Fujitsu Corporation

Tomoyuki Tsunoda

Yoshiaki Ikai

Junji Kaneko

Takahiro Saito

Ken Kobayashi

Yoshimasa Kadooka

Yuhei Umeda

Kazuaki Hiraoka

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 machine learning method includes: generating, by a computer, a sine wave using a basic period of input data having a periodic property; determining a sampling period based on a degree of roundness of an attractor generated from the sine wave; sampling the input data at the determined sampling period to generate a pseudo attractor; and performing a machine learning by using the pseudo attractor.

Method for analyzing time-series data based on machine learning and information processing apparatus

An apparatus obtains time series data to which a label indicating a category of classification is attached, where the time series data contains variations of target values during a period to which the label is attached. The apparatus divides the time series data into a plurality of sections, and generates an attractor for each of the plurality of sections where the attractor indicates a set of numerical values toward which the variations of the target values tends to evolve. The apparatus calculates a Betti sequence in accordance with the generated attractor for each of the plurality of sections, and obtains a feature for the Betti sequences of the plurality of sections by clustering the Betti sequences or by calculating a statistical quantity of the Betti sequences. The apparatus performs machine learning by using the obtained feature as an input.

Learning of a feature based on betti sequences obtained from time series data

A method for computing an index on a sediment disaster, performed by a computer, the method includes: creating a computation model for computing the index on a sediment disaster at a specific point using information on a geometry of a precipitation zone at the specific point and long rainfall and short rainfall at the specific point, the information being created from past precipitation data accumulated in a storage unit; and computing the index on a sediment disaster using the computation model from the information on the geometry of the precipitation zone at the specific point, the information being created from the input precipitation data.

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Kawasaki, Japan

Tomoyuki Tsunoda