本文基于超窄带FDMA电力线通信技术的研究和应用,对分散电力用户的抄表难题提出了经济适用的解决方案。国际上最新的超窄带技术(Ultra Narrow Band Technology)则解决了利用电力线进行远程通信的难题。该技术所选取的频率范围为555～100...本文基于超窄带FDMA电力线通信技术的研究和应用,对分散电力用户的抄表难题提出了经济适用的解决方案。国际上最新的超窄带技术(Ultra Narrow Band Technology)则解决了利用电力线进行远程通信的难题。该技术所选取的频率范围为555～1006Hz,从采集终端到站端数据处理单元(SPU)之间通过超低频电力线通信。因其载波频率低,信号可以穿越各种电压等级的变压器,直接到达变电站处理单元(SPU),实现终端到接收装置的一站式点到点通信,大大降低了系统复杂度,减少了线路铺设和施工成本,从而解决大范围远距离分散用户的集抄问题。展开更多
Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vect...Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.展开更多
文摘本文基于超窄带FDMA电力线通信技术的研究和应用,对分散电力用户的抄表难题提出了经济适用的解决方案。国际上最新的超窄带技术(Ultra Narrow Band Technology)则解决了利用电力线进行远程通信的难题。该技术所选取的频率范围为555～1006Hz,从采集终端到站端数据处理单元(SPU)之间通过超低频电力线通信。因其载波频率低,信号可以穿越各种电压等级的变压器,直接到达变电站处理单元(SPU),实现终端到接收装置的一站式点到点通信,大大降低了系统复杂度,减少了线路铺设和施工成本,从而解决大范围远距离分散用户的集抄问题。
基金supported by the funds of Ningde Normal University Youth Teacher Research Program(2015Q15)The Education Science Project of the Junior Teacher in the Education Department of Fujian province(JAT160532).
文摘Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.
