A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching...A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.展开更多
Separation between water and land is vital for marine scientific research and coastal zone planning and management.The contrasting backscatter properties of land and ocean enable clear water edge line identification i...Separation between water and land is vital for marine scientific research and coastal zone planning and management.The contrasting backscatter properties of land and ocean enable clear water edge line identification in synthetic aperture radar(SAR)imagery.However,SAR images are prone to speckle noise,and the complexity of the water-land boundaries environment makes accurate water-land separation challenging.To overcome noise and complex background interference in remote sensing images,an improved level set method was employed to enhance water-land separation.In the traditional distance regularized level set method,the selection of the image correlation weight coefficient and the edge indicator function directly influences the accuracy of the final segmentation results.A novel level set segmentation algorithm incorporating an improved edge indicator function is proposed to efficiently and accurately separate the water edge lines in SAR images.The distance regularized level set evolution model is enhanced by incorporating the signed pressure force function as an adaptive parameter,which serves as an external constraint for curve evolution.A novel level set model with an adaptive edge indicator function,combining gradient and regional information,is proposed.Experimental results demonstrate that the proposed model enhances the accuracy of waterland separation in SAR images.However,further research is needed to evaluate its potential for detecting boundaries in diverse marine environments and across different types of SAR imagery.展开更多
A cylindrical chamber with a rotating bottom holds significant potential for application in cell culture bioreactors due to its ability to generate more stable swirling flows.In order to control vortex breakdown withi...A cylindrical chamber with a rotating bottom holds significant potential for application in cell culture bioreactors due to its ability to generate more stable swirling flows.In order to control vortex breakdown within the chamber,this study first establishes a computational fluid dynamics simulation coupled with the level set method.Verified by experimental results in literature,this method accurately simulates the position and shape of vortex breakdown,and also predicts the critical Reynolds numbers for the appearance and detachment of vortex breakdown bubbles from the center.Additionally,it precisely captures the gas-liquid interface and depicts the vortex breakdown phenomenon in the air above the liquid for the first time.Finally,it predicts the impact of physical property of gas-liquid systems on vortex breakdown in response to significant changes in viscosity of microbial process systems.展开更多
Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this proble...Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this problem,the traditional particle tracking velocimetry method based on an optical flow was improved.The level set segmentation algorithm was used to obtain the boundary contour of the region with large velocity gradient changes,and the non-uniform flow field was divided into regions according to the boundary contour to obtain sub-regions with uniform velocity distribution.The particle tracking velocimetry method based on optical flow was used to measure the granular flow velocity in each sub-region,thus avoiding the problem of granular flow distribution.The simulation results show that the measurement accuracy of this method is approximately 10%higher than that of traditional methods.The method was applied to a velocity measurement experiment on dense granular flow in silos,and the velocity distribution of the granular flow was obtained,verifying the practicality of the method in granular flow fields.展开更多
To solve the problem of false edges in a flat region of l_(1)norm total variational TV model,an edge extractor based on non-local idea is proposed in this paper.The new edge extractor can effectively suppress the infl...To solve the problem of false edges in a flat region of l_(1)norm total variational TV model,an edge extractor based on non-local idea is proposed in this paper.The new edge extractor can effectively suppress the influence of noise and extract the edge information of the image.The new edge extractor is used as the adaptive function and the weighting function of the l_(p) norm variational model to control the noise reduction ability of the model,and a new model 1 is obtained.Considering that the new model 1 only uses the gradient mode as the image feature operator,which is insufficient to express the image texture information,a new level set curvature gradient variational model 2 combined with the edge extractor is proposed.The new model 2 uses the idea of minimum curvature of the level set of clear images to obtain noise reduction images.By coupling new model 1 and new model 2 to smooth the noise and protect more textures,a new Non-local level set denoising model(NLSDM)for image noise reduction is obtained.The experimental results show that compared with the noise reduction model,the new model has significantly improved the peak signal-to-noise ratio and structural similarity,and the effect of noise reduction and edge preservation is better.展开更多
Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service lif...Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.展开更多
A new multi-phase active contour model is proposed for the image segmentation. It is a generalization of the C-V model with the following characteristics： （1） A key technique, called the technique of painting backg...A new multi-phase active contour model is proposed for the image segmentation. It is a generalization of the C-V model with the following characteristics： （1） A key technique, called the technique of painting background （TPBG）, is developed to remove the information of the background, which blocks the detection of weak boundaries in the object; （2） The two-phase level set is applied multiple times for getting the multi-phase segmentation model （n-1 times for the n-phase model, n〉1）; （3） A scaling-based method is introduced to improve the basic model. Experimental results show that the proposed model is effective for detecting weak boundaries.展开更多
The pyramidal multiphase level set framework （PMLSF） based on the technique of painting background （TPBG） and the Chan-Vese model can detect multiple objects on a given image. However, the boundaries of the sub-ob...The pyramidal multiphase level set framework （PMLSF） based on the technique of painting background （TPBG） and the Chan-Vese model can detect multiple objects on a given image. However, the boundaries of the sub-object obtained by PMLSF-TPBG are not variable since a specialcolor parameter is used in TPBG. To solve the problem, a new technique utilizing a varying parameter is proposed to ensure that PMLSF is effective for the detection of the desired boundaries of the sub-object. The interval of the variable color parameter is proved and the effects of the parameter are also discussed. Experimental results for the brain tumor detection show that different boundaries of the brain tumors can be detected with different color parameters. It is especially useful for clinical diagnoses.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12272144).
