Abstract: An adaptive characteristic spectral line screening method and system based on atomic emission spectrum are provided, the method includes: using a set characteristic screening optimization method to perform a plurality of optimization rounds of characteristic screening, obtaining an initialized spectral dataset of each round of the characteristic screening and initialized characteristic population genes; obtaining an optimal characteristic population gene of each round by a set analysis method, a fitness function, and an iteration of a genetic algorithm; obtaining an optimized characteristic spectral information set when the plurality of optimization rounds reach set optimization rounds; performing combination statistics and discriminant analyses on the optimized characteristic spectral information set to complete an adaptive characteristic spectral line screening.

Abstract: A system for verification of the output of a sensor includes an industrial system comprising a plurality of sensors, one of the plurality of sensors being a target sensor, a plurality of machine learning networks, each machine learning network connecting a plurality of driving sensors associated with the target sensor and trained using simulation data. a selected machine learning network from the plurality of machine learning networks having an output representative of the target sensor, the selected machine learning network being trained with real-time data from the industrial plant and a processor for comparing an output of the selected machine learning network to a real output of the target sensor. Based on the comparison, the real sensor output is provided as final output when the values match, and the estimated value is output when the values do not match and the sensor output is flagged as an error.

Abstract: A product labeling apparatus has a plurality of labelers, an imager for imaging products; and a processor responsive to an output of the imager and operatively connected to a control input of each of the labelers. The processor processes an image received from the imager to identify a portion of a product which portion will pass a target area of a given labeller. The processor then tracks progress of that portion of the product and controls an appropriate one of the labelers to label the portion of the product when that portion of the product is at the target area of the given labeler.

Abstract: A product labelling apparatus has a plurality of labellers, an imager for imaging products; and a processor responsive to an output of the imager and operatively connected to a control input of each of the labellers. The processor processes an image received from the imager to identify a portion of a product which portion will pass a target area of a given labeller. The processor then tracks progress of that portion of the product and controls an appropriate one of the labellers to label the portion of the product when that portion of the product is at the target area of the given labeller.