A method of flow regime identification based on empirical mode decomposition was proposed.
提出了一种基于经验模式分解的气液两相流流型识别方法。
This dissertation focuses on the application of data fusion in two-phase flow regime identification.
将数据融合技术应用于流型辨识研究，对其中的层次、结构等问题进行了深入的探讨。
However this method easily makes a confusion in flow regime identification owing to the influence of gas speed.
由于这一技术受到气泡流速的影响，容易造成流型识别的混淆。
Studies on flow regime identification of two phase flow according pressure fluctuation signal in recent years have been reviewed.
介绍了近年来通过压力（压差）波动过程实现气（汽）液两相流流型识别的最新研究成果。
Flow regime identification in horizontal gas- liquid two- phase flow using differential pressure signal is investigated in this paper.
本文利用差压波动信号对水平管气液两相流流型辨识问题进行了研究。
Entropy is a measure of information in signals. Based on the conception of entropy, two new features-Shannon entropy and Threshold entropy are proposed in flow regime identification.
熵是信号序列信息量的表征，作者在对差压信号的分析中提出了两种基于熵概念的特征——香农熵和阈值熵。
The status of on-line identification for gas-liquid two-phases flow regime using the different pressure fluctuation is reviewed.
介绍了根据压力波动过程实现汽（气）液两相流流型在线识别的最新研究成果；
The status of on-line identification for gas-liquid two-phases flow regime using the different pressure fluctuation is reviewed.
介绍了根据压力波动过程实现汽（气）液两相流流型在线识别的最新研究成果；
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