ABSTRACT: Structural health monitoring (SHM) is critical for ensuring the safety and serviceability of civil infrastructures. Traditional vibration-based damage detection methods often rely on ...

1 Guangzhou Railway Polytechnic, Guangzhou, China. 2 School of Intelligent Construction and Civil Engineering, Zhongyuan University of Technology, Zhengzhou, China. 3 Research Center for Wind ...

Classifies three types of electrical faults from real-world time-series voltage and current waveforms: Short-circuit, Overload, and Ground fault. Developed for ABB Power Systems with <50ms real-time ...

This is a general purpose aimbot, which uses a neural network for enemy/target detection. The aimbot doesn't read/write memory from/to the target process. It is essentially a "pixel bot", designed ...

AWARE uses waveform signatures to detect and classify early-stage grid faults, enabling proactive intervention. The system combines physics-based models with AI/ML to interpret subtle electrical ...

School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India Introduction: In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) ...

Introduction: In seismic structural interpretation, fault detection plays a crucial role as it serves as the foundation and key step for identifying favorable oil and gas zones. Currently, many ...

In telecommunication applications, target impedance serves as a crucial benchmark for power distribution network (PDN) design. It ensures that the die operates within an acceptable level of rail ...

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...