公开(公告)号：US20210019906A1

公开(公告)日：2021-01-21

申请号：US16515867

申请日：2019-07-18

Applicant: Hitachi, Ltd.

Inventor： Yuki WATANABE ,  Ravigopal VENNELAKANTI ,  Manish GUPTA ,  Nam HUYN ,  Akira MAEKI ,  Chandrasekar VENKATRAMAN

IPC: G06T7/70 ,  G06K9/62 ,  G06T7/00 ,  G06T15/06 ,  G06K9/00 ,  G06T7/55 ,  G06T15/08

Abstract: Example implementations described herein are directed to the projection of two dimensional (2D) image recognition results to three dimensional (3D) space by using 3D reconstructed data to realize accurate object counting, identification, scene re-organization, and so on in accordance with the desired implementation. Through the example implementations described herein, more accurate objection detection can be provided than regular 2D object detection.

公开(公告)号：US20250158406A1

公开(公告)日：2025-05-15

申请号：US18388743

申请日：2023-11-10

Applicant: HITACHI, Ltd.

Inventor： Aniruddha Rajendra RAO ,  Chandrasekar VENKATRAMAN ,  Robert ELLIS ,  Chetan GUPTA

IPC: H02J3/14

Abstract: Systems and methods described herein can involve for a selection of one or more lines in a grid to bring down, executing a load shedding optimizing process configured to determine optimal loads in the grid that can be shed while maintaining grid stability, the determination of optimal loads is based on one or more lines chosen to be brought down, load importance, and identification of loads in the grid that can be partially or completely shed; and executing a load shedding process to shed loads in the grid according to the optimal loads in the grid topology to shed.

公开(公告)号：US20180340515A1

公开(公告)日：2018-11-29

申请号：US15605184

申请日：2017-05-25

Applicant: Hitachi, Ltd.

Inventor： Nam HUYN ,  Chandrasekar VENKATRAMAN

IPC: F03D9/25 ,  H02K7/18 ,  F03D7/04

CPC classification number: F03D9/257 ,  F03D7/028 ,  F03D7/045 ,  F03D7/046 ,  F03D7/048 ,  F03D9/11 ,  F03D17/00 ,  F05B2260/821 ,  F05B2260/8211 ,  F05B2260/84 ,  F05B2270/32 ,  F05B2270/335 ,  F05B2270/404 ,  H02K7/183

Abstract: In some examples, a system receives first sensor data from respective wind turbines of a plurality of wind turbines. For instance, the first sensor data may include at least a power output and a wind speed per time interval. The system trains at least one respective model for each respective wind turbine based on the first sensor data received from that respective wind turbine. Further, the system receives, for a second time period, respective second sensor data from the respective wind turbines. The system executes, using the respective second sensor data, the respective model trained using the first sensor data received from that respective wind turbine to determine, for each respective wind turbine, a predicted power output for an upcoming period. The predicted power outputs may be aggregated to determine a total predicted power output and at least one action is performed based on the total predicted power output.

公开(公告)号：US20240249135A1

公开(公告)日：2024-07-25

申请号：US18100933

申请日：2023-01-24

Applicant: Hitachi, Ltd.

Inventor： Aniruddha Rajendra RAO ,  Chandrasekar VENKATRAMAN ,  Chetan GUPTA

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Example implementations described herein involve systems and methods that can include, for receipt of time-series data indicative of energy consumption associated with a type of building of a plurality of different types of buildings and a climatic zone from a plurality of climatic zones, executing random convolutional kernel (RCK) on the time-series data to generate a classification group of the time-series data according to type of building and the climatic zone; and executing a trained functional neural network (FNN) on the time-series data of the classification group to provide a short-term energy consumption forecast.

公开(公告)号：US20230289859A1

公开(公告)日：2023-09-14

申请号：US17693080

申请日：2022-03-11

Applicant: Hitachi, Ltd.

Inventor： Manish GUPTA ,  Dipanjan GHOSH ,  Chandrasekar VENKATRAMAN ,  Archana BELANI ,  Umeshwar DAYAL

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0611 ,  G06Q30/0283

Abstract: Example implementations as described herein are directed to the use of an end-to-end asset recovery platform in conjunction with historical data of a plurality of assets. In example implementations, the end-to-end asset recovery platform tracks assets throughout the use and recovery lifecycles to provide complete visibility and enable informed decision-making for stakeholders.