Research of Selective and Incremental Information Fusion Method Based on Bayesian Network

Li Wei Zhang; Jing Zhang; Yan Sun

doi:10.4028/www.scientific.net/AMM.397-400.2060

Paper Titles

A Polling Control Protocol with Energy-Saving for Wireless Sensor Network
p.2042

RSSI Based Monte-Carlo Localization Boxed Algorithm
p.2048

The Design of MMS and SMS Student Service System Based on PC
p.2052

Design and Actualization of Intrinsically Safe Voice Warning System for Mine
p.2056

Research of Selective and Incremental Information Fusion Method Based on Bayesian Network
p.2060

Investigation of the Impact of BIM&IPD on Change Orders Using Bayesian Network Method
p.2064

Study on the Deposition Methods of Institutional Repositories Based on Sword Protocol
p.2069

Active Queue Management Based on State Variable Feedback Control
p.2077

Synchronization of Two Different Complex Dynamical Networks with Interconnection
p.2081

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 397-400Research of Selective and Incremental Information...

Research of Selective and Incremental Information Fusion Method Based on Bayesian Network

Abstract:

This paper presents a selective incremental information fusion method based on Bayesian network, so that the fusion algorithm can actively select the most relevant information and decision-making, and can make the fusion model to adapt to the dynamic changes in the external environment, and sensor information selection, fusion, decision-making integrated in the framework of Bayesian network . The experimental results show that this method is better than the traditional method.

You might also be interested in these eBooks

Advanced Design and Manufacturing Technology III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 397-400)

Pages:

2060-2063

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.397-400.2060

Citation:

Cite this paper

Online since:

September 2013

Authors:

Li Wei Zhang, Jing Zhang, Yan Sun

Keywords:

Bayesian Network (BN), Incremental Learning, Information Fusion

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] YM. Zhang, Q. Ji. Active and dynamic information fusion for multisensor systems withdynamic Bayesian networks [J]. IEEE Transactions on Systems Man and Cybernetics-Part B: Cybernetics. 2006, 36(2): 467-472.

DOI: 10.1109/tsmcb.2005.859081

Google Scholar

[2] H. Wang, K. Yao, G. Pottie, and D. Estrin. Entropy-based sensor selection heuristic for target localization [C]. 3rd International Symposium on Information Processing in Sensor Networks. 2004, 36-45.

DOI: 10.1145/984622.984628

Google Scholar

[3] YM. Zhang, Q. Ji. Efficient Sensor Selection for Active Information Fusion [J]. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics. 2010, 40(3): 719-728.

DOI: 10.1109/tsmcb.2009.2021272

Google Scholar

[4] WH. Liao, Q. Ji. Efficient Active Fusion for Decision-making via VOI Approximation[C]. 21st AAAI Conference on Artificial Intelligence (AAAI-2006). 2006, 1180-1185.

Google Scholar

[5] C. Kreucher, K. Kastella, and A. O. Hero. Sensor management using an active sensing approach [J]. Signal Process. 2005, 85(3): 607-624.

DOI: 10.1016/j.sigpro.2004.11.004

Google Scholar

[6] B. Guo, M. S. Nixon. Gait feature subset selection by mutual information [J]. IEEE Transactions on Systems, Man, and Cybernetics -Part A: Systems and Humans. 1997, 39(1): 36-46, (2009).

DOI: 10.1109/tsmca.2008.2007977

Google Scholar

[7] Pearl, J. Probabilistic Reasoning in Intelligent Systems[M]. Morgan Kaufmann, SanMateo, CA, (1988).

Google Scholar