Dissimilar AZ31B magnesium alloy and DC56D steel were welded via AA1060 aluminum alloy by magnetic pulse welding.The effects of primary and secondary welding processes on the welded interface were comparatively invest...Dissimilar AZ31B magnesium alloy and DC56D steel were welded via AA1060 aluminum alloy by magnetic pulse welding.The effects of primary and secondary welding processes on the welded interface were comparatively investigated.Macroscopic morphology,microstructure,and interfacial structure of the joints were analyzed using scanning electron microscope,energy dispersive spectrometer,and X-ray diffractometer(XRD).The results show that magnetic pulse welding of dissimilar Mg/Fe metals is achieved using an Al interlayer,which acts as a bridge for deformation and diffusion.Specifically,the AZ31B/AA1060 interface exhibits a typical wavy morphology,and a transition zone exists at the joint interface,which may result in an extremely complex microstructure.The microstructure of this transition zone differs from that of AZ31B magnesium and 1060 Al alloys,and it is identified as brittle intermetallic compounds(IMCs)Al_(3)Mg_(2) and Al_(12)Mg_(17).The transition zone is mainly distributed on the Al side,with the maximum thickness of Al-side transition layer reaching approximately 13.53μm.Incomplete melting layers with varying thicknesses are observed at the primary weld interface,while micron-sized hole defects appear in the transition zone of the secondary weld interface.The AA1060/DC56D interface is mainly straight,with only a small number of discontinuous transition zones distributed intermittently along the interface.These transition zones are characterized by the presence of the brittle IMC FeAl_(3),with a maximum thickness of about 4μm.展开更多
In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Mu...In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.展开更多
Joining dissimilar materials encounters significant engineering challenges due to the contrast in material properties that makes conventional welding not feasible.Magnetic Pulse Welding(MPW)offers a solidstate joining...Joining dissimilar materials encounters significant engineering challenges due to the contrast in material properties that makes conventional welding not feasible.Magnetic Pulse Welding(MPW)offers a solidstate joining technique that overcomes these issues by using impact to create strong bonds without melting the substrate materials.This study investigates the weldability of aluminum alloy Al-5754 with Al-7075 and MARS 380 steel,used in armouring solutions of defense systems,by the use of MPW.In this work,weldability windows are investigated by varying standoff distances between the coating material and its substrate(0.25-4.5 mm)and discharge energies(5-13 kJ)with both O-shape and U-shape inductors.Mechanical strength of the welded joints were assessed through single lap shear tests,identifying optimal welding parameters.Then,the velocity profiles of the flyer plates were measured using heterodyne velocimetry to understand the dynamics of the impact.Then,substructures assembled with the optimal welding conditions were subjected to ballistic testing using 7.62 mm×51 mm NATO and 9 mm×19 mm Parabellum munitions to evaluate the resilience of the welds under ballistic impact.The outcomes demonstrate that MPW effectively joins Al-5754 with both Al-7075 and MARS 380,producing robust welds capable of withstanding ballistic impacts under certain conditions.This research advances the application of MPW in lightweight ballistic protection of defense systems,contributing to the development of more resilient and lighter protective structures.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
Muon scattering tomography(MST) is a powerful noninvasive imaging technique with significant applications in nuclear material detection and security screening.Traditional MST usually relies on the point of closest app...Muon scattering tomography(MST) is a powerful noninvasive imaging technique with significant applications in nuclear material detection and security screening.Traditional MST usually relies on the point of closest approach(PoCA) algorithm to reconstruct images from muon scattering data;however,PoCA often suffers from suboptimal image clarity and resolution.To overcome these challenges,we propose a novel approach that leverages reinforcement learning(RL) to enhance MST reconstruction,termed the μRL-enhanced method.By framing the MST optimization task as an RL problem,we developed an intelligent agent capable of dynamically adjusting the key PoCA parameters.The agent is trained using a multi-objective reward function that guides the optimization toward higher-quality reconstructions.Our experimental results show that theμRL-enhanced method significantly outperforms the traditional PoCA baseline acros s multiple benchmark metrics.Specifically,the proposed approach on average attains a 307% improvement in the intersection over union(IoU),a 79% increase in the structural similarity index measure(SSIM),and a 8.4% enhancement in the peak signal-to-noise ratio(PSNR) across four experiments.Furthermore,when benchmarked against the maximum likelihood scattering and displacement(MLSD)algorithm,the μRL-enhanced method offers modest gains in PS NR and IoU,together with a one-third increase in SSIM.These improvements demonstrate the enhanced reconstruction accuracy and structural fidelity of the μRL-enhanced method,highlighting its potential to advance MST technologies and their applications.展开更多
