Kabushiki Kaisha Toshiba

Johns Hopkins University

Toshiba Medical Systems Corporation

Minoru Nakatsugawa

Takeichiro Nishikawa

Kenji Hirohata

Minoru Yonezawa

Chie Morita

Minoru Mukai

Ryusei Shingaki

Todd R McNutt

Robert Davey

Harry Quon

Scott Patrick Robertson

Zhi Cheng

John Wayne Haller

Rachel-Louise Kvertus Koktava

Kosuke Sakaue

An apparatus for determining a contour of a treatment region in a patient includes a computer processor to receive input regarding a contour of at least one organ-at-risk (OAR) adjacent to the treatment region; receive input regarding an initial contour of the treatment region; predict a radiation toxicity to the at least one OAR based on the contour of the at least one OAR, the initial contour of the treatment region, and a radiation treatment regimen; determine whether the predicted radiation toxicity exceeds a threshold; and determine a contour of the treatment region by iteratively modifying the initial contour of the treatment region, and any subsequent modified contours of the treatment region, until a stopping condition is satisfied. The stopping condition can be a preselected number of iterations or that the predicted radiation toxicity using the contour in place of the initial contour is first calculated is below said threshold.

Method and apparatus for determining treatment region and mitigating radiation toxicity

A server includes a distribution difference calculator and a determining unit. The distribution difference calculator calculates difference information between a first distribution of features calculated from operating data of terminal devices of a first machine type and a second distribution of features calculated from operating data of terminal devices of a second machine type. The determining unit determines whether a model is applicable to the terminal devices of the first machine type to predict occurrence possibility of a failure in each terminal device of the first machine type on basis of the difference information wherein the model is generated on basis of operating data of the terminal devices of the second machine type and is configured to predict occurrence possibility of a failure in each terminal device of the second machine type on basis of each of the features calculated from the operating data thereof.

Server, model applicability/non-applicability determining method and non-transitory computer readable medium

A data preserving apparatus includes: a generator generating scenarios that represent presence or absence of occurrence of a failure, presence or absence of a failure symptom, a time point of occurrence of a failure, and a time point of occurrence of a failure symptom; an evaluator calculating the amount of data loss in the case of occurrence of a failure for the scenario based on first and second backup intervals before and after occurrence of a failure symptom; an backup interval evaluator calculating a number of backups performed until earlier one of a time point of occurrence of a failure and a time point after a predetermined time period; a cost evaluator calculating a cost evaluation values for the scenarios based on the amount of data loss and the number of backups; and a calculator calculating the first and second backup intervals based on cost evaluation values for scenarios.

Data preserving apparatus, method and system therefor

According to one embodiment, a measurement unit periodically measures a usage status of a measurement target, and measures each test item of the measurement target at an arbitrary timing according to a test schedule. A first storage unit stores the usage status and a test result in time series. A second storage unit stores statistic information representing a relationship between the usage status and the test result. A probability calculation unit calculates a first evaluation value representing a load of the measurement target at a recent timing and a second evaluation value representing a load of the measurement target at the arbitrary timing, based on the usage status, and calculates a specific probability of each test item, based on the statistic information, the first evaluation value and the second evaluation value. A test schedule creation unit creates a new test schedule having selected test items, based on the specific probability.

Maintenance system and maintenance method

A user evaluation apparatus includes a parameter determination module which determines parameters of a user evaluation model required to calculate a degree of usage influence so as to maximize a target function. The target function may be defined using weight values, measurement data, and a failure information counting result. The apparatus also includes a user evaluation module which calculates and displays the degree of usage influence using the measurement data and the parameters of the user evaluation model.

User evaluation apparatus depending on hardware usage status

A first module calculates a failure occurrence risk index of each data storage area address. A second module calculates a power saving index of each data storage area address. A third module calculates an access speed index per unit data volume necessary to access each data storage area address. A fourth module generates a distribution table that represents the failure occurrence risk index, the power saving index, and the access speed index for each candidate address, with respect to data to be distributed. A fifth module selects a candidate address in the distribution table such that the power saving index and the access speed index meet restricting conditions and the failure occurrence risk index is minimized, and distributes the data to the candidate address.

Memory device

According to one embodiment, an evaluating apparatus includes an operation data storage unit, a labeling unit, a learning unit, and an evaluating unit. The labeling unit applies a failure label, indicating that a product is broken down, to operation data of the product that is broken down within a designated period of time from the observation date of the operation data, while applies a non-failure label, indicating that the product is not broken down, to the operation data of the product that is not broken down within a designated period of time from the observation date of the operation data. The labeling unit applies neither the failure label nor the non-failure label to the operation data of the product, which is not certain that it is broken down or not within a designated period of time from the observation date of the operation data.

Evaluating apparatus and evaluating program product

There is provided with a method, including: optimizing a first objective function defined by using an explanatory variable belonging an attribute in each sample, a target variable in each sample, and a first conversion parameter to find a value of the first conversion parameter; generating by using the first conversion parameter corresponding to the attribute a conversion function for converting an explanatory variable belonging to the attribute to an intermediate variable with certain range; optimizing a second objective function defined by using a plurality of the intermediate variables corresponding to the plurality of variables in each sample, the target variable in each sample, and a second conversion parameter to find a value of the second conversion parameter; and generating by using the second conversion parameter a probabilistic model for calculating from a plurality of intermediate variables a probability that a predetermined event occurs or does not occur.

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Yokohama, Japan

Minoru Nakatsugawa