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
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
坝体抗震设计和评估需要准确计算无限水库动力响应.基于比例边界有限元法(scaled boundary finite element method,SBFEM)力学推导技术,推导了顺河向地震激励下等横截面无限水域频域响应计算公式,利用Fourier逆变换建立了时域响应控制方...坝体抗震设计和评估需要准确计算无限水库动力响应.基于比例边界有限元法(scaled boundary finite element method,SBFEM)力学推导技术,推导了顺河向地震激励下等横截面无限水域频域响应计算公式,利用Fourier逆变换建立了时域响应控制方程,通过线性叠加推导了顺河、横河、竖直三向组合地震激励下的无限水域频域和时域响应的SBFEM计算公式.结合有限元法,建立了无限水库频域和时域响应的FEM-SBFEM耦合方程.分析了地震激励下的二维、三维等横截面无限水库频域、时域响应,数值验证了所建立计算公式的正确性.所发展的FEM-SBFEM公式体系可推广应用于库底库岸具有吸收性的、横截面有任意几何形状的无限水库谐响应及瞬态响应分析.展开更多
An FEM （Finite Element Method） based damping estimation method of liquid sloshing with small amplitude in rigid container is proposed. Damping of the sloshing is affected by many factors and some of them are very co...An FEM （Finite Element Method） based damping estimation method of liquid sloshing with small amplitude in rigid container is proposed. Damping of the sloshing is affected by many factors and some of them are very complicated. Therefore, this paper aims to provide an estimation range, instead of computing the exact value of damping. This method will consider the dissipation at wall, in the interior, and at the contaminated free surface. Owing to the complexity of viscous damping at the free surface, damping of two extreme conditions are computed to estimate the range of actual damping. An iterative algorithm is designed to solve a special general eigenvalue problem. Comparing the computation results with experimental results, it is found that most of the experimental results are within the range of the numerical estimation. Therefore, the method is effective in estimating the range of the damping of liquid sloshing with small amplitude in rigid container.展开更多
为了模拟喷丸强化过程,实现喷丸强化效果快速预测,基于Abaqus软件采用离散元法-有限元法(Discrete Element Method-Finite Element Method,DEM-FEM)耦合建立随机多丸粒喷丸强化模型,并以TC4钛合金为研究对象,通过喷丸强化试验来验证耦...为了模拟喷丸强化过程,实现喷丸强化效果快速预测,基于Abaqus软件采用离散元法-有限元法(Discrete Element Method-Finite Element Method,DEM-FEM)耦合建立随机多丸粒喷丸强化模型,并以TC4钛合金为研究对象,通过喷丸强化试验来验证耦合模型的准确性。采用Box-Behnken设计(Box-Behnken Design,BBD)法,针对弹丸大小、喷丸速度和喷丸覆盖率3个工艺参数设计了三因素三水平的喷丸仿真试验方案,采用仿真分析获得表面残余应力值及表面粗糙度值,并通过Design-Expert软件进行数值拟合,最终得到喷丸工艺参数与表面残余应力和表面粗糙度之间的函数模型,采用响应面法分析弹丸大小、喷丸速度、喷丸覆盖率三因素之间的交互作用以及对喷丸强化效果的影响规律。结果表明,响应面预测模型结果与仿真计算结果误差低于5%,所建立的响应面预测模型具有较高的近似精度和可靠性,利用此模型可实现喷丸强化效果的有效预测。展开更多
This paper studies the propellant and levitation forces of a prototype maglev system where the propellant forces are provided by a linear motor system. For this purpose, the mathematical model and method using finite ...This paper studies the propellant and levitation forces of a prototype maglev system where the propellant forces are provided by a linear motor system. For this purpose, the mathematical model and method using finite element method coupled to external circuit model is developed. The details of the propellant and levitation forces for a prototype maglev system under different operating conditions are investigated, and some directions are given for practical engineering applications.展开更多
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digit...The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.展开更多
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 the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
文摘坝体抗震设计和评估需要准确计算无限水库动力响应.基于比例边界有限元法(scaled boundary finite element method,SBFEM)力学推导技术,推导了顺河向地震激励下等横截面无限水域频域响应计算公式,利用Fourier逆变换建立了时域响应控制方程,通过线性叠加推导了顺河、横河、竖直三向组合地震激励下的无限水域频域和时域响应的SBFEM计算公式.结合有限元法,建立了无限水库频域和时域响应的FEM-SBFEM耦合方程.分析了地震激励下的二维、三维等横截面无限水库频域、时域响应,数值验证了所建立计算公式的正确性.所发展的FEM-SBFEM公式体系可推广应用于库底库岸具有吸收性的、横截面有任意几何形状的无限水库谐响应及瞬态响应分析.
基金The project supported by the National Natural Science Foundation of China (10172048) The English text was polished by Keren Wang
文摘An FEM （Finite Element Method） based damping estimation method of liquid sloshing with small amplitude in rigid container is proposed. Damping of the sloshing is affected by many factors and some of them are very complicated. Therefore, this paper aims to provide an estimation range, instead of computing the exact value of damping. This method will consider the dissipation at wall, in the interior, and at the contaminated free surface. Owing to the complexity of viscous damping at the free surface, damping of two extreme conditions are computed to estimate the range of actual damping. An iterative algorithm is designed to solve a special general eigenvalue problem. Comparing the computation results with experimental results, it is found that most of the experimental results are within the range of the numerical estimation. Therefore, the method is effective in estimating the range of the damping of liquid sloshing with small amplitude in rigid container.
文摘为了模拟喷丸强化过程,实现喷丸强化效果快速预测,基于Abaqus软件采用离散元法-有限元法(Discrete Element Method-Finite Element Method,DEM-FEM)耦合建立随机多丸粒喷丸强化模型,并以TC4钛合金为研究对象,通过喷丸强化试验来验证耦合模型的准确性。采用Box-Behnken设计(Box-Behnken Design,BBD)法,针对弹丸大小、喷丸速度和喷丸覆盖率3个工艺参数设计了三因素三水平的喷丸仿真试验方案,采用仿真分析获得表面残余应力值及表面粗糙度值,并通过Design-Expert软件进行数值拟合,最终得到喷丸工艺参数与表面残余应力和表面粗糙度之间的函数模型,采用响应面法分析弹丸大小、喷丸速度、喷丸覆盖率三因素之间的交互作用以及对喷丸强化效果的影响规律。结果表明,响应面预测模型结果与仿真计算结果误差低于5%,所建立的响应面预测模型具有较高的近似精度和可靠性,利用此模型可实现喷丸强化效果的有效预测。
文摘This paper studies the propellant and levitation forces of a prototype maglev system where the propellant forces are provided by a linear motor system. For this purpose, the mathematical model and method using finite element method coupled to external circuit model is developed. The details of the propellant and levitation forces for a prototype maglev system under different operating conditions are investigated, and some directions are given for practical engineering applications.
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
文摘The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.
