Abstract: 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.

Abstract: 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.

Abstract: A noise determination method executed by a computer, the noise determination method includes: acquiring time-series data; identifying a shape of a waveform of the time-series data using a persistent diagram; extracting a cluster whose lifetime from birth to death is equal to or greater than a threshold value in the persistent diagram; determining, from statistical information regarding time intervals related to pieces of data included in the cluster, whether or not peaks appear at regular intervals in the waveform of the time-series data; and controlling, based on a result of the determining, notification of an alert indicating that the time-series data includes noise.

Abstract: 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.

Abstract: 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.

Abstract: A power-supply control apparatus includes a processor that executes a process. The process includes calculating, for a first time period, a first predictive value of total power consumption by the power-supply control apparatus and one or more other power-supply control apparatuses to which power is supplied from a power supply; and determining whether to allow a storage battery to be charged in the first time period based on the first predictive value for the first time period and previous information that is related to the first predictive value and obtained in a second time period before the first time period.

Abstract: A control device includes a processor that executes a process including generating a driving signal that drives a switching device, so that an output voltage of a converter circuit that performs a step-down conversion on input power by driving the switching device matches a target value, and modifying the target value so that as an output current of the converter circuit becomes lower the output voltage becomes closer to an upper limit value of the output voltage.

Abstract: A control server according to an embodiment sorts a plurality of notebook PCs into a plurality of groups so that the total value of the remaining energy is a value similar to the total value of the remaining energy of the rechargeable batteries of a plurality of notebook PCs included in a different group. The control server according to the embodiment performs local search individually on the sorted groups, and generates a control plan for the individual notebook PCs.

Abstract: A control scheme creation method according to an embodiment includes executing, on a computer, processing of calculation of the amount of stored or released energy of each of a plurality of energy storage devices for each of a plurality of periods based on estimation value information on the amount of energy consumption within a target area and based on remaining amount information representing the amount of remaining energy of each of the plurality of energy storage devices. Furthermore, the control scheme creation method includes executing, on the computer, processing of determination of storage timing or release timing for the energy storage device for each of the periods based on the calculated amount of stored or released energy.

Abstract: A power-supply control apparatus includes a processor that executes a process. The process includes calculating, for a first time period, a first predictive value of total power consumption by the power-supply control apparatus and one or more other power-supply control apparatuses to which power is supplied from a power supply; and determining whether to allow a storage battery to be charged in the first time period based on the first predictive value for the first time period and previous information that is related to the first predictive value and obtained in a second time period before the first time period.

Abstract: A control device includes a processor that executes a process including generating a driving signal that drives a switching device, so that an output voltage of a converter circuit that performs a step-down conversion on input power by driving the switching device matches a target value, and modifying the target value so that as an output current of the converter circuit becomes lower the output voltage becomes closer to an upper limit value of the output voltage.

Abstract: A control server according to an embodiment sorts a plurality of notebook PCs into a plurality of groups so that the total value of the remaining amounts is a value similar to the total value of the remaining amounts of the rechargeable batteries of a plurality of notebook PCs included in a different group. The control server according to the embodiment performs local search individually on the sorted groups, and generates a control plan for the individual notebook PCs.

Abstract: A system control device, specifically a control device for allowing a robot manipulator to track an object trajectory, can allow a system to track the object trajectory under undesirable conditions such as an unknown physical parameter featuring a dynamic characteristic and random disturbance. The device comprises a parameter estimation unit 1 for estimating the physical parameters of the system; an adaptive linearization input generation unit 2 for generating a partial input for adaptive linearization compensation for the system based on the estimation result; and a robustness partial input generation unit 3 for generating a partial input to improve the robustness of the system linearized by the partial input. Thus, the robustness of the system can be improved even if the physical parameters cannot be accurately estimated.

Abstract: A robust control apparatus for controlling an object corresponding to a robust control theory. A control model generating unit generates an object to be controlled, such as a dynamics model of a robot. In this model, both an error in each of the true values of coefficient matrices and noise are considered. When a corrected partial input of a selective partial input is considered, the generated dynamics model can be treated as a standard probability system model. When the partial input is optimized corresponding to the robust quadratic optimizing criterion, an optimized partial input can be obtained. The object is controlled corresponding to the optimized partial input and the output of the control model generating unit. Thus, in consideration of both the modeling error and the influence of the random disturbances, the robust quadratic optimization can be performed.

Abstract: A simulator includes an initialization unit, an increment generating unit, an approximate value updating unit, and an output unit. The initialization unit enters into the increment generating unit an initial value of a state quantity and a function that describes a system subjected to random disturbance. The increment generating unit uses two sequences of random numbers to generate an increment of the approximate value of the state quantity without calculating a derivative of the function. The approximate value updating unit updates the approximate value using the generated time increment. The output unit provides a result of a simulation of that system, which has a 1.5-order accuracy of a approximation.