To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation ...To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.展开更多
In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps)...In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.展开更多
分布式控制是智能配电网实现广域保护、控制应用的重要方式,报文的实时性是影响分布式控制性能的关键因素。文中在分析配电网分布式控制应用对通信的要求基础上,对现有的快速报文传输方式进行了比较,提出了基于用户数据报协议(UDP),用...分布式控制是智能配电网实现广域保护、控制应用的重要方式,报文的实时性是影响分布式控制性能的关键因素。文中在分析配电网分布式控制应用对通信的要求基础上,对现有的快速报文传输方式进行了比较,提出了基于用户数据报协议(UDP),用通用面向对象变电站事件(GOOSE)机制传输实时报文(GOOSE over UDP)的方案,可实现快速报文在广域网传输。通过试验平台对该方案的传输性能进行了测试,测得配电终端之间传输延时最大不超过10 ms,满足配电网分布式控制应用的需求。展开更多
基金funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No.(44-PRFA-P-48).
文摘To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R 308)。
文摘In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.
文摘分布式控制是智能配电网实现广域保护、控制应用的重要方式,报文的实时性是影响分布式控制性能的关键因素。文中在分析配电网分布式控制应用对通信的要求基础上,对现有的快速报文传输方式进行了比较,提出了基于用户数据报协议(UDP),用通用面向对象变电站事件(GOOSE)机制传输实时报文(GOOSE over UDP)的方案,可实现快速报文在广域网传输。通过试验平台对该方案的传输性能进行了测试,测得配电终端之间传输延时最大不超过10 ms,满足配电网分布式控制应用的需求。
