提出了一种基于离散时间贝叶斯网络的动态故障树分析方法。
A new dynamic fault tree analysis method based on discrete-time Bayesian networks is proposed.
分析了音视频联合建模的层级结构，利用动态贝叶斯网络对不同层级的音视频关联关系建立模型，并基于该模型进行音视频说话人识别的实验。
According to the hierarchical structure of audio-visual bimodal modeling, a new DBN is constructed to describe the natural audio and visual state asynchrony as well as their conditional .
涵盖各个领域的量化分析结合在一起使得基于贝叶斯网权限图的网络安全评估方法更加准确、精确，更加能够适应动态变化的网络。
The combination of analysis covering different kinds of knowledge makes the network security assessment more accurate, more precise and suits to the changes of networks.
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