Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of im...Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.展开更多
In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is...In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.展开更多
This paper describes the study analysis performed to evaluate the available and potential solutions to control the highly increasing short circuit （SC） levels in Kuwait power system. The real Kuwait High Voltage （H...This paper describes the study analysis performed to evaluate the available and potential solutions to control the highly increasing short circuit （SC） levels in Kuwait power system. The real Kuwait High Voltage （HV） network was simulated to examine different measures at both 275 kV and 132 kV stations. The simulation results show that the short circuit currents exceed the permissible levels （40 kA in the 132 kV network and 63 kA in the 275 kV network） in some specific points. The examined measures include the a study on changing the neutral point policy, changing some lines from alternating current （AC） to direct current （DC）, dividing specific bus bars in some generating stations and applying current limiters. The paper also presents a new plan for the transmission network in order to manage the expected increase in short circuit levels in the future.展开更多
We introduce an innovative approach to address a significant challenge in interaction recognition,specificallythe capture of correlation features between different interaction body parts.These features are oftenoverlo...We introduce an innovative approach to address a significant challenge in interaction recognition,specificallythe capture of correlation features between different interaction body parts.These features are oftenoverlooked by traditional graph convolution networks commonly used in interaction recognition tasks.Oursolution,the Merge-and-Split Graph Convolutional Network,takes a unique perspective,treating interactionrecognition as a global problem.It leverages a Merge-and-Split Graph structure to effectively capturedependencies between interaction body parts.To extract the essential interaction features,we introducethe Merge-and-Split Graph Convolution module,which seamlessly combines the Merge-and-Split Graphwith Graph Convolutional Networks.This fusion enables the extraction of rich semantic information betweenadjacent joint points.In addition,we introduce a Short-term Dependence module designed to extract jointand motion characteristics specific to each type of interaction.Furthermore,to extract correlation featuresbetween different hierarchical sets,we present the Hierarchical Guided Attention Module.This module playsa crucial role in highlighting the relevant hierarchical sets that contain essential interaction information.The effectiveness of our proposed model is demonstrated by achieving state-of-the-art performance on 2widely recognized datasets,namely,the NTU60 and NTU120 interaction datasets.Our model’s efficacy isrigorously validated through extensive experiments,and we have made the code available for the researchcommunity at https://github.com/wanghq05/MS-GCN/.展开更多
基金the Artificial Intelligence Key Laboratory of Sichuan Province(Nos.2019RYJ05)National Natural Science Foundation of China(Nos.61971107).
文摘Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.
基金ACKNOWLEDGEMENT This work was supported by National Natural Science Foundation of China （Grant No. 61231008）, National Basic Research Program of China （973 Program） （Grant No. 2009CB320404）, Program for Changjiang Scholars and Innovative Research Team in University （Grant No. IRT0852）, and the 111 Project （Grant No. B08038）.
文摘In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.
文摘This paper describes the study analysis performed to evaluate the available and potential solutions to control the highly increasing short circuit （SC） levels in Kuwait power system. The real Kuwait High Voltage （HV） network was simulated to examine different measures at both 275 kV and 132 kV stations. The simulation results show that the short circuit currents exceed the permissible levels （40 kA in the 132 kV network and 63 kA in the 275 kV network） in some specific points. The examined measures include the a study on changing the neutral point policy, changing some lines from alternating current （AC） to direct current （DC）, dividing specific bus bars in some generating stations and applying current limiters. The paper also presents a new plan for the transmission network in order to manage the expected increase in short circuit levels in the future.
基金funding from the NationalNatural Science Foundation of China under Grant.No.62073004support from the Shenzhen Fundamental ResearchProgram under Grants.No.GXWD20201231165807007-20200807164903001 and JCYJ20200109140410340.
文摘We introduce an innovative approach to address a significant challenge in interaction recognition,specificallythe capture of correlation features between different interaction body parts.These features are oftenoverlooked by traditional graph convolution networks commonly used in interaction recognition tasks.Oursolution,the Merge-and-Split Graph Convolutional Network,takes a unique perspective,treating interactionrecognition as a global problem.It leverages a Merge-and-Split Graph structure to effectively capturedependencies between interaction body parts.To extract the essential interaction features,we introducethe Merge-and-Split Graph Convolution module,which seamlessly combines the Merge-and-Split Graphwith Graph Convolutional Networks.This fusion enables the extraction of rich semantic information betweenadjacent joint points.In addition,we introduce a Short-term Dependence module designed to extract jointand motion characteristics specific to each type of interaction.Furthermore,to extract correlation featuresbetween different hierarchical sets,we present the Hierarchical Guided Attention Module.This module playsa crucial role in highlighting the relevant hierarchical sets that contain essential interaction information.The effectiveness of our proposed model is demonstrated by achieving state-of-the-art performance on 2widely recognized datasets,namely,the NTU60 and NTU120 interaction datasets.Our model’s efficacy isrigorously validated through extensive experiments,and we have made the code available for the researchcommunity at https://github.com/wanghq05/MS-GCN/.
