Abstract: According to one embodiment, an anomaly detection apparatus includes a processing circuit. The processing circuit is configured to: acquire measured values from sensors installed in a system, a first function, a first threshold, and a second function to output a second threshold; generate the predicted values based on the measured value and the first function; detect that a deviation between the measured values and the predicted values exceeds the first threshold; calculate the feature quantities based on the measured values; and determine whether a number of consecutive times is equal to or larger than the second threshold to detect an anomaly or a sign of the anomaly.

Abstract: A storage stores observation data (a set of pairs each consists of a parameter vector value representing a point in a D-dimensional space and an observation value of an objective function at the point). A processor determines a low-dimensional search space (R (2?R<D)-dimensional affine subspace passes through a point represented by a parameter vector value in the D-dimensional space), extracts data (a set of pairs corresponding to points at which similarity to a point included in the search space are more than a predetermined value. The points are among points in the D-dimensional space represented by parameter vector values included in the observation data), and proposes a parameter vector value representing a next point based on the extracted data.

Abstract: An information processing apparatus according to one embodiment, comprising: a regression model generator configured to, by combining two or more of a plurality of variables, generate a plurality of terms that include combinations of two or more of the plurality of variables, respectively, and generate a regression model that regresses a property variable or an objective variable indicating an output of an objective function that includes the property variable, by the plurality of terms; a subgroup generator configured to generate, based on coefficients of the plurality of terms included in the regression model, subgroups that are the combinations of variables included the terms, respectively; and a subspace search processor configured to perform search for each of subspaces spanned by the subgroups based on an optimization criterion for the objective function, and generate pieces of first design value data that include values of the plurality of variables for the subspaces.

Abstract: According to one embodiment, a monitoring apparatus includes a processing circuit. The processing circuit is configured to generate second data including a prediction value of a second sensor correlated with a first sensor from first data including a measurement value of the first sensor of which a measurement value changes suddenly in a case where the control signal changes, detect an anomaly of the system or an anomaly of at least one sensor, and make it difficult to detect the anomaly in a case where the determination signal indicates that there is a change in the control signal.

Abstract: According to an embodiment, a learning device includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: generate a transformation matrix from learning data in which feature quantities and target values are held in a corresponding manner; and learn about parameters of a neural network which includes nodes equal in number to the number of rows of the transformation matrix, a first output layer representing first estimation distribution according to the values of the nodes, and a second output layer representing second estimation distribution decided according to the product of the transformation matrix and the first estimation distribution.

Abstract: According to one embodiment, an abnormality sign detection system comprising one or more computers configured to perform machine learning of an abnormality-sign detection-model that detects at least one of an abnormality in a target facility to be monitored and a sign of the abnormality, wherein the one or more computers are configured to: acquire a plurality of process amounts generated at the target facility; classify each of the plurality of process amounts into either correlation data for which correlation between the plurality of process amounts is learned or decorrelation data for which correlation between the plurality of process amounts is not learned; generate learning input data depending on classification, the learning input data being data in which each of the plurality of process amounts is associated with the correlation data or the decorrelation data; and perform the machine learning by inputting the learning input data to the abnormality-sign detection-model.

Abstract: According to an embodiment, a monitoring apparatus configured to generate time-series predicted data based on time-series measured data and a prediction model that generates predicted data including one or more predicted values predicted to be output from one or more sensors; and generate, for a first sensor among the one or more sensors, a displayed image including a measured value graph representing a temporal change in a measured value included in the time-series measured data in a second period after a first period, a predicted value graph representing a temporal change in a predicted value included in time-series predicted data in the second period, past distribution information representing a distribution of a measured value in the first period, and measurement distribution information representing a distribution of the measured value included in the time-series measured data in the second period.

Abstract: A storage stores observation data (a set of pairs each consists of a parameter vector value representing a point in a D-dimensional space and an observation value of an objective function at the point). A processor determines a low-dimensional search space (R (2?R<D)-dimensional affine subspace passes through a point represented by a parameter vector value in the D-dimensional space), extracts data (a set of pairs corresponding to points at which similarity to a point included in the search space are more than a predetermined value. The points are among points in the D-dimensional space represented by parameter vector values included in the observation data), and proposes a parameter vector value representing a next point based on the extracted data.

Abstract: According to one embodiment, a data processing apparatus includes a processor. The processor calculates, from the first measurement data, a first differential value set that is a set of first differential values in a time direction at a time included in the first period of the measurement values of the sensor of interest. The processor calculates, from the second measurement data, a second differential value set that is a set of second differential values in a time direction at a time included in the second period of the measurement values of the sensor of interest. The processor generates a first differential value distribution and a second differential value distribution using the second differential value set.

Abstract: According to one embodiment, a monitoring apparatus includes a processing circuit. The processing circuit is configured to generate second data including a prediction value of a second sensor correlated with a first sensor from first data including a measurement value of the first sensor of which a measurement value changes suddenly in a case where the control signal changes, detect an anomaly of the system or an anomaly of at least one sensor, and make it difficult to detect the anomaly in a case where the determination signal indicates that there is a change in the control signal.

