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Browsing Research Publications/Outputs by browse.metadata.impactarea "Artificial Intelligence"
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Item Augmented and mixed reality based decision support tool for the integrated resource plan(2021-10) Govender, Devashen; Moodley, Jayandren; Balmahoon, ReevanaIn today’s era, enormous amounts of data are generated and the means to visualize this data is becoming a challenge. Data visualization is an important support tool that allows one to make informed decisions. This paper attempts to provide a possible solution to this major challenge. The solution presented is a data analytic tool capable of visualizing complex data by leveraging the fourth industrial revolution technologies, augmented and mixed reality. Using the disruptive nature of these technologies, it could provide an application that is more intuitive and immersive to the end-user. Moreover, we focus the solution in the energy domain by exploring methods in enhancing energy data visualization to support decision-making of South Africa’s Integrated Resource Plan (IRP).Item Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation(2021-11) Pratt, Lawrence E; Govender, Devashen; Klein, RElectroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules that are otherwise invisible to the naked eye, much the same way an x-ray enables a doctor to detect cracks and fractures in bones. The prevalence of multiple defects, e.g. micro cracks, inactive regions, gridline defects, and material defects, in PV module can be quantified with an EL image. Modern, deep learning techniques for computer vision can be applied to extract the useful information contained in the images on entire batches of PV modules. Defect detection and quantification in EL images can improve the efficiency and the reliability of PV modules both at the factory by identifying potential process issues and at the PV plant by identifying and reducing the number of faulty modules installed. In this work, we train and test a semantic segmentation model based on the u-net architecture for EL image analysis of PV modules made from mono-crystalline and multi-crystalline silicon wafer-based solar cells. This work is focused on developing and testing a deep learning method for computer vision that is independent of the equipment used to generate the EL images, independent of the wafer-based module design, and independent of the image quality.