Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018
No.8 August 2018
No.8 August 2018
No.9 September 2018
No.9 September 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

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

  1. J. Ebling, G. Scheuermann, “Clifford Fourier transform on vector fields,” IEEE Transactions on Visualization & Computer Graphics, 2005, 11(4):469-479P.
  2. S. C. Huang, X. Y. Fang, J. Zhou, “Image Local Fuzzy Measurement Based on BP Neural Network,” Journal of Image and Graphics, 2015, 20(1):20-28P.
  3. S. K. Li, X. Cai, W. K Wang, P. Wang and H. H. Wang, “Large-Scale Flow Field Scientific Visualization,” National Defense Industry Press, 2013:158-162P.
  4. Y. Lin, C. Wang, J. X. Wang, Z. Dou, “A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks,” Sensors, 2016, 16(10): 1-22.
  5. Y. Lin, X. Zhu and Z. Zheng, “The individual identification method of wireless device based on dimensionality reduction and machine learning,” The Journal of Supercomputing, 2017: 1-18.
  6. X. Liu, W. Zhang, N. Zheng, “Flow Feature Extraction Based on Entropy and Clifford Algebra,” Image and Graphics. Springer International Publishing, 2015:292-300P.
  7. H. Obermaier, R. Peikert, “Feature-Based Visualization of Multifields,” Scientific Visualization. Springer London, 2014:189-196.
  8. P. Skraba, B. Wang and G. Chen, “2D Vector Field Simplification Based on Robustness,” Visualization Symposium. IEEE, 2014:49-56P.
  9. J. T. Springenberg, A. Dosovitskiy and T. Brox, “Striving for Simplicity: The All Convolutional Net,” Eprint Arxiv, 2014:1-14P.
  10. M. S. Tang, “3D data field visualization,” Tsinghua University Press, 1999:174-176P.
  11. Y. Wu and W. G. Qiu, “Face Recognition Based on Improved Depth Convolution Neural Network,” Computer Engineering and Design, 2017, 38(8):2246-2250 P.
  12. L. Xu, T. Y. Lee and H. W. Shen, “An information-theoretic framework for flow visualization,” IEEE Transactions on Visualization & Computer Graphics, 2010, 16(6):1216-1224P.
  13. H. X. Xu, S. K. Li, L. Zeng, “A Novel Intelligent Feature Detection and Recognition Method of Fluid Fields,” Computer Engineering & Science, 2009, 31(5):27-30.
  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.

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-04.pdf)IJPE-2018-03-04.pdf[CNN-based Flow Field Feature Visualization Method]1235 Kb
 

CURRENT ISSUE

Prev Next

Program Disturb Research and Error Avoidance Algorithm Design of 3D-TLC NAND Flash Memory

Xiaoshan Yang, Ligu Zhu, Qicong Zhang, Meng Zhang, Fei Wu, and Wei Zhang

Read more

Data Complexity Analysis for Software Defect Detection

Ying Ma, Yichang Li, Junwen Lu, Peng Sun, Yu Sun, and Xiatian Zhu

Read more

Fuzzy AHP-based Comprehensive Evaluation for Smart Grid in Energy Internet Systems

Ying Ma, Yichang Li, Shunzhi Zhu, Nan Qin, Guang Zhao, and Chao Huang

Read more

User Group-based Method for Cold-Start Recommendation

Jing He, Shuo Yuan, Yi Xiang, and Wei Zhou

Read more

Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos

Chunlong Xie, Zhiqian Zhang, Chunsheng Wang, and Zhengqing Liu

Read more

Image Encryption Method based on Hill Matrix and Dynamic DNA Encoding

Xuncai Zhang, Zheng Zhou, Yishan Liu, Guangzhao Cui, Ying Niu, and Yanfeng Wang

Read more

Video Indexing and Retrieval based on Key Frame Extraction

Wenshi Wang, Zhangqin Huang, Weidong Wang, Shuo Zhang, and Rui Tian

Read more

Modeling Approach Combining Performance and Reliability for Mobile Cloud System

Han Xu, Haiqing Wang, Liang Luo, Xiwei Qiu, Sa Meng, and Xun Guo

Read more

Understanding the Similarity of Log Revision Behaviors in Open Source Software

Xu Niu, Shanshan Li, Zhouyang Jia, Shulin Zhou, Wang Li, and Xiangke Liao

Read more

Learning to Predict Price based on E-commerce Online Auction Machine

Xiaohui Li, Hongbin Dong, Xiaowei Wang, and Shuang Han

Read more

Rate Control Algorithm for Multiview Video Coding based on Human Visual Characteristics

Tao Yan, In-Ho Ra, Qiuwen Zhang, Hui Wen, Hang Xu, and Shuqing Chen

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com