Kabushiki Kaisha Toshiba

Kentaro Takagi

Kouta Nakata

Toshihiro Fujiwara

Osamu Watanabe

Yuichi Kato

Kazuaki Ikeda

Masataka Fukuda

Kazuhiro Saito

Shigeru Harada

Yasunori Taguchi

Atsuhide Suzuki

Toshihiro Matsuura

Hisao Fujihashi

Yaling Tao

Satoshi Nagai

Mitsuyoshi Okazaki

Seiichi Asatsu

Kazunari Fujiyama

Hirotsugu Kodama

Susumu Naito

Hitoshi Kichise

Tetsuya Yamanaka

Taiji Hirasawa

Takayuki Iwahashi

Masanori Minowa

Yasunori Ono

Masao Arimura

Yasunari Akikuni

According to one embodiment, the importance analysis apparatus includes an importance calculator and a distribution calculator. Based on a trained model and a plurality of input data samples, the importance calculator calculates an importance of each of a plurality of feature amounts of each of the input data samples. The distribution calculator calculates a distribution of the importances of each of the feature amounts across the input data samples.

Importance analysis apparatus, method, and non-transitory computer readable medium

According to an embodiment, a learning device includes one or more processors. The processors calculate a latent vector of each of a plurality of first target data, by using a parameter of a learning model configured to output a latent vector indicating a feature of a target data. The processors calculate, for each first target data, first probabilities that the first target data belongs to virtual classes on an assumption that the plurality of first target data belong to the virtual classes different from each other. The processors update the parameter such that a first loss of the first probabilities, and a second loss that is lower as, for each of element classes to which a plurality of elements included in each of the plurality of first target data belong, a relation with another element class is lower, become lower.

Learning device, learning method, and computer program product

According to an embodiment, a learning device includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: generate a transformation matrix from learning data in which feature quantities and target values are held in a corresponding manner; and learn about parameters of a neural network which includes nodes equal in number to the number of rows of the transformation matrix, a first output layer representing first estimation distribution according to the values of the nodes, and a second output layer representing second estimation distribution decided according to the product of the transformation matrix and the first estimation distribution.

Learning device, inference device, learning method, and inference method using a transformation matrix generated from learning data

According to one embodiment, an information processing apparatus includes a processing circuit. The processing circuit calculates a first input/output error related to normal data and a second input/output error related to pseudo abnormal data different from the normal data, for each of a plurality of autoencoders having different network structures. The processing circuit outputs relational data indicating a relation between the network structure and the first input/output error and the second input/output error.

Information processing apparatus, information processing method, and storage medium

According to one embodiment, a communication device includes one or more processors. The processors decide on a first-type parameter representing a conversion operation for converting input data into first-type conversion data. The processors calculate first-type predicted distributions based on second-type parameters, each representing one of the plurality of clusters. The processors update the first-type parameter and the second-type parameters so as to achieve optimization of first-type differences representing differences between the first-type predicted distributions and a target distribution, and second-type differences representing differences between the first-type predicted distributions and second-type predicted distributions that indicate probability at which second-type converted data. The second-type converted data is obtained by converting data, which is formed by augmentation of the input data, using the first-type parameter belongs to the clusters. The processors classify the input data into the plurality of clusters based on the first-type predicted distributions calculated using the updated second-type parameters.

Clustering device, clustering method, and computer program product

A method of measuring a steam turbine according to an embodiment has: installing a measuring device into the inside of the steam turbine through an inspection hole or a manhole of the steam turbine or an inspection hole or a manhole of a condenser connected to the steam turbine, when the steam turbine is halted; and measuring a position and a dimension of an axial key or a center key of the steam turbine by using the measuring device, without opening a turbine casing of the steam turbine.

Method of measuring steam turbine, measuring device, and method of manufacturing steam turbine

An operation-supporting apparatus includes: an operation request input unit that inputs information relating to operation request items of plant equipment; an operating conditions setting unit that converts operation request input information to operating parameters; an operation history creation unit that creates an operation history by chronologically processing the operating parameters; a breakdown statistics database that stores an event tree of the plant equipment; an event simulator that calculates the degree of unreliability corresponding to the operating parameters, the operation request information, and the operation history information, and in compliance with the event tree; a risk-cost calculation unit that calculates risk-cost by determining the aggregate product of the degrees of unreliability and restoration costs; an operating method determination unit that determines whether operating conditions are suitable by comparing the risk-cost with profit gained by continuing operation; and an operating method specification unit that specifies specific operation conditions for the plant equipment.

Plant apparatus operation support device

A geothermal turbine comprises a turbine casing, a turbine rotor shaft, a plurality of nozzle diaphragm outer rings fixed to the turbine casing, a plurality of nozzle diaphragm inner rings located radially inside the nozzle diaphragm outer rings, a plurality of nozzles placed between the nozzle diaphragm outer rings and the nozzle diaphragm inner rings, which form a steam passage, a plurality of rotor blades mounted on the turbine rotor shaft, which face the nozzles and form a blade cascade in a circumferential direction, a shroud arranged on tip of the rotor blade, an overhang attached to the nozzle diaphragm outer ring, which extends downstream of the nozzles and has an inner surface facing radially outside the shroud, and a plurality of sealing fins which protrude radially outward from the shroud and face the overhang.

Geothermal turbine

Growing community of inventors

Kanagawa-ken, Japan

Kentaro Takagi