Hitachi, Ltd.

Nam Huyn

Ravigopal Vennelakanti

Chandrasekar Venkatraman

Manish P Gupta

Yuki Watanabe

Akira Maeki

Example implementations described herein are directed to an asset management system configured to facilitate real-time inventory recognition with image analysis tagged with positional information. The example implementations described herein also provide the method and process to improve the accuracy of the pipe detection for counting with various approaches. Referring the expected number of the pipes, example implementations utilize the pipe detection algorithm, as well as the bounding box of the pipe stack, use the knowledge of the physical size of the pipe stack, and analyze a pipe stack from the images of two directions at the end.

Inventory count method and asset management system

Example implementations described herein are directed to a solution to the problem of accurate real-time inventory counting and industrial inspection. The solution, involves a device such as a mobile device that assists a human operator on the field to quickly achieve high quality inspection results. The example implementations detect objects of interest in individual image snapshots and use location and orientation sensors to integrate the snapshots to reconstruct a more accurate virtual representation of the inspection area. This representation can then be reorganized in various ways to derive inventory counts and other information that are not planned originally.

Systems and methods for a real-time intelligent inspection assistant

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.

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Milpitas, CA, United States of America

Nam Huyn