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- W4385575551 abstract "AbstractErrors in Cyber-Physical Systems present a major problem given the current state of technological complexity. Self-diagnosis can contribute to address it, being the standpoint of the present paper. Hence, an application of exploratory Machine Learning models to assess the functioning of robot software in order to identify anomalies that lead to low performance is proposed. More precisely, Hybrid Unsupervised Exploratory Plots (HUEPs) are extended through density-based clustering techniques, that are applied together with unsupervised exploratory projection models. As a result, intuitive and informative visualizations of software performance are obtained, supporting the monitoring and anomaly detection tasks. The proposed clustering extension of HUEPs is thoroughly validated on a massive and up-to-date open dataset, obtaining promising results.Keywords: Anomaly detectionexploratory analysisrobot softwareperformancemachine learningclustering Notes1 European Commission. Key Enabling Technologies (KETs) Observatory. URL: https://knowledge4policy.ec.europa.eu/publication/key-enablingtechnologies-kets-observatory_en2 International Federation of Robotics. IFR presents the World Robotics 2021 reports. URL: https://ifr.org/ifr-press-releases/news/robot-sales-riseagain" @default.
- W4385575551 created "2023-08-05" @default.
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- W4385575551 date "2023-08-04" @default.
- W4385575551 modified "2023-09-27" @default.
- W4385575551 title "A Clustering Extension of HUEPs for the Analysis of Performance Anomalies in Robots" @default.
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- W4385575551 doi "https://doi.org/10.1080/01969722.2023.2240648" @default.
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