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A method analysis for hail cloudy prediction based on CNN

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Abstract

A hailstorm forecast method is proposed in the paper. Edge Detect Cellular Neural Network (EDCNN) method is used to extract the edge of cloud radar images. We have detected the texture of the cloud images. Then the texture image has been processed with wavelet transform. The hail data information from the image has been found. We will get approximate detail coefficients, level detail coefficients, vertical detail coefficients, diagonal detail coefficients, and reconstructed coefficients. Construct hail cloud life feature vector matrix to explain the problem. Found the corresponding rules through the five coefficients. At last, through the simulation experiment achieve the purpose of hail forecast. A feature vector of hail cloud life has been constructed, the rules of hail had been found from the feature vector. And Compared with the contour variance of the hail cloud inner and outer, we will find this paper puts forward the method is more effective. The conclusion is rationality according the simulation experiment verifies.

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References

  1. Chuang, Z., Jiannan, C., Chaohui, Z.: Novel color edge detector derived from CNN mode. Comp. Eng. Appl. 44(2), 17–19 (2008)

    Google Scholar 

  2. Chua, L.O., Roska, T.: Cellular neural networks and visual computing. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  3. Bu-Qing, C.A.O., Jian-Xun, L.I.U., Bin, W.: Currency characteristic extraction and identification research based on PCA and BP neural network. J. Conv. Inform. Technol. 7(2), 38–44 (2012)

    Google Scholar 

  4. Zhou, Y., Zhu, Y.: Identification of a hail cloud clustering method similar to the evolution of research. J. Anhui Agri. Sci. 35(30), 9637–9642 (2007)

    MathSciNet  Google Scholar 

  5. Li Guodong, Xu, Wenxia, Wang Xu: Efficient hail cloudy identification scheme base on CNN. J. Conv. Inform. Technol. 7(22), 669–676 (2012)

    Google Scholar 

  6. Guodong Li, Wenxia Xu. (2011) Process Weather Image By Some CNN, Int. Conf. Green Energy Environ. Sustain. Dev., 1721–1726

  7. Guodong, Li, Wang., Xu: Hail identification analysis from Radar image by CNN. Appl. Mech. Mat. 155, 841–845 (2012)

    Google Scholar 

  8. CHUALO.: Chaos CNN: aversionof complexity, Int. J. Bifurc. 7, 2219–2425 (1997)

  9. M. Lei, L. Min (2004) Robustness design of templates of CNN for detecting inner corners of objects in gray-scale images, in Int. Conf. on Communications, Circuit and Systems, Vol. II: 1090–1093. IEEE Press

  10. Bálya, D., Roska, B., Roska, T., Werblin, F.S.: A CNN framework for modeling parallel processing in a mammalian retina. Int. J. Circuit Theory Appl. (CTA) 30, 363–393 (2002)

    Article  MATH  Google Scholar 

  11. Dan, Yang: MATLAB image processing[M]. Tsinghua University Press, Beijing (2013)

    Google Scholar 

  12. Dongjian, H.: Digital image processing [M]. Xi’an: Xi’an Electronic and Science University press (2004)

  13. Defeng, Zhang: MATLAB digital image processing [M]. Mechanical Industry Press, Beijing (2009)

    Google Scholar 

  14. Zhaoqing, Zhang: Hail cloud based on catastrophe theory prediction research [D]. Tianjin University, Tianjin (2009)

    Google Scholar 

  15. Rekeczky C.: CNN architectures for constrained diffusion based locally adaptive image processing. Int. J. Circuit Theory Appl. (2002)

  16. Zhao, J., Wang, H., Yu, D.: A new approach for edge detection of noisy image based on CNN. Int. J. Circ. Theory Appl. (2003)

  17. Aizenberg, N.N.,Aizenberg, I.N.:CNN-like networks based on multi-valued and universal binary neurons: Learning and application to image processing. In: Proceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications, Rome (1994)

  18. Zhao J.,Yu D.: A new approach for ME multilevel image restoration based on CNN. Int. J. Circ. Theory Appl. (1999)

  19. Guiming, H. Wenyan, J., Min, Z.: A research of segmentation of video moving objects based on cellular neural network. Comp. Modern. (2007)

