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:
According to an embodiment, an information processing device includes one or more hardware processors configured to: set an error function including one or more terms based on a plurality of weights according to features of a plurality of elements, the error function being a function used during learning of a machine learning model into which positions of a plurality of atoms included in an analysis target, and information indicating which of the plurality of elements the plurality of atoms are, are input, and that outputs a physical quantity of the analysis target; and learn the machine learning model using the error function.
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, 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:
There is provided an analysis apparatus including: a first storage to store operation data on an electronic device; a second storage to store a span characteristic concerning a time span in which each of values of a plurality of explanatory variables is changed; an explanatory variable calculator to calculate the explanatory variables based on the operation data; a failure state information calculator to calculate failure state information for the electronic device based on the explanatory variables calculated by the explanatory variable calculator, and calculate, when the failure state information represents a risky state, an overall span concerning in what time span the failure state information is possibly to represent a safe state due to changes in the values of the explanatory variables; and a diagnosis unit to diagnose the electronic device based on the failure state information and the overall span characteristic.
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 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 data preserving apparatus includes: a generator generating scenarios that represent presence or absence of occurrence of a failure, presence or absence of a failure symptom, a time point of occurrence of a failure, and a time point of occurrence of a failure symptom; an evaluator calculating the amount of data loss in the case of occurrence of a failure for the scenario based on first and second backup intervals before and after occurrence of a failure symptom; an backup interval evaluator calculating a number of backups performed until earlier one of a time point of occurrence of a failure and a time point after a predetermined time period; a cost evaluator calculating a cost evaluation values for the scenarios based on the amount of data loss and the number of backups; and a calculator calculating the first and second backup intervals based on cost evaluation values for scenarios.
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:
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.