Three different data fusion intervals: 15, 30, and 60 min, have been tested.
It is proposed in the paper the interval data fusion procedure intended for determination of an interval to be consistent with maximal number of given initial intervals (not necessary consistent among each other) and to be with maximal likelihood including a value x∗ that can serve as representative of all the given intervals.
The highest ranked value, accepted as a result of the fusion, guarantees improved accuracy and robustness of the interval data fusion procedure outputs.
The interval data fusion procedures can be widely applied in interlaboratory comparisons, prediction of fundamental constant values on the base of different measured values, conformity testing, enhancement of multisensor readings accuracy in sensor networks, etc.
By applying an intelligent rating system, the back-propagation network (BPN) method, and FTA technique, a new approach was created with a powerful learning ability and a flexible data fusion capability, to assist in determining inspection intervals for new aircraft structures, especially in composite structure.
In addition, Interval Type-2 Fuzzy Logic System (IT2FLS) is utilized for data fusion and building a model for spatial masking in the Riesz wavelet domain.
Couple their sensor capabilities with analytic data fusion software and you have real-time dynamic physiological data.
In [51], various decision fusion, data fusion, and source fusion techniques have been studied.
Data fusion can be implemented using two different approaches: group fusion and similarity fusion.
There are mainly two fusion rules of CSS: data fusion rule and decision fusion rule.
