniform grain sizes across different regions.For the first time,the orientation evolution of grains with different morphologies during multi-directional compressions was visualized microscopically.Columnar grains were found to be more easily subdivided than equiaxed grains due to local strain accumulation.Under cumulative compressions,grain orientations gradually rotated from uniform to random,driving continuous dynamic recrystallization(CDRX).Slip system interactions and concentrated misorientation led to the formation and extension of transition and shear bands,inducing grain fragmentation dominated by transgranular subdivided CDRX.Smooth grain boundaries transformed into serrated ones after multiple passes,providing additional nucleation sites for discontinuous dynamic recrystallization(DDRX)and facilitating boundary expand CDRX.The interaction of diverse DRX mechanisms was the fundamental cause of grain refinement.This study clarified the principles of refining and homogenizing millimeter-grade coarse grains under increasing forging strain,offering valuable insights for the development of primary hot processing techniques for as-castβtitanium alloys.展开更多
The superplasticity of the Mg−8.59Gd−3.85Y−1.14Zn−0.49Zr alloy was investigated before and after multi-directional forging(MDF)and the mechanisms affecting superplastic deformation were analyzed.The results indicate t...The superplasticity of the Mg−8.59Gd−3.85Y−1.14Zn−0.49Zr alloy was investigated before and after multi-directional forging(MDF)and the mechanisms affecting superplastic deformation were analyzed.The results indicate that after MDF at a temperature of 350℃and strain rates of 0.1 and 0.01 s^(−1)(1-MDFed and 2-MDFed),the superplasticity of the alloy can be significantly improved.The elongations of the MDFed alloys exceed 400%under the strain rate of 6.06×10^(−4)s^(−1)and temperatures of 350,375,and 400℃,and reach the maximum values of 766%(1-MDFed)and 693%(2-MDFed)at 375℃.The grain boundary sliding of the MDFed alloy is sufficient,and the energy barrier of deformation decreases.Theβphase limits the grain growth and promotes dynamic recrystallization,maintaining the stability of the fine-grained structure during superplastic deformation.Several Y-rich phases nucleate in the high-strain region(i.e.,the final fracture region)at high temperatures,accelerating the fracture of the specimen.展开更多
With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance systems.This technology plays a critical role in enhan...With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance systems.This technology plays a critical role in enhancing public safety.However,traditional methods typically process images and text separately,applying upstream models directly to downstream tasks.This approach significantly increases the complexity ofmodel training and computational costs.Furthermore,the common class imbalance in existing training datasets limitsmodel performance improvement.To address these challenges,we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology andMulti-Instance Negative Pooling(VPM-Net).First,we incorporate the Contrastive Language-Image Pre-training(CLIP)pre-trained model to accurately map visual and textual features into a unified embedding space,effectively mitigating inconsistencies in data distribution and the training process.To enhancemodel adaptability and generalization,we introduce an efficient and task-specific Visual Prompt Tuning(VPT)technique,which improves the model’s relevance to specific tasks.Additionally,we design two key modules:the Knowledge-Aware Network(KAN)and theMulti-Instance Negative Pooling(MINP)module.The KAN module significantly enhances the model’s understanding of complex scenarios through deep contextual semantic modeling.MINP module handles samples,effectively improving the model’s ability to distinguish fine-grained features.The experimental outcomes across diverse datasets underscore the remarkable performance of VPM-Net.These results vividly demonstrate the unique advantages and robust reliability of VPM-Net in fine-grained retrieval tasks.展开更多
This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Tradi...This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments.To address this,we implement real-time tool condition recognition by introducing deep learning technology.Aiming to the insufficient recognition accuracy,we propose a pyramid pooling-based vision Transformer network(P2ViT-Net)method for tool condition recognition.Using images as input effectively mitigates the issue of low-dimensional signal features.We enhance the vision Transformer(ViT)framework for image classification by developing the P2ViT model and adapt it to tool condition recognition.Experimental results demonstrate that our improved P2ViT model achieves 94.4%recognition accuracy,showing a 10%improvement over conventional ViT and outperforming all comparative convolutional neural network models.展开更多
