NDLI: Multi-Sensor Data Fusion using Geometric Transformations for Gas Transmission Pipeline Inspection

Content Provider	IEEE Xplore Digital Library
Author	Oagaro, J.A. Mandayam, S.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ (Oagaro, J.A.; Mandayam, S.)
Abstract	This paper presents a technique that can be used to fuse data from multiple sensors that are employed in nondestructive evaluation (NDE) applications, specifically for the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage (MFL), ultrasonic (UT) and thermal imaging methods. The results presented in this paper indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.
Starting Page	1734
Ending Page	1737
File Size	1458426
Page Count	4
File Format	PDF
ISBN	9781424415403
ISSN	10915281
DOI	10.1109/IMTC.2008.4547324
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-12
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pipelines Inspection Magnetic flux leakage Fuses Gas detectors Sensor fusion Artificial neural networks Testing Ultrasonic imaging Neural networks inverse problems Image processing pattern recognition industrial monitoring
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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