Table of Contents
Advances in Artificial Intelligence
Volume 2013, Article ID 241260, 11 pages
http://dx.doi.org/10.1155/2013/241260
Research Article

Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion

1Low and Medium Voltage Division, SIEMENS, Cairo, Egypt
2IEEE Senior Member, Engineering Science Department, Suez University, Suez, Egypt

Received 27 May 2013; Revised 2 September 2013; Accepted 4 September 2013

Academic Editor: Djamel Bouchaffra

Copyright © 2013 Waleed A. Abdulhafiz and Alaa Khamis. 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.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Muhammad Abu Bakr, and Sukhan Lee, “A general framework for data fusion and outlier removal in distributed sensor networks,” 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 91–96, . View at Publisher · View at Google Scholar
  • Alaa Khamis, “Minefield mapping using distributed mobile sensors,” 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), pp. 1–6, . View at Publisher · View at Google Scholar
  • Sanjay Kumar Singh, Neel Mani, and Bharat Singh, “A Framework for Extracting Reliable Information from Unstructured Uncertain Big Data,” Intelligent Decision Technologies 2016, vol. 57, pp. 175–185, 2016. View at Publisher · View at Google Scholar
  • Mohammad M. Alyannezhadi, Ali A. Pouyan, and Vahid Abolghasemi, “An efficient algorithm for multisensory data fusion under uncertainty condition,” Journal of Electrical Systems and Information Technology, 2016. View at Publisher · View at Google Scholar
  • Muhammad Abu Bakr, and Sukhan Lee, “Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency,” Sensors, vol. 17, no. 11, pp. 2472, 2017. View at Publisher · View at Google Scholar
  • Alexander I. Dolgiy, Sergey M. Kovalev, and Anna E. Kolodenkova, “Processing Heterogeneous Diagnostic Information on the Basis of a Hybrid Neural Model of Dempster-Shafer,” Artificial Intelligence, vol. 934, pp. 79–90, 2018. View at Publisher · View at Google Scholar
  • Sukhan Lee, and Muhammad Abu Bakr, “Covariance Projection as a General Framework of Data Fusion and Outlier Removal,” Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, vol. 501, pp. 5–21, 2018. View at Publisher · View at Google Scholar
  • Muhammad Abu Bakr, and Sukhan Lee, “A Framework of Covariance Projection on Constraint Manifold for Data Fusion,” Sensors, vol. 18, no. 5, pp. 1610, 2018. View at Publisher · View at Google Scholar