In recent years,the demand for synchronous acquisition of three-dimensional(3D)shape and col-or texture has surged in fields such as cultural heritage preservation and healthcare.Addressing this need,this paper propos...In recent years,the demand for synchronous acquisition of three-dimensional(3D)shape and col-or texture has surged in fields such as cultural heritage preservation and healthcare.Addressing this need,this paper proposes a novel method for simultaneous 3D shape and color texture capture.First,a linear model correlating camera exposure time with grayscale values is established.Through exposure time calibration,the projected red,green and blue(RGB)light and white-light grayscale values captured by a monochrome cam-era are aligned.Then,three sets of color fringes are projected onto the object to identify optimal pixels for 3D reconstruction.And,three pure-color patterns are projected to synthesize the color texture.Experimental res-ults show that this method effectively achieves synchronous 3D shape and color texture acquisition,offering high speed and precision,and avoids color crosstalk interference common in 3D reconstruction of colored ob-jects using a monochrome camera.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
To address the challenges of rapid bit failure and high drilling costs associated with hard limestone in Sichuan Basin of China,we conducted rock-breaking experiments and simulations of shaped(cylindrical,ridge,and ch...To address the challenges of rapid bit failure and high drilling costs associated with hard limestone in Sichuan Basin of China,we conducted rock-breaking experiments and simulations of shaped(cylindrical,ridge,and chopper)cutters.Rock mechanics,drillability,and acoustic emission indentation tests revealed the drilling resistance characteristics of the limestone:average uniaxial compressive strength of 202.472 MPa,tensile strength of 7.092 MPa,and drillability of 7.866.We evaluated the performance differences between the shaped cutters before introducing an efficient and innovative finite-discrete-infinite element method(FDIEM)to establish an interaction model between the shaped cutters and limestone.The simulation results indicated the following:(1)The shaped cutters demonstrated superior rock-breaking performance compared to the traditional cylindrical cutter.(2)Compared with the cylindrical cutter,the ridge cutter yielded the lowest peak indentation force and mechanical specific energy,with reductions of 8.71%and 33.83%,respectively.This confirmed that the ridge cutter had the optimal tooth profile for the target formation.Its rock-breaking mechanism relied on the convex edges to induce localized high stress in the rock,which enabled efficient rock fragmentation via a plowing mode while mitigating frictional resistance from cuttings.(3)The novel chopper cutter with its secondary step surface exerted a buffering effect on the cuttings,thereby achieving high cutting stability.This study provides theoretical and technical support for the design of personalized drill bits and the acceleration of the rate of penetration(ROP)in deep hard rock formations.展开更多
In the past few years,efforts have been made to extend the sensitivity of surface nuclear magnetic resonance(SNMR)to short relaxation times,typical for strongly bound water,which,for example,occurs in partially satura...In the past few years,efforts have been made to extend the sensitivity of surface nuclear magnetic resonance(SNMR)to short relaxation times,typical for strongly bound water,which,for example,occurs in partially saturated soils.The two limiting factors for the sensitivity are the dead time after the excitation pulse and the duration of the pulse itself.To enable short pulses,while also achieving proper depths of investigation,high pulse amplitudes are needed.This makes it necessary to consider the Bloch-Siegert effect,i.e.the counter-rotating component and the parallel component of the excitation field have significant influence on the excitation.If an untuned transmitter circuit is used,the pulse shape will also be non-sinusoidal.In this paper,we demonstrate that this influences SNMR measurements with short pulses in two ways:On one hand,the pulse shape influences the phase of the fundamental frequency oscillation.On the other,at very high pulse amplitudes,other frequency components of the excitation field start to influence the excitation.The behavior of the macroscopic magnetizations in the subsurface during the pulse is simulated by solving the Bloch equations,using the pulse shape as an input.Since these calculations are computational expensive,we propose a lookup scheme that allows a time efficient modeling of the obtained SNMR data.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
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,mate...The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,material strength dictates the resistance to plastic deformation and flow,a contrast to the shockwave-dominated interactions where compressibility is key.This paper presents a self-consistent compressible penetration theory that considers both the axial penetration and radial crater growth of shaped charge jets into targets.An integrated approach where the axial and radial dynamics are coupled has been proposed,influencing each other through shared physical principles rather than being treated as separate,empirically linked phenomena.The presented theory is rooted in the compressible Bernoulli equation and the linear Rankine-Hugoniot relation.These foundational equations are employed to accurately model the high-pressure shock state and subsequent material flow at the jet-target interface,providing a robust physical basis for the penetration model.Notably,it considers the target material's compressibility,which elevates the pressure at the jet-target interface beyond that observed with incompressible materials.This pressure increase is directly proportional to the target's degree of compressibility.As such,this model of compressible penetration reorients the analytical approach:rather than merely estimating penetration resistance,it determines this value from the target material's specific compressibility and yield strength.This shift from empirical correlations to a physics-based derivation of penetration resistance enhances the model's predictive power,particularly for novel target materials or engagement conditions outside established experimental datasets.This investigation establishes a quantitative link between the material's yield strength and its penetration resistance.The accuracy of this penetration resistance value is paramount,as it significantly influences the predicted crater