By Topic

The dynamic classification fusion algorithm using triangular fuzzy clustering based on multi-sensor information

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Fu Guo Qiang ; Educational Technology Information Center, Shenzhen Polytechnic, Shenzhen, China

Due to various complex environmental impact, coupled with unpredictable equipment failure, the data collected by a single sensor may be untrue inaccurate. Simple multisensor data fusion does also not accurately reflect the true state of the monitored object. An algorithm using triangular fuzzy variables as the distance of cluster analysis is developed to classify all data to some clusters according strong correlation after sensor data are cleanup on Grubbs rule. Then data fusions are done inside each of clusters using weighted data fusion. The algorithm takes advantage of integrations of data from different time and space to reduce the rate of false positives and rate of missing situations. The algorithm makes it possible to capture data classes with the strong correlation matching to the event dynamically and accurately, and improve the level of intelligence of the sensor network.

Published in:

Computer Science & Education (ICCSE), 2012 7th International Conference on

Date of Conference:

14-17 July 2012