It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clu...It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.展开更多
The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-rel...The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.展开更多
Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical ...Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge.This study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the agent.The management of experience data are also modified to fully utilize its value and improve learning efficiency.Reinforcement learning agents usually learn from an ignorant state,which is inefficient.As such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task scheduling.These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level.The proposed algorithm is compared with six other methods in simulation experiments.Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.展开更多
The unified analysis of omni-directional wheeled mobile manipulator(OWMM) through the dimensionally nonhomogeneous Jacobian matrix may lead to unreliable results. The existing researches focus on the integrated perfor...The unified analysis of omni-directional wheeled mobile manipulator(OWMM) through the dimensionally nonhomogeneous Jacobian matrix may lead to unreliable results. The existing researches focus on the integrated performance evaluation of OWMM, without taking the influence of velocity difference into consideration. This paper presents a new approach to formulate the dimensionally homogeneous Jacobian matrix and a new index for OWMM. First, the universal transformational matrix of the link coordinate frame is derived based on double quaternion. The degree of locomotion of a mobile platform and the degrees of manipulation of a manipulator are treated equally as the joints of a redundant composite robot. Then the integrated modeling of OWMM is established, and the non-dimensional Jacobian matrix is obtained from the above matrix. Next, by examining the concept of directional manipulability, a new index is proposed to evaluate the directional manipulability of OWMM along the specified task direction. Furthermore, for the same task and the time of simulation, the kinematic performance of the fixed base operation of the manipulator and the operation of OWMM are analyzed by numerical simulation. The results suggest that the proposed approach is equivalent to the method of traditional kinematic modeling, with the error of numerical solution being 10-7 , and the varying frequency of DMTR is higher than that of the condition number. This indicates that DMTR is more effective to reflect the variation of the task direction. The proposed method can be used to analyze the manipulating capability of OWMM, and has a simple structure and generality in the unified analysis of OWMM.展开更多
为研究ø139.7 mm×9.17 mm IEU S135特殊螺纹钻杆接头的上扣力学特性,建立了包含进、退刀槽结构的双台肩钻杆接头三维非线性有限元模型,考虑材料非线性、结构非对称、螺旋升角和各啮合面接触等因素,采用ABAQUS/Explicit有限元...为研究ø139.7 mm×9.17 mm IEU S135特殊螺纹钻杆接头的上扣力学特性,建立了包含进、退刀槽结构的双台肩钻杆接头三维非线性有限元模型,考虑材料非线性、结构非对称、螺旋升角和各啮合面接触等因素,采用ABAQUS/Explicit有限元方法对上扣扭矩作用下钻杆接头关键结构的应力分布特性进行分析。研究结果表明:钻杆接头整体等效应力分布不均,在横截面内呈轴对称分布,关键结构处应力集中显著,最大等效应力未超过材料屈服强度;钻杆接头台肩上的径向预留间隙和其过渡圆角处的结构非连续对台肩面的等效应力及接触压力分布影响显著;钻杆接头螺纹段的等效应力和接触压力均呈“U”形分布,应力整体分布均匀,螺纹接头前3扣与后3扣的承载比之差为8.6%,表明接头承载性能较佳。对有限元模拟结果进行实物验证试验对比分析,试验结果与有限元模拟结果基本符合。分析结果可为钻具优化设计及安全应用提供数据支撑。展开更多
基金This work was supported by the National Natural Science and Technology Innovation 2030 Major Project of Ministry of Science and Technology of China(2018AAA0101200)the National Natural Science Foundation of China(61502522,61502534)+4 种基金the Equipment Pre-Research Field Fund(JZX7Y20190253036101)the Equipment Pre-Research Ministry of Education Joint Fund(6141A02033703)Shaanxi Provincial Natural Science Foundation(2020JQ-493)the Military Science Project of the National Social Science Fund(WJ2019-SKJJ-C-092)the Theoretical Research Foundation of Armed Police Engineering University(WJY202148).
文摘It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.
基金supported by National Natural Science Foundation of China(Grant No.62071377,62101442,62201456)Natural Science Foundation of Shaanxi Province(Grant No.2023-YBGY-036,2022JQ-687)The Graduate Student Innovation Foundation Project of Xi’an University of Posts and Telecommunications under Grant CXJJDL2022003.
文摘The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.
基金supported by the National Key Research and Development Program of China(No.2021YFE0116900)National Natural Science Foundation of China(Nos.42275157,62002276,and 41975142)Major Program of the National Social Science Fund of China(No.17ZDA092).
文摘Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge.This study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the agent.The management of experience data are also modified to fully utilize its value and improve learning efficiency.Reinforcement learning agents usually learn from an ignorant state,which is inefficient.As such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task scheduling.These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level.The proposed algorithm is compared with six other methods in simulation experiments.Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.
基金supported by National Natural Science Foundation of China (Grant No. 51075005)
文摘The unified analysis of omni-directional wheeled mobile manipulator(OWMM) through the dimensionally nonhomogeneous Jacobian matrix may lead to unreliable results. The existing researches focus on the integrated performance evaluation of OWMM, without taking the influence of velocity difference into consideration. This paper presents a new approach to formulate the dimensionally homogeneous Jacobian matrix and a new index for OWMM. First, the universal transformational matrix of the link coordinate frame is derived based on double quaternion. The degree of locomotion of a mobile platform and the degrees of manipulation of a manipulator are treated equally as the joints of a redundant composite robot. Then the integrated modeling of OWMM is established, and the non-dimensional Jacobian matrix is obtained from the above matrix. Next, by examining the concept of directional manipulability, a new index is proposed to evaluate the directional manipulability of OWMM along the specified task direction. Furthermore, for the same task and the time of simulation, the kinematic performance of the fixed base operation of the manipulator and the operation of OWMM are analyzed by numerical simulation. The results suggest that the proposed approach is equivalent to the method of traditional kinematic modeling, with the error of numerical solution being 10-7 , and the varying frequency of DMTR is higher than that of the condition number. This indicates that DMTR is more effective to reflect the variation of the task direction. The proposed method can be used to analyze the manipulating capability of OWMM, and has a simple structure and generality in the unified analysis of OWMM.
文摘为研究ø139.7 mm×9.17 mm IEU S135特殊螺纹钻杆接头的上扣力学特性,建立了包含进、退刀槽结构的双台肩钻杆接头三维非线性有限元模型,考虑材料非线性、结构非对称、螺旋升角和各啮合面接触等因素,采用ABAQUS/Explicit有限元方法对上扣扭矩作用下钻杆接头关键结构的应力分布特性进行分析。研究结果表明:钻杆接头整体等效应力分布不均,在横截面内呈轴对称分布,关键结构处应力集中显著,最大等效应力未超过材料屈服强度;钻杆接头台肩上的径向预留间隙和其过渡圆角处的结构非连续对台肩面的等效应力及接触压力分布影响显著;钻杆接头螺纹段的等效应力和接触压力均呈“U”形分布,应力整体分布均匀,螺纹接头前3扣与后3扣的承载比之差为8.6%,表明接头承载性能较佳。对有限元模拟结果进行实物验证试验对比分析,试验结果与有限元模拟结果基本符合。分析结果可为钻具优化设计及安全应用提供数据支撑。
