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CNN-based Flow Field Feature Visualization Method

Volume 14, Number 3, March 2018, pp. 434-444
DOI: 10.23940/ijpe.18.03.p4.434444

Tang Bina and Li Yib

aCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin, 150001, China
bCollege of Computer Science & Technology, Harbin Engineering University, Harbin, 150001, China

(Submitted on December 6, 2017; Revised on January 24, 2018; Accepted on February 15, 2018)


Abstract:

The feature-based visualization method can separate important areas for users from flow field data, which can better highlight the feature structure. However, most of the current feature extraction methods are only applicable to single typical features, and they need complex mathematical analysis. Based on the above reasons, this paper proposes a universal feature visualization method, recognizes demand in the region of flow data, shows the characteristics of structure protruding from the global visual effect in the design of a multi-dimension parallel convolution kernel that contains the recognition model, and further puts forward the method of feature visualization based on a convolutional neural network. Compared with the classical three level BP neural network model, our model gets a high accuracy rate. We verify the effectiveness of the method and solve the problem of insufficient expansion of existing methods.

 

References: 15

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  14. F. Zhao, C. S. Wu, S. F. Huang and Z. R. Zhang, “Route map on virtual tank,” Journal of Ship Mechanics, 2014, 18(8):924-932P.
  15. Z. Y. Zheng and S. Y. Gu, “Tensorflow: Actual Google deep learning framework,” Electronic Industry Press, 2017:144-146P.

 

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