多媒体业务在异构网络传输过程中,由于现有的QoS(Quality of Service)类映射方法存在灵活性不足的问题,从而降低了系统端到端效能。针对这个问题,该文在深入分析当前的QoS类映射方法基础上,结合用户QoE(Quality of Experience)特点,借...多媒体业务在异构网络传输过程中,由于现有的QoS(Quality of Service)类映射方法存在灵活性不足的问题,从而降低了系统端到端效能。针对这个问题,该文在深入分析当前的QoS类映射方法基础上,结合用户QoE(Quality of Experience)特点,借助于网络微积分理论,构建了QoS类映射的数学分析模型,并进行了理论分析。基于该数学分析模型,该文从用户QoE角度提出了具有弹性的QoS类映射方法(Elastic QoS Class Mapping Method,EQCMM),该方法根据当前网络资源的使用情况,通过灵活地调整QoS类映射,充分利用网络现有资源,提高了端到端带宽资源的利用率,改善了系统端到端的效能。最后,通过仿真验证了该方法的有效性。展开更多
针对空间光通信中跟踪系统的高精度、宽带宽要求,提出了一种基于PID神经元网络(Proportional integral differential neural network-PIDNN)的控制方案。采用MATLAB对所建立的跟踪系统模型进行了仿真分析研究,对采用PIDNN控制器的精跟...针对空间光通信中跟踪系统的高精度、宽带宽要求,提出了一种基于PID神经元网络(Proportional integral differential neural network-PIDNN)的控制方案。采用MATLAB对所建立的跟踪系统模型进行了仿真分析研究,对采用PIDNN控制器的精跟踪系统的在线训练能力及学习、调整功能进行了仿真验证,同时加入扰动源对精跟踪系统的稳态、动态性能及鲁棒性进行了仿真测试。仿真和测试结果表明:通过PIDNN控制的精跟踪系统具有良好的稳态及动态性能和很强的鲁棒性,系统跟踪精度高且系统带宽较宽。展开更多
The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning al...The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.展开更多
文摘多媒体业务在异构网络传输过程中,由于现有的QoS(Quality of Service)类映射方法存在灵活性不足的问题,从而降低了系统端到端效能。针对这个问题,该文在深入分析当前的QoS类映射方法基础上,结合用户QoE(Quality of Experience)特点,借助于网络微积分理论,构建了QoS类映射的数学分析模型,并进行了理论分析。基于该数学分析模型,该文从用户QoE角度提出了具有弹性的QoS类映射方法(Elastic QoS Class Mapping Method,EQCMM),该方法根据当前网络资源的使用情况,通过灵活地调整QoS类映射,充分利用网络现有资源,提高了端到端带宽资源的利用率,改善了系统端到端的效能。最后,通过仿真验证了该方法的有效性。
基金National Basic Research Program of China“973 Program”(2007CB307101,2007CB307106)Program of Introducing Talents of Discipline to Universities“111 Project”(B08002)+1 种基金Cultivation Fund of the Key Scientific and Technical Innovation Project,Ministry of Education of China(706005)National Natural Science Funds(60772043)
文摘针对空间光通信中跟踪系统的高精度、宽带宽要求,提出了一种基于PID神经元网络(Proportional integral differential neural network-PIDNN)的控制方案。采用MATLAB对所建立的跟踪系统模型进行了仿真分析研究,对采用PIDNN控制器的精跟踪系统的在线训练能力及学习、调整功能进行了仿真验证,同时加入扰动源对精跟踪系统的稳态、动态性能及鲁棒性进行了仿真测试。仿真和测试结果表明:通过PIDNN控制的精跟踪系统具有良好的稳态及动态性能和很强的鲁棒性,系统跟踪精度高且系统带宽较宽。
基金supported by the Shaanxi Province Natural Science Basic Research Plan Project(2023-JC-YB-244).
文摘The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.
