公开(公告)号：US20240152756A1

公开(公告)日：2024-05-09

申请号：US18548805

申请日：2022-03-25

Applicant: Intel Corporation

Inventor： Barath Lakshmanan ,  Ashish B. Datta ,  Craig D. Sperry ,  David J. Austin ,  Caleb Mark McMillan ,  Neha Purushothaman ,  Rita H. Wouhaybi

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: In one embodiment, a method of training an autoencoder neural network includes determining autoencoder design parameters for the autoencoder neural network, including an input image size for an input image, a compression ratio for compression of the input image into a latent vector, and a latent vector size for the latent vector. The input image size is determined based on a resolution of training images and a size of target features to be detected. The compression ratio is determined based on entropy of the training images. The latent vector size is determined based on the compression ratio. The method further includes training the autoencoder neural network based on the autoencoder design parameters and the training dataset, and then saving the trained autoencoder neural network on a storage device.

公开(公告)号：US20220066435A1

公开(公告)日：2022-03-03

申请号：US17523559

申请日：2021-11-10

Applicant: Intel Corporation

Inventor： Tara K. Thimmanaik ,  Rita Chattopadhyay ,  David J. Austin

IPC: G05B23/02 ,  G06T7/00

Abstract: In one embodiment, a device comprises interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a video stream captured by a camera during performance of an industrial process, wherein the video stream comprises a sequence of frames; detects, based on analyzing the sequence of frames, a degree of particle scatter that occurs during performance of the industrial process; and determines, based on the degree of particle scatter, that an anomaly occurs during performance of the industrial process.