Ajou University Industry-academic Cooperation Foundation

Dong Yeon Yoo

Jung Won Lee

Ye Seul Park

Yang Gon Kim

Su Bin Bae

Chang Nam Lim

Jin Se Kim

A method and system for health monitoring of a collaborative robot are provided. The method includes calling a test program installed in a collaborative robot for health monitoring of the collaborative robot when the collaborative robot satisfies a call condition of the test program, performing a test by operating the collaborative robot based on the test program, and collecting and analyzing a result of the test by the collaborative robot.

Method and system for health monitoring of collaborative robot

The present invention relates to a method and device for diagnosing a defect of a collaborative robot, the method comprising the steps in which: an electronic device generates a sensing data structure for managing sensing data collected from at least one collaborative robot; the electronic device generates an operation data structure for managing operation data associated with the operation of the collaborate robot; the electronic device generates a malfunction data structure for managing malfunction data of a point in which the severity of the operation equals to or is higher than a threshold; and the electronic device stores data collected from the at least one collaborative robot in accordance with the structures. Application to other embodiments is also possible.

Data management method and device for diagnosing defect of collaborative robot

The present disclosure relates to a method for filtering selectively obstacle to be an obstacle to machine learning according to a learning purpose and a system thereof. A system for filtering obstacle data in machine learning of medical images may include an obstacle data definition unit configured to receive definitions of obstacle data according to a machine learning purpose; a filter generation unit configured to generate a filter for filtering the obstacle data; and a filtering unit configured to remove obstacle data in machine learning using the generated filter.

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Suwon-si, South Korea

Dong Yeon Yoo