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-vision algorithms with edge-computing processing for the automatic detection of non-conformities. Machine-learning techniques will be applied to optimize cutting parameters, and the module will be
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/10.54499/2023.11234.PEX , funded by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning applied in Applied to Fluid Dynamics Simulation Admission requirements
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improve existing degradation models. T3 - combine computer vision and machine learning models to identify, delimit and classify the facades’ anomalies in photographs and thermal images; integrate the new
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for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration, with nº 15456, and
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://doi.org/10.54499/2023.11234.PEX , funded by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning applied in Applied to Fluid Dynamics Simulation Admission
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within the framework of project 2023.14874.PEX - anomaly - Machine Learning-based Models for Advanced Anomaly Detection in Dam Structural Health Monitoring, with DOI https://doi.org/10.54499/2023.14874.PEX