ID 原文 译文
4243 首先，介绍了当前太赫兹信道的研究进展，包括信道建模、信道测量及信道估计。 Research progresses of terahertzchannel, such as terahertz channel model, terahertz channel measurement and terahertz channel estimation, were first re-viewed.
4244 在此基础上，分析了单用户基本通信场景和多用户复杂通信场景，并针对各个场景中存在的问题列举了可能的解决方案。 Based on these characteristics of terahertz channel, the underlying problems in basic single-user communicationscenarios and more complicated multi-user communication scenarios were respectively analyzed.
4245 最后，展望了太赫兹通信未来可行的研究方向。 For each scenario pos-sible solutions were concluded. Last but not least, some prospect future research directions on terahertz communicationswere discussed.
4246 鉴于深度学习、频谱、时频分析方法间的优势互补，设计了由卷积网络、傅里叶变换和小波包分解组合的多流分析处理框架，对非平稳信号进行组合分析。 Considering the complementarity between the deep learning, spectrum and time frequency analysis methods, amulti-stream framework was designed by combining the convolutional network, Fourier transform and wavelet packagedecomposition methods, with the aim to analyze the non-stationary signal.
4247 提出了一种基于非平稳信号组合分析的故障诊断方法，提取信号的多属性特征并加权融合。 Accordingly, a none-stationary signal com-bined analysis based fault diagnosis method was proposed to extract features in difference aspects.
4248 应用于故障诊断的实验结果表明，所提出的信号组合分析方法能够更加稳定、准确地刻画故障类型，在不显著增加计算复杂度的前提下有效提高了故障诊断的分类准确率。 The fault diagnosisexperiments demonstrate that the combined analysis method can efficiently and stably depict the fault and significantlyimprove the performance of fault diagnosis.
4249 针对目前网络安全态势预测方法的精确度不足问题，以自修正系数修匀法为基础提出一种新的网络安全态势预测模型。 In order to solve the problem of insufficient accuracy of current network security situation prediction methods,a new network security situation prediction model was proposed based on self-correcting coefficient smoothing.
4250 首先，设计一种网络安全态势评估量化方法，基于熵关联度将警报信息转化为态势实际值时间样本序列。 Firstly, anetwork security assessment quantification method was designed to transform the alarm information into situation realvalue time series based on the entropy correlation degree.
4251 然后，计算静态修匀系数自适应解并利用可变域空间获取预测初始值。 Then, the adaptive solution of the static smoothing coefficientwas calculated and the predicted initial value was obtained by using the variable domain space.
4252 最后，为了进一步提高预测精度，基于偏差类别并采用时变加权马尔可夫链对网络安全态势初始预测结果进行修正。 Finally, based on the errorcategory, the time-changing weighted Markov chain was built to modify the initial network situation prediction result andthe prediction accuracy was further raised.