Sheng Ding
Papers
2
Total Citations
19
H-Index
2
About
Dr. Sheng Ding is a leading researcher in the reliability and safety of industrial automation and cyber-physical systems (CPS). His work centers on developing advanced deep learning models for error detection and anomaly detection, addressing the growing complexity of modern automated environments. Dr. Ding’s most influential contribution, "Model-Based Error Detection for Industrial Automation Systems Using LSTM Networks" (2020), has garnered 15 citations, establishing a foundational approach for using recurrent neural networks to identify system faults in real time. He further advanced the field with "Anomaly Detection for Cyber-Physical Systems Using Transformers" (2021), pioneering the application of transformer architectures to enhance detection accuracy in safety-critical CPS. By tackling the increasing structural and behavioral complexity of automation systems, Dr. Ding’s work directly mitigates risks of economic loss and system failures. His research is essential for students and engineers seeking robust, data-driven solutions for industrial reliability, bridging the gap between theoretical machine learning and practical industrial safety.
Research Focus
Key Achievements
Top Papers
- 1
- 2Anomaly Detection for Cyber-Physical Systems Using Transformers4 citations · 2021