The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Min...The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Ming and Song,2011).Adult quiescent hippocampal neural stem cells(NSCs)are bona fide stem cells and,when activated,give rise to newborn granule neurons in the adult brain,which play vital roles in learning,memory,mood,and affective cognition(Bonaguidi et al.,2011;Ming and Song,2011).展开更多
The effect of forging passes on the refinement of high purity aluminum during multi-forging was investigated. The attention was focused on the structure uniformity due to deformation uniformity and the grain refinemen...The effect of forging passes on the refinement of high purity aluminum during multi-forging was investigated. The attention was focused on the structure uniformity due to deformation uniformity and the grain refinement limitation with very high strains. The results show that the fine grain zone in the center of sample expands gradually with the increase of forging passes. When the forging passes reach 6, an X-shape fine grain zone is initially formed. With a further increase of the passes, this X-shape zone tends to spread the whole sample. Limitation in the structural refinement is observed with increasing strains during multi-forging process at the room temperature. The grains size in the center is refined to a certain size （110 μm as forging passes reach 12, and there is no further grain refinement in the center with increasing the forging passes to 24. However, the size of the coarse grains near the surface is continuously decreased with increasing the forging passes to 24.展开更多
基金the financial supports from the Key Research and Development Program of Hunan Province,China(No.2023GK2020)。
文摘The homogenized Mg−5.6Gd−0.8Zn(wt.%)alloys were treated with water cooling and furnace cooling to obtain specimens without and with the 14H long-period stacking ordered(LPSO)phase.Subsequently,multi-directional forging(MDF)experiments were carried out.The microstructure and mechanical properties of different regions(the center,middle and edge regions)in the MDFed alloys were systematically investigated,and the effect of LPSO phase on them was discussed.The results show that the alloys in different regions undergo significant grain refinement during the MDF process.Inhomogeneous microstructures with different degrees of dynamic recrystallization(DRX)are formed,resulting in microhardness heterogeneity.The alloy with the LPSO phase has higher microstructure homogeneity,a higher degree of recrystallization,and better comprehensive mechanical properties than the alloy without the LPSO phase.The furnace-cooled alloy after 18 passes of MDF has the best comprehensive mechanical properties,with an ultimate compressive strength of 488 MPa,yield strength of 258 MPa,and fracture strain of 21.2%.DRX behavior is closely related to the LPSO phase and deformation temperature.The kinked LPSO phase can act as a potential nucleation site for DRX grains,while the fragmented LPSO phase promotes DRX nucleation through the particle-stimulated nucleation mechanism.
文摘This review explores multi-directional functionally graded(MDFG)nanostructures,focusing on their material characteristics,modeling approaches,and mechanical behavior.It starts by classifying different types of functionally graded(FG)materials such as conventional,axial,bi-directional,and tri-directional,and the material distribution models like power-law,exponential,trigonometric,polynomial functions,etc.It also discusses the application of advanced size-dependent theories like Eringen’s nonlocal elasticity,nonlocal strain gradient,modified couple stress,and consistent couple stress theories,which are essential to predict the behavior of structures at small scales.The review covers the mechanical analysis of MDFG nanostructures in nanobeams,nanopipes,nanoplates,and nanoshells and their dynamic and static responses under different loading conditions.The effect of multi-directional material gradation on stiffness,stability and vibration is discussed.Moreover,the review highlights the need for more advanced analytical,semi-analytical,and numerical methods to solve the complex vibration problems ofMDFG nanostructures.It is evident that the continued development of these methods is crucial for the design,optimization,and real-world application of MDFG nanostructures in advanced engineering fields like aerospace,biomedicine,and micro/nanoelectromechanical systems(MEMS/NEMS).This study is a reference for researchers and engineers working in the domain of MDFG nanostructures.