The increasing occurrence of corrosion-related damage in steel pipelines has led to the growing use of composite-based repair techniques as an efficient alternative to traditional replacement methods.Computer modeling...The increasing occurrence of corrosion-related damage in steel pipelines has led to the growing use of composite-based repair techniques as an efficient alternative to traditional replacement methods.Computer modeling and structural analysis were performed for the repair reinforcement of a steel pipeline with a composite bandage.A preliminary analysis of possible contact interaction schemes was implemented based on the theory of cylindrical shells,taking into account transverse shear deformations.The finite element method was used for a detailed study of the stress state of the composite bandage and the reinforced section of the pipeline.The limit state of the reinforced section was assessed based on the von Mises criterion for steel and the Tsai-Wu criterion for composites.The effectiveness of the repair was demonstrated on a pipeline whose wall thickness had decreased by 20%as a result of corrosion damage.At a nominal pressure of P=6 MPa,the maximum normal stress in the weakened area reached 381 MPa.The installation of a composite bandage reduced this stress to 312 MPa,making the repaired section virtually as strong as the undamaged pipeline.Due to the linearity of the problem,the results obtained can be easily used to find critical internal pressure values.展开更多
Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused ...Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.展开更多
In this study,a 1400 MPa-grade ultra-high-strength steel thin-plate butt-welded joint was selected as the research object,and the joint was fabricated using the metal inert gas(MIG)welding process with ER307Si filler ...In this study,a 1400 MPa-grade ultra-high-strength steel thin-plate butt-welded joint was selected as the research object,and the joint was fabricated using the metal inert gas(MIG)welding process with ER307Si filler wire.Residual stress distributions were measured via the hole-drilling method,while micro-hardness was assessed using a micro-hardness tester.Simultaneously,both transverse shrinkage and angular distortion of the welded joint were experimentally determined.According to the hardness distribution of the joint,a thermalmetallurgical-mechanical finite element model was developed based on SYSWELD software platform.This model incorporates solid-state phase transformations(SSPT)and softening effect in the HAZ,as well as strain hardening and annealing behaviors in the weld metal.The temperature field,residual stress distribution,and welding deformation of single-pass butt-welded joint were simulated by the developed computational method.The simulation results were validated against experimental measurements,confirming the accuracy and reliability of the proposed computational approach.Furthermore,based on the numerical results,the influence mechanisms of SSPT and material softening on residual stress and deformation were analyzed.The findings indicate that SSPT exhibits considerable influences on the magnitude and distribution of welding residual stress.It reduces the peak longitudinal residual stress from 1620 MPa to 1350 MPa and increases the peak transverse residual stress from 350 MPa to 402 MPa.The results also manifest that the softening effect further reduces the peak longitudinal residual stress by 300 MPa,while exhibits minor effect on transverse residual stress.However,the results show that neither the SSPT nor the softening effect presents obvious influence on welding deformation.展开更多
Driven by efforts toward carbon-neutral steelmaking,increased scrap usage elevates Sn content in steels.While the general effects of Sn on steel have been studied,its specific influence on resistance spot welding(RSW)...Driven by efforts toward carbon-neutral steelmaking,increased scrap usage elevates Sn content in steels.While the general effects of Sn on steel have been studied,its specific influence on resistance spot welding(RSW)remains unclear.This study investigates Sn’s impact on the mechanical properties of RSW joint of 460 MPa HSLA steel.Cross-tension tests reveal that both the RSW joint without Sn and the RSW joint·containing 0.09wt%Sn exhibit pull-out failure.The RSW joint containing 0.09wt%Sn showing higher peak load and energy absorption attributed to Sn’s solid–solution strengthening.Conversely,the RSW joint containing 0.52wt%Sn exhibited the partial interface failure mode,significantly reducing the peak load and energy absorption.The primary reason is the segregation of Sn in the interdendritic regions of the fusion zone,which weakens atomic cohesion and reduces fracture toughness.Such severe segregation arises from RSW’s high cooling rates,which shift the primary solidification phase from δ-ferrite to austenite.Fortunately,double-pulse RSW mitigates Sn segregation,restoring failure mode and mechanical performance.This study assesses the impact of Sn on RSW joint properties,and these findings highlight the broader significance of understanding scrap-related residual element effects in sustainable steel production.展开更多
