Diyar Altinses

Diyar Altinses is a researcher at the Department of Electrical Power Engineering, South Westphalia University of Applied Sciences (Soest, Germany). Holding an M.Sc. in Systems Engineering and Engineering Management (double degree from University of Bolton and FH-SWF, 2019-2021), he completed his Dr. rer. nat. in 2021 with a focus on multimodal fault-tolerance through machine learning.

Research interests span

• Fault-tolerant machine learning for industrial sensor data reconstruction
• Drone logistics optimization with 5G and multimodal networks
• Synthetic dataset generation for industrial process modeling
• Self-optimizing systems in metal forming and packaging

Recent publications highlight applications of neural fusion techniques in drone landing precision, imputation methods for time series decomposition, and benchmarking data-driven industrial approaches. His work connects computational techniques with real-world process optimization challenges.