Kabushiki Kaisha Toshiba

Naoki Imasaki

Takashi Koiso

Toshiaki Tanaka

Hideyuki Aisu

Takeichiro Nishikawa

Hideki Kobayashi

Ryohei Orihara

Junichi Kiji

Kanako Hattori

Shigeru Kambayashi

Takufumi Yoshida

Tomoko Murakami

Makoto Kano

Kyoko Makino

Satoshi Sekine

Tsunekazu Endo

Shoji Nakai

Mitsuyo Yamaura

Tatsuo Yoshitsugu

Susumo Kubo

Ichiro Toyoshima

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 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.

Detection system, detection method, and detection computer program product

A monitoring method includes acquiring tag identification information unique to a radio frequency identification tag carried by a person, and reader identification information about a tag reader that detects the radio frequency identification tag. Next, attribute information about the person is acquired based on the tag identification information, and area information about a detection area of the tag reader is acquired based on the reader identification information. Finally, it is determined whether the person carrying the radio frequency identification tag having the attribute information acquired is allowed to enter into the detection area based on the area information.

Method of and computer program product for monitoring person's movements

A behavior of a group of persons is monitored. Each person is given a radio frequency identification tag. Tag identification information that is unique to the radio frequency identification tag, and reader identification information that is unique to a tag reader, which detects the radio frequency identification tag, are acquired. A position of each of the persons in the group is detected based on the tag identification information and the reader identification information. A position of majority of the persons in the group is regarded as the position of the group. If the position of a person is different from the position of the group, then that person is determined as having parted from the group.

Method of and system for monitoring behavior of group of persons, and computer program product

Each of a plurality of units 2 has the learning section 9 which updates the parameters PRM and performs learning. The learning means 9 modifies the parameters PRM based on the error signal OS' propagated from one unit to another. To build a system consisting of such units 2, the user uses the GUI or some other method to select the units 2 as well as the signal paths for connecting the units via the user interface, displays the system configuration status, and adjusts the functions of units 2. Therefore, an easy-to-build data processing system and a system-build system and method used to build such a system can be provided.

Data processing system, system-build system, and system-build method

In a vertically movable elevator group management control apparatus for control of a plurality of transversely shiftable cars among plural shafts, control is done by storing route data with respect to each said car, generating target floor data including a target floor, based on car call data obtained in correspondence with each said car and station call data as obtained correspondingly to each floor, estimating the time taken for said car to reach said target floor, based on at least said route data, said target floor data and said car call data, and assigning a certain car to a certain floor call, based on the estimated arrival time.

Elevator group management control apparatus and elevator group

The basic state feedback gain is derived and selected by the on-line identification based on the on-line input/output data of a controlled object, and at the same time, the sensitivity of the state feedback gain is directly and sequentially calculated based on the on-line input/output data of the controlled object. The state feedback gain is finely adjusted when the state feedback gain is not optimum. Further, the sensitivity of the state feedback gain of the controlled object is derived by the feedback gain sensitivity calculating unit according to the on-line input/output data of the controlled object. When the sensitivity is determined to be zero based on the criterion by the controller, the state feedback gain is determined to be optimum. When the sensitivity is determined to be near zero, the state feedback gain is finely adjusted by use of the sensitivity. When the sensitivity is determined to be greatly deviated from zero, an initialization instruction is supplied to the on-line identifier to initialize the covariance matrix in the on-line identification. Thus, a satisfactory response speed and precision can be obtained even when the present invention is applied to a controlled object dynamic characteristic of which is significantly changed.

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Kamakura, Japan

Naoki Imasaki