Abstract: A non-transitory computer-readable recording medium stores a program that causes a computer to execute a process including: inputting input data including one or more records that have one of a plurality of formats, each of the plurality of formats including a plurality of items; generating conversion data by generating an integrated record having an integrated format from the one or more records; and causing a learner to execute a learning process using the conversion data as input tensor, the learner performing deep learning by performing tensor decomposition on input tensor.

Abstract: An apparatus identifies partial tensor data that contributes to machine learning using tensor data in a tensor format obtained by transforming training data having a graph structure. Based on the partial tensor data and the training data, the apparatus generates expanded training data to be used in the machine learning by expanding the training data.

Abstract: A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes acquiring training data in which information that indicates whether or not an attack is performed from a first device to a second device is associated with each of a specific operation log from the first device to the second device and a plurality of operation logs that includes operation logs from the first device to the second device before and after the specific operation log; generating order matrix data that includes a graph structure that corresponds to each of the plurality of operation logs and an order relationship of the specific operation log and the operation logs before and after the specific operation log; and generating a machine learning model based on the training data by inputting the data of the order matrix data to a neural network.

Abstract: An apparatus identifies partial tensor data that contributes to machine learning using tensor data in a tensor format obtained by transforming training data having a graph structure. Based on the partial tensor data and the training data, the apparatus generates expanded training data to be used in the machine learning by expanding the training data.

Abstract: A computer-implemented machine learning method of a machine learning model includes: performing first training of the machine learning model by using pieces of training data associated with a correct label; determining, from the pieces of training data, a set of pieces of training data that are close to each other in a feature space based on a core tensor generated by the trained machine learning model and have a same correct label; generating extended training data based on the determined set of pieces of training data; and performing second training of the trained machine learning model by using the generated extended training data.

Abstract: A machine learning model, in which core tensors are generated, is trained by a computer. The computer performs a process including: extracting, from a plurality of items of pseudo training data generated from a plurality of items of training data for the machine learning model, a plurality of items of determined pseudo training data that are determined as pseudo training data that promotes training of the machine learning model; and training the machine learning model by using the plurality of items of determined pseudo training data.

Abstract: An apparatus for determining an abnormality includes: a memory; and a processor coupled to the memory, the processor being configured to execute setting processing that includes setting a determination region so as to exclude range of a frequency component of which waveforms of vibrations in a step before a change and a step after a change coincide each other in a predetermined error range, in a frequency spectrum for each step, the frequency spectrum being obtained from vibration data that is obtained by detecting, by a sensor, a vibration of a monitoring target apparatus executing a plurality of steps in a predetermined order using a rotary part, and execute generating processing that includes generating abnormality determination criterion information indicating a frequency region used to determine an abnormality of the monitoring target apparatus, in accordance with peaks of the frequency spectrum detected in the determination region.

Abstract: A non-transitory computer-readable recording medium stores a program that causes a computer to execute a process including: inputting input data including one or more records that have one of a plurality of formats, each of the plurality of formats including a plurality of items; generating conversion data by generating an integrated record having an integrated format from the one or more records; and causing a learner to execute a learning process using the conversion data as input tensor, the learner performing deep learning by performing tensor decomposition on input tensor.

Abstract: A non-transitory computer-readable recording medium stores a program that causes a computer to execute a process including: inputting input data generated from a plurality of logs, the input data including one or more records that have a plurality of items; generating conversion data by complementing, regarding a target record, included in the input data, in which one or more values in the plurality of items has been lost, at least one of the one or more lost values by a candidate value; and causing a learner to execute a learning process using the conversion data as input tensor, the learner performing deep learning by performing tensor decomposition on input tensor.

Abstract: A non-transitory computer-readable recording medium having stored therein an abnormality detection program for causing a computer to execute a process, the process includes referring to a storage that stores a plurality of determination criteria for each of processes based on a rotation frequency of the rotating part and harmonic frequencies of the rotation frequency in each of a plurality of processes, the rotation frequency and the harmonic frequencies of the rotation frequency being specified from a frequency spectrum of vibration data obtained by measuring, by a sensor, vibration of a monitoring target device that executes the plurality of processes in a predetermined order by using a rotating part that rotates, and detecting occurrence of an abnormality of the monitoring target device from the vibration data measured by the sensor based on the predetermined order and the plurality of determination criteria for each of the processes.

