This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analy...This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.展开更多
Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coeffici...Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.展开更多
The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humani...The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humanity.In this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems.Results from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance.However,this improvement has been difficult to achieve for high-reliability applications.Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.展开更多
Static Random Access Memory(SRAM) based Field Programmable Gate Array(FPGA) is widely applied in the field of aerospace, whose anti-SEU(Single Event Upset) capability becomes more and more important. To improve anti-F...Static Random Access Memory(SRAM) based Field Programmable Gate Array(FPGA) is widely applied in the field of aerospace, whose anti-SEU(Single Event Upset) capability becomes more and more important. To improve anti-FPGA SEU capability, the registers of the circuit netlist are tripled and divided into three categories in this study. By the packing algorithm, the registers of triple modular redundancy are loaded into different configurable logic block. At the same time, the packing algorithm considers the effect of large fan-out nets. The experimental results show that the algorithm successfully realize the packing of the register of Triple Modular Redundancy(TMR). Comparing with Timing Versatile PACKing(TVPACK), the algorithm in this study is able to obtain a 11% reduction of the number of the nets in critical path, and a 12% reduction of the time delay in critical path on average when TMR is not considered. Especially, some critical path delay of circuit can be improved about 33%.展开更多
The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi...The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.展开更多
Redundancy control can effectively enhance the stability and robustness of a system.Based on the conventional redundancy control switchover and majority arbitration strategy,this paper introduces the concept of hetero...Redundancy control can effectively enhance the stability and robustness of a system.Based on the conventional redundancy control switchover and majority arbitration strategy,this paper introduces the concept of heterogeneity and dynamics,constructs a dynamic heterogeneous redundancy-based microcontroller architecture DHR-MCU,and designs a fixed-leader distributed consensus algorithm that satisfies the program running state control of this architecture.The theoretical analysis and actual measurement of the prototype system prove that this architecture has good anti-attack and self-recovery capabilities under normal functions and performances and meets the general robust features in terms of safety and security.展开更多
Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PS...Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability.展开更多
The problem of stochastically allocating redundant com- ponents to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime...The problem of stochastically allocating redundant com- ponents to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with sub- systems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution,展开更多
This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration spa...This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.展开更多
This paper presents the forward displacement analysis of an 8-PSS(prismatic-spherical-spherical)redundant parallel manipulator whose moving platform is linked to the base platform by eight kinemtic chains consisting o...This paper presents the forward displacement analysis of an 8-PSS(prismatic-spherical-spherical)redundant parallel manipulator whose moving platform is linked to the base platform by eight kinemtic chains consisting of a PSS joint and a strut with fixed length.A general approximation algorithm is used to solve the problem.To avoid the extraction of root in the approximation process,the forward displacement analysis of the 8-PSS redundant parallel manipulator is transformed into another equivalent problem on the assumption that the strut is extensible while the slider is fixed.The problem is solved by a modified approximation algorithm which predicates that the manipulator will move along a pose vector to reduce the difference between the desired configuration and an instantaneous one,and the best movement should be with minimum norm and least quadratic sum.The characteristic of this modified algorithm is that its convergence domain is larger than that of the general approximation algorithm.Simulation results show that the modelified algorithm is general and can be used for the forward displacement analysis of the redundant parallel manipulator actuated by a revolute joint.展开更多
The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-...The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-Copulabased reliability evaluation method is proposed to estimate the reliability of system components with multiple PCs.Specifically,the marginal degradation reliability of each PC is built by using the Wiener stochastic process based on the PC’s degradation mechanism.The joint degradation reliability of the component with multiple PCs is established by connecting the marginal reliability of PCs using D-vine.In addition,two RSM decision models are developed to ensure the system accomplishes the next mission.The genetic algorithm(GA)is used to solve the constraint optimization problem of the models.A numerical example illustrates the application of the proposed RSM method.展开更多
基金supported by the National HighTech ResearchDevelopment Program of China (863) under Grant No.2012AA050801
文摘This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.
基金supported by National Natural Science Foundation of China (No. 71171199)Natural Science Foundation of Shaanxi Province of China (No. 2013JM1003)
文摘Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.
