This session focuses on the integration of AI engineering, data analytics, machine learning, deep learning, and IoT technologies to support the future of smart electrical engineering education.
Track Description
This track focuses on the application of AI engineering, machine learning, deep learning, data analytics, and Internet of Things (IoT) to enhance smart electrical engineering education. It welcomes research on intelligent learning systems, smart laboratories, power quality monitoring, predictive maintenance, and data-driven approaches for improving teaching, learning, and practical training in electrical engineering.
Scope and Areas of Interest
Topics of interest include, but are not limited to:
Artificial Intelligence for Electrical Engineering Education
AI-based learning systems, intelligent tutoring systems, adaptive learning platforms, and AI-assisted laboratory instruction.
Machine Learning Applications in Electrical Engineering
Machine learning models for electrical signal analysis, system diagnosis, classification, prediction, optimization, and educational experiments.
Deep Learning for Smart Electrical Systems
Deep learning techniques for image-based inspection, fault detection, load forecasting, power system analysis, and intelligent automation.
Internet of Things for Smart Electrical Laboratories
IoT-based monitoring systems, smart sensors, remote laboratories, real-time data acquisition, and cloud-connected electrical engineering experiments.
Power Quality Analysis and Monitoring
AI and data analytics approaches for voltage sag, harmonic distortion, transient detection, power factor analysis, and smart power quality monitoring.
Predictive Maintenance for Electrical Engineering Education
Predictive maintenance models for electrical machines, motors, transformers, laboratory equipment, and smart training systems.
Data Analytics for Electrical Engineering Learning
Learning analytics, performance prediction, student behavior analysis, data visualization, and decision-support systems.
Smart Electrical Engineering Training Platforms
Intelligent training kits, simulation-based learning, virtual laboratories, digital twins, and AI-enhanced practical learning environments.
AIoT for Electrical Engineering Applications
Integration of AI and IoT for smart energy systems, smart grids, industrial automation, and educational demonstration systems.
Emerging Technologies in Electrical Engineering Education
Applications of edge AI, cloud computing, embedded systems, cyber-physical systems, and digital transformation in electrical engineering education.
Asst. Prof. Dr. Pakpoom Chansri
King Mongkut’s University of Technology Thonburi (KMUTT), Thailand
pakpoom.cha@kmutt.ac.th