基金supported by the National Natural Science Foundation of China(Grant Nos.62274002,62304001,and 62201005)the Anhui Provincial Natural Science Foundation(Grant Nos.2308085QF213 and 2408085QF211)the Natural Science Research Project of the Anhui Educational Committee(Grant No.2023AH050072)。
文摘Edge deployment solutions based on convolutional neural networks(CNNs)have garnered significant attention because of their potential applications.However,traditional CNNs rely on pooling to reduce the feature size,leading to substantial information loss and reduced network robustness.Herein,we propose a more robust adaptive pooling network(APN)method implemented using memristor technology.Our method introduces an improved pooling layer that reduces input features to an arbitrary scale without compromising their importance.Different coupling coefficients of the pooling layer are stored as conductance values in arrays.We validate the proposed APN on generic datasets,demonstrating significant performance improvements over previously reported CNN architectures.Additionally,we evaluate the APN on a CAPTCHA recognition task with perturbations to assess network robustness.The results show that the APN achieves 92.6% accuracy in 4-digit CAPTCHA recognition and exhibits higher robustness.This brief presents a highly robust and novel scheme for edge computing using memristor technology.
基金supported by the National Key Research and Development Program of China(No.2022YFB3706901)the National Natural Science Foundation of China(No.52274382)。
文摘The complex grain fragmentation mechanisms of coarse grains in titanium alloys under multi-directional forging(MDF)directly influence the optimization and control of primary hot working processes.This study conducted MDF experiments onβ-phase as-cast Ti-6554 alloy and simulated non-uniform deformation during cyclic multi-directional compression through macro-and micro-deformation modeling.The results revealed that friction and surface cooling caused low strain and tensile stress concentration at billet edges,leading to mixed grain structures.In contrast,high strain and triaxial compressive stress at billet centers facilitated uniform grain refinement.After 14 compressions and 4 intermediate reheating processes,coarse grains of the billet were refined from 2-5 mm to 0.25-0.50 mm,achieving uniform grain sizes across different regions.For the first time,the orientation evolution of grains with different morphologies during multi-directional compressions was visualized microscopically.Columnar grains were found to be more easily subdivided than equiaxed grains due to local strain accumulation.Under cumulative compressions,grain orientations gradually rotated from uniform to random,driving continuous dynamic recrystallization(CDRX).Slip system interactions and concentrated misorientation led to the formation and extension of transition and shear bands,inducing grain fragmentation dominated by transgranular subdivided CDRX.Smooth grain boundaries transformed into serrated ones after multiple passes,providing additional nucleation sites for discontinuous dynamic recrystallization(DDRX)and facilitating boundary expand CDRX.The interaction of diverse DRX mechanisms was the fundamental cause of grain refinement.This study clarified the principles of refining and homogenizing millimeter-grade coarse grains under increasing forging strain,offering valuable insights for the development of primary hot processing techniques for as-castβtitanium alloys.
基金supported by the National Natural Science Foundation of China(No.52127808)。
文摘The superplasticity of the Mg−8.59Gd−3.85Y−1.14Zn−0.49Zr alloy was investigated before and after multi-directional forging(MDF)and the mechanisms affecting superplastic deformation were analyzed.The results indicate that after MDF at a temperature of 350℃and strain rates of 0.1 and 0.01 s^(−1)(1-MDFed and 2-MDFed),the superplasticity of the alloy can be significantly improved.The elongations of the MDFed alloys exceed 400%under the strain rate of 6.06×10^(−4)s^(−1)and temperatures of 350,375,and 400℃,and reach the maximum values of 766%(1-MDFed)and 693%(2-MDFed)at 375℃.The grain boundary sliding of the MDFed alloy is sufficient,and the energy barrier of deformation decreases.Theβphase limits the grain growth and promotes dynamic recrystallization,maintaining the stability of the fine-grained structure during superplastic deformation.Several Y-rich phases nucleate in the high-strain region(i.e.,the final fracture region)at high temperatures,accelerating the fracture of the specimen.
