SenDaL trains a router to switch between a linear and a deep calibration model, achieving deep-model accuracy at near-linear-model speed on low-cost fine-dust sensors.
Soft sensor validation for monitor- ing and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders,
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SenDaL: An Effective and Efficient Calibration Framework of Low-Cost Sensors for Daily Life
SenDaL trains a router to switch between a linear and a deep calibration model, achieving deep-model accuracy at near-linear-model speed on low-cost fine-dust sensors.