Reinforcement learning(RL),as an important branch of machine learning,has recently achieved extensive attention and success in many applications.Its main idea is to enable agents to continuously learn to make optimal ...Reinforcement learning(RL),as an important branch of machine learning,has recently achieved extensive attention and success in many applications.Its main idea is to enable agents to continuously learn to make optimal decisions by trying to maximize a reward function for their actions and interactions with the environment.However,making highquality decisions in complex and uncertain real-world scenarios is a challenging task.The interference and attacks in such scenarios tend to destroy the existing strategies.Maintaining RL's optimal performance in various cases and adapting to changing environments remains an important challenge.This article presents a comprehensive review of recent advancements in robust reinforcement learning(RRL),and analyzes them from the perspectives of challenges,methodologies,and applications.It systematically evaluates current progress in RRL and summarizes the commonly used benchmark platforms.Finally,several open challenges are discussed to stimulate further research and guide future developments in this area.展开更多
Low heat input welding is widely used in the industry.The microstructure and toughness of the welded joints under low heat input conditions have received less attention than those under high heat input.The impact toug...Low heat input welding is widely used in the industry.The microstructure and toughness of the welded joints under low heat input conditions have received less attention than those under high heat input.The impact toughness,microstructure and failure mechanisms of the coarse-grain heat-affected zone(CGHAZ)in a micro-alloyed steel were investigated by welding thermal simulation with the heat input ranging from 15 to 65 kJ/cm.The impact toughness of CGHAZ is highly sensitive to variations in low heat input.The failure mechanisms were discussed from the viewpoints of micro-voids formation and micro-cracks propagation.The micro-voids are preferred to be formed and grow at soft phase of grain boundary ferrite(GBF).At the heat inputs no more than 22 kJ/cm,martensite was dominantly formed,and the micro-cracks initiated from the GBF were propagated into the grain interiors,leading to the brittle fracture and low toughness.When the heat input was increased to 31.2 kJ/cm,granular bainite became the dominant constitute,causing cracks to deflect away from GBF and propagate into prior austenite grains.The high density high-angle and low-angle grain boundaries and the presence of retained austenite,effectively restricted the crack propagation,resulting in ductile fracture behavior and enhanced toughness.High heat input(62.3 kJ/cm)promoted coarse GBF formation,providing continuous paths for microcrack propagation.This direct intergranular crack progression caused brittle fracture and low toughness.Industrial cold cracking in the CGHAZ can thus be controlled by heat input optimization to maximize toughness.展开更多
With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier...With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.展开更多
Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the po...Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform.Effective pre-strategies can improve the prevention ability for high-impact and low-probability(HILP)events and provide the foundation for measures in the response and restoration stages.First,this paper proposes a resilience reserve to expand the existing research on power system resilience.Secondly,this paper puts forward an interactive method of deep reinforcement learning,which considers the interests of both the power grid dispatching center and the ride-hailing platform.It improves the resilience reserve by achieving the order dispatch,orderly charging management of ride-hailing electric vehicles,and the pricing strategy of charging stations.Finally,this paper uses a practical example covering about 107.32 km2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.展开更多
Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunitie...Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems.展开更多
