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 šŸš€. With a solid academic background, he earned his B.Sc. in Mechanical Engineering šŸ› ļø from Mazandaran University, followed by an M.Sc. in Aerospace Engineering āœˆļø from Tarbiat Modares University, and completed his PhD šŸŽ“ at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles šŸ“„, books šŸ“š, and research projects šŸ” to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization šŸ’§, fluid mechanics šŸŒŠ, and sustainable energy applications āš”. As a dedicated educator šŸ‘Øā€šŸ« and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations šŸ¤, publishing, and mentoring.

Profile

Education šŸŽ“

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering šŸ› ļø from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering āœˆļø at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics šŸŒ¬ļø. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering šŸš€ from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems šŸ”„, and energy-efficient solutions šŸ’”. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems šŸ”§. Through rigorous coursework, experimental research, and publications šŸ“, Dr. Rostami has cultivated profound expertise that bridges theory and practice šŸ§ šŸ’Ŗ, enabling him to address modern aerospace challenges with innovative thinking šŸš€.

Experience šŸ‘Øā€šŸ«

Dr. Elyas Rostami serves as a faculty member šŸ‘Øā€šŸ« at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering šŸš€. His career spans academic teaching šŸ“–, research leadership šŸ”¬, article peer reviewing šŸ§, and supervising research projects šŸ”. He has authored multiple scientific papers šŸ“ and books šŸ“š, advancing the field of propulsion systems, spray technology šŸ’§, and fluid mechanics šŸŒŠ. Beyond academia, Dr. Rostami actively engages with industry via research collaborations šŸ¤ and applied projects that explore new energies šŸŒ± and aerospace applications āœˆļø. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator šŸŽ“āš™ļø. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse šŸŒ.

Awards & Recognitions šŸ…

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction šŸŽ–ļø. His scholarly contributions earned him recognition through research publications in reputable journals šŸ…, invitations to review articles šŸ§, and participation in collaborative research projects šŸ¤. He has authored a scientific book šŸ“˜ and continuously advances his field through active publishing and innovation šŸŒŸ. His expertise in aerospace propulsion šŸ”„ and fluid dynamics šŸŒŠ 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 šŸŒ. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement šŸ’”šŸ”¬.

Research Interests šŸ”¬

Dr. Elyas Rostamiā€™s research focuses on fluid mechanics šŸŒŠ, aerospace propulsion systems šŸš€, spray and atomization processes šŸ’§, and the use of renewable energies in industrial applications šŸŒ±. His investigations address the thermodynamics of propulsion systems šŸ”„, advancing efficiency and performance in aerospace technology āœˆļø. He integrates experimental and computational approaches šŸ–„ļø to explore new energy solutions and optimize spray behavior under different conditions āš™ļø. Through research projects šŸ”¬, journal articles šŸ“„, and book authorship šŸ“š, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation šŸ’”, making significant strides in the development of sustainable and high-performance propulsion technologies šŸŒšŸš€.

PublicationsĀ 

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 Xiā€™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) ļ¼š25186-25196. 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