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：US20200327886A1

公开(公告)日：2020-10-15

申请号：US16380343

申请日：2019-04-10

Applicant: Hitachi, Ltd.

Inventor： Walid SHALABY ,  Chetan GUPTA ,  Maria Teresa GONZALEZ DIAZ ,  Adriano ARANTES

IPC: G10L15/22 ,  G10L15/18

Abstract: Example implementations involve a framework for knowledge base construction of components and problems in short texts. The framework extracts domain-specific components and problems from textual corpora such as service manuals, repair records, and public Q/A forums using: 1) domain-specific syntactic rules leveraging part of speech tagging (POS), and 2) a neural attention-based seq2seq model which tags raw sentences end-to-end identifying components and their associated problems. Once acquired, this knowledge can be leveraged to accelerate the development and deployment of intelligent conversational assistants for various industrial AI scenarios (e.g., repair recommendation, operations, and so on) through better understanding of user utterances. The example implementations give better tagging accuracy on various datasets outperforming well known off-the-shelf systems.

公开(公告)号：US20200294220A1

公开(公告)日：2020-09-17

申请号：US16355419

申请日：2019-03-15

Applicant: Hitachi, Ltd.

Inventor： Maria Teresa GONZALEZ DIAZ ,  Dipanjan GHOSH ,  Adriano ARANTES ,  Michiko YOSHIDA ,  Jiro HASHIZUME ,  Chetan GUPTA ,  Phawis THAMMASORN

IPC: G06T7/00 ,  G06N20/20 ,  G06K9/62 ,  G06T7/73

Abstract: Example implementations described herein involve defect analysis for images received from a camera system, which can involve applying a first model configured to determine regions of interest of the object from the images, applying a second model configured to identify localized areas of the object based on the regions of interest on the images; and applying a third model configured to identify defects in the localized ones of the images.

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：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.

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：US20200075027A1

公开(公告)日：2020-03-05

申请号：US16121837

申请日：2018-09-05

Applicant: HITACHI, LTD.

Inventor： Adriano Siqueira ARANTES ,  Marcos VIEIRA ,  Chetan GUPTA ,  Ahmed Khairy FARAHAT ,  Maria Teresa GONZALEZ DIAZ

IPC: G10L17/22 ,  G06N99/00 ,  G10L13/04 ,  G10L17/00

Abstract: In some examples, a system may receive from a device, speech sound patterns corresponding to a voice input related to equipment. Further, the system may determine an identity of a person associated with the device, and may identify the equipment related to the voice input. Using at least one of the received speech sound patterns or a text conversion of the speech sound patterns, along with an equipment history of the identified equipment, as input to one or more machine learning models, the system may determine, at least partially, an instruction related to the equipment. Additionally, the system may send, to the device, the instruction related to the equipment as an audio file for playback on the device.