Abstract: A learning device performs learning by an autoencoder, using waveform data with changes over time that is obtained from an intrinsic movement of an object. The learning device performs persistent homology conversion to calculate a change in the number of connected component according to a threshold change in a value direction for the waveform data. The learning device determines abnormality based on a determination result of a learner to which output data of the autoencoder obtained from the waveform data and output data obtained from the persistent homology conversion are input, and in which machine learning about abnormality of the waveform data has been performed.

Abstract: A method includes: executing a first generation process that includes generating a Betti number series corresponding to a contribution rate by performing persistent homology processing on a first point set, the first point set being generated by using a plurality of pieces of time series data and the contribution rate of each of the plurality of pieces of time series data, each of points included in the first point set being represented by coordinates; executing a second generation process that includes generating a characteristic image from a plurality of the Betti number series, the plurality of Betti number series being generated by performing the first generation process on each of the plurality of contribution rates; and executing a third generation process that includes generating machine learning data in which the characteristic image and a classification corresponding to the plurality of pieces of time series data are associated with each other.

Abstract: A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: setting each of scores to each of a plurality of sets of unlabeled data with regard to each of labels used in a plurality of sets of labeled data based on a distance of each of the plurality of sets of unlabeled data with respect to each of the labels; and causing a learning model to learn using a neural network by using the plurality of sets of labeled data respectively corresponding to the labels of the plurality of sets of labeled data, and the plurality of sets of unlabeled data respectively corresponding to the scores of the plurality of sets of unlabeled data with regard to the labels.

Abstract: A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: setting a label vector having one or a plurality of labels as components to corresponding data to be learned ; and learning a learning model including a neural network using the data to be learned and the label vector correspondingly set to the data to be learned.

Abstract: A non-transitory computer-readable recording medium stores a data-acquisition-instruction generating program that causes a computer to execute a process including: first generating a plurality of change curves of each of control parameters based on requisite density information, the requisite density information being related to a data measurement density in a data measurement region specified by a combination of a plurality of control parameters, the plurality of control parameters being used by a device subject to the data measurement; and second generating a data acquisition instruction to perform measurement at a plurality of measurement points with respect to the device to be measured in an order in which change of each control parameter becomes change corresponding to the change curves, and new measurement is performed such that only one of the control parameters changes from previous measurement.

Abstract: A learning apparatus sets a score, for each of one or more labels assigned to each set of data to be subjected to learning, based on an attribute of the set of data to be subjected to learning, or a relation between the set of data to be subjected to learning and another set of data to be subjected to learning. The learning apparatus then causes learning to be performed with a neural network by use of the score set for the label assigned to the set of data to be subjected to learning.

Abstract: A detection device adds, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data. The detection device generates a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added. The detection device generates a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation. The detection device detects a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.

Abstract: A non-transitory computer-readable recording medium storing a program that causes a computer to execute a procedure, the procedure includes generating, for each of a plurality of wafers, extended coordinates including a position on the wafer and a value calculated from a distance from a center of the wafer and a contribution parameter, for each defect on the wafer by using information of a defect position on the wafer, generating a Betti number group by persistent homology processing for a plurality of extended coordinates generated for each of the plurality of wafers generating, for each of the plurality of wafers, a defect pattern image from a plurality of Betti number groups generated for the plurality of values of contribution parameter, and generating machine learning data associating a plurality of defect pattern images generated for the plurality of wafers with determination information associated with the plurality of wafers.

Abstract: A non-transitory computer-readable recording medium stores a model generation program that causes a computer to execute a process. The process includes obtaining measurement data on measurement points sequentially measured along a measurement path curve generated from a Hilbert curve laid out in a normalized space in which measurement target ranges of respective plurality of control parameters related to control of an object to be measured are normalized, the measurement points being more in number in a specific area of the measurement target ranges than in an area other than the specific area, and the numbers of measurement points lying on two sides in each group of two adjacent sides of the measurement path curve and where a control parameter changes in opposite directions being balanced; and generating a control model of the object to be measured on the basis of the obtained measurement data of the measurement points.

Abstract: A determination apparatus extracts a plurality of specific events that have values greater than an event determination threshold from among a plurality of events that have occurred in chronological order. The determination apparatus generates a feature amount related to adjacent occurrence intervals of the plurality of specific events, using the plurality of specific events. The determination apparatus generates array data corresponding to the plurality of events using points each having components of the event determination threshold and the feature amount, while changing the event determination threshold. The determination apparatus determines a type of the plurality of events using the array data.

