AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An automated inspection framework based on deep feature extraction and machine learning classification to assess insulator and grid component conditions that offers a scalable and accurate inspection tool that can reduce field inspection costs, support utility maintenance planning and enhance grid reliability.
The image-based identification of surface contamination on high-voltage insulators may support preventive inspection and maintenance. Existing deep learning approaches frequently employ single-stage classification, which can make it difficult to distinguish visually similar contamination types. This study proposes a hi...
The increasing integration of distributed energy resources (DERs) complicates fault detection in active distribution networks, rendering traditional model-based approaches inadequate for handling complex, time-varying power system characteristics. This paper proposes a novel weighted ensemble learning framework integra...
Deep learning-based fault diagnosis for wind turbine blades has been widely adopted owing to its end-to-end feature extraction capability, yet it continues to face considerable challenges. First, wind turbines predominantly operate under normal conditions, resulting in an acute scarcity of labeled fault data. Second, t...
In response to the accuracy loss caused by lightweight networks on edge devices and the difficulty in accurately detecting small defect targets, this paper studies the lightweight You Only Look Once version 8 (YOLOv8) transmission line insulator defect detection algorithm based on Three-Branch Attention Mechanism (TBAM...
Experimental results indicate that WT-DETR achieves highly competitive performance for buried sewer pipeline defect detection, reaching a precision of 88.5%, demonstrating the effectiveness of the WT-DETR model in buried sewer pipeline defect detection.
The intelligent transformation of manufacturing processes increasingly relies on automated visual and parametric inspection of intermediate products, where machine learning models classify surface defects from sensor-derived features. Despite the rapid progress of deep tabular learning architectures, the relative perfo...
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