Dr. Zhao Tianjiao | Design Education | Best Researcher Award

Zhao Tianjiao is an Associate Professor in the Visual Art Department at Tianjin University and a Master’s Degree Supervisor in Industrial Design. He holds a Ph.D. in Design from The Hong Kong Polytechnic University and has extensive experience in research on user experience, design thinking, and deep learning applications in design. His work focuses on emotion-embedded design, creative idea generation, and intelligent design tools. He has published numerous SCI/SSCI-indexed research papers and has led several national research projects in China. 🎨📚🔬

Profile

Education 🎓

Zhao Tianjiao completed his Bachelor’s (2009) and Master’s (2011) degrees in Mechatronics Engineering from Harbin Institute of Technology. He pursued his Ph.D. in Design (2015) from The Hong Kong Polytechnic University. He was also a Visiting Scholar (2013-2014) at National Taiwan University, where he explored interdisciplinary research in design and planning. His education provided a strong foundation in integrating engineering, design, and user experience methodologies. 🎓📖💡

Experience 👨‍🏫

Zhao Tianjiao began his academic career as a Lecturer (2016-2020) in the Visual Art Department at Tianjin University. He was later promoted to Associate Professor (2021-present) and has been a Master’s Degree Supervisor (2017-present) in Industrial Design. His teaching focuses on User Experience Design, Design Thinking, and Animation Derivative Design. Over the years, he has contributed to curriculum innovation and research-driven education. 🎭👨‍🏫📐

Awards & Recognitions 🏅

Zhao Tianjiao has been recognized for his academic excellence and teaching contributions. He received the Tianjin University Elite Scholar Program Award, was selected for the Tianjin 131 Talent Project, and won Second Prize in the Tianjin University Youth Teacher Lecture Competition. Additionally, he was honored with the Excellent Guide Teacher Award at the National College Digital Art and Design Awards. 🏅🎨🎖️

Research Interests 🔬

His research explores emotion-driven design, deep learning applications in creative design, and user experience optimization. He investigates how AI can enhance design processes and has led multiple National Natural Science Foundation of China projects on design big data, creative education, and intelligent product development. His work bridges the gap between human emotions and AI-driven design methodologies. 🤖🎨

Publications

1. Zhao T.J.,Jia J.Y., Zhu T.F.,Yang J.Y.(2023 Accept).Research on emotion-embedded design fow based on deeplearningtechnology. International Journal of Technology and Design Education. (SCI/SSCI)

2. Zhao T.J.,Zhang X.Y.,Zhang H.C.,Meng Y.F.(2023 Accept), A study on users’ attention distribution to product featuresunderdifferent emotions.Behaviour & Information Technology.(SCI/SSCI)

3. Zhao T.J.,Yang J.Y.，Zhang H.C.，Kin Wai Michael Siu (2019).Creative idea generation method based on deeplearning，International Journal of Technology and Design Education,2021,31, 421-440. (SCI/SSCI)

4. Zhao, T.J., & Siu, K. W. M. (2015). The needs of quality urban rail transit life in an Asian metropolitan city. Applied ResearchinQuality of Life, 10(4):647~665.(SSCI)

5. Zhao, T.J., & Siu, K. W. M. (2014). The Boundaries of Public Space: A Case Study of Hong Kong Mass Transit Railway.
International Journal of Design, 8(2), 43-60. (SCI/SSCI )

6. Zhao T.J., Zhang H.C., Yang J.Y. (2018). Research on theDesign Form Exploring Method from Daily Life: ExplorationandPractice on the Teaching of Form Design Course. ZHUANGSHI, 306(10), 104-107. (CSSCI)

7. Zhao T.J.,Chen M.J.,Liu W.F.(2022).Research on Image Retrieval Optimization Based on Eye Movement Experiment
Data,Journal of Education and Training Studies, 10(4).

8. Zhao T.J., Zhang H.C.,Pan L.(2021).The Teaching Reform of User Experience Design under the NewEngineeringBackground——”Multy Integration”Curriculum Model Practice,Creation and Design,2021(5):7.

9. Yang J.Y.,Zhao, T.J., Zhang H.C.(2020).Visual strategy boosts service design.Tsinghua Publishing.

10. Zhao, T.J., Zhu, T.F.(2019). Exploration of product design emotion based on three-level theory of emotional design, Advancesin Intelligent Systems and Computing, v 1018, p 169-175. (EI Conference)

11. Zhao T.J., Siu K W M, Sun H.(2017). Discovering subway design opportunities using social network data: Theimage-need-design opportunity model. International Conference on Social Computing and Social Media. Springer, Cham, 2017, 451-466. (EI Conference)

12. Zhao, T.J., and Kin Wai Michael Siu (2017). The Production of URT Public Space: People, Occupation, and PracticeintheHong Kong Mass Transit Railway. The International Journal of Architectonic, Spatial, and Environmental Design .11(3): 35-48.

13. Zhao T. J, Gao K, Li X, et al(2017). Deep Learning based Design Image Management: Proceedings of the International
Conference on Environmental Science and Sustainable Energy,Ed.by ZhaoYang Dong[M]// ESSE 2017. 453-461.

14. Zhao, T.J., & Siu, K. W. M. (2014). Freedom and Control: A state of balance in public space. Facilities, 32(11/12), 606-623.

15. Zhao T. J., Siu K. W. M.(2012). The role of subway in the urban life: Case study in Hong Kong Mass Transit Railway. Humanities and Social Sciences Review, 2012.

Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

Profile

Orcid
Scopus

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

Developing an AI-based method for reading pointer meters using human-like reading sequences.
Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

Intelligent Measurement with Deep Learning
Equipment Status Monitoring and Fault Diagnosis
Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

[1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
[2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
10.1109/JSEN.2024.3417231
[3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
10.1109/access.2024.3368755.
4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
[5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929

Zhao Tianjiao | Design Education | Best Researcher Award