Publicación: ASSESSMENT OF CONVOLUTIONAL NEURAL NETWORKS FOR ASSET DETECTION IN DYNAMIC AUTOMATION CONSTRUCTION ENVIRONMENTS
Fecha
2023
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IEEE CONFERENCIAS
Resumen
THIS PAPER PROPOSES A TOOL FOR THE EVALUATION OF ALGORITHMS FOR GRID SYNCHRONIZATION USING THE DELFINO LAUNCHPAD TMS320F28379D PLATFORM, WITH THE OBJECTIVE OF SUPPORTING DEVELOPERS AND RESEARCHERS IN THE VALIDATION BEFORE THEIR TEST IN GRID-CONNECTED EQUIPMENT. THE PROPOSAL USES THE DIGITAL TO ANALOG OUTPUTS AVAILABLE IN THE LAUNCHPAD TO EMULATE THE GRID VOLTAGE, PROVIDING VARIATIONS IN AMPLITUDE, FREQUENCY, AND PHASE AND AT THE SAME TIME, PROVIDING RELEVANT SIGNALS TO EVALUATE THE PERFORMANCE OF THE TESTED ALGORITHM IN A SCOPE. THE WORK PRESENTS (I) A GLOBAL CLASSIFICATION OF SYNCHRONIZATION TECHNIQUES AND EVALUATION SCENARIOS, (II) THE TOOL CHARACTERISTICS AND ITS IMPLEMENTATION METHODOLOGY, (III) A STUDY CASE AND ITS RESULTS WHERE TWO SYNCHRONIZATION TECHNIQUES ARE IMPLEMENTED IN AN OPEN-SOURCE DEVICE, AND (IV) FINAL DISCUSSIONS OF THE TOOL AND THE RESULT.
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robotics, convolutional neural network, Automation in construction, 3D printing
