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- W2091567885 abstract "We present a systematic approach for fusion of multi-sensory nondestructive testing data. Our data set consists of impact-echo, ultrasonic pulse echo and ground penetrating radar data collected on a large-scale concrete specimen with built-in honeycombing defects. From each data set, the most significant signatures of honeycombs were extracted in the form of features. We applied two simple data fusion algorithms to the data: Dempster’s rule of combination and the Hadamard product. The performance of the fusion rules versus the single-sensor testing was evaluated. The fusion rules exhibit a slight improvement of false alarm rate over the best single sensor." @default.
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- W2091567885 date "2015-04-01" @default.
- W2091567885 modified "2023-10-04" @default.
- W2091567885 title "Multi sensor data fusion approach for automatic honeycomb detection in concrete" @default.
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- W2091567885 doi "https://doi.org/10.1016/j.ndteint.2015.01.003" @default.
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