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
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
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
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
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
The aerodynamic optimization design of high-speed trains(HSTs)is crucial for energy conservation,environmental preservation,operational safety,and speeding up.This study aims to review the current state and progress o...The aerodynamic optimization design of high-speed trains(HSTs)is crucial for energy conservation,environmental preservation,operational safety,and speeding up.This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs.First,the study explores the impact of train nose shape parameters on aerodynamic performance.The parameterization methods involved in the aerodynamic multiobjective optimization ofHSTs are summarized and classified as shape-based and disturbance-based parameterizationmethods.Meanwhile,the advantages and limitations of each parameterizationmethod,aswell as the applicable scope,are briefly discussed.In addition,the NSGA-II algorithm,particle swarm optimization algorithm,standard genetic algorithm,and other commonly used multi-objective optimization algorithms and the improvements in the field of aerodynamic optimization for HSTs are summarized.Second,this study investigates the aerodynamic multi-objective optimization technology for HSTs using the surrogate model,focusing on the Kriging surrogate models,neural network,and support vector regression.Moreover,the construction methods of surrogate models are summarized,and the influence of different sample infill criteria on the efficiency ofmulti-objective optimization is analyzed.Meanwhile,advanced aerodynamic optimization methods in the field of aircraft have been briefly introduced to guide research on the aerodynamic optimization of HSTs.Finally,based on the summary of the research progress of the aerodynamicmulti-objective optimization ofHSTs,future research directions are proposed,such as intelligent recognition technology of characteristic parameters,collaborative optimization of multiple operating environments,and sample infill criterion of the surrogate model.展开更多
A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set ...A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating t...The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400℃is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7].展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
As an essential field of multimedia and computer vision,3D shape recognition has attracted much research attention in recent years.Multiview-based approaches have demonstrated their superiority in generating effective...As an essential field of multimedia and computer vision,3D shape recognition has attracted much research attention in recent years.Multiview-based approaches have demonstrated their superiority in generating effective 3D shape representations.Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors.However,there exist two disadvantages:First,the mainstream methods ignore the comprehensive exploration of local information in each view.Second,many approaches roughly aggregate multiview features by adding or concatenating them together.The information loss for some discriminative characteristics limits the representation effectiveness.To address these problems,a novel architecture named region-based joint attention network(RJAN)was proposed.Specifically,the authors first design a hierarchical local information exploration module for view descriptor extraction.The region-to-region and channel-to-channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning.Subsequently,a novel relation-aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation,considering the view-to-view relationships.Extensive experiments were conducted on three public databases:ModelNet40,ModelNet10,and ShapeNetCore55.RJAN achieves state-of-the-art performance in the tasks of 3D shape classification and 3D shape retrieval,which demonstrates the effectiveness of RJAN.The code has been released on https://github.com/slurrpp/RJAN.展开更多
Unlike conventional spherical charges,a shaped charge generates not only a strong shock wave and a pulsating bubble,but also a high strain rate metal jet and a ballistic wave during the underwater explosion.They show ...Unlike conventional spherical charges,a shaped charge generates not only a strong shock wave and a pulsating bubble,but also a high strain rate metal jet and a ballistic wave during the underwater explosion.They show significant characteristic differences and couple each other.This paper designs and conducts experiments with shaped charges to analyze the complicated process.The effects of liner angle and weight of shaped charge on the characteristics of metal jets,waves,and bubbles are discussed.It is found that in underwater explosions,the shaped charge generates the metal jet accompanied by the ballistic wave.Then,the shock wave propagates and superimposes with the ballistic wave,and the generated bubble pulsates periodically.It is revealed that the maximum head velocity of the metal jet versus the liner angle a and length-to-diameter ratio k of the shaped charge follows the laws of 1/(α/180°)^(0.55)andλ^(0.16),respectively.The head shape and velocity of the metal jet determine the curvature and propagation speed of the initial ballistic wave,thus impacting the superposition time and region with the shock wave.Our findings also reveal that the metal jet carries away some explosion products,which hinders the bubble development,causing an inward depression of the bubble wall near the metal jet.Therefore,the maximum bubble radius and pulsation period are 5.2%and 3.9%smaller than the spherical charge with the same weight.In addition,the uneven axial energy distribution of the shaped charge leads to an oblique bubble jet formation.展开更多
