Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,convention...Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,conventional sequential-modular-based process-simulation techniques present challenges regarding computationally intensive calculations and significant central processing unit(CPU)time requirements,particularly in large-scale design and optimization tasks.To address these challenges,this paper proposes a novel process-simulation parallel computing framework(PSPCF).This framework achieves layered parallelism in recycling processes at the unit operation level.Notably,PSPCF introduces a groundbreaking concept of formulating simulation problems as task graphs and utilizes Taskflow,an advanced task graph computing system,for hierarchical parallel scheduling and the execution of unit operation tasks.PSPCF also integrates an advanced work-stealing scheme to automatically balance thread resources with the demanding workload of unit operation tasks.For evaluation,both a simpler parallel column process and a more complex cracked gas separation process were simulated on a flowsheeting platform using PSPCF.The framework demonstrates significant time savings,achieving over 60%reduction in processing time for the simpler process and a 35%–40%speed-up for the more complex separation process.展开更多
Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with p...Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with previous research that focuses on developing efficient heuristic,we focus on the pre-process of the task graph before the HW/SW partitioning in this paper,that is,enumerating all the sub-graphs that meet the requirements.Experimental results showed that the original graph can be reduced to 67% in the worst-case scenario and 58% in the best-case scenario.In conclusion,the reduced task graph saved hardware area while improving partitioning speed and accuracy.展开更多
Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in im...Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.展开更多
This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent...This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.展开更多
The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ap...The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.展开更多
To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony sy...To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3305900)the National Natural Science Foundation of China(Key Program)(62136003)+2 种基金the National Natural Science Foundation of China(62394345)the Major Science and Technology Projects of Longmen Laboratory(LMZDXM202206)the Fundamental Research Funds for the Central Universities.
文摘Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,conventional sequential-modular-based process-simulation techniques present challenges regarding computationally intensive calculations and significant central processing unit(CPU)time requirements,particularly in large-scale design and optimization tasks.To address these challenges,this paper proposes a novel process-simulation parallel computing framework(PSPCF).This framework achieves layered parallelism in recycling processes at the unit operation level.Notably,PSPCF introduces a groundbreaking concept of formulating simulation problems as task graphs and utilizes Taskflow,an advanced task graph computing system,for hierarchical parallel scheduling and the execution of unit operation tasks.PSPCF also integrates an advanced work-stealing scheme to automatically balance thread resources with the demanding workload of unit operation tasks.For evaluation,both a simpler parallel column process and a more complex cracked gas separation process were simulated on a flowsheeting platform using PSPCF.The framework demonstrates significant time savings,achieving over 60%reduction in processing time for the simpler process and a 35%–40%speed-up for the more complex separation process.
基金Supported by the National Natural Science Foundation of China (60970016,61173032)
文摘Hardware/software（HW/SW） partitioning is one of the key processes in an embedded system.It is used to determine which system components are assigned to hardware and which are processed by software.In contrast with previous research that focuses on developing efficient heuristic,we focus on the pre-process of the task graph before the HW/SW partitioning in this paper,that is,enumerating all the sub-graphs that meet the requirements.Experimental results showed that the original graph can be reduced to 67% in the worst-case scenario and 58% in the best-case scenario.In conclusion,the reduced task graph saved hardware area while improving partitioning speed and accuracy.
基金supported by the Talent Fund of Beijing Jiaotong University(No.2023XKRC028)CCFLenovo Blue Ocean Research Fund and Beijing Natural Science Foundation under Grant(No.L221003).
文摘Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFB1714700Postdoctoral Research Foundation of China,Grant/Award Number:2024M752364Postdoctoral Fellowship Program of CPSF,Grant/Award Number:GZB20240525。
文摘This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.
文摘The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
文摘To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.