  20. Destri. G., Marenzoni, P.: Cellular neural networks as a gen-eral massively parallel computational paradigm. Int. J. Circ. Theory Appl. (1996)

  21. Guozhen, B., Jiehao, Y.: Self tuning of PID parameters based on Improved Fuzzy Neural Network[J]. Appl. Res. Comp. (2016)

  22. Fang, X.: Dynamics study on two-dimensional neural networks. Appl. Res. Comp. (2011)

  23. Feng, Q., Sheng, L., Zhang, W.: A new method of image restoration based on cellular neural network. J. Image Graph. (2009)

  24. Sun, X., Pan, G.: Study on the moving target tracking algorithm based on cellular neural network. Comp. Dev. Appl. (2008)

  25. Zhang, C., Zhang, X.: A sufficient condition for the stability of cellular neural networks. J. Beijing Union Univ. Nat. Sci. (2006)

  26. Zhang, X., Zhu, Y., Wang.: Performance analysis and application of super chaotic synchronization system of cellular neural network. Comp Appl Software 2009

  27. Dogaru, R., et al.: Pyramidal cells: A novel class of adaptive coupling cells and their application for cellular neural networks. Fundamental Theory and Applications, IEEE Transactions on Circuits and Systems-I (1998)

  28. Jing, G., Dianxun, S.: The faster higher-order cellular automaton for hyper-parallel undistorted data compression. J. Comp. Sci. Technol. (2000)

  29. Jing, G., Dianxun, S.: A new parallel-by-cell approach to undistorted data compression based on cellular automaton and genetic algorithm. J. Comp. Sci. Technol. (1999)

  30. Shuai, D.X.,Gu, J.: The faster high-order cellular automaton for hyper-parallel undistorted data compression. J. Comp. Sci. Technol. (2000)

  31. Xuan, J., Luo, X., Zhang, G., Lu, J., Xu, Z.: Uncertainty analysis for the keyword system of web events. IEEE Trans. Syst. Man Cyber. Syst. 46(6), 829–842 (2016)

    Article  Google Scholar 

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Acknowledgments

The work presented is supported by the financial supports given by research outlay item, The National Natural Science Fund China (No. 11461063), National Social Science Fund China (No. 14BTJ021), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 13YJAZH040). The Xinjiang Uygur Autonomous Region university research project (The research on hail forecast model design in Aksu region), The Fund of the Key Research Center of Humanities and Social Sciences in the general Colleges and Universities of Xin Jiang Uygur Autonomous Region (Grant Number: 050315B03). An Empirical Study on the Leading Role of the Financial Industry and the Logistics Industry Linkage to the New Urbanization in Xinjiang (Project No. XJUFE2016K012) Financial Services Research of Foreign Direct Investment of Xinjiang Enterprise (Project No. XJUFE2016K046).

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Authors and Affiliations

  1. Department of Applied Mathematics, Xinjiang University of Finance and Economics, 830012, Xinjiang, People’s Republic of China

    Li Guodong, Yang Bing & Buhailiqiemu Awudong

  2. Xinjiang Weather Modification Office, Xinjiang, Urumqi, 830002, People’s Republic of China

    Xu Wenxia

  3. School of Computer Science Technology and Information Engineering, Harbin Normal University, Harbin, Heilongjiang, 150025, People’s Republic of China

    Chang Xiaojuan

  4. Research Center of Xinjiang Social and Economic Statistics of Xinjiang, University Of Finance and Economics, Xinjiang, Urumqi, 830012, People’s Republic of China

    Li Guodong

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  1. Li Guodong
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  2. Xu Wenxia
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  3. Yang Bing
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  4. Buhailiqiemu Awudong
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  5. Chang Xiaojuan
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Correspondence to Chang Xiaojuan.

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Guodong, L., Wenxia, X., Bing, Y. et al. A method analysis for hail cloudy prediction based on CNN. Cluster Comput 19, 2015–2026 (2016). https://doi.org/10.1007/s10586-016-0632-3

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