Shaped charge has been widely used for penetrating concrete.However,due to the obvious difference between the propagation of shock waves and explosion products in water and air,the theory governing the formation of sh...Shaped charge has been widely used for penetrating concrete.However,due to the obvious difference between the propagation of shock waves and explosion products in water and air,the theory governing the formation of shaped charge jets in water as well as the underwater penetration effect of concrete need to be studied.In this paper,we introduced a modified forming theory of an underwater hemispherical shaped charge,and investigated the behavior of jet formation and concrete penetration in both air and water experimentally and numerically.The results show that the modified jet forming theory predicts the jet velocity of the hemispherical liner with an error of less than 10%.The underwater jets exhibit at least 3%faster and 11%longer than those in air.Concrete shows different failure modes after penetration in air and water.The depth of penetration deepens at least 18.75%after underwater penetration,accompanied by deeper crater with 65%smaller radius.Moreover,cracks throughout the entire target are formed,whereas cracks exist only near the penetration hole in air.This comprehensive study provides guidance for optimizing the structure of shaped charge and improves the understanding of the permeability effect of concrete in water.展开更多
This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs...This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs)through the blood vessels of the human body.The rheology of gold-blood nanofluid is treated as magnetohydrodynamic(MHD)flow with ferromagnetic properties.The AuNPs take different shapes as bricks,cylinders,and platelets which are considered in changing the nanofluid flow behavior.Physiologically,the blood is circulated under the kinetics of the peristaltic action.The mixed properties of the slip flow,the gravity,the space porosity,the transverse ferromagnetic field,the thermal radiation,the nanoparticles shape factors,the peristaltic amplitude ratio,and the concentration of the AuNPs are interacted and analyzed for the gold-blood circulation in the inclined tube.The appropriate model for the thermal conductivity of the nanofluid is chosen to be the effective Hamilton-Crosser model.The undertaken nanofluid can be treated as incompressible non-Newtonian ferromagnetic fluid.The solutions of the partial differential governing equations of the MHD nanofluid flow are executed by the strategy of perturbation approach under the assumption of long wavelength and low Reynolds number.Graphs for the streamwise velocity distributions,temperature distributions,pressure gradients,pressure drops,and streamlines are presented under the influences of the pertinent properties.The practical implementation of this research finds application in treating cancer through a technique known as photothermal therapy(PTT).The results indicate the control role of the magnetism,the heat generation,the shape factors of the AuNPs,and its concentration on the enhancement of the thermal properties and the streamwise velocity of the nanofluid.The results reveal a marked enhancement in the temperature profiles of the nanofluid,prominently influenced by both the intensified heat source and the heightened volume fractions of the nanoparticles.Furthermore,the platelet shape is regarded as most advantageous for heat conduction owing to its highest effective thermal conductivity.AuNPs proved strong efficiency in delivering and targeting the drug to reach the affected area with tumors.These results offer valuable insights into evaluating the effectiveness of PTT in addressing diverse cancer conditions and regulating their progression.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
The cavity characteristics in liquid-filled containers caused by high-velocity impacts represent an important area of research in hydrodynamic ram phenomena.The dynamic expansion of the cavity induces liquid pressure ...The cavity characteristics in liquid-filled containers caused by high-velocity impacts represent an important area of research in hydrodynamic ram phenomena.The dynamic expansion of the cavity induces liquid pressure variations,potentially causing catastrophic damage to the container.Current studies mainly focus on non-deforming projectiles,such as fragments,with limited exploration of shaped charge jets.In this paper,a uniquely experimental system was designed to record cavity profiles in behind-armor liquid-filled containers subjected to shaped charge jet impacts.The impact process was then numerically reproduced using the explicit simulation program ANSYS LS-DYNA with the Structured Arbitrary Lagrangian-Eulerian(S-ALE)solver.The formation mechanism,along with the dimensional and shape evolution of the cavity was investigated.Additionally,the influence of the impact kinetic energy of the jet on the cavity characteristics was analyzed.The findings reveal that the cavity profile exhibits a conical shape,primarily driven by direct jet impact and inertial effects.The expansion rates of both cavity length and maximum radius increase with jet impact kinetic energy.When the impact kinetic energy is reduced to 28.2 kJ or below,the length-to-diameter ratio of the cavity ultimately stabilizes at approximately 7.展开更多
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 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.
