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Performance Evaluation Technique of Learning Model Based on Feature Cluster in Sensing Data of Collaborative Robots
Jinse Kim, Subin Bea, Ye-Seul Park, Jung-Won Lee
http://doi.org/10.5626/JOK.2022.49.10.824
Recently, attempts have been made to apply an artificial intelligence model to PHM(Prognostics and Health Management) of collaborative robots, a representative equipment of smart factories. However, typical models are developed in a heuristic way without preprocessing or analysis of sensing data collected by operating test programs. Therefore, in this paper, we proposed a model performance evaluation method based on feature cluster concept which could analyze features of time series sensing data with cycles collected from cooperative robots. To demonstrate the effectiveness of the proposed method, we applied it to a program classification model, an internal component of the motion fault detection network, and identified characteristics of data that contributed to performance degradation, which has not been revealed by existing method. This results enabled a qualitative evaluation of the performance of the model and provided directions to improving model performance.
Development and Application of Guidelines for Compliance with IEC 62304 International Standards for AI Medical Device Software
DongYeop Kim, Ye-Seul Park, Byungjeong Lee, Jung-Won Lee
http://doi.org/10.5626/JOK.2021.48.1.71
Medical device software developers must implement the processes required by IEC 62304, the international standard for medical device software life-cycle processes, and they must also have a large amount of artifacts to obtain a license. Recently, AI medical device software based on medical images has been actively developed, and since it is treated as standalone software, it must be approved in accordance with IEC 62304 for medical device software. The international standard for AI technology is currently in the discussion stage, and the developer should arbitrarily establish the life-cycle process of AI medical device software, and by matching the specifications of IEC 62304, the performance and safety of AI products will be evaluated. It is unclear which quality management technique should be used to produce the best artifact. This paper provides a quality control technique for fulfilling the scope and requirements of IEC 62304 compliance for AI medical device software in the form of guidelines. These guidelines are also applied to actual AI products to check their potential use in real applications.
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