The increasing demand for mobile simulation tools has opened new possibilities in engineering applications,particularly in composite material modelling.This paper introduces original engineering software developed to ...The increasing demand for mobile simulation tools has opened new possibilities in engineering applications,particularly in composite material modelling.This paper introduces original engineering software developed to simulate composite materials on smartphones.The research explores the capabilities of mobile devices to perform simulations that are traditionally confined to desktop systems.Key challenges,such as computational limitations and the optimization of software architecture,now with integrated quantitative performance metrics such as computation time,accuracy,and memory efficiency,are addressed through the use of finite element analysis(FEA)and other advanced numerical methods.The software utilizes HTML-based coding for cross-platform accessibility,allowing engineers and researchers to conduct simulations anytime,anywhere.Strategies like parallel processing,cloud-assisted computation,and algorithmic optimization were implemented to enhance performance.The software’s real-time feedback and adaptive modelling provide accurate simulations of composite materials such as fiber-reinforced polymers.Furthermore,this paper reviews existing mobile-based simulation tools,highlighting their strengths and areas for improvement,while proposing novel solutions to increase efficiency,accuracy,and usability.The findings demonstrate that mobile devices,with optimized software,can successfully handle complex simulations,democratizing access to advanced engineering tools.展开更多
Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully use...Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully usedwhen Simulation of Urban MObility(SUMO)is used as an environment simulator for RL.We propose a multi-process framework under value-basedRL.First,we propose a shared memory mechanism to improve exploration efficiency.Second,we use the weight sharing mechanism to solve the problem of asynchronous multi-process agents.We also explained the reason shared memory in ATSC does not lead to early local optima of the agent.Wehave verified in experiments the sampling efficiency of the 10-process method is 8.259 times that of the single process.The sampling efficiency of the 20-process method is 13.409 times that of the single process.Moreover,the agent can also converge to the optimal solution.展开更多
文摘The increasing demand for mobile simulation tools has opened new possibilities in engineering applications,particularly in composite material modelling.This paper introduces original engineering software developed to simulate composite materials on smartphones.The research explores the capabilities of mobile devices to perform simulations that are traditionally confined to desktop systems.Key challenges,such as computational limitations and the optimization of software architecture,now with integrated quantitative performance metrics such as computation time,accuracy,and memory efficiency,are addressed through the use of finite element analysis(FEA)and other advanced numerical methods.The software utilizes HTML-based coding for cross-platform accessibility,allowing engineers and researchers to conduct simulations anytime,anywhere.Strategies like parallel processing,cloud-assisted computation,and algorithmic optimization were implemented to enhance performance.The software’s real-time feedback and adaptive modelling provide accurate simulations of composite materials such as fiber-reinforced polymers.Furthermore,this paper reviews existing mobile-based simulation tools,highlighting their strengths and areas for improvement,while proposing novel solutions to increase efficiency,accuracy,and usability.The findings demonstrate that mobile devices,with optimized software,can successfully handle complex simulations,democratizing access to advanced engineering tools.
基金Gansu Education Department:[Grant Number 2021CXZX-515]National Natural Science Foundation of China:[Grant Number 61763028].
文摘Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully usedwhen Simulation of Urban MObility(SUMO)is used as an environment simulator for RL.We propose a multi-process framework under value-basedRL.First,we propose a shared memory mechanism to improve exploration efficiency.Second,we use the weight sharing mechanism to solve the problem of asynchronous multi-process agents.We also explained the reason shared memory in ATSC does not lead to early local optima of the agent.Wehave verified in experiments the sampling efficiency of the 10-process method is 8.259 times that of the single process.The sampling efficiency of the 20-process method is 13.409 times that of the single process.Moreover,the agent can also converge to the optimal solution.
