Abstract: An inspection system for a fabricated object formed by layering powder includes an acquisition unit that acquires an image of a surface of each of layers, an identification unit that identifies a defect portion (protruding portion or recessed portion) on the surface of the powder and a position of the defect portion based on the acquired image, and a determination unit that determines that an abnormality occurs when the defect portion successively occurs at a same position in the plurality of layers.

Abstract: An edge device is configured to automatically establish a connection with a remote monitoring device when an abnormality has occurred in a plant. The edge device includes a controller configured to detect the abnormality based on data regarding an observation target device, and a connection establishment unit configured to establish a connection with the remote monitoring device such that the remote monitoring device is able to remotely access the edge device when the controller has detected the abnormality and determined that the abnormality matches a predetermined condition, wherein the remote access is a connection for remotely checking a state of the observation target device, or for enabling a remote manipulation of the observation target device.

Abstract: An evaluating device includes a first acquisition unit configured to acquire a first index, a second acquisition unit configured to acquire a second index, and an evaluating unit configured to evaluate reliability. The first index indicates the difference between learning input data and actual operation input data in data space. The second index indicates the difference in the ignition tendency of the neurons between the time of input of the learning input data in the learning model of the neural network and the time of input of the actual operation input data in the learning model of the neural network. The evaluating unit evaluates the reliability of the prediction value output from the learning model with respect to the actual operation input data based on the first index acquired by the first acquisition unit and the second index acquired by the second acquisition unit.

Abstract: Provided is learning device that is a learning device of a neural network model whose bond strength between multiple neurons is represented as a weighting coefficient. The learning device is configured to optimize the weighting coefficient by using an evaluation function that includes a model reliability based on the degree of firing of each neuron of the multiple neurons and a prediction error obtained using the neural network model.

Abstract: A monitoring system performs monitoring using a monitoring device connected to a facility to be monitored through the Internet via communication, wherein the facility is provided with a facility body and a data processing unit configured to process acquired data acquired from the facility body, the data processing unit includes: a low-density data acquisition unit configured to acquire low-density data, a high-density data acquisition unit configured to acquire high-density data having a larger data amount per unit time than the low-density data; a data conversion unit configured to convert the high-density data into feature quantity data which is reduced in density of the high-density data; and a transmission unit configured to transmit monitoring data including the low-density data and the feature quantity data, and the monitoring device configured to perform monitoring on the basis of the monitoring data transmitted from the transmission unit in the data processing unit.

Abstract: A model optimization device is configured to optimize a prediction model configured to generate predicted values of a target variable for an explanatory variable. The model optimization device includes a generating unit configured to generate expanded MR data by transforming training data, and an optimization unit configured to cause the prediction model to learn and optimize the prediction model based on a first predicted value generated by the prediction model based on the training data, and a second predicted value generated by the prediction model based on the MR data.

Abstract: A model evaluating device is configured to evaluate performance of a prediction model configured to generate predicted values of a target variable for an explanatory variable. The model evaluating device includes a generating unit configured to generate expanded MR data by transforming evaluation data, and an evaluating unit configured to evaluate the performance of the prediction model based on a first predicted value generated by the prediction model based on the evaluation data, and a second predicted value generated by the prediction model based on the MR data.

Abstract: In the present invention, an input unit receives input of production conditions relating to a plurality of parameters relating to production of a biogas in a biogas production facility. An index value corresponds to an amount of heat per unit quantity of a biogas generated by the biogas production facility under the production conditions. A condition classifying unit classifies the production conditions on the basis of the plurality of parameters and the index value. An output unit outputs information relating to the result of classification by the condition classifying unit.

Abstract: A rotation detection device to solve the above-described problems includes: accelerometers installed to be spaced apart from each other at predetermined pitches around a rotating shaft of a rotary machine, the accelerometers detecting rotation vibration of the rotary machine and outputting vibration signals; and a signal processing unit that acquires the vibration signals from the accelerometers. The signal processing unit includes: an installation information acquisition means for acquiring installation radii of the accelerometers with respect to the rotating shaft; an analysis processing means for acquiring frequency responses and coherences for the vibration signals; a determination means for determining whether or not the coherences are greater than a predetermined threshold value; and a rotation period calculation means for calculating a rotation period of the rotating shaft from the frequency, the phase difference, the predetermined pitch, and the installation radii.

Abstract: An edge device is configured to automatically establish a connection with a remote monitoring device when an abnormality has occurred in a plant. The edge device includes a controller configured to detect an abnormality based on data regarding an observation target device, and a connection establishment unit configured to establish a connection with the remote monitoring device such that the remote monitoring device is able to remotely access the edge device when the controller has detected the abnormality and determined that the abnormality matches a predetermined condition, wherein the remote access is a connection for remotely checking a state of the observation target device, or for enabling a remote manipulation of the observation target device.

