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Kamran Paynabar

As seen in: arXiv, Taylor & Francis Online
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Articles

Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties

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By Giovanna Capizzi, Kamran Paynabar, Mariafrancesca Patalano
| Taylor & Francis Online
Abstract Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD are widely applied in advanced manufacturing processes, including additive, subtractive, and hybrid manufacturing. To ensure the consistency of analysis and avoid false alarms, preprocessing steps such as registration and mesh reconstruction are commonly applied prior to monitoring.
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[2511.02452] An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity

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By Davide Cacciarelli, Kamran Paynabar
| arXiv
arXiv:2511.02452 (stat) View PDF HTML (experimental) Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2511.02452 [stat.ML] (or arXiv:2511.02452v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2511.02452 Submission history From: Davide Cacciarelli [ view email] [v1] Tue, 4 Nov 2025 10:30:20 UTC (14,330 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?)...
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Stream-Based Active Learning for Process Monitoring

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By Antonio J. Lepore, Kamran Paynabar, Christian Capezza
| Taylor & Francis Online
Abstract Statistical process monitoring (SPM) methods are essential tools in quality management to assess the stability of industrial processes, i.e., to dynamically classify the process state as in control (IC), under normal operating conditions, or out of control (OC), otherwise. Although traditional SPM methods are based on unsupervised approaches, supervised methods leverage process data with labels revealing the true process state.
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