公开(公告)号：US20220335183A1

公开(公告)日：2022-10-20

申请号：US17763473

申请日：2019-10-02

申请人： NIPPON TELEGRAPH AND TELEPHONE CORPORATION

发明人： Koshin TO ,  Akira NAKAYAMA ,  Masaru MIYAMOTO ,  Shinya OI ,  Yusuke TANAKA ,  Takayuki ADACHI ,  Mitsutoshi NAGAHAMA ,  Maiko NAYA

IPC分类号： G06F30/27 ,  G06F30/17

摘要： A vector field setting unit (102) sets a vector field for providing virtual force for reproducing, in a region in which a moving body moves, movement of the moving body in a certain direction, and a simulation unit (104) simulates the movement of the moving body in the region, based on a movement model to which a parameter for simulating the movement of the moving body is set and the vector field set by the vector field setting unit, whereby the movement of the moving body is appropriately simulated even when the movement of the moving body is unusual.

公开(公告)号：US20220366307A1

公开(公告)日：2022-11-17

申请号：US17763409

申请日：2019-10-02

申请人： NIPPON TELEGRAPH AND TELEPHONE CORPORATION

发明人： Koshin TO ,  Masaru MIYAMOTO ,  Shinya OI ,  Akira NAKAYAMA ,  Yusuke TANAKA ,  Takayuki ADACHI ,  Mitsutoshi NAGAHAMA ,  Maiko NAYA

IPC分类号： G06N20/00 ,  G08G9/00

摘要： A first learning unit (101) learns a difference model (111) for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points; a second learning unit (102) learns a prediction model (past) (112) for predicting variation of the monitoring target using the past monitoring data; a first generation unit (103) generates corrected past data using the difference model (111), by correcting a difference between the past monitoring data and the current monitoring data; and a third learning unit (104) learns a prediction model (current) (113) for predicting variation of the monitoring target using the current monitoring data, the difference model (111), the prediction model (past) (112), and the corrected past data, whereby variation of the monitoring target can be appropriately predicted even when the monitoring target involves irregular variation.