公开(公告)号：US11720091B2

公开(公告)日：2023-08-08

申请号：US17371940

申请日：2021-07-09

Applicant: Ohio State Innovation Foundation

Inventor： Alper Yilmaz ,  Nima Ajam Gard ,  Ji Hyun Lee ,  Tunc Aldemir ,  Richard Denning

IPC: G06N3/08 ,  G05B23/02 ,  G05B13/02 ,  G21D3/00 ,  G21D3/04 ,  G06F11/34 ,  G06N3/045

CPC classification number: G05B23/024 ,  G05B13/027 ,  G06F11/3495 ,  G06N3/045 ,  G06N3/08 ,  G21D3/001 ,  G21D3/04 ,  G05B2219/32335

Abstract: Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.

公开(公告)号：US11156995B2

公开(公告)日：2021-10-26

申请号：US17264122

申请日：2019-08-22

Applicant: Ohio State Innovation Foundation

Inventor： Alper Yilmaz ,  Nima Ajam Gard ,  Ji Hyun Lee ,  Tunc Aldemir ,  Richard Denning

IPC: G05B13/02 ,  G05B23/02 ,  G06N3/04 ,  G21D3/00 ,  G21D3/04

Abstract: Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.