Calibration device, calibration method, and computer-readable recording medium
AssigneeYokogawa Electric Corporation
InventorsWataru Kobayashi, Risa Hara, Kodai Murayama, Souichirou Shimoda
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Abstract
A calibration device generates a data set of a reference sample including spectral data of the reference sample containing a plurality of components and each objective variable determined by a content of each of the components of the reference sample, and trains, by machine learning using the data set of the reference sample, a machine learning model that outputs at least one objective variable among the objective variables of each of the components in response to input of the spectral data.
Claims8
A calibration device, comprising:
- a generation unit that generates a data set of a reference sample including spectral data of the reference sample containing a plurality of components and each objective variable determined by a content of each of the components of the reference sample;
- and
- a training unit that trains, by machine learning using the data set of the reference sample, a machine learning model that outputs at least one objective variable among the objective variables of each of the components in response to input of the spectral data.
A calibration method comprising:
- generating a data set of a reference sample including spectral data of the reference sample containing a plurality of components and each objective variable determined by a content of each of the components of the reference sample;
- and
- training, by machine learning using the data set of the reference sample, a machine learning model that outputs at least one objective variable among the objective variables of each of the components in response to input of the spectral data.
A computer-readable recording medium having stored therein a calibration program that causes a computer to execute a process comprising:
- generating a data set of a reference sample including spectral data of the reference sample containing a plurality of components and each objective variable determined by a content of each of the components of the reference sample;
- and
- training, by machine learning using the data set of the reference sample, a machine learning model that outputs at least one objective variable among the objective variables of each of the components in response to input of the spectral data.
Dependencies are inferred from the claim text.