Abstract

In this study, a cloud computing model was established and research and development of food technology network course education based on cloud computing is a kind of practice and the accumulation of the technology, which can develop a perfect function, with good interactive performance, can solve various problems of food technology. And the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the food science student’s needs. The application of network technology based on cloud computing, can avoid the weaknesses of the traditional teaching methods, enrich the teaching contents, improve teaching effect, perfect assess system and also play an important role in training food science students' the team spirit.

Keywords:

Food technology, interactive performance, network cloud computing,

References

1. Cordero-Grande, L., G. Vegas-Sánchez-Ferrero, P. Casaseca-de-la-Higuera, J.A. San-Román-Calvar, A. Revilla-Orodea, M. Martín-Fernández and C. Alberola-López, 2011. Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model. Med. Image Anal., 2011(15): 283-301.
   CrossRef    PMid:21354361    Direct Link
2. Geng, Y., J. He and K. Pahlavan, 2013a. Modeling the effect of human body on TOA based indoor human tracking. Int. J. Wirel. Inform. Netw., 20(4): 306-317.
   CrossRef    Direct Link
3. Geng, Y., J. He, H. Deng and K. Pahlavan, 2013b. Modeling the effect of human body on TOA ranging for indoor human tracking with wrist mounted sensor. Proceeding of the 16th International Symposium on Wireless Personal Network Communications (WPMC, 2013). Atlantic City, NJ.
   Direct Link
4. Li, S., Y. Geng, J. He and K. Pahlavan, 2012. Analysis of three-dimensional maximum likelihood algorithm for capsule endoscopy localization. Proceeding of the 5th International Conference on Biomedical Engineering and Informatics (BMEI, 2012). Chongqing, China, pp: 721-725.
   CrossRef    Direct Link
5. Nguyen, M.X., Q.M. Le, V. Pham, T. Tran and B.H. Le, 2011. Multi-scale sparse representation for robust face recognition. Proceeding of the Conference on Knowledge and Systems Engineering (KSE), pp: 195-199.
   CrossRef    Direct Link
6. Yikui, Z., G. Junying and L. Jingwen, 2011. Study of occluded robust face recognition approach based on homotopy algorithm and color information fusion. Signal Process., 27(11): 1762-1768.
   Direct Link
7. Zhang, Q. and B. Li, 2010. Discriminative K-SVD for dictionary learning in face recognition. Proceeding of the Conference on Computer Vision and Pattern Recognition (CVPR, 2010), pp: 2691-2698.
   CrossRef    Direct Link

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.