Abstract: A manufacturing control apparatus has an acquiring part that acquires past manufacturing data in which a target value of an output value of the intermediate product, the output value of the intermediate product, and quality of a final product produced from the manufacturing apparatus are associated with one another, an output predicting part that predicts, based on the target value of the intermediate product and the output value of the intermediate product in the past manufacturing data, the output value of the intermediate product for each of possible target values of the intermediate product, and a quality predicting part that predicts, based on the output value of the intermediate product and the quality of the final product in the past manufacturing data, the quality of the final product from a predicted value of the output value of the intermediate product for each of the predicted possible target values.

Abstract: An optimization device includes an output data acquisitor that acquires output data having a second number of dimensions obtained by performing an experiment or a simulation, an evaluation value calculator that calculates and outputs an evaluation value of the output data, a features extractor that extracts an output data features having a third number of dimensions different from the second number of dimensions, an input parameter converter that generates a conversion parameter related to the output data features predicted from the input parameters, a next input parameter determinator that determines a next input parameter to be acquired by the output data acquisitor, based on the conversion parameter and the corresponding evaluation value, and an iterative determinator that repeats processes of the output data acquisitor, the input/output data storage, the evaluation value calculator, the features extractor, the input parameter converter, and the next input parameter determinator.

Abstract: An optimization apparatus includes an output data acquirer to acquire output data expressing a result of experiment or simulation based input parameters, input/output data storage to store the input parameters and the output data corresponding to the input parameters, as a pair, an evaluation value calculator to calculate evaluation values of the output data, an input parameter converter to generate conversion parameters of a dimension number changed from the dimension number of the input parameters, a next-input parameter decider to decide next input parameters based on pairs of the conversion parameters and the evaluation values corresponding to the conversion parameters, and a repetition determiner to repeat operations of the output data acquirer, the input/output data storage, the evaluation value calculator, the input parameter converter, and the next-input parameter decider, until satisfying a predetermined condition.

Abstract: An information processing apparatus has an output data acquisition unit configured to acquire an output value obtained by performing an experiment or simulation based on an input parameter of a predetermined number of dimensions, an evaluation value calculation unit configured to calculate and output an evaluation value of the output value, an outlier processing unit configured to output a converted evaluation value including a specified value obtained by converting the evaluation value that does not satisfy a predetermined criterion, a next input parameter determination unit configured to determine a next input parameter based on the input parameter and the converted evaluation value corresponding to the input parameter, and an iteration determination unit configured to repeat processing of the output data acquisition unit, the evaluation value calculation unit, the outlier processing unit, and the next input parameter determination unit until a predetermined condition is satisfied.

Abstract: An information processing apparatus includes a data acquisition unit that acquires data including a missing value, a missing rate calculation unit that calculates a missing rate indicating a ratio of missing values included in the data, and a covariance matrix estimation unit that estimates a covariance matrix based on the missing rate. According to the information processing apparatus, since the covariance matrix is estimated based on the missing rate, the estimation accuracy of the covariance matrix can be improved.

Abstract: A regression analysis apparatus includes a regression model constructor and a similar feature extractor. A regression model constructor is configured to construct a regression model that represents an objective variable with a plurality of explanatory variables that correspond to any one of a plurality of features and with a regression coefficient of the plurality of explanatory variables by performing regression analysis using analysis target data including the plurality of features with one of the plurality of features as the objective variable. A similar feature extractor is configured to calculate a similarity degree between a feature other than a feature that corresponds to the objective variable in the analysis target data and the plurality of explanatory variables, and each of the plurality of explanatory variables, and configured to extract a similar feature having the similarity degree higher than a predetermined value.

Abstract: A method and system for detecting anomalies in waveforms in an industrial plant. During a learning stage, one or more training waveforms are received from sensors monitoring a plurality of equipment in the industrial plant. The one or more training waveforms are used to generate a representative waveform and deviations of the one or more training waveforms from the representative waveform are determined. Based on the deviations, groups are created. A model may be associated with each group for building an expected waveform pattern. When test waveforms are received, based on the electrical and physical properties of the test waveforms, each test waveform is classified into one of the groups. Thereafter, each waveform is compared with the expected waveform pattern associated with the group to which the respective test waveform belongs, to detect the anomaly.

Abstract: An optimization device includes an output data acquisitor that acquires output data having a second number of dimensions obtained by performing an experiment or a simulation, an evaluation value calculator that calculates and outputs an evaluation value of the output data, a features extractor that extracts an output data features having a third number of dimensions different from the second number of dimensions, an input parameter converter that generates a conversion parameter related to the output data features predicted from the input parameters, a next input parameter determinator that determines a next input parameter to be acquired by the output data acquisitor, based on the conversion parameter and the corresponding evaluation value, and an iterative determinator that repeats processes of the output data acquisitor, the input/output data storage, the evaluation value calculator, the features extractor, the input parameter converter, and the next input parameter determinator.

Abstract: A waveform segmentation device has a state level estimation unit that estimates a state level of input waveform data, and a segmentation identification unit that segments the waveform data at a plurality of segmentation points based on the state level estimated by the state level estimation unit. The segmentation identification unit may identify the plurality of segmentation points such that a feature value of the waveform data is included between two adjacent segmentation points among the segmentation points.