文摘Dissimilar AZ31B magnesium alloy and DC56D steel were welded via AA1060 aluminum alloy by magnetic pulse welding.The effects of primary and secondary welding processes on the welded interface were comparatively investigated.Macroscopic morphology,microstructure,and interfacial structure of the joints were analyzed using scanning electron microscope,energy dispersive spectrometer,and X-ray diffractometer(XRD).The results show that magnetic pulse welding of dissimilar Mg/Fe metals is achieved using an Al interlayer,which acts as a bridge for deformation and diffusion.Specifically,the AZ31B/AA1060 interface exhibits a typical wavy morphology,and a transition zone exists at the joint interface,which may result in an extremely complex microstructure.The microstructure of this transition zone differs from that of AZ31B magnesium and 1060 Al alloys,and it is identified as brittle intermetallic compounds(IMCs)Al_(3)Mg_(2) and Al_(12)Mg_(17).The transition zone is mainly distributed on the Al side,with the maximum thickness of Al-side transition layer reaching approximately 13.53μm.Incomplete melting layers with varying thicknesses are observed at the primary weld interface,while micron-sized hole defects appear in the transition zone of the secondary weld interface.The AA1060/DC56D interface is mainly straight,with only a small number of discontinuous transition zones distributed intermittently along the interface.These transition zones are characterized by the presence of the brittle IMC FeAl_(3),with a maximum thickness of about 4μm.
文摘In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.
基金funded on the one hand by Agence de l'Innovation de Défense(AID)grant reference number 2021650044on the other hand by Ecole Centrale de Nantes。
文摘Joining dissimilar materials encounters significant engineering challenges due to the contrast in material properties that makes conventional welding not feasible.Magnetic Pulse Welding(MPW)offers a solidstate joining technique that overcomes these issues by using impact to create strong bonds without melting the substrate materials.This study investigates the weldability of aluminum alloy Al-5754 with Al-7075 and MARS 380 steel,used in armouring solutions of defense systems,by the use of MPW.In this work,weldability windows are investigated by varying standoff distances between the coating material and its substrate(0.25-4.5 mm)and discharge energies(5-13 kJ)with both O-shape and U-shape inductors.Mechanical strength of the welded joints were assessed through single lap shear tests,identifying optimal welding parameters.Then,the velocity profiles of the flyer plates were measured using heterodyne velocimetry to understand the dynamics of the impact.Then,substructures assembled with the optimal welding conditions were subjected to ballistic testing using 7.62 mm×51 mm NATO and 9 mm×19 mm Parabellum munitions to evaluate the resilience of the welds under ballistic impact.The outcomes demonstrate that MPW effectively joins Al-5754 with both Al-7075 and MARS 380,producing robust welds capable of withstanding ballistic impacts under certain conditions.This research advances the application of MPW in lightweight ballistic protection of defense systems,contributing to the development of more resilient and lighter protective structures.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
基金supported by the National Natural Science Foundation of China (No.12222502)。
文摘Muon scattering tomography(MST) is a powerful noninvasive imaging technique with significant applications in nuclear material detection and security screening.Traditional MST usually relies on the point of closest approach(PoCA) algorithm to reconstruct images from muon scattering data;however,PoCA often suffers from suboptimal image clarity and resolution.To overcome these challenges,we propose a novel approach that leverages reinforcement learning(RL) to enhance MST reconstruction,termed the μRL-enhanced method.By framing the MST optimization task as an RL problem,we developed an intelligent agent capable of dynamically adjusting the key PoCA parameters.The agent is trained using a multi-objective reward function that guides the optimization toward higher-quality reconstructions.Our experimental results show that theμRL-enhanced method significantly outperforms the traditional PoCA baseline acros s multiple benchmark metrics.Specifically,the proposed approach on average attains a 307% improvement in the intersection over union(IoU),a 79% increase in the structural similarity index measure(SSIM),and a 8.4% enhancement in the peak signal-to-noise ratio(PSNR) across four experiments.Furthermore,when benchmarked against the maximum likelihood scattering and displacement(MLSD)algorithm,the μRL-enhanced method offers modest gains in PS NR and IoU,together with a one-third increase in SSIM.These improvements demonstrate the enhanced reconstruction accuracy and structural fidelity of the μRL-enhanced method,highlighting its potential to advance MST technologies and their applications.
