The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regressi...The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.展开更多
针对数据量剧增的配电物联网中存在的带宽利用率低和业务数据服务质量(quality of service,QoS)难以满足通信需求等问题,提出一种多优先级排队论的带宽分配方法。首先,对感知终端到边缘物联网关的业务数据传输过程进行改进,改进后的传...针对数据量剧增的配电物联网中存在的带宽利用率低和业务数据服务质量(quality of service,QoS)难以满足通信需求等问题,提出一种多优先级排队论的带宽分配方法。首先,对感知终端到边缘物联网关的业务数据传输过程进行改进,改进后的传输过程可根据不同业务数据对QoS的不同要求进行数据优先级的划分,对不同优先级数据设置不同的服务机制;然后,对业务数据传输中的马尔科夫过程进行分析,基于改进后的数据传输过程建立以带宽利用率为目标,丢包率和延时时间为约束的多优先级排队论带宽分配模型;并将所提出的带宽分配方法与传统方法进行对比。结果表明:QoS指标有所改善,而且带宽利用率比传统不分优先级带宽分配方法高9.73%,比弹性系数法高31.17%。最后,探究多优先级排队论带宽分配方法的动态性能,结果表明适当地提高带宽可以改善QoS指标,但要注意带宽增大时所带来的带宽利用率减小问题。合理的带宽分配可以避免资源的浪费。展开更多
A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of inter...A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these ＂training events＂ are represented in matrix form and transpose-multiplied to generate time- domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window （approxi- mately the width of the training event） is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigen- vectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled （50 Hz） data from six observatories, each equipped with three- component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.展开更多
文摘The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.
文摘针对数据量剧增的配电物联网中存在的带宽利用率低和业务数据服务质量(quality of service,QoS)难以满足通信需求等问题,提出一种多优先级排队论的带宽分配方法。首先,对感知终端到边缘物联网关的业务数据传输过程进行改进,改进后的传输过程可根据不同业务数据对QoS的不同要求进行数据优先级的划分,对不同优先级数据设置不同的服务机制;然后,对业务数据传输中的马尔科夫过程进行分析,基于改进后的数据传输过程建立以带宽利用率为目标,丢包率和延时时间为约束的多优先级排队论带宽分配模型;并将所提出的带宽分配方法与传统方法进行对比。结果表明:QoS指标有所改善,而且带宽利用率比传统不分优先级带宽分配方法高9.73%,比弹性系数法高31.17%。最后,探究多优先级排队论带宽分配方法的动态性能,结果表明适当地提高带宽可以改善QoS指标,但要注意带宽增大时所带来的带宽利用率减小问题。合理的带宽分配可以避免资源的浪费。
文摘A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these ＂training events＂ are represented in matrix form and transpose-multiplied to generate time- domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window （approxi- mately the width of the training event） is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigen- vectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled （50 Hz） data from six observatories, each equipped with three- component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.