基金supported by the Sichuan Science and Technology Program(2023JDRC0062)National Natural Science Foundation of China(12172308)Project of State Key Laboratory of Traction Power(2023TPL-T05).
文摘The aerodynamic optimization design of high-speed trains(HSTs)is crucial for energy conservation,environmental preservation,operational safety,and speeding up.This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs.First,the study explores the impact of train nose shape parameters on aerodynamic performance.The parameterization methods involved in the aerodynamic multiobjective optimization ofHSTs are summarized and classified as shape-based and disturbance-based parameterizationmethods.Meanwhile,the advantages and limitations of each parameterizationmethod,aswell as the applicable scope,are briefly discussed.In addition,the NSGA-II algorithm,particle swarm optimization algorithm,standard genetic algorithm,and other commonly used multi-objective optimization algorithms and the improvements in the field of aerodynamic optimization for HSTs are summarized.Second,this study investigates the aerodynamic multi-objective optimization technology for HSTs using the surrogate model,focusing on the Kriging surrogate models,neural network,and support vector regression.Moreover,the construction methods of surrogate models are summarized,and the influence of different sample infill criteria on the efficiency ofmulti-objective optimization is analyzed.Meanwhile,advanced aerodynamic optimization methods in the field of aircraft have been briefly introduced to guide research on the aerodynamic optimization of HSTs.Finally,based on the summary of the research progress of the aerodynamicmulti-objective optimization ofHSTs,future research directions are proposed,such as intelligent recognition technology of characteristic parameters,collaborative optimization of multiple operating environments,and sample infill criterion of the surrogate model.
基金National Natural Science Foundation ofChina( No.90 2 0 5 0 0 6) and Shanghai Rising Star Program( No.0 2 QG14 0 3 1)
文摘A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the National Natural Science Foundation of China(Nos.52201207 and 52271169)the Fundamental Research Funds for the Central University(No.3072024LJ1002).
文摘The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400℃is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7].
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金the National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704the National Natural Science Foundation of China,Grant/Award Number:62272337。
文摘As an essential field of multimedia and computer vision,3D shape recognition has attracted much research attention in recent years.Multiview-based approaches have demonstrated their superiority in generating effective 3D shape representations.Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors.However,there exist two disadvantages:First,the mainstream methods ignore the comprehensive exploration of local information in each view.Second,many approaches roughly aggregate multiview features by adding or concatenating them together.The information loss for some discriminative characteristics limits the representation effectiveness.To address these problems,a novel architecture named region-based joint attention network(RJAN)was proposed.Specifically,the authors first design a hierarchical local information exploration module for view descriptor extraction.The region-to-region and channel-to-channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning.Subsequently,a novel relation-aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation,considering the view-to-view relationships.Extensive experiments were conducted on three public databases:ModelNet40,ModelNet10,and ShapeNetCore55.RJAN achieves state-of-the-art performance in the tasks of 3D shape classification and 3D shape retrieval,which demonstrates the effectiveness of RJAN.The code has been released on https://github.com/slurrpp/RJAN.
基金funded by the National Natural Science Founda-tion of China(52071109).
文摘Unlike conventional spherical charges,a shaped charge generates not only a strong shock wave and a pulsating bubble,but also a high strain rate metal jet and a ballistic wave during the underwater explosion.They show significant characteristic differences and couple each other.This paper designs and conducts experiments with shaped charges to analyze the complicated process.The effects of liner angle and weight of shaped charge on the characteristics of metal jets,waves,and bubbles are discussed.It is found that in underwater explosions,the shaped charge generates the metal jet accompanied by the ballistic wave.Then,the shock wave propagates and superimposes with the ballistic wave,and the generated bubble pulsates periodically.It is revealed that the maximum head velocity of the metal jet versus the liner angle a and length-to-diameter ratio k of the shaped charge follows the laws of 1/(α/180°)^(0.55)andλ^(0.16),respectively.The head shape and velocity of the metal jet determine the curvature and propagation speed of the initial ballistic wave,thus impacting the superposition time and region with the shock wave.Our findings also reveal that the metal jet carries away some explosion products,which hinders the bubble development,causing an inward depression of the bubble wall near the metal jet.Therefore,the maximum bubble radius and pulsation period are 5.2%and 3.9%smaller than the spherical charge with the same weight.In addition,the uneven axial energy distribution of the shaped charge leads to an oblique bubble jet formation.
