Elyas Rostami | Mechanical Engineering | Best Researcher Award

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Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🤝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications 

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

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