文摘A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.
基金The National Natural Science Foundation of China under contract Nos 61701416 and 61901195Natural Science Foundation of Jiangsu Province of China under contract No.BK20211341China Postdoctoral Science Foundation under contract No.2022M712687.
文摘Separation between water and land is vital for marine scientific research and coastal zone planning and management.The contrasting backscatter properties of land and ocean enable clear water edge line identification in synthetic aperture radar(SAR)imagery.However,SAR images are prone to speckle noise,and the complexity of the water-land boundaries environment makes accurate water-land separation challenging.To overcome noise and complex background interference in remote sensing images,an improved level set method was employed to enhance water-land separation.In the traditional distance regularized level set method,the selection of the image correlation weight coefficient and the edge indicator function directly influences the accuracy of the final segmentation results.A novel level set segmentation algorithm incorporating an improved edge indicator function is proposed to efficiently and accurately separate the water edge lines in SAR images.The distance regularized level set evolution model is enhanced by incorporating the signed pressure force function as an adaptive parameter,which serves as an external constraint for curve evolution.A novel level set model with an adaptive edge indicator function,combining gradient and regional information,is proposed.Experimental results demonstrate that the proposed model enhances the accuracy of waterland separation in SAR images.However,further research is needed to evaluate its potential for detecting boundaries in diverse marine environments and across different types of SAR imagery.
基金National Natural Science Foundation of China(22178228,22178326)
文摘A cylindrical chamber with a rotating bottom holds significant potential for application in cell culture bioreactors due to its ability to generate more stable swirling flows.In order to control vortex breakdown within the chamber,this study first establishes a computational fluid dynamics simulation coupled with the level set method.Verified by experimental results in literature,this method accurately simulates the position and shape of vortex breakdown,and also predicts the critical Reynolds numbers for the appearance and detachment of vortex breakdown bubbles from the center.Additionally,it precisely captures the gas-liquid interface and depicts the vortex breakdown phenomenon in the air above the liquid for the first time.Finally,it predicts the impact of physical property of gas-liquid systems on vortex breakdown in response to significant changes in viscosity of microbial process systems.
文摘Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this problem,the traditional particle tracking velocimetry method based on an optical flow was improved.The level set segmentation algorithm was used to obtain the boundary contour of the region with large velocity gradient changes,and the non-uniform flow field was divided into regions according to the boundary contour to obtain sub-regions with uniform velocity distribution.The particle tracking velocimetry method based on optical flow was used to measure the granular flow velocity in each sub-region,thus avoiding the problem of granular flow distribution.The simulation results show that the measurement accuracy of this method is approximately 10%higher than that of traditional methods.The method was applied to a velocity measurement experiment on dense granular flow in silos,and the velocity distribution of the granular flow was obtained,verifying the practicality of the method in granular flow fields.
基金funded by National Nature Science Foundation of China,grant number 61302188.
文摘To solve the problem of false edges in a flat region of l_(1)norm total variational TV model,an edge extractor based on non-local idea is proposed in this paper.The new edge extractor can effectively suppress the influence of noise and extract the edge information of the image.The new edge extractor is used as the adaptive function and the weighting function of the l_(p) norm variational model to control the noise reduction ability of the model,and a new model 1 is obtained.Considering that the new model 1 only uses the gradient mode as the image feature operator,which is insufficient to express the image texture information,a new level set curvature gradient variational model 2 combined with the edge extractor is proposed.The new model 2 uses the idea of minimum curvature of the level set of clear images to obtain noise reduction images.By coupling new model 1 and new model 2 to smooth the noise and protect more textures,a new Non-local level set denoising model(NLSDM)for image noise reduction is obtained.The experimental results show that compared with the noise reduction model,the new model has significantly improved the peak signal-to-noise ratio and structural similarity,and the effect of noise reduction and edge preservation is better.
基金fundings supported by Sichuan Science and Technology Program(2025YFHZ0065).
文摘Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.
文摘A new multi-phase active contour model is proposed for the image segmentation. It is a generalization of the C-V model with the following characteristics： （1） A key technique, called the technique of painting background （TPBG）, is developed to remove the information of the background, which blocks the detection of weak boundaries in the object; （2） The two-phase level set is applied multiple times for getting the multi-phase segmentation model （n-1 times for the n-phase model, n〉1）; （3） A scaling-based method is introduced to improve the basic model. Experimental results show that the proposed model is effective for detecting weak boundaries.
文摘The pyramidal multiphase level set framework （PMLSF） based on the technique of painting background （TPBG） and the Chan-Vese model can detect multiple objects on a given image. However, the boundaries of the sub-object obtained by PMLSF-TPBG are not variable since a specialcolor parameter is used in TPBG. To solve the problem, a new technique utilizing a varying parameter is proposed to ensure that PMLSF is effective for the detection of the desired boundaries of the sub-object. The interval of the variable color parameter is proved and the effects of the parameter are also discussed. Experimental results for the brain tumor detection show that different boundaries of the brain tumors can be detected with different color parameters. It is especially useful for clinical diagnoses.