diameter;indeed,the crater diameter's sensitivity to this resistance underscores the necessity for its precise determination.Ultimately,by integrating the yield strength of the target material,this framework enables the prediction of both the penetration depth and the resultant crater diameter from a shaped charge jet.The theory's validation involved two experimental sets:the first focused on shaped charge jet penetration into 45#steel at varied stand-offs,while the second utilized targets of high-to ultrahigh-strength steel-fiber reactive powder concrete(RPC)with differing strength characteristics.These experimental campaigns were specifically chosen to test the theory against both ductile metallic alloys,where plastic flow is significant,and advanced quasi-brittle cementitious composites,presenting a broad spectrum of material responses and penetration challenges.Resulting hole profiles derived from theoretical calculations demonstrated a strong correspondence with empirical measurements for both material types.展开更多
A new method was proposed for preparing AZ31/1060 composite plates with a corrugated interface,which involved cold-pressing a corrugated surface on the Al plate and then hot-pressing the assembled Mg/Al plate.The resu...A new method was proposed for preparing AZ31/1060 composite plates with a corrugated interface,which involved cold-pressing a corrugated surface on the Al plate and then hot-pressing the assembled Mg/Al plate.The results show that cold-pressing produces intense plastic deformation near the corrugated surface of the Al plate,which promotes dynamic recrystallization of the Al substrate near the interface during the subsequent hot-pressing.In addition,the initial corrugation on the surface of the Al plate also changes the local stress state near the interface during hot pressing,which has a large effect on the texture components of the substrates near the corrugated interface.The construction of the corrugated interface can greatly enhance the shear strength by 2−4 times due to the increased contact area and the strong“mechanical gearing”effect.Moreover,the mechanical properties are largely depended on the orientation relationship between corrugated direction and loading direction.展开更多
Accurate mechanical modeling is essential for robotic belt grinding(RBG), a process characterized by compliant contact mechanisms that make force prediction particularly challenging. However, existing mechanical model...Accurate mechanical modeling is essential for robotic belt grinding(RBG), a process characterized by compliant contact mechanisms that make force prediction particularly challenging. However, existing mechanical models predominantly focus on macroscale compliance while neglecting grain-scale compliant motion. Moreover, abrasive grains are typically idealized as regular shapes, overlooking the inherent stochasticity of real grain geometries. This study proposes a shapeequivalence method for modeling stochastic abrasive grains and develops a multiscale compliant force model for RBG. Specifically, an individual grain is represented as a polygonal pyramid with stochastic edges that is mathematically equivalent to a cone;this method unifies the treatment of grain geometries and streamlines the modeling process. The mathematical equivalence relationship for random grain shapes is further derived based on a grain-compliant contact model. By integrating grain geometric characteristics and progressive grain wear, an analytical mechanical model that captures both the static contact force and dynamic grinding force is established, thereby describing the transition from grain-workpiece compliant interaction to belt-workpiece elastic contact. Grinding experiments were conducted using abrasive belts with different grain shape distributions to validate the model. The results demonstrated reliable predictions of the tangential grinding force and its component characteristics. Additional analyses were performed to reveal how the tangential grinding force varies with wear time and grinding parameters.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the orig...The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.展开更多
Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability...Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.展开更多
As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^(...As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).展开更多
Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the co...Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.展开更多
Dithering optimization techniques can be divided into the phase-optimized technique and the intensity-optimized technique. The problem with the former is the poor sensitivity to various defocusing amounts, and the pro...Dithering optimization techniques can be divided into the phase-optimized technique and the intensity-optimized technique. The problem with the former is the poor sensitivity to various defocusing amounts, and the problem with the latter is that it cannot enhance phase quality directly nor efficiently. In this paper, we present a multi-objective optimization framework for three-dimensional(3D) measurement by utilizing binary defocusing technique. Moreover, a binary patch optimization technique is used to solve the time-consuming issue of genetic algorithm. It is demonstrated that the presented technique consistently obtains significant phase performance improvement under various defocusing amounts.展开更多
文摘In recent years,the demand for synchronous acquisition of three-dimensional(3D)shape and col-or texture has surged in fields such as cultural heritage preservation and healthcare.Addressing this need,this paper proposes a novel method for simultaneous 3D shape and color texture capture.First,a linear model correlating camera exposure time with grayscale values is established.Through exposure time calibration,the projected red,green and blue(RGB)light and white-light grayscale values captured by a monochrome cam-era are aligned.Then,three sets of color fringes are projected onto the object to identify optimal pixels for 3D reconstruction.And,three pure-color patterns are projected to synthesize the color texture.Experimental res-ults show that this method effectively achieves synchronous 3D shape and color texture acquisition,offering high speed and precision,and avoids color crosstalk interference common in 3D reconstruction of colored ob-jects using a monochrome camera.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
基金the National Science and Technology Major Project(Grant No.2025ZD1008300)the Major Scientific Research Instrument Development Project of the National Natural Science Foundation of China(Grant No.52327803).
