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- W2078040233 abstract "Sensor fusion is a widely used strategy where the system-variables are unobservable. In this work a (back-propagation neural network) BPNN based sensor fusion model has been developed for tool-condition-monitoring (TCM) in flexible manufacturing applications (as a case study) and different performance improvement strategies have been incorporated with notes on generalized applicability to other sensor fusion models. Novel strategies like applicability of signal-level segmentation for temporal registration, effect of number and quality of datasets, usage of cross-correlation indices for sensor-selection, feature-space filtering, outlier-removal, estimation-space filtering etc. have been proposed. Statistical dataset-generation for better sensor-fusion modeling, and applicability of recurrent-type sensor fusion models have been introduced. All the propositions are verified with results obtained from the TCM sensor-fusion model and cost-effective peer-performing sensor collection is suggested." @default.
- W2078040233 created "2016-06-24" @default.
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- W2078040233 date "2006-01-01" @default.
- W2078040233 modified "2023-10-15" @default.
- W2078040233 title "Few strategies to improve sensor fusion" @default.
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- W2078040233 doi "https://doi.org/10.1109/icit.2006.372586" @default.
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