Tianyuan Liu | Machine Learning | Best Researcher Award

Published on by Technology Scientists

Assoc. Prof. Dr. Tianyuan Liu | Machine Learning | Best Researcher Award

Master’s Supervisor | Donghua University | China

Assoc. Prof. Dr. Tianyuan Liu, affiliated with Donghua University, Shanghai, China, is a distinguished researcher specializing in industrial intelligence, human-centric manufacturing, and vision-based quality inspection. With 43 publications, 1,103 citations, and an h-index of 17, Dr. Liu’s work reflects significant academic impact and steady scholarly growth in intelligent industrial systems. His research integrates cognitive computing, deep learning, and large language models to enhance manufacturing precision, reliability, and adaptability. Notably, his 2025 article “Analysis of causes of welding defects in bridge weathering steel based on large language models” in the Journal of Industrial Information Integration demonstrates his pioneering approach to applying AI-driven diagnostic systems in structural materials engineering. Another major contribution, “Causal deep learning for explainable vision-based quality inspection under visual interference” published in Journal of Intelligent Manufacturing, advances explainable AI (XAI) frameworks for real-time industrial inspection, ensuring transparency and accuracy in automated decision-making. His review, “Towards cognition-augmented human-centric assembly: A visual computation perspective”, underscores his vision for augmenting human intelligence with computational cognition to achieve collaborative, efficient, and sustainable manufacturing systems. Furthermore, his book chapter “Industrial Intelligence: Methods and Applications” provides a comprehensive view of the synergy between AI and industrial processes, shaping the academic and applied discourse in smart factories. Assoc. Prof. Dr. Liu’s contributions collectively enhance the fusion of AI, cognition, and industrial engineering, driving forward the next generation of intelligent, explainable, and human-oriented manufacturing ecosystems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., & Liu, S. (2022). A reinforcement learning method for human-robot collaboration in assembly tasks. Robotics and Computer-Integrated Manufacturing, 73, 102227.
Cited by: 182.

2. Zhou, B., Bao, J., Li, J., Lu, Y., Liu, T., & Zhang, Q. (2021). A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robotics and Computer-Integrated Manufacturing, 71, 102160.
Cited by: 152.

3. Zhou, B., Shen, X., Lu, Y., Li, X., Hua, B., Liu, T., & Bao, J. (2023). Semantic-aware event link reasoning over industrial knowledge graph embedding time series data. International Journal of Production Research, 61(12), 4117–4134.
Cited by: 123.

4. Zhou, B., Li, X., Liu, T., Xu, K., Liu, W., & Bao, J. (2024). CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing. Advanced Engineering Informatics, 59, 102333.
Cited by: 114.

5. Liu, T., Bao, J., Wang, J., & Zhang, Y. (2018). A hybrid CNN–LSTM algorithm for online defect recognition of CO₂ welding. Sensors, 18(12), 4369.
Cited by: 105.

Assoc. Prof. Dr. Tianyuan Liu’s research bridges artificial intelligence and industrial engineering, advancing smart, explainable, and human-centric manufacturing solutions that empower global industry transformation.

Jaouher Ben Ali | Prognostics | Best Paper Award

Published on by Technology Scientists

Prof. Jaouher Ben Ali | Prognostics | Best Paper Award

Professor | Tunis University | Tunisia

Prof. Jaouher Ben Ali, from the École Nationale Supérieure d’Ingénieurs de Tunis, Tunisia, is a prominent researcher in machine learning, signal processing, and intelligent fault diagnosis, with impactful work spanning biomedical and industrial applications. He has authored 41 publications, accumulated 2,656 citations, and achieved an h-index of 18, reflecting the strong influence of his research. Prof. Ben Ali’s work focuses on developing advanced algorithms for fault detection, condition monitoring, and health prediction using cutting-edge computational methods such as Empirical Mode Decomposition (EMD), Higher-Order Statistics (HOS), and deep learning models like LSTM-XGBoost fusion. His recent study, “Optimization of blood glucose prediction with LSTM-XGBoost fusion and integration of statistical features for enhanced accuracy” (2025, Biomedical Signal Processing and Control), showcases his efforts to integrate artificial intelligence with biomedical signal analysis for more precise and reliable health monitoring. He has also contributed significantly to mechanical fault diagnosis, as seen in works such as “Fault Diagnosis in Rolling Element Bearings Using Bi-Spectrum-Based EMD and Simplified Fuzzy ARTMAP” and “Advanced Feature Extraction Techniques for Bearing Fault Diagnosis Using Higher-Order Statistics and Machine Learning.” By combining data-driven techniques, nonlinear modeling, and adaptive learning, Prof. Ben Ali advances both theoretical understanding and practical applications of intelligent diagnostics. His interdisciplinary research strengthens links between academia and industry, promoting innovations that enhance system reliability, healthcare accuracy, and sustainable industrial performance worldwide.

Profiles: Scopus | Google Scholar

Featured Publications

1. Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16–27.
Cited by: 900

2. Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150–172.
Cited by: 625

3. Saidi, L., Ali, J. B., Bechhoefer, E., & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120, 1–8.
Cited by: 279

4. Saidi, L., Ali, J. B., & Fnaiech, F. (2015). Application of higher order spectral features and support vector machines for bearing faults classification. ISA Transactions, 54, 193–206.
Cited by: 237

5. Saidi, L., Ali, J. B., & Fnaiech, F. (2014). Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis. ISA Transactions, 53(5), 1650–1660.
Cited by: 192

Dr. Ben Ali’s research advances intelligent diagnostic technologies that enhance system reliability, healthcare precision, and industrial safety—driving progress toward a smarter, data-driven, and sustainable future.