Abstract: A system performs operation monitoring in which a learning model in operation is monitored. In the operation monitoring, the system performs a first certainty factor comparison to determine, each time the learning model in operation to which input data is input outputs output data, whether or not a certainty factor of the learning model is below a first threshold. In a case where a result of the first certainty factor comparison is true, the system replaces the learning model in operation with any of candidate learning models having a certainty factor higher than the certainty factor of the learning model in operation in which the result of true is obtained among one or more candidate learning models (one or more learning models each having a version different from a version of the learning model in operation), as a learning model of an operation target.

Abstract: A computer system manages model information for defining a U-Net configured to execute, on the input time-series data, an encoding operation for extracting a feature map relating to the target wave by using downsampling blocks and a decoding operation for outputting data for predicting the first motion time of the target wave by using upsampling blocks, executes the encoding operation and the decoding operation on the input time-series data by using the model information. The downsampling blocks and the upsampling blocks each includes a residual block. The residual block includes a time attention block calculates a time attention for emphasizing a specific time domain in the feature map. The time attention block includes an arithmetic operation for calculating attentions different in time width, and calculates a feature map to which the time attention is added by using the attentions.

Abstract: A training model creation system includes a first server (a mother server 100) that diagnoses a state of an inspection target in a first base (a mother base) using a first model (a mother model) of a neural network and a plurality of second servers (child servers 200) that diagnose a state of an inspection target in each base of the plurality of second bases using a second model (a child model) of the neural network. In the training model creation system, the first server receives feature values of the trained second model from the respective plurality of second servers, merges a received plurality of feature values of the second model and a feature value of the trained first model, and reconstructs and trains the first model based on a merged feature value.

Abstract: A system performs operation monitoring in which a learning model in operation is monitored. In the operation monitoring, the system performs a first certainty factor comparison to determine, each time the learning model in operation to which input data is input outputs output data, whether or not a certainty factor of the learning model is below a first threshold. In a case where a result of the first certainty factor comparison is true, the system replaces the learning model in operation with any of candidate learning models having a certainty factor higher than the certainty factor of the learning model in operation in which the result of true is obtained among one or more candidate learning models (one or more learning models each having a version different from a version of the learning model in operation), as a learning model of an operation target.