Abstract: A robot control system includes circuitry configured to acquire a plurality of disturbance torques and a plurality of axis positions for a plurality of axes of a robot. The plurality of disturbance torques corresponds to the plurality of axis positions, and the plurality of disturbance torques and the plurality of axis positions correspond to a particular motion of the robot. The circuitry is further configured to transmit input data to a learned model for external force. The input data includes the plurality of disturbance torques or the plurality of axis positions. The circuitry is further configured to receive output data from the learned model for external force, calculate an estimated external force having acted on an end effector of the robot based on the output data, and control the robot based on the estimated external force. The output data includes transmission torque data or external force data.

Abstract: A data analysis system comprises a memory configured to store a data analysis model trained in advance by using training data configured to output an output value indicating whether a target to be analyzed is in a specific state in response to the data analysis model receives input data on the target to be analyzed, the data analysis model including a parameter that includes a random variable; and circuitry configured to input the input data to the data analysis model repeatedly a plurality of times and calculate an estimate indicating a degree of being in the specific state based on a distribution of a plurality of the output values output from the data analysis model for the plurality of times.

Abstract: Provided is a training image generating method that facilitates preparation of training images for constructing an image recognition model and reduces a period of time required for collecting data on images of defective products to be used as the training images. The training image generating method includes creating a SinGAN model including a generator and a discriminator in each of a plurality of layers based on a first image having a portion of interest shown partially on a target object, generating an input image by compositing a target object image and a portion-of-interest image, and generating, based on the SinGAN model and the input image, a second image exhibiting a portion of interest different in mode from that of the first image.

Abstract: An event estimation system includes an upper device, and a lower controller device including first circuitry that acquires operation information of a control target device connected to the lower controller device, estimates a presence or absence of an abnormality based on the operation information, holds the operation information for a certain time period, and transmits, based on the presence or absence of an abnormality and to the upper device, the operation information related to the estimation of the presence or absence of the abnormality. The upper device has second circuitry that receives the operation information from the lower controller device, and operates according to the presence or absence of the abnormality, inputs, using an upper neural network model, the operation information, output event information, and estimates an event.

Abstract: A control system may include: a motor configured to power a driven object; and circuitry configured to: generate a first driving force command to drive the motor during a first control; estimate a first force acting on the motor during the first control based, at least in part, on the first driving force command; generate a second driving force command to drive the motor during a second control after the first control; estimate a second force acting on the motor during the second control based, at least in part, on the second driving force command; and estimate an external force acting on the driven object during the second control based, at least in part, on a comparison between the first force and the second force.

Abstract: An abnormality information estimation system includes processing circuitry that identifies, based on operation data related to an operation of an industrial device that controls a mechanism, multiple unit phenomena due to the operation, and estimates abnormality information about an abnormality occurring in the mechanism based on the multiple of unit phenomena.

Abstract: Provided a machine learning data generation device including: at least one processor; and at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, causes the at least one processor to execute: acquiring , in association with a predetermined label actual time series information; executing physical simulation of generating a plurality of pieces of virtual time series information; identifying parameter values based on the plurality of pieces of virtual time series information and the actual time series information, and to associate the identified parameter values with the label; generating a new parameter value and the label based on the identified parameter values; generating virtual time series information corresponding to a new internal state by executing physical simulation through use of the new parameter value; and generating new machine learning data.

Abstract: A control system may include: a motor configured to power a driven object; and circuitry configured to: generate a first driving force command to drive the motor during a first control; estimate a first force acting on the motor during the first control based, at least in part, on the first driving force command; generate a second driving force command to drive the motor during a second control after the first control; estimate a second force acting on the motor during the second control based, at least in part, on the second driving force command; and estimate an external force acting on the driven object during the second control based, at least in part, on a comparison between the first force and the second force.

Abstract: Provided is a machine learning model determination system including: at least one server and at least one client terminal; an evaluation information database which stores evaluation information being information on an evaluation of machine learning; an evaluation information update module which updates the evaluation information based on a specific value of a parameter and an evaluation of the machine learning through use of specific teaching data; a teaching data input module; a verification data input module; a parameter determination module which determines the specific value of the parameter based on the evaluation information; and a machine learning engine which includes a learning module which executes learning for a machine learning model through use of the specific teaching data, and an evaluation module which evaluates a result of the machine learning through use of the specific verification data.

Abstract: A state estimation device is provided, which includes: a machine controlling unit for controlling a work machine based on a sensor value acquired from a sensor configured to output the sensor value related to an operation by the work machine; and a state estimation unit for estimating a state of the sensor based on the sensor value. In addition, a system is provided, which includes: the state estimation device; the work machine; and the sensor. In addition, a method of manufacturing a manufacture item by a work machine is provided, which includes: controlling the work machine based on a sensor value acquired from a sensor configured to output a sensor value related to an operation by the work machine on the manufacture item; and estimating a state of the sensor based on the sensor value.

Abstract: An event estimation system includes an upper device, and a lower controller device including first circuitry that acquires operation information of a control target device connected to the lower controller device, estimates a presence or absence of an abnormality based on the operation information, holds the operation information for a certain time period, and transmits, based on the presence or absence of an abnormality and to the upper device, the operation information related to the estimation of the presence or absence of the abnormality. The upper device has second circuitry that receives the operation information from the lower controller device, and operates according to the presence or absence of the abnormality, inputs, using an upper neural network model, the operation information, output event information, and estimates an event.