CNN-based flow field feature visualization method

25Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Bin, T., & Yi, L. (2018). CNN-based flow field feature visualization method. International Journal of Performability Engineering, 14(3), 434–444. https://doi.org/10.23940/ijpe.18.03.p4.434444

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free