The dataset consists of acoustic signals acquired from the surface of the knee of 14 subjects. The description of study group and methodology of the experiment can be found in the publication: https://doi.org/10.3390/s21196495.
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This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques.
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Acoustic measurement data from Multilayer Ceramic Capacitors (MLCCs). Contains preprocessed data from intact and damaged MLCCs for damage detection (classification) purposes.
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