Abstract: An abnormality determination device includes one or more processors. The processors input first input data to a first model to obtain first output data. The first output data is formed by restoring data with the reduced dimension to data with the same dimension as that of the first input data. The processors input second input data, which is a difference between the first input data and the first output data, to a second model, and obtain second output data. The second output data is formed by restoring data with the reduced dimension to data with the same dimension as that of the second input data. The processors obtain restored data that is a sum of the first output data and the second output data. The processors compare the first input data with the restored data and determine an abnormality in the first input data based on the comparison result.

Abstract: A semiconductor device includes a first electrode, a second electrode, a semiconductor layer that includes a first semiconductor region, a second semiconductor region, and a third semiconductor region, a third electrode, a first insulating region, a second insulating region, a fourth electrode that has a plurality of portions consecutive in a first direction, the plurality of portions including a first portion that has a first width in a second direction, a second portion that is located closer to the second electrode than the first portion in the first direction and has a second width smaller than the first width in the second direction, and a third portion that is adjacent to the second portion, located closer to the second electrode than the second portion in the first direction, and has a third width larger than the second width in the second direction, and a third insulating region.

Abstract: According to one embodiment, a design support method includes inputting a design value group to a simulator. The design value group includes design values relating to a semiconductor element. The method further includes acquiring a characteristic value group output from the simulator according to the input of the design value group. The characteristic value group includes characteristic values of the semiconductor element. The characteristic values include a first and a second characteristic values respectively indicating an on-resistance and a breakdown voltage. The method further includes calculating an acquisition function of a Bayesian inference from history data including not less than one data set. The data set includes the design value group and a score. The portion of the characteristic value group includes the first and second characteristic values. The method further includes generating a new design value group based on the acquisition function.

Abstract: According to one embodiment, a parameter optimization apparatus stores data sets which each include a first parameter value of a first number of dimensions and an observed value of an objective function corresponding to the first parameter value. The apparatus determines a search space of a second number of dimensions smaller than the first number of dimensions. The apparatus acquires one or more data sets each having a first parameter value present within a predetermined distance from the search space. The apparatus searches the search space for a first parameter value that may optimize the objective function, using a surrogate model of an objective function based on one or more data sets acquired.

Abstract: According to one embodiment, a semiconductor device includes first to third electrodes, a semiconductor member, a conductive member, and an insulating member. The semiconductor member includes first to third semiconductor regions. The first semiconductor region includes first and second partial regions. The second semiconductor region is between the first partial region and the third semiconductor region. The conductive member is located between the second partial region and the third electrode. The conductive member includes a first end portion and a first other-end portion. The first end portion is between the first other-end portion and the third electrode. The conductive member includes first to third portions. The second portion is between the third portion and the third electrode. The first portion is between the second portion and the third electrode. The first portion includes the first end portion. The second portion contacts the first and third portions.

Abstract: According to one embodiment, a processing circuit classifies a time-series data corresponding to process amounts generated in a target facility into groups. For each of groups, the processing circuit applies time-series data included in the group to a first auto-encoder, which differs depending upon each group, and outputs time-series data. The processing circuit applies input difference data, which are based on output time-series data on the process amounts and the input time-series data, to a single second auto-encoder, and outputs difference data. The processing circuit determines an abnormality of the target facility, based on the comparison between addition data which are based on the output difference data and the output time-series data, and the input time-series data.

Abstract: According to one embodiment, an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns.

Abstract: According to an embodiment, a monitoring apparatus configured to generate time-series predicted data based on time-series measured data and a prediction model that generates predicted data including one or more predicted values predicted to be output from one or more sensors; and generate, for a first sensor among the one or more sensors, a displayed image including a measured value graph representing a temporal change in a measured value included in the time-series measured data in a second period after a first period, a predicted value graph representing a temporal change in a predicted value included in time-series predicted data in the second period, past distribution information representing a distribution of a measured value in the first period, and measurement distribution information representing a distribution of the measured value included in the time-series measured data in the second period.

Abstract: According to an embodiment, a learning device includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: generate a transformation matrix from learning data in which feature quantities and target values are held in a corresponding manner; and learn about parameters of a neural network which includes nodes equal in number to the number of rows of the transformation matrix, a first output layer representing first estimation distribution according to the values of the nodes, and a second output layer representing second estimation distribution decided according to the product of the transformation matrix and the first estimation distribution.

Abstract: An object positioning apparatus comprising: a radiographic image input interface configured to acquire a radiographic image that is generated by causing a fluoroscopic imaging apparatus to image an object and includes a first region and a second region, the first region depicting an index region for positioning of the object, the second region depicting a non-index region other than the index region; and a positioning processor configured to perform the positioning of the object by performing matching processing between a previously generated reference image and the first region that is specified from the radiographic image based on three-dimensional model information of the non-index region.