Printed Circuit boards (PCBs) are one of the most important stages in making electronic products. A small defect in PCBs can cause significant flaws in the final product. Hence, detecting all defects in PCBs and locating them is essential. In this paper, we propose an approach based on denoising convolutional autoencoders for detecting defective PCBs and to locate the defects. Denoising autoencoders take a corrupted image and try to recover the intact image. We trained our model with defective PCBs and forced it to repair the defective parts. Our model not only detects all kinds of defects and locates them, but it can also repair them as well. By subtracting the repaired output from the input, the defective parts are located. The experimental results indicate that our model detects the defective PCBs with high accuracy (97.5%) compare to state of the art works.
An essential task in antibody/nanobody therapeutics discovery is to rapidly identify whether an antibody/nanobody has specificity and cross-reactivity to one or multiple tar- gets. Multiple target specificity and cross-reactivity of antibodies can be demonstrated by screening the third Complementarity Determining Region on the heavy chain (CDR-H3) of antibody sequences. However, the existing methods are costly and labor-intensive as repet- itive wet-lab experimentation is required to explore the sequences space. Here, we present a deep learning dimensionality reduction model based on Variational Autoencoder (VAE) and Residual Neural Network (Resnet), which we named VAEResDR. Our VAEResDR can efficiently learn the sequences’ key features while scaling down high-dimensional an- tibody sequences into a two-dimensional visualization representation for coherent analysis and rapid screening. We demonstrate that our VAEResDR can provide a tool to precisely analyze CDR-H3 sequences within the hidden patterns and effectively improve antibody/- nanobody CDR-H3 sequence clustering.
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