Abstract: A manufacturing control apparatus has an acquiring part that acquires past manufacturing data in which a target value of an output value of the intermediate product, the output value of the intermediate product, and quality of a final product produced from the manufacturing apparatus are associated with one another, an output predicting part that predicts, based on the target value of the intermediate product and the output value of the intermediate product in the past manufacturing data, the output value of the intermediate product for each of possible target values of the intermediate product, and a quality predicting part that predicts, based on the output value of the intermediate product and the quality of the final product in the past manufacturing data, the quality of the final product from a predicted value of the output value of the intermediate product for each of the predicted possible target values.

Abstract: Present disclosure discloses a method and system for detecting anomalies in waveforms in an industrial plant. During a learning stage, one or more training waveforms are received from a plurality of sensors monitoring a plurality of equipment in the industrial plant. The one or more training waveforms are used to generate a representative waveform and deviations of the one or more training waveforms from the representative waveform is determined. Based on the deviations, a plurality of groups is created. A model may be associated with each group for building an expected waveform pattern (reference waveform pattern). In real-time a plurality of test waveforms is received. Based on the electrical and physical properties of the plurality of test waveforms, each test waveform is classified into one of the plurality of groups. Thereafter, each waveform is compared with the reference waveform pattern associated with the group to which the respective test waveform belongs, to detect the anomaly.

Abstract: An information processing apparatus includes a data acquisition unit that acquires data including a missing value, a missing rate calculation unit that calculates a missing rate indicating a ratio of missing values included in the data, and a covariance matrix estimation unit that estimates a covariance matrix based on the missing rate. According to the information processing apparatus, since the covariance matrix is estimated based on the missing rate, the estimation accuracy of the covariance matrix can be improved.

Abstract: An optimization apparatus includes an output data acquirer to acquire output data expressing a result of experiment or simulation based input parameters, input/output data storage to store the input parameters and the output data corresponding to the input parameters, as a pair, an evaluation value calculator to calculate evaluation values of the output data, an input parameter converter to generate conversion parameters of a dimension number changed from the dimension number of the input parameters, a next-input parameter decider to decide next input parameters based on pairs of the conversion parameters and the evaluation values corresponding to the conversion parameters, and a repetition determiner to repeat operations of the output data acquirer, the input/output data storage, the evaluation value calculator, the input parameter converter, and the next-input parameter decider, until satisfying a predetermined condition.

Abstract: An apparatus according to one embodiment of the present invention detects an abnormality of a monitoring target on the basis of state data of the target and includes an estimated data calculator, a deviation degree calculator, an abnormality degree calculator, and an abnormality determiner. The estimated data calculator calculates estimated data of a second period on the basis of the state data of the first period. The deviation degree calculator calculates a degree of deviation of the second period on the basis of the state data and the estimated data of the second period. The abnormality degree calculator calculates a degree of abnormality of the second period on the basis of the degree of deviation of the second period. The abnormality determiner determines presence or absence of an abnormality of the target in the second period on the basis of the degree of abnormality in the second period.

Abstract: According to an embodiment of the present invention, a storage management apparatus to manage a plurality of storage combinations which are combinations of storages included in a storage group, includes a combination failure probability calculator and an allocation ratio calculator. The combination failure probability calculator calculates a failure probability of the storage combination based on a failure probability of each of the storages. The allocation ratio calculator calculates an allocation ratio of received data with respect to the storage combination based on the failure probability of the storage combination.

Abstract: According to one embodiment, a time series data analysis device includes a feature vector calculator and an updater. The feature vector calculator calculates feature amounts of a plurality of feature waveforms based on distances between a partial time series and the feature waveforms, the partial time series being data belonging to each of a plurality of intervals which are set in a plurality of pieces of time series data. The updater updates the feature waveforms based on the feature amounts.

Abstract: According to one embodiment, physical position information on errors on a recording medium is acquired, physical position relationship between the errors on the recording medium is calculated based on the position information, and a failure mode related to the errors is determined based on the position relationship.

Abstract: According to one embodiment, there is provided a quality controlling device including: a predictor, a frequency calculator, and an implementing signal creator. The predictor employs a prediction model that associates an inspection result value of a first inspection with a predicted value being a value relating to a possibility of pass or failure in a second inspection and calculates the predicted value from an inspection result value that is obtained for an inspection target in the first inspection. The frequency calculator calculates, for the inspection target, an implementation frequency to implement the second inspection in accordance with the predicted value calculated by the predictor. The implementing signal creator creates a signal that indicates, for the inspection target, necessity of implementing the second inspection in accordance with the implementation frequency.

Abstract: An analysis apparatus includes a regression model constructor and a similar feature extractor. A regression model constructor is configured to construct a regression model that represents a target variable with a plurality of explanatory variables that correspond to any one of a plurality of features and with a regression coefficient of the plurality of explanatory variables by performing regression analysis using analysis target data including the plurality of features with one of the plurality of features as the target variable. A similar feature extractor is configured to calculate a similarity degree between a feature other than the feature that corresponds to the target variable in the analysis target data and the plurality of explanatory variables, and each of the plurality of explanatory variables, and configured to extract a similar feature having the similarity degree higher than a predetermined value.

Abstract: An apparatus according to one embodiment of the present invention detects an abnormality of a monitoring target on the basis of state data of the target and includes an estimated data calculator, a deviation degree calculator, an abnormality degree calculator; and an abnormality determiner. The estimated data calculator calculates estimated data of a second period on the basis of the state data of the first period. The deviation degree calculator calculates a degree of deviation of the second period on the basis of the state data and the estimated data of the second period. The abnormality degree calculator calculates a degree of abnormality of the second period on the basis of the degree of deviation of the second period. The abnormality determiner determines presence or absence of an abnormality of the target in the second period on the basis of the degree of abnormality in the second period.