With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provi...With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.展开更多
This paper summarizes the basic content of network curriculum design based on online learning mode and the basic flow, as well as network course should have the factors that suitable of the mode and attention matters ...This paper summarizes the basic content of network curriculum design based on online learning mode and the basic flow, as well as network course should have the factors that suitable of the mode and attention matters in the design collaboration mode of network course. Based on this, other researchers and practitioners can conveniently and effectively design network course based on the cooperation mode. Through the analysis of the network curriculum development and the actual case, verify advantage of collaborative online learning mode.展开更多
Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to rep...Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.展开更多
Learning has come a long way from the conventional blackboard that marked the earlier decades to the new age Blackboard Collaborate which enables learners to work and learn in a collaborative online environment. Virtu...Learning has come a long way from the conventional blackboard that marked the earlier decades to the new age Blackboard Collaborate which enables learners to work and learn in a collaborative online environment. Virtual classrooms, especially with learners sharing in academic workload on portals such as Google Docs, have arrived as a natural development of the ICT （In Circuit Tester） wave. In addition to offering ample scope for peer interaction, Blackboard Collaborate gives learners the electronic environment they are most familiar with and keeps them updated with the latest educational tools. However, for it to succeed, the perception of the teachers is a factor that needs close scrutiny before this tool can be incorporated into the system. The aim of this paper is to investigate the perceptions of teachers at Qassim University, KSA （Kingdom of Saudi Arabia） towards the inclusion of Blackboard Collaborate into the teaching-learning environment, and its efficacy as a learning tool in the university＇s EFL （English as a Foreign Language） situation.展开更多
基金supported by the National Natural Science Foundation of China under Grant 52077146.
文摘With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.
文摘This paper summarizes the basic content of network curriculum design based on online learning mode and the basic flow, as well as network course should have the factors that suitable of the mode and attention matters in the design collaboration mode of network course. Based on this, other researchers and practitioners can conveniently and effectively design network course based on the cooperation mode. Through the analysis of the network curriculum development and the actual case, verify advantage of collaborative online learning mode.
基金supported by the National Natural Science Foundation of China (No. 61977003),entitled “Research on learning style for adaptive learning: modelling, identification and applications”
文摘Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.
文摘Learning has come a long way from the conventional blackboard that marked the earlier decades to the new age Blackboard Collaborate which enables learners to work and learn in a collaborative online environment. Virtual classrooms, especially with learners sharing in academic workload on portals such as Google Docs, have arrived as a natural development of the ICT （In Circuit Tester） wave. In addition to offering ample scope for peer interaction, Blackboard Collaborate gives learners the electronic environment they are most familiar with and keeps them updated with the latest educational tools. However, for it to succeed, the perception of the teachers is a factor that needs close scrutiny before this tool can be incorporated into the system. The aim of this paper is to investigate the perceptions of teachers at Qassim University, KSA （Kingdom of Saudi Arabia） towards the inclusion of Blackboard Collaborate into the teaching-learning environment, and its efficacy as a learning tool in the university＇s EFL （English as a Foreign Language） situation.