文摘To address the challenges of rapid bit failure and high drilling costs associated with hard limestone in Sichuan Basin of China,we conducted rock-breaking experiments and simulations of shaped(cylindrical,ridge,and chopper)cutters.Rock mechanics,drillability,and acoustic emission indentation tests revealed the drilling resistance characteristics of the limestone:average uniaxial compressive strength of 202.472 MPa,tensile strength of 7.092 MPa,and drillability of 7.866.We evaluated the performance differences between the shaped cutters before introducing an efficient and innovative finite-discrete-infinite element method(FDIEM)to establish an interaction model between the shaped cutters and limestone.The simulation results indicated the following:(1)The shaped cutters demonstrated superior rock-breaking performance compared to the traditional cylindrical cutter.(2)Compared with the cylindrical cutter,the ridge cutter yielded the lowest peak indentation force and mechanical specific energy,with reductions of 8.71%and 33.83%,respectively.This confirmed that the ridge cutter had the optimal tooth profile for the target formation.Its rock-breaking mechanism relied on the convex edges to induce localized high stress in the rock,which enabled efficient rock fragmentation via a plowing mode while mitigating frictional resistance from cuttings.(3)The novel chopper cutter with its secondary step surface exerted a buffering effect on the cuttings,thereby achieving high cutting stability.This study provides theoretical and technical support for the design of personalized drill bits and the acceleration of the rate of penetration(ROP)in deep hard rock formations.
基金funded by the German Research Foundation(Deutsche Forschungsgemeinschaft-DFG)under grant MU 3318/8-1.
文摘In the past few years,efforts have been made to extend the sensitivity of surface nuclear magnetic resonance(SNMR)to short relaxation times,typical for strongly bound water,which,for example,occurs in partially saturated soils.The two limiting factors for the sensitivity are the dead time after the excitation pulse and the duration of the pulse itself.To enable short pulses,while also achieving proper depths of investigation,high pulse amplitudes are needed.This makes it necessary to consider the Bloch-Siegert effect,i.e.the counter-rotating component and the parallel component of the excitation field have significant influence on the excitation.If an untuned transmitter circuit is used,the pulse shape will also be non-sinusoidal.In this paper,we demonstrate that this influences SNMR measurements with short pulses in two ways:On one hand,the pulse shape influences the phase of the fundamental frequency oscillation.On the other,at very high pulse amplitudes,other frequency components of the excitation field start to influence the excitation.The behavior of the macroscopic magnetizations in the subsurface during the pulse is simulated by solving the Bloch equations,using the pulse shape as an input.Since these calculations are computational expensive,we propose a lookup scheme that allows a time efficient modeling of the obtained SNMR data.
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 by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金the Fundamental Research Funds for the Central Universities of Nanjing University of Science and Technology(CN)under Grant No.30924010803。
文摘The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,material strength dictates the resistance to plastic deformation and flow,a contrast to the shockwave-dominated interactions where compressibility is key.This paper presents a self-consistent compressible penetration theory that considers both the axial penetration and radial crater growth of shaped charge jets into targets.An integrated approach where the axial and radial dynamics are coupled has been proposed,influencing each other through shared physical principles rather than being treated as separate,empirically linked phenomena.The presented theory is rooted in the compressible Bernoulli equation and the linear Rankine-Hugoniot relation.These foundational equations are employed to accurately model the high-pressure shock state and subsequent material flow at the jet-target interface,providing a robust physical basis for the penetration model.Notably,it considers the target material's compressibility,which elevates the pressure at the jet-target interface beyond that observed with incompressible materials.This pressure increase is directly proportional to the target's degree of compressibility.As such,this model of compressible penetration reorients the analytical approach:rather than merely estimating penetration resistance,it determines this value from the target material's specific compressibility and yield strength.This shift from empirical correlations to a physics-based derivation of penetration resistance enhances the model's predictive power,particularly for novel target materials or engagement conditions outside established experimental datasets.This investigation establishes a quantitative link between the material's yield strength and its penetration resistance.The accuracy of this penetration resistance value is paramount,as it significantly influences the predicted crater diameter;indeed,the crater diameter's sensitivity to this resistance underscores the necessity for its precise determination.Ultimately,by integrating the yield strength of the target material,this framework enables the prediction of both the penetration depth and the resultant crater diameter from a shaped charge jet.The theory's validation involved two experimental sets:the first focused on shaped charge jet penetration into 45#steel at varied stand-offs,while the second utilized targets of high-to ultrahigh-strength steel-fiber reactive powder concrete(RPC)with differing strength characteristics.These experimental campaigns were specifically chosen to test the theory against both ductile metallic alloys,where plastic flow is significant,and advanced quasi-brittle cementitious composites,presenting a broad spectrum of material responses and penetration challenges.Resulting hole profiles derived from theoretical calculations demonstrated a strong correspondence with empirical measurements for both material types.