Abstract: A thermoelectric conversion device includes: a thermoelectric conversion element in which a p-type thermoelectric material and an n-type thermoelectric material that are provided between an upper electrode and a lower electrode of the thermoelectric conversion element are alternately connected in series via the upper electrode and the lower electrode; an insulating layer that is provided between the upper electrode and the lower electrode and covers the p-type thermoelectric material and the n-type thermoelectric material; and an electric storage element that is provided between the upper electrode and the lower electrode and is covered by the insulating layer.

Abstract: A thermoelectric conversion module includes: a first substrate having water permeability and thermal conductivity; a thermoelectric conversion element provided on the first substrate; a heat insulator having water permeability provided around the thermoelectric conversion element on the first substrate; and a second substrate disposed on the thermoelectric conversion element and the heat insulator and having water permeability and thermal conductivity.

Abstract: A thermoelectric conversion device includes: a thermoelectric conversion element in which a p-type thermoelectric material and an n-type thermoelectric material that are provided between an upper electrode and a lower electrode of the thermoelectric conversion element are alternately connected in series via the upper electrode and the lower electrode; an insulating layer that is provided between the upper electrode and the lower electrode and covers the p-type thermoelectric material and the n-type thermoelectric material; and an electric storage element that is provided between the upper electrode and the lower electrode and is covered by the insulating layer.

Abstract: A thermoelectric conversion module includes a pair of heat transfer plates, p-type semiconductor blocks and n-type semiconductor blocks arranged between the heat transfer plates, and terminal electrodes formed respectively on inner surfaces of the heat transfer plates and connecting the semiconductor blocks in series. The heat transfer plates include holes reaching from an outer surface to the terminal electrodes, and grooves each formed between the terminal electrodes and communicating between the adjacent holes. If a disconnection occurs, for example, a pin of a tester is brought into contact with the terminal electrode via the hole to specify a disconnected portion, and the terminal electrodes are electrically connected by injecting conductive paste into the holes in the disconnected portion as well as the groove.

Abstract: Thermoelectric generating parts having a plate-shape or film-shape are stacked in a thickness direction. Each of the thermoelectric generating parts generates an electric power as a temperature difference is generated in the thickness direction. Thermal conducting members are disposed between two of the thermoelectric generating parts adjacent in a stacked direction and on outer surfaces of outermost two thermoelectric generating parts. A first thermal coupling member is connected to and thermally coupled to the every other thermal conducting members. A second thermal coupling member is connected to and thermally coupled to the thermal conducting members not connected to the first thermal coupling member.

Abstract: A thermoelectric conversion module includes: a first substrate having water permeability and thermal conductivity; a thermoelectric conversion element provided on the first substrate; a heat insulator having water permeability provided around the thermoelectric conversion element on the first substrate; and a second substrate disposed on the thermoelectric conversion element and the heat insulator and having water permeability and thermal conductivity.

Abstract: A thermoelectric conversion module includes a pair of heat transfer plates, p-type semiconductor blocks and n-type semiconductor blocks arranged between the heat transfer plates, and terminal electrodes formed respectively on inner surfaces of the heat transfer plates and connecting the semiconductor blocks in series. The heat transfer plates include holes reaching from an outer surface to the terminal electrodes, and grooves each formed between the terminal electrodes and communicating between the adjacent holes. If a disconnection occurs, for example, a pin of a tester is brought into contact with the terminal electrode via the hole to specify a disconnected portion, and the terminal electrodes are electrically connected by injecting conductive paste into the holes in the disconnected portion as well as the groove.

Abstract: Thermoelectric generating parts having a plate-shape or film-shape are stacked in a thickness direction. Each of the thermoelectric generating parts generates an electric power as a temperature difference is generated in the thickness direction. Thermal conducting members are disposed between two of the thermoelectric generating parts adjacent in a stacked direction and on outer surfaces of outermost two thermoelectric generating parts. A first thermal coupling member is connected to and thermally coupled to the every other thermal conducting members. A second thermal coupling member is connected to and thermally coupled to the thermal conducting members not connected to the first thermal coupling member.