plates. -The title particles are prepared by a two-step method based on the sol-gel process. In the first step Ph(MeO)3Si is hydrolyzed under acidic conditions (aq. HNO3, 60°C, 0.5-4.0 min). In the second step the condensation of the silane progresses under basic conditions (NH4OH, 1 h). The samples are characterized by SEM, TEM, and 29 Si NMR spectroscopy. The particle size and hollow diameter of the particles can be controlled by variation of the hydrolysis time. The particles are soluble in some organic solvents and may be useful in many application fields such as coating technology, catalysis, delivery systems, nanoreactors, and composite materials. -(HAH, H. J.; KIM, J. S.; JEON, B. J.; KOO*, S. M.; LEE, Y. E.; Chem.
Soft sensors suffer from high manufacturing tolerances and signal drift from long‐term usage, which degrades their practicality. Although deep learning has recently been proposed to address these issues, it is expensive in terms of data collection and processing. Therefore, an adaptive calibration method is proposed for soft sensors, suitable for mass production and long‐term usage. In addition to maintaining the original benefits of deep learning characterization, this method enables fast and accurate calibration by capturing the change in the characteristics of the sensor through domain adaptation, using optimal transportation. An offline calibration method is first described, which is for alleviating the difficulty in calibrating every single unit from mass produced soft sensors. The main advantage is that identically manufactured soft sensors in a large volume with variations can be calibrated with reduced time and effort for collecting and processing data. Online calibration is then discussed, which compensates for the parameter changes when a soft sensor is continuously used for an extended period of time. For a single sensor, even though the sensor shows signal drift from the long‐term usage, the calibrated network weights can be quickly adjusted online. Finally, the proposed adaptive calibration is experimentally evaluated using actual soft sensors.
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