The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃...The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.展开更多
For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete ...For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.展开更多
Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutiv...Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.展开更多
在现代电力系统中,配网低电压问题常由多种因素引起,包括不平衡负荷、过长的供电距离或设备老化等。这些问题不仅影响电能质量,而且会降低用户满意度和电网运行效率。而智能电表的广泛部署为实时监控和管理配网提供了新的可能。文章深...在现代电力系统中,配网低电压问题常由多种因素引起,包括不平衡负荷、过长的供电距离或设备老化等。这些问题不仅影响电能质量,而且会降低用户满意度和电网运行效率。而智能电表的广泛部署为实时监控和管理配网提供了新的可能。文章深入探讨基于智能电表高速电力线载波(High-speed Power Line Carrier,HPLC)模块96点数据的配网低电压治理方法,根据自动识别和分类低电压问题,开发数据驱动的电压调节策略,并结合预测模型和预防措施,提出一套综合治理方案。然后,通过系统测试,验证所提方法的有效性和实用性。展开更多
With the increasing utilization of High-Speed Trains (HSTs), the need for a reliable and high-bandwidth Internet access under high-speed mobility scenarios has become more demanding. In static, walking, and low mobi...With the increasing utilization of High-Speed Trains (HSTs), the need for a reliable and high-bandwidth Internet access under high-speed mobility scenarios has become more demanding. In static, walking, and low mobility environments, TCP/IP (transmission control protocol/Internet protocol) can work well. However, TCP/IP cannot work well in high-speed scenarios because of reliability and handoff delay problems. This is mainly because the mobile node is required to maintain the connection to the corresponding node when it handovers to another access point node. In this paper, we propose a named data networking wireless mesh network architecture for HST wireless communication (NDN-Mesh-T), which combines the advantages of Wireless Mesh Networks (WMNs) and NDN architectures. We attempt to solve the reliability and handoff delay problems to enable high bandwidth and low latency in Internet access in HST scenarios. To further improve reliability and bandwidth utilization, we propose a Direction-Aware Forwarding (DAF) strategy to forward Interest packet along the direction of the running train. The simulation results show that the proposed scheme can significantly reduce the packet loss rate by up to 51% compared to TCP/IP network architecture. Moreover, the proposed mechanism can reduce the network load, handoff delay, and data redundancy.展开更多
高速铁路信号系统安全数据网承载着我国高速铁路列控系统核心业务,对网络和设备的可靠性和实时性具有极高要求,目前的网络安全防护技术手段尚不能很好地适用于信号系统安全数据网。采用软件定义网络架构,从网络统一管控的角度出发进行...高速铁路信号系统安全数据网承载着我国高速铁路列控系统核心业务,对网络和设备的可靠性和实时性具有极高要求,目前的网络安全防护技术手段尚不能很好地适用于信号系统安全数据网。采用软件定义网络架构,从网络统一管控的角度出发进行探索性研究,提出并设计实现了针对我国高速铁路信号系统安全数据网的安全防护方案SD-SSDN(Software-defined Signal Safety Data Network)。实验结果表明:该防护方案可以有效阻断黑客攻击,提高信号安全数据网的安全性;同时,为了保障高实时性和可靠性,还提出基于SDN多级流表的冗余流表映射技术和自适应链路聚合技术;在10个交换机组成的环网中,链路失效恢复时间约为8ms,可以满足列控系统业务需求。展开更多
文摘The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.
文摘For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.72171236 and 71701216)the National Key R&D Program of China(Grant No.2020YFB1600400)+2 种基金the China Scholarship Council(202008360277)the Key Science and Technology Research Program of the Educational Department of Jiangxi Province(Grant No.GJJ200605)the Natural Science Foundation of Hunan Province(Grant No.2020JJ5783).
文摘Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.
文摘在现代电力系统中,配网低电压问题常由多种因素引起,包括不平衡负荷、过长的供电距离或设备老化等。这些问题不仅影响电能质量,而且会降低用户满意度和电网运行效率。而智能电表的广泛部署为实时监控和管理配网提供了新的可能。文章深入探讨基于智能电表高速电力线载波(High-speed Power Line Carrier,HPLC)模块96点数据的配网低电压治理方法,根据自动识别和分类低电压问题,开发数据驱动的电压调节策略,并结合预测模型和预防措施,提出一套综合治理方案。然后,通过系统测试,验证所提方法的有效性和实用性。
基金supported by the National Natural Science Foundation of China (No. 61309025)the Hunan Provincial Natural Science Foundation of China (No. 2017JJ2332)+1 种基金the National Key Technology R&D Program (No. 2015BAH05F02)the Fundamental Research Funds for the Central Universities of Central South University (No. 2017zzts146)
文摘With the increasing utilization of High-Speed Trains (HSTs), the need for a reliable and high-bandwidth Internet access under high-speed mobility scenarios has become more demanding. In static, walking, and low mobility environments, TCP/IP (transmission control protocol/Internet protocol) can work well. However, TCP/IP cannot work well in high-speed scenarios because of reliability and handoff delay problems. This is mainly because the mobile node is required to maintain the connection to the corresponding node when it handovers to another access point node. In this paper, we propose a named data networking wireless mesh network architecture for HST wireless communication (NDN-Mesh-T), which combines the advantages of Wireless Mesh Networks (WMNs) and NDN architectures. We attempt to solve the reliability and handoff delay problems to enable high bandwidth and low latency in Internet access in HST scenarios. To further improve reliability and bandwidth utilization, we propose a Direction-Aware Forwarding (DAF) strategy to forward Interest packet along the direction of the running train. The simulation results show that the proposed scheme can significantly reduce the packet loss rate by up to 51% compared to TCP/IP network architecture. Moreover, the proposed mechanism can reduce the network load, handoff delay, and data redundancy.
文摘高速铁路信号系统安全数据网承载着我国高速铁路列控系统核心业务,对网络和设备的可靠性和实时性具有极高要求,目前的网络安全防护技术手段尚不能很好地适用于信号系统安全数据网。采用软件定义网络架构,从网络统一管控的角度出发进行探索性研究,提出并设计实现了针对我国高速铁路信号系统安全数据网的安全防护方案SD-SSDN(Software-defined Signal Safety Data Network)。实验结果表明:该防护方案可以有效阻断黑客攻击,提高信号安全数据网的安全性;同时,为了保障高实时性和可靠性,还提出基于SDN多级流表的冗余流表映射技术和自适应链路聚合技术;在10个交换机组成的环网中,链路失效恢复时间约为8ms,可以满足列控系统业务需求。