文摘The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humanity.In this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems.Results from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance.However,this improvement has been difficult to achieve for high-reliability applications.Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.
基金Supported by the National Natural Science Foundation of China(No.61106033)
文摘Static Random Access Memory(SRAM) based Field Programmable Gate Array(FPGA) is widely applied in the field of aerospace, whose anti-SEU(Single Event Upset) capability becomes more and more important. To improve anti-FPGA SEU capability, the registers of the circuit netlist are tripled and divided into three categories in this study. By the packing algorithm, the registers of triple modular redundancy are loaded into different configurable logic block. At the same time, the packing algorithm considers the effect of large fan-out nets. The experimental results show that the algorithm successfully realize the packing of the register of Triple Modular Redundancy(TMR). Comparing with Timing Versatile PACKing(TVPACK), the algorithm in this study is able to obtain a 11% reduction of the number of the nets in critical path, and a 12% reduction of the time delay in critical path on average when TMR is not considered. Especially, some critical path delay of circuit can be improved about 33%.
基金supported by National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154,62202147)the key Research and Development Program of Hubei Province,China(Grant No.2023BEB024).
文摘The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.
文摘Redundancy control can effectively enhance the stability and robustness of a system.Based on the conventional redundancy control switchover and majority arbitration strategy,this paper introduces the concept of heterogeneity and dynamics,constructs a dynamic heterogeneous redundancy-based microcontroller architecture DHR-MCU,and designs a fixed-leader distributed consensus algorithm that satisfies the program running state control of this architecture.The theoretical analysis and actual measurement of the prototype system prove that this architecture has good anti-attack and self-recovery capabilities under normal functions and performances and meets the general robust features in terms of safety and security.
基金This work has been supported by the Ningbo National Natural Science Foundation(2019A610124)General Project of Education Department of Zhejiang Province(Y201737089).
文摘Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability.
基金supported by the National Natural Science Foundation of China(7117217271101158+3 种基金71272058)the Program for New Century Excellent Talents in University(NCET-10-0043)the Key Project Cultivation Fund of the Scientific and Technical Innovation Program of Beijing Institute of Technology(2011CX01001)the Special Fund of International Science and Technology Cooperation Program of Beijing Institute of Technology(GZ2014215101)
文摘The problem of stochastically allocating redundant com- ponents to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with sub- systems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution,
文摘This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.
基金Funded by the National Natural Science Foundation of China(Grant No.50905102)the China Postdoctoral Science Foundation(Grant No.200801199)the Natural Science Foundation of Guangdong Province(Grant No.8351503101000001)
文摘This paper presents the forward displacement analysis of an 8-PSS(prismatic-spherical-spherical)redundant parallel manipulator whose moving platform is linked to the base platform by eight kinemtic chains consisting of a PSS joint and a strut with fixed length.A general approximation algorithm is used to solve the problem.To avoid the extraction of root in the approximation process,the forward displacement analysis of the 8-PSS redundant parallel manipulator is transformed into another equivalent problem on the assumption that the strut is extensible while the slider is fixed.The problem is solved by a modified approximation algorithm which predicates that the manipulator will move along a pose vector to reduce the difference between the desired configuration and an instantaneous one,and the best movement should be with minimum norm and least quadratic sum.The characteristic of this modified algorithm is that its convergence domain is larger than that of the general approximation algorithm.Simulation results show that the modelified algorithm is general and can be used for the forward displacement analysis of the redundant parallel manipulator actuated by a revolute joint.
基金supported by the Aeronautical Science Foundation of China(20150863003).
文摘The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-Copulabased reliability evaluation method is proposed to estimate the reliability of system components with multiple PCs.Specifically,the marginal degradation reliability of each PC is built by using the Wiener stochastic process based on the PC’s degradation mechanism.The joint degradation reliability of the component with multiple PCs is established by connecting the marginal reliability of PCs using D-vine.In addition,two RSM decision models are developed to ensure the system accomplishes the next mission.The genetic algorithm(GA)is used to solve the constraint optimization problem of the models.A numerical example illustrates the application of the proposed RSM method.
