US 11,720,091 B2
Systems and methods for real-time data processing and for emergency planning
Alper Yilmaz, Lewis Center, OH (US); Nima Ajam Gard, Dublin, OH (US); Ji Hyun Lee, Columbus, OH (US); Tunc Aldemir, Columbus, OH (US); and Richard Denning, Columbus, OH (US)
Assigned to Ohio State Innovation Foundation, Columbus, OH (US)
Filed by Ohio State Innovation Foundation, Columbus, OH (US)
Filed on Jul. 9, 2021, as Appl. No. 17/371,940.
Application 17/371,940 is a division of application No. 17/264,122, granted, now 11,156,995, previously published as PCT/US2019/047745, filed on Aug. 22, 2019.
Claims priority of provisional application 62/721,273, filed on Aug. 22, 2018.
Prior Publication US 2022/0027731 A1, Jan. 27, 2022
Int. Cl. G06N 3/08 (2023.01); G05B 23/02 (2006.01); G05B 13/02 (2006.01); G21D 3/00 (2006.01); G21D 3/04 (2006.01); G06F 11/34 (2006.01); G06N 3/045 (2023.01)
CPC G05B 23/024 (2013.01) [G05B 13/027 (2013.01); G06F 11/3495 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G21D 3/001 (2013.01); G21D 3/04 (2013.01); G05B 2219/32335 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A modular neural network system for real-time processing of data, comprising:
a plurality of graphical processors, executing stored programming codes stored in a non-transitory computer storage medium to process scenario data, wherein the programming codes are configured as:
a first processing segment and a second processing segment, each of the first and the second processing segment comprising respective plurality of processing bulks, wherein:
a first bulk of the first and the second processing segment is a front portion and a last bulk of the first and the second processing segment is a back portion,
the first bulk has a shallower data processing block depth than subsequent processing bulks, such that processing block depth increases as a flow of the scenario data increases towards the back portion;
each processing bulk of the first and the second processing segment comprises a plurality of processing blocks for processing certain micro tasks;
each processing block having a different kernel size at a same level for capturing features with different data sizes in the scenario data;
the kernels are selected from one of: a convolution kernel, a transpose convolution kernel;
such that the first segment is responsible for learning special tasks, which the learning will be used in the second segment to reconstruct a volumetric input.