About us

Sensor & AI System Lab conducts research on embedded software, intelligent measurement devices, and AI-based smart systems. Our work spans robotics, autonomous driving, and smart energy, focusing on object tracking, recognition, positioning, predictive control, and deep learning algorithms. We also study convergence applications such as smart factories, cities, and energy systems, aiming to deliver reliable, high-impact technologies that create practical value for future industries and society. 

Intelligent Control and Measurement Devices and Embedded Systems 

Our lab focuses on developing intelligent control and measurement devices as well as embedded systems by integrating advanced sensor technologies, signal processing, and hardware–software co-design. We apply cutting-edge control theories and AI-based fault diagnosis to robotics, autonomous vehicles, and smart factories, aiming to deliver reliable, high-precision systems that drive future smart industries.

Object Tracking, Recognition, and Positioning Technology for Intelligent Robots and Autonomous Driving Systems 

Our lab leads research in object tracking, recognition, and positioning for intelligent robots and autonomous systems, developing advanced AI and deep learning algorithms such as vision-based tracking, 3D pose estimation, and sensor fusion. We also design innovative positioning methods for GPS-denied environments and real-time traffic safety solutions, driving breakthroughs in autonomous driving, smart mobility, and intelligent robotics. 

Wind Energy Conversion System and Smart Energy Control 

Our lab actively researches wind energy conversion systems (WECS) and smart energy control, applying advanced methods such as predictive control, adaptive fuzzy sliding mode control, and sampled-data control to maximize efficiency and stability under real-world uncertainties. We also extend our work to energy storage and applications, including second-life battery–based smart waterway management, contributing to sustainable energy utilization and next-generation smart infrastructure.

2025 Representative Publications

Mayilsamy, G., Lee, S. R., Joo, Y. H., & Jeong, J. H. (2025). A Predictive Rotor Position Error Compensation Scheme for Enhanced PMVG Operation in Wind Turbine System Applications. IEEE Transactions on Instrumentation and Measurement. https://ieeexplore.ieee.org/abstract/document/10985822

Ganesh, M., & Jeong, J. H. (2025). A direct predictive DC-link voltage control with warm-starting iterative approach for three-level NPC BTB converter fed PMSG-based wind turbine systems. Expert Systems with Applications, 127747. https://www.sciencedirect.com/science/article/pii/S0957417425013697

Gopalan, R. S., Mani, M. A., & Jeong, J. H. (2025). Resilient containment control of fractional-order multi-agent systems with uncertainty and time delay via non-fragile approaches. AIMS Mathematics, 10(8), 19712-19737. https://www.aimspress.com/article/doi/10.3934/math.2025879

Jo, S. H., Woo, J., & Jeong, J. H. (2025). TAMS: A CNN-based time attention network for time series sensor data with feature points of bicycle accident. Expert Systems with Applications, 272, 126739. https://www.sciencedirect.com/science/article/pii/S0957417425003616

KS, S. S., Joo, Y. H., & Jeong, J. H. (2025). Keypoint prediction enhanced Siamese networks with attention for accurate visual object tracking. Expert Systems with Applications, 268, 126237. https://www.sciencedirect.com/science/article/pii/S095741742403104X

Ganesh Mayilsamy, JaeHoon Jeong. (2025). Extended Research on Predictive Rotor Position Bias With Efficient Bias Angle Search Strategy in PMVG-WTS Power Factor Improvement. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11257857