Abstract: An anomaly detection device includes: an initial-stage detection information acquisition unit that acquires detection information at an initial stage in a series of a machining step performed using one tool; a map-function identification unit that identifies a mapping function for mapping the detection information at the initial stage, on reference detection information; a wear-progress-stage detection information acquisition unit that acquires detection information at a wear progress stage that follows the initial stage in the series of the machining step; a map-function application unit that applies the mapping function to the detection information at the wear progress stage; an anomaly-degree calculation unit that calculates an anomaly degree indicating a degree of difference, from the reference detection information, of the detection information at the wear progress stage to which the mapping function is applied; and an anomaly detecting unit that detects an abnormality of the tool based on determination

Abstract: In the present invention, an input unit receives input of production conditions relating to a plurality of parameters relating to production of a biogas in a biogas production facility. An index value corresponds to an amount of heat per unit quantity of a biogas generated by the biogas production facility under the production conditions. A condition classifying unit classifies the production conditions on the basis of the plurality of parameters and the index value. An output unit outputs information relating to the result of classification by the condition classifying unit.

Abstract: An edge device that automatically establishes a connection with a remote monitoring device when an abnormality has occurred in a plant is provided. The edge device includes a controller configured to detect an abnormality on the basis of data regarding an observation target device, and a connection establishment unit configured to establish a connection with a remote monitoring device such that the remote monitoring device is able to remotely access the edge device when the controller has detected the abnormality and determined that the detected abnormality matches a predetermined condition, wherein the remote access is a connection for remotely checking a state of the observation target device, or for enabling a remote manipulation of the observation target device.

Abstract: A monitoring system performs monitoring using a monitoring device connected to a facility to be monitored through the Internet via communication, wherein the facility is provided with a facility body and a data processing unit configured to process acquired data acquired from the facility body, the data processing unit includes: a low-density data acquisition unit configured to acquire low-density data, a high-density data acquisition unit configured to acquire high-density data having a larger data amount per unit time than the low-density data; a data conversion unit configured to convert the high-density data into feature quantity data which is reduced in density of the high-density data; and a transmission unit configured to transmit monitoring data including the low-density data and the feature quantity data, and the monitoring device configured to perform monitoring on the basis of the monitoring data transmitted from the transmission unit in the data processing unit.

Abstract: A vibration control device that can achieve the best vibration control effect within an allowable stroke range of a movable mass includes a controller that calculates at least one of a displacement deviation between a target displacement of a movable mass and a displacement of the movable mass detected by a mass displacement sensor and a velocity deviation between a target velocity of the movable mass and a velocity of the movable mass detected by a mass velocity sensor, and generates a control command for making a motor drive the movable mass on the basis of at least one of the displacement deviation and the velocity deviation. The target displacement and the target velocity are set in a manner that the amplitude of the movable mass becomes constant and that phases thereof are delayed 90 degrees with respect to the vibration of a building.

Abstract: This electricity-demand prediction device is provided with a data reception unit, an individual-model generation unit, and a demand-prediction computation unit. Vehicle probe data in which specific driving states are recorded is inputted to the data reception unit, and on the basis of said vehicle probe data, the individual-model generation unit generates an individual model for each specific vehicle or for each user of a specific vehicle. Said individual models represent correlations between historical values associated with factor information regarding factors in decisions to charge specific vehicles at a specific charging facility and historical values representing electricity demand for said specific vehicles at said specific charging facility. The demand-prediction computation unit computes a predicted electricity demand for the specific vehicles at the specific charging facility on the basis of the generated individual models.

Abstract: This electricity-demand prediction device is provided with an area-feature-value prediction unit, an individual-model acquisition unit, and a demand-prediction computation unit. Measured data relating to vehicles is input to the area-feature-value prediction unit, and for each of a plurality of areas, the area-feature-value prediction unit predicts an area feature value for a feature relating to vehicles associated with the area in question. The individual-model acquisition unit acquires individual models for specific vehicles. The individual models take area feature values as input and output electricity-demand values for the specific vehicles at a specific charging facility. The demand-prediction computation unit inputs the predicted area values to the individual models to compute a predicted electricity demand for the specific vehicles corresponding to the individual models at the aforementioned specific charging facility.

Abstract: A vibration control device that can achieve the best vibration control effect within an allowable stroke range of a movable mass includes a controller that calculates at least one of a displacement deviation between a target displacement of a movable mass and a displacement of the movable mass detected by a mass displacement sensor and a velocity deviation between a target velocity of the movable mass and a velocity of the movable mass detected by a mass velocity sensor, and generates a control command for making a motor drive the movable mass on the basis of at least one of the displacement deviation and the velocity deviation. The target displacement and the target velocity are set in a manner that the amplitude of the movable mass becomes constant and that phases thereof are delayed 90 degrees with respect to the vibration of a building.