Publications by authors named "Mohammadreza Aghaei"
Article Synopsis
- - This study introduces a vision transformer for identifying visual defects on steel surfaces, using an open-source dataset to categorize conditions into six fault types: crazing, inclusion, rolled in, pitted surface, scratches, and patches.
- - The model undergoes resizing of defect images and various hyperparameter adjustments to find the best configuration, achieving a high accuracy rate of 96.39% for defect detection and classification.
- - The research not only highlights the effectiveness of the vision transformer architecture compared to other methods, like convolutional neural networks (CNNs), but also demonstrates its potential for real-time condition monitoring of various defects.
The lack of regeneration of injured neurons in the central and peripheral neural system leads to the failure of damaged tissue repair in patients. While there is no definitive cure for most neurodegenerative diseases, new therapeutic methods that cause the proliferation and differentiation of neurons are of interest. Challenges such as the inability of neuronal cells to proliferate after injury, the lack of a stimulus for initial stimulation, and the presence of the microenvironment around CNS neurons contain several inhibitory factors that prevent neuron regeneration, thus, creating a structure similar to the extracellular matrix helps the cell proliferation in current treatment.
View Article and Find Full Text PDFFaults in photovoltaic (PV) modules may occur due to various environmental and physical factors. To prevent faults and minimize investment losses, fault diagnosis is crucial to ensure uninterrupted power production, extended operational lifespan, and a high level of safety in PV modules. Recent advancements in inspection techniques and instrumentation have significantly reduced the cost and time required for inspections.
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