文摘The increasing occurrence of corrosion-related damage in steel pipelines has led to the growing use of composite-based repair techniques as an efficient alternative to traditional replacement methods.Computer modeling and structural analysis were performed for the repair reinforcement of a steel pipeline with a composite bandage.A preliminary analysis of possible contact interaction schemes was implemented based on the theory of cylindrical shells,taking into account transverse shear deformations.The finite element method was used for a detailed study of the stress state of the composite bandage and the reinforced section of the pipeline.The limit state of the reinforced section was assessed based on the von Mises criterion for steel and the Tsai-Wu criterion for composites.The effectiveness of the repair was demonstrated on a pipeline whose wall thickness had decreased by 20%as a result of corrosion damage.At a nominal pressure of P=6 MPa,the maximum normal stress in the weakened area reached 381 MPa.The installation of a composite bandage reduced this stress to 312 MPa,making the repaired section virtually as strong as the undamaged pipeline.Due to the linearity of the problem,the results obtained can be easily used to find critical internal pressure values.
基金supported in part by the National Natural Science Foundation of China(grants 62203073 and 62573068)the Natural Science Foundation of Chongqing,China(grant CSTB2022NSCQMSX0577)。
文摘Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.
基金funded by the National Natural Science Foundation of China(Grant No.52471032).
文摘In this study,a 1400 MPa-grade ultra-high-strength steel thin-plate butt-welded joint was selected as the research object,and the joint was fabricated using the metal inert gas(MIG)welding process with ER307Si filler wire.Residual stress distributions were measured via the hole-drilling method,while micro-hardness was assessed using a micro-hardness tester.Simultaneously,both transverse shrinkage and angular distortion of the welded joint were experimentally determined.According to the hardness distribution of the joint,a thermalmetallurgical-mechanical finite element model was developed based on SYSWELD software platform.This model incorporates solid-state phase transformations(SSPT)and softening effect in the HAZ,as well as strain hardening and annealing behaviors in the weld metal.The temperature field,residual stress distribution,and welding deformation of single-pass butt-welded joint were simulated by the developed computational method.The simulation results were validated against experimental measurements,confirming the accuracy and reliability of the proposed computational approach.Furthermore,based on the numerical results,the influence mechanisms of SSPT and material softening on residual stress and deformation were analyzed.The findings indicate that SSPT exhibits considerable influences on the magnitude and distribution of welding residual stress.It reduces the peak longitudinal residual stress from 1620 MPa to 1350 MPa and increases the peak transverse residual stress from 350 MPa to 402 MPa.The results also manifest that the softening effect further reduces the peak longitudinal residual stress by 300 MPa,while exhibits minor effect on transverse residual stress.However,the results show that neither the SSPT nor the softening effect presents obvious influence on welding deformation.
基金financially supported by the National Nat-ural Science Foundation of China(Nos.52293390 and 52293393)Liaoning Academy of Materials,China.
