Mingli Ding
Papers
3
Total Citations
23
H-Index
3
About
Dr. Mingli Ding is a robotics and sensor networks researcher whose work focuses on the intersection of mobile robot reliability, intelligent navigation, and fault diagnosis. Her key research areas include mobile robot path planning, wireless sensor network (WSN) localization, and predictive maintenance using deep learning. Dr. Ding’s most cited work, "Mobile Robot Motor Bearing Fault Detection and Classification on Discrete Wavelet Transform and LSTM Network" (13 citations), introduces a pioneering method that combines signal processing with long short-term memory networks to predict motor bearing failures, significantly enhancing robot uptime and industrial safety. She also developed an innovative algorithm for mobile robot path planning using RSSI potential fields from WSNs (5 citations), and advanced WSN mobile node localization through an efficient Metropolis-Hastings particle filter (5 citations), offering a cost-effective, noise-robust navigation solution. Her contributions bridge the gap between theoretical signal processing and practical robotics, providing tools for more autonomous and reliable mobile systems. Dr. Ding’s work is particularly valuable for students and researchers in robotics, industrial automation, and sensor networks, demonstrating how deep learning and probabilistic methods can solve real-world challenges in robot navigation and maintenance.
Research Focus
Key Achievements
Top Papers
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