Abstract: A determination apparatus generates an interval vector having a plurality of components that are adjacent occurrence intervals between a plurality of events that have occurred in chronological order. The determination apparatus generates a plurality of local variable points each of which includes specific components as one set of coordinates, using a predetermined number of consecutive interval vectors in the chronological order. The determination apparatus generates a Betti sequence by applying persistent homology transform to the plurality of local variable points for which the interval vectors serving as starting points are different. The determination apparatus determines a type of the plurality of events based on the Betti sequence.

Abstract: An extraction apparatus generates a plurality of Betti series based on Betti numbers obtained by performing a persistent homology transform on a plurality of pseudo-attractors generated from a plurality of pieces of time-series data. The extraction apparatus generates a plurality of transformed Betti series in which a region with a larger radius at the time of generating the Betti numbers is weighted more than a region with a smaller radius from the plurality of Betti series. The extraction apparatus extracts abnormality candidates from the plurality of pieces of time-series data based on the Betti number in the plurality of transformed Betti series.

Abstract: A non-transitory computer-readable recording medium stores therein a visualization program that causes a computer to execute a process including: generating a plurality of conversion vectors, from a plurality of vectors generated from plural pieces of input data, by a dimensional compression in a positional relation between the plurality of vectors; and plotting the plurality of conversion vectors.

Abstract: A model generating device for generating model data regarding an operation state of a target object to be controlled, includes a memory and a processor coupled to the memory and configured to acquire first measurement data regarding the operation state of the target object under each of a plurality of measurement conditions set in a first sequence, acquire second measurement data regarding the operation state of the target object under each of the plurality of measurement conditions set in a second sequence different from the first sequence, estimate third measurement data indicating the operation state in a case where the target object enters a steady state in each of the plurality of measurement conditions, based on the first measurement data and the second measurement data, generate the model data regarding the operation state of the target object based on the third measurement data, and output the model data.

Abstract: A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Abstract: A non-transitory computer-readable recording medium stores therein a state classifying program that causes a computer to execute a process including: generating an attractor containing a plurality of points that correspond to a plurality of sets of time series data, coordinate values of each of the plurality of points being values corresponding to the sets of time series data; generating Betti number sequence data by applying a persistent homology process on the attractor; and classifying a state that is represented by the plurality of sets of time series data based on the Betti number sequence data.

Abstract: An engine control apparatus controls an engine, by generating first and second instruction values to adjust an MAF of fresh air supplied to the engine and an MAP indicating a pressure of air supplied to the engine to respective target values, based on measured values of a first sensor which detects the MAF and a second sensor which detects the MAP, regardless of limiting conditions related to a totally closed or fully open state of an EGR and an VNT, switching a supplying destination of the measured values of the first and second sensors for a certain time after the generated first or second instruction value saturates, and generating the first and second instruction values to adjust the MAF and the MAP to the respective target values, based on the measured values of the first and second sensors, under the limiting conditions, when the switching occurs.

Abstract: A planning method for planning to order a product, the planning method being executed by a computer, the planning method includes: generating a predicted value of a demand of the product at each plurality of prices based on a selling price and a number of sales in association with each other for each past sale date; calculating an order quantity that yields a highest profit, for each of the plurality of prices, using the predicted value of the demand of the product at the plurality of prices; storing, for the plurality of prices, the calculated order quantity and a profit in association with each other into a memory; and identifying a combination of a price and an order quantity that yields the highest profit, with reference to the memory.

Abstract: A non-transitory computer-readable recording medium stores a data-acquisition-instruction generating program that causes a computer to execute a process including: first generating a plurality of change curves of each of control parameters based on requisite density information, the requisite density information being related to a data measurement density in a data measurement region specified by a combination of a plurality of control parameters, the plurality of control parameters being used by a device subject to the data measurement; and second generating a data acquisition instruction to perform measurement at a plurality of measurement points with respect to the device to be measured in an order in which change of each control parameter becomes change corresponding to the change curves, and new measurement is performed such that only one of the control parameters changes from previous measurement.

Abstract: A disclosed machine learning method includes: calculating a first output error between a label and an output in a case where dropout in which values are replaced with 0 is executed for a last layer of a first channel among plural channels in a parallel neural network; calculating a second output error between the label and an output in a case where, the dropout is not executed for the last layer of the first channel; and identifying at least one channel from the plural channels based on a difference between the first output error and the second output error to update parameters of the identified channel.