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Mathematical Problems in Engineering
Volume 2014, Article ID 124156, 13 pages
http://dx.doi.org/10.1155/2014/124156
Research Article

A Comprehensive Decision-Making Approach Based on Hierarchical Attribute Model for Information Fusion Algorithms’ Performance Evaluation

Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an 710072, China

Received 14 October 2014; Revised 29 November 2014; Accepted 29 November 2014; Published 29 December 2014

Academic Editor: Hui Zhang

Copyright © 2014 Lianhui Li and Rong Mo. 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. E. A. Lee, “Computing foundations and practice for cyber-physical systems: a preliminary report,” Tech. Rep. UCB/EECS-2007-72, University of California, Berkeley, Calif, USA, 2007. View at Google Scholar
  2. J. Lin, S. Sedigh, and A. Miller, “A general framework for quantitative modeling of dependability in cyber-physical systems: a proposal for doctoral research,” in Proceedings of the 33rd Annual IEEE International Computer Software and Applications Conference (COMPSAC '09), pp. 668–671, Seattle, Wash, USA, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Sastry, “Networked embedded systems: from sensor webs to cyber-physical systems,” in Hybrid Systems: Computation and Control, vol. 4416 of Lecture Notes in Computer Science, p. 1, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  4. M. Branicky, “CPS initiative overview,” in Proceedings of the IEEE/RSJ International Conference on Robotics and Cyber-Physical Systems, IEEE, Washington, DC, USA, 2008.
  5. R. Rajkumar, I. Lee, L. Sha, and J. Stankovic, “Cyber-physical systems: the next computing revolution,” in Proceedings of the 47th Design Automation Conference (DAC '10), pp. 731–736, ACM, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Zhang, M. D. Ilíc, and O. Tonguz, “Application of support vector machine classification to enhanced protection relay logic in electric power grids,” in Proceedings of the Large Engineering Systems Conference on Power Engineering, pp. 31–38, IEEE, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Tan, S. Goddard, and L. C. Pérez, “A prototype architecture for cyber-physical systems,” ACM SIGBED Review, vol. 5, no. 1, pp. 1–2, 2008. View at Publisher · View at Google Scholar
  8. T. W. Hnat, I. T. Sookoor, P. Hooimeijer et al., “Macrolab: a vector-based macroprogramming framework for cyber-physical systems,” in Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 225–238, ACM, 2008.
  9. M. D. de Amorim, A. Ziviani, Y. Viniotis, and L. Tassiulas, “Practical aspects of mobility in wireless self-organizing networks,” IEEE Wireless Communications, vol. 15, no. 6, pp. 6–7, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. K. D. Kang and S. H. Son, “Real-time data services for cyber physical systems,” in Proceedings of the 28th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS '08), pp. 483–488, Beijing, China, June 2008. View at Publisher · View at Google Scholar
  11. T. Abdelzaher, “Research challenges in distributed cyber-physical systems,” in Proceedings of the IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC '08), p. 5, Shanghai, China, December 2008. View at Publisher · View at Google Scholar
  12. T. W. Hnat, T. I. Sookoor, P. Hooimeijer et al., “MacroLab: a vector-based macroprogramming framework for cyber-physical systems,” in Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys '08), pp. 225–238, ACM, New York, NY, USA, 2008. View at Publisher · View at Google Scholar
  13. D. T. Larose, “k-nearest neighbor algorithm,” in Discovering Knowledge in Data: An Introduction to Data Mining, p. 106, 2005. View at Google Scholar
  14. M. Hayat and A. Khan, “Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou's PseAAC,” Protein & Peptide Letters, vol. 19, no. 4, pp. 411–421, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Zhang and J. Wang, “Combined feedback-feedforward tracking control for networked control systems with probabilistic delays,” Journal of the Franklin Institute, vol. 351, no. 6, pp. 3477–3489, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. N. Tomašev, M. Radovanović, D. Mladenić, and M. Ivanović, “Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification,” International Journal of Machine Learning and Cybernetics, vol. 5, no. 3, pp. 445–458, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Zhang, X. Zhang, and J. Wang, “Robust gain-scheduling energy-to-peak control of vehicle lateral dynamics stabilisation,” Vehicle System Dynamics, vol. 52, no. 3, pp. 309–340, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Ran and J.-H. Zhang, “Distributed system data fusion algorithm based on track fuzzy membership,” Signal Processing, no. 2, pp. 226–229, 2011. View at Google Scholar
  19. A. Moosavian, H. Ahmadi, A. Tabatabaeefar, and M. Khazaee, “Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing,” Shock and Vibration, vol. 20, no. 2, pp. 263–272, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Zhang, Y. Shi, and J. Wang, “On energy-to-peak filtering for nonuniformly sampled nonlinear systems: a markovian jump system approach,” IEEE Transactions on Fuzzy Systems, vol. 22, no. 1, pp. 212–222, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Zhang and J. Wang, “State estimation of discrete-time Takagi-Sugeno fuzzy systems in a network environment,” IEEE Transactions on Cybernetics, 2014. View at Publisher · View at Google Scholar
  22. X.-F. Huang and Q.-Z. Wu, “An algorithm of weighted covariance for centralized asynchronous fusion based on Kalman,” in Proceedings of the International Conference on Industrial Control and Electronics Engineering (ICICEE '12), pp. 1554–1557, IEEE, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Zhang, X. Liu, J. Wang, and H. R. Karimi, “Robust H sliding mode control with pole placement for a fluid power electrohydraulic actuator (EHA) system,” The International Journal of Advanced Manufacturing Technology, vol. 73, no. 5–8, pp. 1095–1104, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Zhang, Y. Shi, and A. Saadat Mehr, “Robust static output feedback control and remote PID design for networked motor systems,” IEEE Transactions on Industrial Electronics, vol. 58, no. 12, pp. 5396–5405, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. R. L. Rothrock and O. E. Drummond, “Performance metrics for multiple-sensor multiple-target tracking,” in Signal and Data Processing of Small Targets, vol. 4048 of Proceedings of SPIE, pp. 521–531, 2000. View at Publisher · View at Google Scholar
  26. W. H. Qiu, Management Decision Entropy and Its Applications, China Electric Power Press, Beijing, China, 2011.
  27. G. Chi, G. Li, and Y. Cheng, “The human all-round development evaluation model based on AHP and standard deviation and empirical study,” Chinese Journal of Management, vol. 2, article 022, 2010. View at Google Scholar
  28. D. Diakoulaki, G. Mavrotas, and L. Papayannakis, “Determining objective weights in multiple criteria problems: the critic method,” Computers and Operations Research, vol. 22, no. 7, pp. 763–770, 1995. View at Publisher · View at Google Scholar · View at Scopus
  29. C. L. Hwang and K. Yoon, Multiple Attribute Decision Making, Springer, Berlin, Germany, 1981. View at MathSciNet
  30. M. Zhao, W.-H. Qiu, and B.-S. Liu, “Relative entropy evaluation method for multiple attribute decision making,” Control and Decision, vol. 25, no. 7, pp. 1098–1100, 2010. View at Google Scholar
  31. T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley-Interscience, Hoboken, NJ, USA, 2nd edition, 2006. View at MathSciNet