公开(公告)号：US20210374500A1

公开(公告)日：2021-12-02

申请号：US16886344

申请日：2020-05-28

Applicant: Hitachi, Ltd.

Inventor： Dipanjan GHOSH ,  Maria Teresa GONZALEZ DIAZ ,  Mahbubul ALAM ,  Ahmed FARAHAT ,  Chetan GUPTA ,  Lijing WANG

IPC: G06N3/04 ,  G06N20/20 ,  G06N3/08 ,  G06N5/04

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

公开(公告)号：US20240420450A1

公开(公告)日：2024-12-19

申请号：US18210221

申请日：2023-06-15

Applicant: HITACHI, Ltd.

Inventor： Lasitha Sandaruwan VIDYARATNE ,  Xian Yeow LEE ,  Mahbubul ALAM ,  Ahmed FARAHAT ,  Dipanjan GHOSH ,  Maria Teresa GONZALEZ DIAZ ,  Chetan GUPTA

IPC: G06V10/762 ,  G06V10/74 ,  G06V10/77 ,  G06V10/774

Abstract: Systems and methods described herein which can involve for a first input of a plurality of labeled images of a new domain task, processing the first plurality of labeled images through a plurality of backbone snapshots, each of the backbone snapshots representative of a model trained across a plurality of other domain tasks, each of the plurality of backbone snapshots configured to output a first plurality of features responsive to the input; processing a second input of second plurality of unlabeled images through the plurality of backbone snapshots to output a second plurality of features responsive to the second input; and generating a representative model for the new domain task from the clustering and transformation of the first plurality of features and as associated from the clustered and transformed second plurality of features.

公开(公告)号：US20230206111A1

公开(公告)日：2023-06-29

申请号：US17561397

申请日：2021-12-23

Applicant: Hitachi, Ltd.

Inventor： Mahbubul ALAM ,  Dipanjan GHOSH ,  Ahmed FARAHAT ,  Laleh JALALI ,  Chetan GUPTA ,  Shuai Zheng

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Example implementations described herein can involve systems and methods involving, for receipt of input data from one or more assets, identifying and separating different event contexts from the input data; training a plurality of machine learning models for each of the different event contexts; selecting a best performing model from the plurality of machine learning models to form a compound model; selecting a best performing subset of the input data for the compound model based on maximizing a metric; and deploying the compound model for the selected subset.

公开(公告)号：US20240152787A1

公开(公告)日：2024-05-09

申请号：US17981107

申请日：2022-11-04

Applicant: Hitachi, Ltd.

Inventor： Mahbubul ALAM ,  Ahmed FARAHAT ,  Dipanjan GHOSH ,  Jana BACKHUS ,  Teresa GONZALEZ ,  Chetan GUPTA

IPC: G06N5/04 ,  G06N20/00

CPC classification number: G06N5/043 ,  G06N20/00

Abstract: Example implementations described herein involve systems and methods for efficient learning for mixture of domains which can include applying a clustering technique to a set of data comprised of multiple domains to obtain an initial domain separation of the set of data into one or more clusters; training one or more experts associated with each of the one or more clusters based on the initial domain separation where each expert corresponds with one domain of the multiple domains; inputting all data points to the one or more experts for refining each of the one or more clusters using expert output probabilities; retraining the one or more experts based on the refined one or more clusters; and training a gating mechanism to route an input to an appropriate expert of the one or more experts based on the refined one or more clusters.

公开(公告)号：US20230153982A1

公开(公告)日：2023-05-18

申请号：US17525807

申请日：2021-11-12

Applicant: Hitachi, Ltd.

Inventor： Maria Teresa GONZALEZ DIAZ ,  Dipanjan GHOSH ,  Mahbubul ALAM ,  Chetan GUPTA ,  Eman T. Hassan

IPC: G06T7/00 ,  G06N3/08 ,  G06K9/62

CPC classification number: G06T7/0008 ,  G06N3/084 ,  G06K9/6257 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30252

Abstract: Example implementations involve systems and methods to create robust visual inspection datasets and models. The novel method learns and transfers damage representation from few samples to new images. The proposed method introduces a generative region-of-interest based adversarial network with the aim of learning a common damage representation and transferring it to an unseen image. The proposed approach shows the benefit of adding damage-region-based component, since existing methods fail to transfer the damages. The proposed method successfully generated images with variations in context and conditions to improve model generalization for small datasets.

公开(公告)号：US20220187819A1

公开(公告)日：2022-06-16

申请号：US17118081

申请日：2020-12-10

Applicant: Hitachi, Ltd.

Inventor： Walid SHALABY ,  Mahbubul ALAM ,  Dipanjan GHOSH ,  Ahmed FARAHAT ,  Chetan GUPTA

IPC: G05B23/02 ,  G06N3/08 ,  G06K9/62

Abstract: Example implementations involve systems and methods for predicting failures and remaining useful life (RUL) for equipment, which can involve, for data received from the equipment comprising fault events, conducting feature extraction on the data to generate sequences of event features based on the fault events; applying deep learning modeling to the sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; and executing optimization on the model.

公开(公告)号：US20220187076A1

公开(公告)日：2022-06-16

申请号：US17119896

申请日：2020-12-11

Applicant: Hitachi, Ltd.

Inventor： Maria Teresa GONZALEZ DIAZ ,  Adriano S. ARANTES ,  Dipanjan GHOSH ,  Mahbubul ALAM ,  Gregory SIN ,  Chetan GUPTA

IPC: G01C21/20 ,  G06T7/00 ,  G06N3/08 ,  G06T11/00 ,  G06F30/10

Abstract: Example implementations involve systems and methods to advance data acquisition systems for automated visual inspection using a mobile camera infrastructure. The example implementations address the uncertainty of localization and navigation under semi-controlled environments. The approach combines object detection models and navigation planning to control the quality of visual inputs in the inspection process. The solution guides the operator (human or robot) to collect only valid viewpoints to achieve higher accuracy. Finally, the learning models and navigation planning are generalized to multiple type and size of inspection objects.