基金supported by the National Science Foundation of China(Grant Nos.12372361,12102427,12372335 and 12102202)the Fundamental Research Funds for the Central Universities(Grant No.30923010908)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX23_0520).
文摘Shaped charge has been widely used for penetrating concrete.However,due to the obvious difference between the propagation of shock waves and explosion products in water and air,the theory governing the formation of shaped charge jets in water as well as the underwater penetration effect of concrete need to be studied.In this paper,we introduced a modified forming theory of an underwater hemispherical shaped charge,and investigated the behavior of jet formation and concrete penetration in both air and water experimentally and numerically.The results show that the modified jet forming theory predicts the jet velocity of the hemispherical liner with an error of less than 10%.The underwater jets exhibit at least 3%faster and 11%longer than those in air.Concrete shows different failure modes after penetration in air and water.The depth of penetration deepens at least 18.75%after underwater penetration,accompanied by deeper crater with 65%smaller radius.Moreover,cracks throughout the entire target are formed,whereas cracks exist only near the penetration hole in air.This comprehensive study provides guidance for optimizing the structure of shaped charge and improves the understanding of the permeability effect of concrete in water.
文摘This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs)through the blood vessels of the human body.The rheology of gold-blood nanofluid is treated as magnetohydrodynamic(MHD)flow with ferromagnetic properties.The AuNPs take different shapes as bricks,cylinders,and platelets which are considered in changing the nanofluid flow behavior.Physiologically,the blood is circulated under the kinetics of the peristaltic action.The mixed properties of the slip flow,the gravity,the space porosity,the transverse ferromagnetic field,the thermal radiation,the nanoparticles shape factors,the peristaltic amplitude ratio,and the concentration of the AuNPs are interacted and analyzed for the gold-blood circulation in the inclined tube.The appropriate model for the thermal conductivity of the nanofluid is chosen to be the effective Hamilton-Crosser model.The undertaken nanofluid can be treated as incompressible non-Newtonian ferromagnetic fluid.The solutions of the partial differential governing equations of the MHD nanofluid flow are executed by the strategy of perturbation approach under the assumption of long wavelength and low Reynolds number.Graphs for the streamwise velocity distributions,temperature distributions,pressure gradients,pressure drops,and streamlines are presented under the influences of the pertinent properties.The practical implementation of this research finds application in treating cancer through a technique known as photothermal therapy(PTT).The results indicate the control role of the magnetism,the heat generation,the shape factors of the AuNPs,and its concentration on the enhancement of the thermal properties and the streamwise velocity of the nanofluid.The results reveal a marked enhancement in the temperature profiles of the nanofluid,prominently influenced by both the intensified heat source and the heightened volume fractions of the nanoparticles.Furthermore,the platelet shape is regarded as most advantageous for heat conduction owing to its highest effective thermal conductivity.AuNPs proved strong efficiency in delivering and targeting the drug to reach the affected area with tumors.These results offer valuable insights into evaluating the effectiveness of PTT in addressing diverse cancer conditions and regulating their progression.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
基金financial support from the National Natural Science Foundation of China(Grant No.11572159).
文摘The cavity characteristics in liquid-filled containers caused by high-velocity impacts represent an important area of research in hydrodynamic ram phenomena.The dynamic expansion of the cavity induces liquid pressure variations,potentially causing catastrophic damage to the container.Current studies mainly focus on non-deforming projectiles,such as fragments,with limited exploration of shaped charge jets.In this paper,a uniquely experimental system was designed to record cavity profiles in behind-armor liquid-filled containers subjected to shaped charge jet impacts.The impact process was then numerically reproduced using the explicit simulation program ANSYS LS-DYNA with the Structured Arbitrary Lagrangian-Eulerian(S-ALE)solver.The formation mechanism,along with the dimensional and shape evolution of the cavity was investigated.Additionally,the influence of the impact kinetic energy of the jet on the cavity characteristics was analyzed.The findings reveal that the cavity profile exhibits a conical shape,primarily driven by direct jet impact and inertial effects.The expansion rates of both cavity length and maximum radius increase with jet impact kinetic energy.When the impact kinetic energy is reduced to 28.2 kJ or below,the length-to-diameter ratio of the cavity ultimately stabilizes at approximately 7.
