Information Density quantified by phase in eigen space and mutual information enables virtual sensing that replaces physical sensors with under 3.21% mean error on real Madrid smart-city data.
Dynamic sparse pca: a dimensional reduction method for sensor data in virtual metrology
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Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
Information Density quantified by phase in eigen space and mutual information enables virtual sensing that replaces physical sensors with under 3.21% mean error on real Madrid smart-city data.