Kabushiki Kaisha Toshiba

Toshiba Tec Kabushiki Kaisha

Toshiba Energy Systems & Solutions Corporation

Takashi Koiso

Tomonori Ikumi

Masami Takahata

Naoki Imasaki

Masaki Narahashi

Hideki Kobayashi

Naoki Sekine

Ryohei Orihara

Takufumi Yoshida

Kanako Hattori

Toru Yano

Shigeru Kambayashi

Shinji Saegusa

Tomoko Murakami

Ryosuke Takeuchi

Ryusei Shingaki

Yoshiyuki Sakamoto

Shuuichiro Imahara

Shinya Umeno

Ichiro Toyoshima

Hiroshi Mizuno

According to one embodiment, an anomaly factor diagnosis apparatus includes an identifier configured to identify a first measurement data item corresponding to a first time point when an anomaly factor occurs in a power generation apparatus, among measurement data items in the power generation apparatus, based on anomaly factor occurrence data identifying the first time point; a distribution generator configured to calculate a first distribution function representing a distribution of a feature value of the first measurement data item; and an evaluator configured to calculate an evaluation value of possibility of occurrence of the anomaly factor in the power generation apparatus, based on the first distribution function, and measurement data items to be tested in the power generation apparatus.

Anomaly factor diagnosis apparatus and method, and anomaly factor diagnosis system

According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.

Behavior estimation apparatus, threshold calculation apparatus, behavior estimation method and non-transitory computer readable medium thereof

According to one embodiment, the flow line recognition system includes a first device, a first recording unit, a second device, a second recording unit and a generation unit. The first device detects a position of a moving object in a monitoring area by laser scanning. The second device detects a position of the moving object in a specific area, which is a part of the monitoring area, more accurately than the first device. The generation unit generates flow line information indicative of a path of the moving object moving in the monitoring area, based on the moving object information for the monitoring area recorded in the first recording unit, and the moving object information for the specific area recorded in the second recording unit.

Flow line recognition system

A trajectory processing apparatus comprises a trajectory database configured to store a position coordinate of a movable body detected from a camera image in association with data that specifies the camera image from which the movable body is detected, and a camera image database configured to store the camera image. A control section fetches the position coordinate of the movable body and the specifying data for the camera image from which the movable body is detected from the trajectory database. Further, the position coordinate of the movable body fetched from the trajectory database is displayed in a display section as a trajectory of the movable body. Furthermore, the control section acquires from the camera image database the camera image specified by the specifying data fetched from the trajectory database. Moreover, this camera image is displayed in the display section.

Trajectory processing apparatus and method

The invention provides a personal behavior analysis apparatus capable of statistically analyzing a personal behavior. To analyze personal behaviors, a population data file is provided that stores flow-line data for individual persons as a population acquired by tracking the behaviors of persons within a monitored area. After input of a condition for narrowing down the flow-line data as analysis objects is accepted via a condition input section, the population data file is searched based on the condition and flow-line data meeting the condition is acquired. The acquired flow-line data is stored in a subpopulation data file as a subpopulation. A control section of the apparatus calculates statistics values related to personal behavior analysis using the flow-line data stored in the subpopulation data file and displays them on a display section.

Apparatus and method for analyzing personal behavior

A behavior prediction apparatus includes an input unit inputting sole behaviors of a human and simultaneous occurrence probability of behaviors, a behavior discrimination unit specifying a behavior pattern based on correspondence between behaviors corresponding to simultaneous occurrence probability and actual behaviors, an information generation/recording unit generating behavior history information of the specified behavior pattern, and to generate and record information of each of a behavior transition probability, a behavior time and a behavior occurrence probability based on the behavior history information, a behavior prediction unit obtaining a starting behavior from the behavior occurrence probability, select another behavior pattern in order of behavior occurrence probability, and add the behavior time to the selected behavior pattern to output behavior prediction information, and a social value calculation unit obtaining a social value per unit time using the behavior prediction information, social value unit information and the prediction period.

Behavior prediction apparatus and method therefor

According to an aspect of the present invention, there is provided with a behavior prediction apparatus including: an area attribute database which stores area attributes of each of a plurality of areas in an activity area of customers; an inter-area attribute database which stores inter-area attributes between two areas; a behavior history database which stores behavior history of the customers in the activity area; a movement frequency database generator which generates a movement frequency database representing a movement frequency between the two areas on the basis of the individual behavior history database; and a movement frequency model estimator which estimates a value of a parameter in a movement frequency model having area attributes and inter-area attributes as its input and a movement frequency as its output.

System and method for customer behavior movement frequency prediction in a store

According to an aspect of the present invention, there is provided with a behavior determining apparatus including: an elemental-behavior extracting unit configured to extract, from trajectory data in which positions of a traveling body in an observation area are held at predetermined time intervals, L (L is an integer equal to or larger than 1) elemental behavior quantities among a stopping time, a stop frequency, a walking time, a running time, a direction change frequency, a sum of angle change quantities at the times when the traveling body changes a traveling direction, a sum of absolute values of angle change quantities at the times when the traveling body changes a traveling direction, a gravitational center set vector, and a pre-stop average speed; and a behavior determining unit configured to determine a behavior of the traveling body from the L elemental behavior quantities.

Behavior recognition using vectors of motion properties based trajectory and movement type

A trail analysis method of movable object includes acquiring first stay event data indicating a first location, a first arrival time of a first movable object to the first location and a first staying time, and second stay event data indicating a second location, a second arrival time of a second movable object to the second location and a second staying time, evaluating coincidence between the first and second locations based on the first and second stay event data, evaluating whether a first time zone specified by the first arrival time and the first staying time and a second time zone specified by the second arrival time and the second staying time overlap, from the first and second stay event data, and evaluating contact between the first and second movable objects using location and staying time evaluation results.

Trail analysis method and apparatus

A detection system includes a sensitiveness level setup control section and a receiver control section. The sensitiveness level setup control section looks into a sensitiveness level setup file and finds the sequence of levels of sensitiveness to the received signals corresponding to the setup ID. The receiver control section converts, for each component, a sequence of levels of sensitiveness to received signals received from the sensitiveness level switching section into signals for designating levels of sensitiveness to received signals corresponding to the respective receivers, and transmits the signals for designating levels of sensitiveness to received signals through the communication network.

Growing community of inventors

Kawasaki, Japan

Takashi Koiso