Kabushiki Kaisha Toshiba

Toshiba Energy Systems & Solutions Corporation

Kouta Nakata

Yuichi Kato

Yasunori Taguchi

Kentaro Takagi

Susumu Naito

Toshio Aoki

Taira Ashikawa

Tomoo Ikeda

Osamu Nishiyama

Shun Hirao

Shinya Tominaga

Yaling Tao

Ryota Miyake

Chikashi Miyamoto

Akira Kuroda

Kazunori Imoto

Kouji Ueno

Ryohei Orihara

Takahiro Takimoto

Yoshiaki Mizuoka

Koji Ueno

Ayana Yamamoto

Naoyuki Takado

Isaku Nagura

Yusuke Terakado

Eiichi Ookuma

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 one embodiment, a data processing apparatus includes a processor. The processor calculates, from the first measurement data, a first differential value set that is a set of first differential values in a time direction at a time included in the first period of the measurement values of the sensor of interest. The processor calculates, from the second measurement data, a second differential value set that is a set of second differential values in a time direction at a time included in the second period of the measurement values of the sensor of interest. The processor generates a first differential value distribution and a second differential value distribution using the second differential value set.

Data processing apparatus, method, and program

According to one embodiment, system includes a determination unit, a first storage, a second storage, a search unit and a display. The determination unit determines a feature quantity of the process-targeted manufacturing data. The first storage stores cause-unidentified manufacturing data. The second storage stores cause-identified manufacturing data. The search unit searches, based on the feature quantity of the process-targeted manufacturing data, the first storage and the second storage for the cause-unidentified manufacturing data and the cause-identified manufacturing data that have a feature quantity similar to that of the process-targeted manufacturing data. The display displays the search result.

Similar-defect search/display system, apparatus and method

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 one embodiment, a monitoring apparatus includes a processing circuit. The processing circuit is configured to generate second data including a prediction value of a second sensor correlated with a first sensor from first data including a measurement value of the first sensor of which a measurement value changes suddenly in a case where the control signal changes, detect an anomaly of the system or an anomaly of at least one sensor, and make it difficult to detect the anomaly in a case where the determination signal indicates that there is a change in the control signal.

Monitoring apparatus, method, and program

A classification device includes a generation unit, a learning unit, a classification unit, and an output control unit. The generation unit generates pseudo data having a feature similar to a feature of training data. The learning unit learns, by using the training data and the pseudo data, a classification model that classifies data into one of a pseudo class for classifying the pseudo data and a plurality of classification classes other than the pseudo class and that is constructed by a neural network. The classification unit classifies, by using the classification model, input data as a target for classification into one of the pseudo class and the plurality of classification classes. The output control unit outputs information indicating that the input data classified into the pseudo class is data not belonging to any of the plurality of classification classes.

Classification device, classification 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, an estimation device acquires a data set from history data. The history data includes a plurality of data IDs, path information, first and second qualitative variables. The data IDs respectively indicate a plurality of data flowing through a plurality of nodes. The path information indicates a path of the nodes for each of the data. The first and second qualitative variables are mutually-independent and indicate classifications of each of the data IDs. The data set includes a part of the data IDs having a first variable value assigned as the first qualitative variable. The estimation device estimates an overall relevance indicating a relevancy to the data set for each of the nodes. The estimation device generates a plurality of partial data sets. The estimation device estimates a partial relevance indicating a relevancy to each of the partial data sets for each of the nodes.

Estimation device, display control device, estimation system, and estimation method

According to an embodiment, a monitoring apparatus configured to generate time-series predicted data based on time-series measured data and a prediction model that generates predicted data including one or more predicted values predicted to be output from one or more sensors; and generate, for a first sensor among the one or more sensors, a displayed image including a measured value graph representing a temporal change in a measured value included in the time-series measured data in a second period after a first period, a predicted value graph representing a temporal change in a predicted value included in time-series predicted data in the second period, past distribution information representing a distribution of a measured value in the first period, and measurement distribution information representing a distribution of the measured value included in the time-series measured data in the second period.

Monitoring apparatus, monitoring method, computer program product, and model training apparatus

An abnormality determination device includes one or more processors. The processors input first input data to a first model to obtain first output data. The first output data is formed by restoring data with the reduced dimension to data with the same dimension as that of the first input data. The processors input second input data, which is a difference between the first input data and the first output data, to a second model, and obtain second output data. The second output data is formed by restoring data with the reduced dimension to data with the same dimension as that of the second input data. The processors obtain restored data that is a sum of the first output data and the second output data. The processors compare the first input data with the restored data and determine an abnormality in the first input data based on the comparison result.

Abnormality determination device, learning device, and abnormality determination method

A first information processing apparatus includes a record set acquiring unit that acquires a record set, a record identification information acquiring unit that acquires record identification information, a concealing unit that generates a concealment record set from the record set using conversion information and generates concealment record identification information based on the record identification information and the conversion information, and a first transmitter that transmits the concealment record set and the concealment record identification information to a second information processing apparatus. The second information processing apparatus includes a second receiver that receives the concealment record set and the concealment record identification information from the first information processing apparatus and a data analyzer that analyzes each record in the concealment record set identified based on the concealment record identification information using a learning model or a numerical model.

Information processing system, information processing apparatus, information processing method, and computer program product

According to one embodiment, a learning apparatus includes a first rule generator, a feature value calculator, a related word extractor, a second rule generator, and a learning unit. The first rule generator generates a first rule to label the event candidate, the first rule including a keyword of the event candidate. The feature value calculator calculates feature values of other words included in the text other than the event candidate. The related word extractor extracts a related word relating to the keyword from the other words using the feature values. The second rule generator generates a second rule to label the event candidate, the second rule being different from the first rule and including the related word. The learning unit generates learning data associating the keyword, the related word, and labeled event candidate with each other.

Learning apparatus, learning method, and learning program

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

According to one embodiment, a data connection device includes a first log storage, a second log storage, and a hardware processor. The first log storage is configured to store a plurality of first logs indicating a first behavior of a first user. The second log storage is configured to store a plurality of second logs which are independent of the plurality of first logs and indicate a second behavior of a second user. The hardware processor is configured to acquire a set of first logs indicating a specific first behavior, from the plurality of first logs, acquire a set of second logs having a feature in common with the set of first logs, and connect the set of first logs with the set of second logs.

Growing community of inventors

Tokyo, Japan

Kouta Nakata