基金supported by Guangdong Major Project of Basic and Applied Basic Research, China (No. 2020B0301030006)Fundamental Research Funds for the Central Universities, China (No. SWU-XDJH202313)+1 种基金Chongqing Postdoctoral Science Foundation Funded Project, China (No. 2112012728014435)the Chongqing Postgraduate Research and Innovation Project, China (No. CYS23197)。
文摘A new method was proposed for preparing AZ31/1060 composite plates with a corrugated interface,which involved cold-pressing a corrugated surface on the Al plate and then hot-pressing the assembled Mg/Al plate.The results show that cold-pressing produces intense plastic deformation near the corrugated surface of the Al plate,which promotes dynamic recrystallization of the Al substrate near the interface during the subsequent hot-pressing.In addition,the initial corrugation on the surface of the Al plate also changes the local stress state near the interface during hot pressing,which has a large effect on the texture components of the substrates near the corrugated interface.The construction of the corrugated interface can greatly enhance the shear strength by 2−4 times due to the increased contact area and the strong“mechanical gearing”effect.Moreover,the mechanical properties are largely depended on the orientation relationship between corrugated direction and loading direction.
基金supported by the National Natural Science Foundation of China (Grant Nos.52505554,52575571)the Postdoctoral Fellowship Program of CPSF (Grant No.GZB20250348)。
文摘Accurate mechanical modeling is essential for robotic belt grinding(RBG), a process characterized by compliant contact mechanisms that make force prediction particularly challenging. However, existing mechanical models predominantly focus on macroscale compliance while neglecting grain-scale compliant motion. Moreover, abrasive grains are typically idealized as regular shapes, overlooking the inherent stochasticity of real grain geometries. This study proposes a shapeequivalence method for modeling stochastic abrasive grains and develops a multiscale compliant force model for RBG. Specifically, an individual grain is represented as a polygonal pyramid with stochastic edges that is mathematically equivalent to a cone;this method unifies the treatment of grain geometries and streamlines the modeling process. The mathematical equivalence relationship for random grain shapes is further derived based on a grain-compliant contact model. By integrating grain geometric characteristics and progressive grain wear, an analytical mechanical model that captures both the static contact force and dynamic grinding force is established, thereby describing the transition from grain-workpiece compliant interaction to belt-workpiece elastic contact. Grinding experiments were conducted using abrasive belts with different grain shape distributions to validate the model. The results demonstrated reliable predictions of the tangential grinding force and its component characteristics. Additional analyses were performed to reveal how the tangential grinding force varies with wear time and grinding parameters.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
文摘The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.
基金supported by the National Natural Science Foundation of China(Nos.52473080,52403167 and 52173079)the Fundamental Research Funds for the Central Universities(Nos.xtr052023001 and xzy012023037)+1 种基金the Postdoctoral Research Project of Shaanxi Province(No.2024BSHSDZZ054)the Shaanxi Laboratory of Advanced Materials(No.2024ZY-JCYJ-04-12).
文摘Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.
基金supported by the National Natural Science Foundation of China (Grant No.52405033)。
文摘As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).
文摘Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.
基金Project supported by the Zhejiang Provincial Welfare Technology Applied Research Project,China(Grant No.2017C31080)
文摘Dithering optimization techniques can be divided into the phase-optimized technique and the intensity-optimized technique. The problem with the former is the poor sensitivity to various defocusing amounts, and the problem with the latter is that it cannot enhance phase quality directly nor efficiently. In this paper, we present a multi-objective optimization framework for three-dimensional(3D) measurement by utilizing binary defocusing technique. Moreover, a binary patch optimization technique is used to solve the time-consuming issue of genetic algorithm. It is demonstrated that the presented technique consistently obtains significant phase performance improvement under various defocusing amounts.
