Fujitsu Corporation

Kumamoto University

Yuhei Umeda

Ken Kobayashi

Tsugito Maruyama

Hirokazu Anai

Masaru Todoriki

Hiroya Inakoshi

Takashi Katoh

Yoshimasa Kadooka

Yoshiaki Ikai

Koji Maruhashi

Tsutomu Ishida

Akira Ura

Tomoyuki Tsunoda

Saeru Yamamuro

Kouki Kawabata

Kazuaki Hiraoka

Meryll Dindin

Frederic Chazal

Yasushi Sakurai

Yasuko Matsubara

Yasuaki Irifune

A storage medium storing a training processing program that causes at least one computer to execute a process that includes acquiring a deviation degree of a feature in a training dataset, by using a determination model, the training dataset being unlabeled; selecting one or more pieces of data included in the training dataset based on the deviation degree; outputting the selected one or more pieces of data or related data related to the selected one or more pieces of data; receiving an input of a determination result by a user for the one or more pieces of data; and determining an adjustment standard used to adjust a feature of each piece of the data included in the training dataset based on the received determination result, wherein when determination target data is determined by the determination model, a feature of the determination target data is adjusted based on the adjustment standard.

Storage medium, information processing device, and training processing method

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 in accordance with a change of a threshold for a value of the first 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.

Computer-readable recording medium, abnormality determination method, and abnormality determination device

A detection device generates plural attractors based on brainwave data. Subsequently, the detection device calculates a Betti number by subjecting the attractors to persistent homology transform. The detection device determines an onset of encephalopathy based on a first order component of a Betti sequence calculated based on the Betti number.

Non-transitory computer-readable recording medium, encephalopathy determination method, and information processing apparatus

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.

Computer-readable recording medium, determination method, and determination apparatus for classifying time series data

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.

Machine learning method, machine learning apparatus, and computer-readable recording medium

An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.

Allocation method, allocating device, and computer-readable recording medium

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.

Computer-readable recording medium, determination method, and determination apparatus

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.

Data generation apparatus, data generation method, and non-transitory computer-readable storage medium for storing program

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.

Analysis method, analysis device, and recording medium

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.

Data generation method and data generation device

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.

Computer-readable recording medium, detection method, and detection device

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.

Visualization method, visualization device, and recording medium

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.

Method and apparatus for machine learning

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.

Data-acquisition-instruction generating method, data-acquisition-instruction generating device, and computer-readable recording medium

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.

Growing community of inventors

Kawasaki, Japan

Yuhei Umeda