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Wireless Communications and Mobile Computing
Volume 2018, Article ID 5795037, 13 pages
https://doi.org/10.1155/2018/5795037
Research Article

SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education, 1600 Gajeon-ri, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31253, Republic of Korea

Correspondence should be addressed to Chang-Heon Oh; rk.ca.hcetaerok@hohc

Received 28 July 2017; Accepted 18 December 2017; Published 29 January 2018

Academic Editor: Yong Ren

Copyright © 2018 Hyun-Jun Shin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Taisch, B. Stahl, and G. Tavola, “ICT in manufacturing: trends and challenges for 2020—an European view,” in Industrial Informatics (INDIN), Proceedings of the IEEE International Conference on Industrial Informatics, pp. 941–946, 2012. View at Google Scholar
  2. J. Park, “Technology and issue on embodiment of smart factory in small-medium manufacturing business,” The Journal of Korean Institute of Communications and Information Sciences, vol. 40, no. 12, pp. 2491–2502, 2015. View at Publisher · View at Google Scholar
  3. H. Syed, P. Athul, K. Andreas, P. Apostolds, and J. S. Song, “Recent trends in standards related to the internet of things and machine-to-machine communications,” Journal of Information and Communication Convergence Engineering, vol. 12, pp. 228–236, 2014. View at Google Scholar
  4. W. Guo, Y. Zhang, and L. Li, “The integration of CPS, CPSS, and ITS: a focus on data,” Tsinghua Science and Technology, vol. 20, no. 4, pp. 327–335, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Jin, B. A. Weiss, D. Siegel, J. Lee, and J. Ni, “Present status and future growth of advanced maintenance technology and strategy in US manufacturing,” International Journal of Prognostics and Health Management, vol. 7, 2016. View at Google Scholar
  6. M. Qiu and E. H.-M. Sha, “Energy-aware online algorithm to satisfy sampling rates with guaranteed probability for sensor applications,” in Proceedings of the High Performance Computing and Communications, pp. 156–167.
  7. F. Tao, Y. Zuo, L. D. Xu, and L. Zhang, “IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1547–1557, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. Q. Jing, A. V. Vasilakos, J. Wan, J. Lu, and D. Qiu, “Security of the internet of things: perspectives and challenges,” Wireless Networks, vol. 20, no. 8, pp. 2481–2501, 2014. View at Publisher · View at Google Scholar
  9. X. Xu, “From cloud computing to cloud manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 28, no. 1, pp. 75–86, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. Q. Liu, J. Wan, and K. Zhou, “Cloud manufacturing service system for industrial-cluster-oriented application,” Journal of Internet Technology, vol. 15, no. 3, pp. 373–380, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Wan, D. Zhang, Y. Sun, K. Lin, C. Zou, and H. Cai, “VCMIA: a novel architecture for integrating vehicular cyber-physical systems and mobile cloud computing,” Mobile Networks and Applications, vol. 19, no. 2, pp. 153–160, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Zhang, H. Lim, and H. J. Lee, “The design of an efficient proxy-based framework for mobile cloud computing,” Journal of Information and Communication Convergence Engineering, vol. 13, no. 1, pp. 15–20, 2015. View at Publisher · View at Google Scholar
  13. Z. Yang, M. Awasthi, M. Ghosh, and N. Mi, “A fresh perspective on total cost of ownership models for flash storage in datacenters,” in Proceedings of the 8th IEEE International Conference on Cloud Computing Technology and Science, pp. 245–252, December 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Bhimani, Z. Yang, M. Leeser, and N. Mi, “Accelerating big data applications using lightweight virtualization framework on enterprise cloud,” in Proceedings of the IEEE High-Performance Extreme Computing Conference, pp. 1–7, September 2017. View at Publisher · View at Google Scholar
  15. H. Gao, Z. Yang, J. Bhimani et al., “AutoPath: harnessing parallel execution paths for efficient resource allocation in multi-stage big data frameworks,” in Proceedings of the 26th International Conference on Computer Communication and Networks, pp. 1–9, July 2017. View at Publisher · View at Google Scholar
