Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to...Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.展开更多
In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criti...In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.展开更多
Aiming at developing a node scheduling protocol for sensor networks with fewer active nodes,we propose a coordinated node scheduling protocol based on the presentation of a solution and its optimization to determine w...Aiming at developing a node scheduling protocol for sensor networks with fewer active nodes,we propose a coordinated node scheduling protocol based on the presentation of a solution and its optimization to determine whether a node is redundant.The proposed protocol can reduce the number of working nodes by turning off as many redundant nodes as possible without degrading the coverage and connectivity.The simulation result shows that our protocol outperforms the peer with respect to the working node number and dynamic coverage percentage.展开更多
A promising technology named epitaxy on nano-scale freestanding fin （ENFF） is firstly proposed for hetero- epitaxy. This technology can effectively release total strain energy and then can reduce the probability of ...A promising technology named epitaxy on nano-scale freestanding fin （ENFF） is firstly proposed for hetero- epitaxy. This technology can effectively release total strain energy and then can reduce the probability of gener- ating mismatch dislocations. Based on the calculation, dislocation defects can be eliminated completely when the thickness of the Si freestanding fin is less than 10nm for the epitaxial Ge layer. In addition, this proposed ENFF process can provide sufficient uniaxial stress for the epitaxy layer, which can be the major stressor for the SiGe or Ge channel fin field-effect transistor or nanowire at the 10nm node and beyond. According to the results of technology computer-aided design simulation, nanowires integrated with ENFF show excellent electrical perfor- mance for uniaxial stress and band offset. The ENFF process is compatible with the state of the art mainstream technology, which has a good potential for future applications.展开更多
With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple res...With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.展开更多
With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy s...With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.展开更多
A scheduling algorithm for the edge nodes of optical burst switching (OBS) networks is proposed to guarantee the delay requirement of services with different CoS (Class of Service) and provide lower burst loss ratio a...A scheduling algorithm for the edge nodes of optical burst switching (OBS) networks is proposed to guarantee the delay requirement of services with different CoS (Class of Service) and provide lower burst loss ratio at the same time. The performance of edge nodes based on the proposed algorithm is presented.展开更多
基金the State Grid Liaoning Electric Power Supply Co.,Ltd.(Research on Scheduling Decision Technology Based on Interactive Reinforcement Learning for Adapting High Proportion of New Energy,No.2023YF-49).
文摘Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.
基金the Project Program of Science and Technology on Micro-System Laboratory,No.6142804220101.
文摘In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.
基金the National Natural Science Foundation of China(Grant No.60533110 and No.90604013)the Scientific Research Foundation of Harbin Institute of Technology(Grant No. HIT2002.74)
文摘Aiming at developing a node scheduling protocol for sensor networks with fewer active nodes,we propose a coordinated node scheduling protocol based on the presentation of a solution and its optimization to determine whether a node is redundant.The proposed protocol can reduce the number of working nodes by turning off as many redundant nodes as possible without degrading the coverage and connectivity.The simulation result shows that our protocol outperforms the peer with respect to the working node number and dynamic coverage percentage.
基金Supported by the National Key Research and Development Program of China(2016YFA0301701)the Youth Innovation Promotion Association of CAS under Grant No 2016112
文摘A promising technology named epitaxy on nano-scale freestanding fin （ENFF） is firstly proposed for hetero- epitaxy. This technology can effectively release total strain energy and then can reduce the probability of gener- ating mismatch dislocations. Based on the calculation, dislocation defects can be eliminated completely when the thickness of the Si freestanding fin is less than 10nm for the epitaxial Ge layer. In addition, this proposed ENFF process can provide sufficient uniaxial stress for the epitaxy layer, which can be the major stressor for the SiGe or Ge channel fin field-effect transistor or nanowire at the 10nm node and beyond. According to the results of technology computer-aided design simulation, nanowires integrated with ENFF show excellent electrical perfor- mance for uniaxial stress and band offset. The ENFF process is compatible with the state of the art mainstream technology, which has a good potential for future applications.
基金This paper is supported by Shaanxi Natural Science Foundation of China under Grant No2004E202
文摘With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.
基金supported by the National Natural Science Foundation of China(60973139,60773041)the Natural Science Foundation of Jiangsu Province(BK2008451)+2 种基金Special Fund for Software Technology of Jiangsu Province,Postdoctoral Foundation(0801019C,20090451240,20090451-241)Science&Technology Innovation Fund for higher education institutions of Jiangsu Province(CX09B_153Z,CX08B-086Z)the six kinds of Top Talent of Jiangsu Province(2008118)
文摘With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.
文摘A scheduling algorithm for the edge nodes of optical burst switching (OBS) networks is proposed to guarantee the delay requirement of services with different CoS (Class of Service) and provide lower burst loss ratio at the same time. The performance of edge nodes based on the proposed algorithm is presented.
