A fusion toolbox for sensor data fusion in industrial recycling

Information from different sensors can be fused in various ways. It is often difficult to choose the most suitable method for solving a fusion problem. In a measurement situation, the measured signal is often corrupted by disturbances (noise, etc.). It is, therefore, meaningless to compare crisp val...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2002-02, Vol.51 (1), p.144-149
Main Authors: Karlsson, B., Jarrhed, J.-O., Wide, P.
Format: Article
Language:English
Subjects:
AC motors
Data fusion
DC motors
Eddy currents
Fuzzy
Fuzzy logic
Fuzzy neural networks
Fuzzy systems
Gaussian
Machine vision
Neural networks
Noise measurement
Recycling
Robot vision systems
Sensor fusion
Sensors
Shape measurement
Statistics
TECHNOLOGY
TEKNIKVETENSKAP
Training data
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Summary:Information from different sensors can be fused in various ways. It is often difficult to choose the most suitable method for solving a fusion problem. In a measurement situation, the measured signal is often corrupted by disturbances (noise, etc.). It is, therefore, meaningless to compare crisp values without the corresponding uncertainty intervals. This paper describes a toolbox including nine different fusing methods. All methods are applied on training data, and the most suitable method is then used for solving the real fusion problem. In the example, fusion is performed on data for classification in an industrial recycling operation. The data is from different vision systems and an eddy current system. The fusion methods included in the toolbox are fuzzy logic with triangular and Gaussian shaped membership functions, fuzzy measures with triangular and Gaussian shapes, Bayes' statistics, artificial neural networks, multivariate analysis (PCA), a knowledge-based system, and a neurofuzzy system.
ISSN:0018-9456
1557-9662
1557-9662
DOI:10.1109/19.989918