  16. F. Soliman and M. A. Youssef, “Internet-based E-commerce and its impact on manufacturing and business operations,” Industrial Management & Data Systems, vol. 103, no. 8-9, pp. 546–552, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. Gartner’s 2015 Hype Cycle for Emerging Technologies Identifies the Computing Innovations that Organizations Should Monitor, http://www.gartner.com/newsroom/id/3114217.
  18. H. S. Kang, J. Y. Lee, S. Choi et al., “Smart manufacturing: past research, present findings, and future directions,” International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 13, pp. 111–128, 2016. View at Google Scholar
  19. J. Lee, C. Jin, and B. Bagheri, “Cyber physical systems for predictive production systems,” Production Engineering Research and Development, vol. 11, no. 2, pp. 155–165, 2017. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Dworschak and H. Zaiser, “Competences for cyber-physical systems in manufacturing-first findings and scenarios,” Procedia CIRP, vol. 25, pp. 345–350, 2014. View at Google Scholar
  21. L. Monostori, “Cyber-physical production systems: roots, expectations and R&D challenges,” Procedia CIRP, vol. 17, pp. 9–13, 2014. View at Google Scholar
  22. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  23. M. D. Odom and R. Sharda, “A neural network model for bankruptcy prediction,” in Proceedings of the International Joint Conference on Neural Prediction Networks (IJCNN '90), pp. 163–168, 1990. View at Scopus
  24. J. C. Beard, C. Epstein, and R. D. Chamberlain, “Online automated reliability classification of queueing models for streaming processing using support vector machines,” in Proceedings of the International Conference on Parallel and Distributed Computing, pp. 325–328, 2015.
  25. C. J. Lee, S. O. Song, and E. S. Yoon, “The monitoring of chemical process using the support vector machine,” Korean Chemical Engineering Research, vol. 42, pp. 538–544, 2004. View at Google Scholar
  26. Y. Oh, H. S. Park, A. Yoo et al., “A product quality prediction model using real-time process monitoring in manufacturing supply chain,” Journal of Korean Institute of Industrial Engineers, vol. 39, no. 4, pp. 271–277, 2013. View at Publisher · View at Google Scholar
  27. S. Neumeyer, K. Exner, S. Kind, H. Hayka, and R. Stark, “Virtual prototyping and validation of cpps within a new software framework,” Computation, vol. 5, no. 1, article 10, 2017. View at Publisher · View at Google Scholar
  28. B. Bagheri, S. Yang, H.-A. Kao, and J. Lee, “Cyber-physical systems architecture for self-aware machines in Industry 4.0 environment,” International Federation of Automatic Control, vol. 48, no. 3, pp. 1622–1627, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Marques, C. Agostinho, R. Poler, G. Zacharewicz, and R. Jardim-Goncalves, “An architecture to support responsive production in manufacturing companies,” in Proceedings of the 8th IEEE International Conference on Intelligent Systems, pp. 40–46, September 2016. View at Publisher · View at Google Scholar · View at Scopus
  30. U. N. Bhat, An Introduction to Queueing Theory: Modeling and Analysis in Applications, Springer, New York, NY, USA, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  31. MathWorks, SimEvents User's Guide, https://kr.mathworks.com/help/pdf_doc/simevents/simevents_ug.pdf.
  32. M. A. Gray, “Discrete event simulation: a review of SimEvents,” Computing in Science & Engineering, vol. 9, pp. 62–66, 2007. View at Google Scholar
  33. A. A. Alsebae, M. S. Leeson, and R. J. Green, “SimEvents-based modeling and simulation study of stop-and-wait protocol,” in Proceedings of the 5th International Conference on Modelling, Identification and Control, pp. 239–244, September 2013. View at Scopus
  34. N. Galaske, D. Strang, and R. Anderl, “Response behavior model for process deviations in cyber-physical production systems,” in Proceedings of the World Congress on Engineering and Computer Science, pp. 443–455, 2015.
  35. S. C. Lee, T. G. Jeon, H. S. Hwang, and C. S. Kim, “Design and implementation of wireless sensor based-monitoring system for smart factory,” in Proceedings of the International Conference on Computational Science and Its Applications, pp. 584–592, 2007.
  36. J. Jang and E. J. Kim, “Survey on industrial wireless network technologies for smart factory,” Journal of Platform Technology, vol. 4, pp. 3–10, 2016. View at Google Scholar