文摘Driven by efforts toward carbon-neutral steelmaking,increased scrap usage elevates Sn content in steels.While the general effects of Sn on steel have been studied,its specific influence on resistance spot welding(RSW)remains unclear.This study investigates Sn’s impact on the mechanical properties of RSW joint of 460 MPa HSLA steel.Cross-tension tests reveal that both the RSW joint without Sn and the RSW joint·containing 0.09wt%Sn exhibit pull-out failure.The RSW joint containing 0.09wt%Sn showing higher peak load and energy absorption attributed to Sn’s solid–solution strengthening.Conversely,the RSW joint containing 0.52wt%Sn exhibited the partial interface failure mode,significantly reducing the peak load and energy absorption.The primary reason is the segregation of Sn in the interdendritic regions of the fusion zone,which weakens atomic cohesion and reduces fracture toughness.Such severe segregation arises from RSW’s high cooling rates,which shift the primary solidification phase from δ-ferrite to austenite.Fortunately,double-pulse RSW mitigates Sn segregation,restoring failure mode and mechanical performance.This study assesses the impact of Sn on RSW joint properties,and these findings highlight the broader significance of understanding scrap-related residual element effects in sustainable steel production.
文摘Reinforcement learning(RL),as an important branch of machine learning,has recently achieved extensive attention and success in many applications.Its main idea is to enable agents to continuously learn to make optimal decisions by trying to maximize a reward function for their actions and interactions with the environment.However,making highquality decisions in complex and uncertain real-world scenarios is a challenging task.The interference and attacks in such scenarios tend to destroy the existing strategies.Maintaining RL's optimal performance in various cases and adapting to changing environments remains an important challenge.This article presents a comprehensive review of recent advancements in robust reinforcement learning(RRL),and analyzes them from the perspectives of challenges,methodologies,and applications.It systematically evaluates current progress in RRL and summarizes the commonly used benchmark platforms.Finally,several open challenges are discussed to stimulate further research and guide future developments in this area.
基金supported by the National Natural Science Foundation of China(No.51804232)Beijing Municipal Natural Science Foundation(No.2212041)+1 种基金supported by the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(FRF-IDRY-20-020)GIMRT Program of the Institute for Materials Research,Tohoku University(202303-RDKGE-0518).
文摘Low heat input welding is widely used in the industry.The microstructure and toughness of the welded joints under low heat input conditions have received less attention than those under high heat input.The impact toughness,microstructure and failure mechanisms of the coarse-grain heat-affected zone(CGHAZ)in a micro-alloyed steel were investigated by welding thermal simulation with the heat input ranging from 15 to 65 kJ/cm.The impact toughness of CGHAZ is highly sensitive to variations in low heat input.The failure mechanisms were discussed from the viewpoints of micro-voids formation and micro-cracks propagation.The micro-voids are preferred to be formed and grow at soft phase of grain boundary ferrite(GBF).At the heat inputs no more than 22 kJ/cm,martensite was dominantly formed,and the micro-cracks initiated from the GBF were propagated into the grain interiors,leading to the brittle fracture and low toughness.When the heat input was increased to 31.2 kJ/cm,granular bainite became the dominant constitute,causing cracks to deflect away from GBF and propagate into prior austenite grains.The high density high-angle and low-angle grain boundaries and the presence of retained austenite,effectively restricted the crack propagation,resulting in ductile fracture behavior and enhanced toughness.High heat input(62.3 kJ/cm)promoted coarse GBF formation,providing continuous paths for microcrack propagation.This direct intergranular crack progression caused brittle fracture and low toughness.Industrial cold cracking in the CGHAZ can thus be controlled by heat input optimization to maximize toughness.
文摘With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.
文摘Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform.Effective pre-strategies can improve the prevention ability for high-impact and low-probability(HILP)events and provide the foundation for measures in the response and restoration stages.First,this paper proposes a resilience reserve to expand the existing research on power system resilience.Secondly,this paper puts forward an interactive method of deep reinforcement learning,which considers the interests of both the power grid dispatching center and the ride-hailing platform.It improves the resilience reserve by achieving the order dispatch,orderly charging management of ride-hailing electric vehicles,and the pricing strategy of charging stations.Finally,this paper uses a practical example covering about 107.32 km2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.
基金funded by the U.S.Department of Education under Grant Number ED#P116S210005the National Science Foundation under Grant Numbers 2226936 and 2420405.
文摘Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems.
