Automated solar filament detection using deep learning faces several challenges. Semantic segmentation of solar filaments is a complicated multiscale feature extraction task with long-tail distribution.
The paper proposes a hybrid approach for detecting surface defects on solar panels by combining handcrafted features—Local Binary Pattern, Histogram of Gradients, and Gabor Filters—with deep features extracted from DenseNet‑169. The concatenated feature set is classified using SVM, XGBoost, and LGBM, with the DenseNet‑169 + Gabor (SVM) configuration achieving the highest accuracy of 99.17% on an augmented dataset. This method aims to provide an automated, accurate, and flexible defect‑detection system for large‑scale, remote solar power plants.
By Muhammad Junaid Asif, Muhammad Saad Rafaqat, Usman Nazakat, Uzair Khan, Rana Fayyaz Ahmad
The paper presents a reproducible workflow built in the Galaxy environment for automated crack identification and damage assessment from scanning electron microscopy images of fusion materials. It processes SEM images and metadata to generate crack masks, skeletonized networks, quality‑control visualizations, and scalar damage descriptors without needing image‑specific tuning across different tungsten grades, microstructures, magnifications, and damage states. The workflow was demonstrated on a dataset of 418 images from 114 experiments covering five tungsten grades and three microstructural states, and it includes a crack‑density descriptor that serves as a standardized input for downstream machine‑learning prediction and physics‑based crack simulation modules.
By Rinkle Juneja, Viktor Reshniak, Richard K. Archibald, John W. Duggan, Gregory R. Watson, Cory D. Hauck, Gary M. Staebler
arXiv:2609.16773v1 Announce Type: new
Abstract: Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accu...
By Ruibo Wang, Ziyi Shen, Huaming Wu, Dong Liang, Kun Shang
Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accuracy, they rely heavily on long-range spatial fea...
The paper introduces a mesh‑free multiresolution deep energy method for phase‑field modeling of brittle fracture. A single neural network represents displacement and phase fields, trained by minimizing incremental energy with multiresolution B‑spline feature encoding and stratified Monte Carlo integration. Across six benchmark problems, the method reproduces load‑displacement curves and crack patterns with high accuracy, outperforming a deep Ritz baseline on a random multi‑crack dataset.
By Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh, Mohammad Vahab, Cosmin Anitescu, Timon Rabczuk, Elena Atroshchenko