The accurate identification of fault locations in power transmission networks is critical for ensuring system reliability and reducing downtime. Traditional fault location methods, such as impedance-based techniques, have been widely used, but they often suffer from limitations due to system complexity and changing network conditions. Recently, artificial intelligence (AI)-based approaches, including machine learning (ML) and deep learning (DL) models have emerged as promising alternatives for improving fault location accuracy. This paper reviews various traditional and AI-based fault location methods, highlighting their advantages, challenges, and applications in modern power systems. The study provides insights into the effectiveness of these techniques and suggests future research directions for enhancing fault location accuracy in transmission networks.
The use of Advanced Driver Assistance Systems (ADAS) heavily depends on the perception models to make real-time decisions but the traditional methods have tended to use specific confidence thresholds to make the trade-offs between missed detections and false alarms to be not opti…
Radiography, renowned for its diagnostic prowess and affordability, plays a key role in detecting diseases, including critical conditions. Chest radiography, focusing on a vital body area, poses interpretational challenges, necessitating experienced radiologists for accurate diag…
We present a comprehensive automated solution for 3D seismic fault detection and interpretation that combines deep learning with advanced geometric post-processing. The method integrates a 3D U-Net neural network trained on synthetic data with normalized distance function targets…
Blockchain-integrated deep learning intrusion detection systems for the Internet of Things have attracted growing research attention, yet the relationship between detection depth and blockchain trust scope in these architectures has not been examined systematically. This analysis…
This report presents eleven frozen prospective HR7/PFA assessments of emerging artificial intelligence systems across scientific, industrial, enterprise, and frontier research domains. The assessments cover AI-assisted drug discovery, autonomous robotics, enterprise cybersecurity…
# Overview The **V2X Collision Avoidance System** is an intelligent transportation platform that integrates **Vehicle-to-Everything (V2X)** communication, **embedded systems**, **artificial intelligence**, and **sensor fusion** to improve road safety through real-time collision p…