基金funded by the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)the Young and Middle-aged Scientific and Technological Innova-tion Team Plan in Higher Education Institutions inHubei Province,China(GrantNo.T2023007)the key projects ofHubei Provincial Department of Education(No.D20161403).
文摘With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance systems.This technology plays a critical role in enhancing public safety.However,traditional methods typically process images and text separately,applying upstream models directly to downstream tasks.This approach significantly increases the complexity ofmodel training and computational costs.Furthermore,the common class imbalance in existing training datasets limitsmodel performance improvement.To address these challenges,we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology andMulti-Instance Negative Pooling(VPM-Net).First,we incorporate the Contrastive Language-Image Pre-training(CLIP)pre-trained model to accurately map visual and textual features into a unified embedding space,effectively mitigating inconsistencies in data distribution and the training process.To enhancemodel adaptability and generalization,we introduce an efficient and task-specific Visual Prompt Tuning(VPT)technique,which improves the model’s relevance to specific tasks.Additionally,we design two key modules:the Knowledge-Aware Network(KAN)and theMulti-Instance Negative Pooling(MINP)module.The KAN module significantly enhances the model’s understanding of complex scenarios through deep contextual semantic modeling.MINP module handles samples,effectively improving the model’s ability to distinguish fine-grained features.The experimental outcomes across diverse datasets underscore the remarkable performance of VPM-Net.These results vividly demonstrate the unique advantages and robust reliability of VPM-Net in fine-grained retrieval tasks.
基金supported by China Postdoctoral Science Foundation(No.2024M754122)the Postdoctoral Fellowship Programof CPSF(No.GZB20240972)+3 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent(No.2024ZB194)Natural Science Foundation of Jiangsu Province(No.BK20241389)Basic Science ResearchFund of China(No.JCKY2023203C026)2024 Jiangsu Province Talent Programme Qinglan Project.
文摘This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments.To address this,we implement real-time tool condition recognition by introducing deep learning technology.Aiming to the insufficient recognition accuracy,we propose a pyramid pooling-based vision Transformer network(P2ViT-Net)method for tool condition recognition.Using images as input effectively mitigates the issue of low-dimensional signal features.We enhance the vision Transformer(ViT)framework for image classification by developing the P2ViT model and adapt it to tool condition recognition.Experimental results demonstrate that our improved P2ViT model achieves 94.4%recognition accuracy,showing a 10%improvement over conventional ViT and outperforming all comparative convolutional neural network models.
基金supported by National Institutes of Health(R35NS137480,R35NS116843,and RF1AG079557)by Dr.Miriam and Sheldon G.Adelson Medical Research Foundation.
文摘The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Ming and Song,2011).Adult quiescent hippocampal neural stem cells(NSCs)are bona fide stem cells and,when activated,give rise to newborn granule neurons in the adult brain,which play vital roles in learning,memory,mood,and affective cognition(Bonaguidi et al.,2011;Ming and Song,2011).
基金Projects(51204053,51074048,51204048)supported by the National Natural Science Foundation of ChinaProject(20110491518)supported by China Postdoctoral Science FoundationProject(2012CB619506)supported by the National Basic Research Program of China
文摘The effect of forging passes on the refinement of high purity aluminum during multi-forging was investigated. The attention was focused on the structure uniformity due to deformation uniformity and the grain refinement limitation with very high strains. The results show that the fine grain zone in the center of sample expands gradually with the increase of forging passes. When the forging passes reach 6, an X-shape fine grain zone is initially formed. With a further increase of the passes, this X-shape zone tends to spread the whole sample. Limitation in the structural refinement is observed with increasing strains during multi-forging process at the room temperature. The grains size in the center is refined to a certain size （110 μm as forging passes reach 12, and there is no further grain refinement in the center with increasing the forging passes to 24. However, the size of the coarse grains near the surface is continuously decreased with increasing the forging passes to 24.
