Abstract: A controller for controlling a plurality of robots includes a failure prediction section configured to predict a failure time for each of the robots; and a load adjustment section configured to perform adjustment of a work load of each of the robots according to each of the predicted failure times, so that each of the robots operates until a maintenance time determined in common to each of the robots.

Abstract: A control apparatus that controls a plurality of robots, includes a failure prediction part that predicts a time of failure with respect to each component of the robots, a maintenance time adjustment part that adjusts maintenance times of the plurality of robots based on the components for which the times of failure are predicted, and a load adjustment part that adjust workloads of the robots according to the predicted times of failure for activation until the maintenance times.

Abstract: A class discrimination method includes: (a) a step of preparing, for each class, a known feature spectrum group obtained based on an output of a specific layer among a plurality of vector neuron layers when a plurality of pieces of training data are input to a machine learning model; and (b) a step of executing a class discrimination processing of the data to be discriminated using the machine learning model and the known feature spectrum group. The step (b) includes: (b1) a step of calculating a feature spectrum based on an output of the specific layer according to the data to be discriminated to the machine model; (b2) a step for each of the one or more classes; (b3) a step of creating an explanatory text of a class discrimination result for the data to be discriminated according to the similarity; and (b4) a step of outputting the explanatory text.

Abstract: A method causes one or more processors to execute a method in which a machine learning model of a vector neural network type is used. The model is learned to reproduce correspondence between first images and a pre-label corresponding to each of the first images, and includes one or more neuron layers. First intermediate data output by the one or more neurons when the first images are input to the learned model is stored in one or more memories in correlation with the neurons. The method includes inputting a second image of an object to the machine learning model and acquiring second intermediate data based on at least one of a second vector and a second activation included in the one or more neurons, calculating a similarity degree between the first and second intermediate data, generating an evidence image corresponding to the similarity degree, and displaying the generated evidence image.

Abstract: An apparatus including: memory that stores a machine learning model of a vector neural; and one or more processors that execute an arithmetic operation. The machine model has a plurality of vector neuron layers each including a plurality of nodes. When one of the plurality of vector layers is referred to as an upper layer and a vector layer below is referred to as a lower layer, one or more processors execute outputting one output vector by using output vectors from the plurality of nodes of the lower layer as an input for each node of the upper layer, the outputting including: obtaining a prediction vector, obtaining a sum vector based on a linear combination of the vectors, obtaining a normalization coefficient, and obtaining the output vector of the target node by dividing the sum vector by the norm and multiplying the divided sum vector by the normalization coefficient.

Abstract: A method for causing one or more processors to execute: performing learning of a first model of a capsule network type including one or more capsule layers each having one or more capsules to reproduce correspondence between a plurality of first data elements included in a first data set and a pre-label corresponding to each of the plurality of first data elements; and inputting the first data set into the learned first model and acquiring first intermediate data based on at least one of a first activation and a first pose included in the one or more capsules, for the one or more capsule layers.

Abstract: A method of causing one or more processors to execute: performing learning of a model that is an algorithm of a vector neural network type to reproduce correspondence between a plurality of first data elements included in a first data set and a pre-label corresponding to each of the plurality of first data elements, in which the model has one or more neuron layers, each of the one or more neuron layers has one or more neuron groups, each of the one or more neuron groups has one or more neurons, and each of the one or more neurons outputs first intermediate data based on at least one of a first vector and a first activation; and inputting the first data set into the learned model and acquiring the first intermediate data output by the one or more neurons by being associated with the neuron.

Abstract: A control apparatus that controls a plurality of robots, includes a failure prediction part that predicts a time of failure with respect to each component of the robots, a maintenance time adjustment part that adjusts maintenance times of the plurality of robots based on the components for which the times of failure are predicted, and a load adjustment part that adjust workloads of the robots according to the predicted times of failure for activation until the maintenance times.

Abstract: A controller for controlling a plurality of robots includes a failure prediction section configured to predict a failure time for each of the robots; and a load adjustment section configured to perform adjustment of a work load of each of the robots according to each of the predicted failure times, so that each of the robots operates until a maintenance time determined in common to each of the robots.