Chaotic recurrence characteristics analysis of information entropy of gas-liquid two phase flow image

2011 ◽  
Author(s):  
Wen peng Hong ◽  
Xuan yu Wang ◽  
Fei yu Teng ◽  
Yan Liu
Keyword(s):  
Information Entropy ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Image ◽  
Characteristics Analysis

Classification of natural circulation two-phase flow image patterns based on self-organizing maps of full frame DCT coefficients

2018 ◽  
Vol 335 ◽  
pp. 161-171 ◽  
Author(s):  
Roberto N. de Mesquita ◽  
Leonardo F. Castro ◽  
Walmir M. Torres ◽  
Marcelo da S. Rocha ◽  
Pedro E. Umbehaun ◽  
...  
Keyword(s):  
Two Phase Flow ◽  
Natural Circulation ◽  
Phase Flow ◽  
Two Phase ◽  
Self Organizing Maps ◽  
Flow Image ◽  
Dct Coefficients ◽  
Self Organizing

Pattern Recognition of the Soft Abrasive Flow Based on Wavelet Packet

2012 ◽  
Vol 588-589 ◽  
pp. 756-760
Author(s):  
Ke Dong Hu ◽  
Yi Xuan Ji ◽  
Da Peng Tan
Keyword(s):  
Pattern Recognition ◽  
Flow Pattern ◽  
Information Entropy ◽  
Two Phase Flow ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Phase Flow ◽  
Two Phase ◽  
Monitoring Point ◽  
Solid Liquid

A Pattern Recognition method based on wavelet packet and artificial neural is proposed for solid-liquid two-phase flow characteristic parameters and the non-linear relationship between flow pattern. This method firstly established the physical and dynamic model, then set a monitoring point. To get the optimum wavelet tree and its information entropy, six floors of wavelet packet was used to decompose the collected velocity fluctuation signal. Transported the proper vector which is component by information entropy into Back Propagation neural network to train and identify. The recognition results show that this method can effectively overcome the subjectivity of traditional identification methods. It has good recognition effect, thus provide an effective choice for solid-liquid two-phase flow pattern recognition.

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