There is an obvious gap in sprint level at home and abroad,and there are different opinions on the reasons.According to the analysis,physical energy and its distribution in each segment are the main factors restrictin...There is an obvious gap in sprint level at home and abroad,and there are different opinions on the reasons.According to the analysis,physical energy and its distribution in each segment are the main factors restricting the sprint performance in China.Different from middle and long-distance running,we must rely on accurate sectional timing technology to master the law of speed-physical energy change in the process of sprint.Each stage is an integral part of the whole dash process,and each part restricts each other.Each stage has a relative best achievement.Simply pursuing the optimal state of segment is not only not helpful to the final result,but also counterproductive.展开更多
Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that operate in parallel ...Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that operate in parallel at different levels. In this paper we propose a more realistic biologically inspired hybrid neural network architecture that uses two kinds of neural networks simultaneously to consider short-term and long-term characteristics of the signal. The first of these networks quickly adapts to new modes of operation whereas the second one provides more accurate learning within a specific mode. We call these networks the surfacing and deep learning agents and show that this hybrid architecture performs complementary functions that improve the overall learning. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It is shown that the proposed architecture provides a superior performance based on the RMS error criterion.展开更多
Aerodynamic noise is the dominant noise source of the high-speed train.It not only seriously affects the passenger comfort and people’s normal life along the railway line,but also may cause fatigue damage to the surr...Aerodynamic noise is the dominant noise source of the high-speed train.It not only seriously affects the passenger comfort and people’s normal life along the railway line,but also may cause fatigue damage to the surrounding equipment and buildings.This manuscript carried out the simulation and experimental study on the external aerodynamic noise of high-speed train,in order to increase the understanding of the noise and hence to be better able to control it.The on-line tests were performed to verify that it is reasonable to simplify the high-speed train model.The turbulent air flow model was then developed,and the external steady flow field was computed by Realizable k-εturbulence model.Based on the steady flow field,aerodynamic noise sources on the train surface and the external transient flow field were calculated by broadband acoustics source model and large eddy simulation(LES)respectively.The pressures on the train surface were obtained from the results of the transient model.Considering the transient flow field,the far-field aerodynamic noise generated by the high-speed train was finally obtained based on Lighthill-Curle theory.Through the comparison between simulations and on-line tests,it is shown that the numerical model gives reliable aerodynamic noise predictions.This research is significant to the study and control of the aerodynamic noise of high-speed train.展开更多
针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation,VT-BCE)算法。基于叠加训练(Superimposed Training,ST)方案,将训练序列和符号序列线...针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation,VT-BCE)算法。基于叠加训练(Superimposed Training,ST)方案,将训练序列和符号序列线性叠加,使得训练序列和符号序列的信道信息一致,提高信号的跟踪能力;基于置信传播,双向信道估计(Bidirectional Channel Estimation,BCE)算法将一个数据块分成多个短块,利用整个数据块的信息估计当前短块信道,实现对当前短块的精准信道估计。将ST方案、BCE算法和信道均衡(频域)以迭代的方式相结合,使估计的符号序列可以作为信道估计的虚拟训练(Virtual Training,VT)序列,提升信道的估计性能,进而提高系统的解码性能。最后,通过计算机仿真和水池试验,验证了所提算法的有效性。展开更多
国家电力调度通信中心(简称国调)承担着特大规模交直流混合运行的全国互联电网的调度管理任务。文章介绍了国调调度员培训模拟系统(dispatcher training simulator,DTS)的技术路线、软硬件结构,详细阐述了其突出的技术特点,内容包括:22...国家电力调度通信中心(简称国调)承担着特大规模交直流混合运行的全国互联电网的调度管理任务。文章介绍了国调调度员培训模拟系统(dispatcher training simulator,DTS)的技术路线、软硬件结构,详细阐述了其突出的技术特点,内容包括:220kV外网准确自动拼接建模、在线精确仿真、多回交直流混合运行电力系统仿真、HVDC详细模型仿真、国调DTS与网调DTS互联协同仿真、基于安全模式下WEB交互操作的跨级调度联合反事故演习等。展开更多
文摘There is an obvious gap in sprint level at home and abroad,and there are different opinions on the reasons.According to the analysis,physical energy and its distribution in each segment are the main factors restricting the sprint performance in China.Different from middle and long-distance running,we must rely on accurate sectional timing technology to master the law of speed-physical energy change in the process of sprint.Each stage is an integral part of the whole dash process,and each part restricts each other.Each stage has a relative best achievement.Simply pursuing the optimal state of segment is not only not helpful to the final result,but also counterproductive.
文摘Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that operate in parallel at different levels. In this paper we propose a more realistic biologically inspired hybrid neural network architecture that uses two kinds of neural networks simultaneously to consider short-term and long-term characteristics of the signal. The first of these networks quickly adapts to new modes of operation whereas the second one provides more accurate learning within a specific mode. We call these networks the surfacing and deep learning agents and show that this hybrid architecture performs complementary functions that improve the overall learning. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It is shown that the proposed architecture provides a superior performance based on the RMS error criterion.
基金supported by National Natural Science Foundation of China(51705068)the fundamental research funds for the central universities(N150303003)research initiation funds for the PhD of Liaoning Province(201601005).
文摘Aerodynamic noise is the dominant noise source of the high-speed train.It not only seriously affects the passenger comfort and people’s normal life along the railway line,but also may cause fatigue damage to the surrounding equipment and buildings.This manuscript carried out the simulation and experimental study on the external aerodynamic noise of high-speed train,in order to increase the understanding of the noise and hence to be better able to control it.The on-line tests were performed to verify that it is reasonable to simplify the high-speed train model.The turbulent air flow model was then developed,and the external steady flow field was computed by Realizable k-εturbulence model.Based on the steady flow field,aerodynamic noise sources on the train surface and the external transient flow field were calculated by broadband acoustics source model and large eddy simulation(LES)respectively.The pressures on the train surface were obtained from the results of the transient model.Considering the transient flow field,the far-field aerodynamic noise generated by the high-speed train was finally obtained based on Lighthill-Curle theory.Through the comparison between simulations and on-line tests,it is shown that the numerical model gives reliable aerodynamic noise predictions.This research is significant to the study and control of the aerodynamic noise of high-speed train.
文摘针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation,VT-BCE)算法。基于叠加训练(Superimposed Training,ST)方案,将训练序列和符号序列线性叠加,使得训练序列和符号序列的信道信息一致,提高信号的跟踪能力;基于置信传播,双向信道估计(Bidirectional Channel Estimation,BCE)算法将一个数据块分成多个短块,利用整个数据块的信息估计当前短块信道,实现对当前短块的精准信道估计。将ST方案、BCE算法和信道均衡(频域)以迭代的方式相结合,使估计的符号序列可以作为信道估计的虚拟训练(Virtual Training,VT)序列,提升信道的估计性能,进而提高系统的解码性能。最后,通过计算机仿真和水池试验,验证了所提算法的有效性。
文摘国家电力调度通信中心(简称国调)承担着特大规模交直流混合运行的全国互联电网的调度管理任务。文章介绍了国调调度员培训模拟系统(dispatcher training simulator,DTS)的技术路线、软硬件结构,详细阐述了其突出的技术特点,内容包括:220kV外网准确自动拼接建模、在线精确仿真、多回交直流混合运行电力系统仿真、HVDC详细模型仿真、国调DTS与网调DTS互联协同仿真、基于安全模式下WEB交互操作的跨级调度联合反事故演习等。
