Elyas Rostami | Mechanical Engineering | Best Researcher Award

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

Profile

Education πŸŽ“

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering πŸ› οΈ 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 πŸš€ from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems πŸ”₯, and energy-efficient solutions πŸ’‘. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems πŸ”§. Through rigorous coursework, experimental research, and publications πŸ“, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠πŸ’ͺ, enabling him to address modern aerospace challenges with innovative thinking πŸš€.

Experience πŸ‘¨β€πŸ«

Dr. Elyas Rostami serves as a faculty member πŸ‘¨β€πŸ« at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering πŸš€. His career spans academic teaching πŸ“–, research leadership πŸ”¬, article peer reviewing 🧐, and supervising research projects πŸ”. He has authored multiple scientific papers πŸ“ and books πŸ“š, advancing the field of propulsion systems, spray technology πŸ’§, 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 πŸŽ“βš™οΈ. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions πŸ…

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction πŸŽ–οΈ. His scholarly contributions earned him recognition through research publications in reputable journals πŸ…, invitations to review articles 🧐, and participation in collaborative research projects 🀝. He has authored a scientific book πŸ“˜ and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion πŸ”₯ 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 πŸ’‘πŸ”¬.

Research Interests πŸ”¬

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

PublicationsΒ 

Guoliang Wang | Control Science and Engineering | Best Researcher Award

Prof. Guoliang Wang | Control Science and Engineering | Best Researcher Award

Β Guoliang Wang is a Professor at the Department of Automation, School of Information and Control Engineering, Liaoning Petrochemical University. πŸ“š With extensive expertise in control theory and automation, he has made significant contributions to Markov jump systems, stochastic system theory, and big data-driven fault detection. πŸš€ He has published 86 journal articles indexed in SCI and Scopus, authored books, and holds 9 patents. πŸ… As a postdoctoral researcher at Nanjing University of Science and Technology (2011-2016), he furthered his research in control engineering. πŸŽ“ His professional memberships include the Chinese Association of Automation and the Chinese Mathematical Society. πŸ† He has received the Liaoning Province Natural Science Academic Achievement Second Prize for his contributions. His innovative work in optimization, reinforcement learning, and system modeling continues to impact academia and industry. 🌍

Profile

Education πŸŽ“

Guoliang Wang earned his Ph.D. in Control Theory and Control Engineering from Northeastern University (2007-2010). πŸŽ“ Prior to that, he completed his Master’s degree in Operations Research and Control Theory at the School of Science, Northeastern University (2004-2007). πŸ“– His academic foundation is built on advanced mathematical modeling, stochastic systems, and automation, equipping him with expertise in complex system analysis. πŸ—οΈ His research has consistently focused on optimizing control mechanisms and enhancing stability in dynamic environments. As a Postdoctoral Researcher at Nanjing University of Science and Technology (2011-2016), he deepened his understanding of control theory, reinforcement learning, and system dynamics. πŸ… His education has been pivotal in developing innovative methodologies for automation, fault detection, and big data-driven decision-making.

Experience πŸ‘¨β€πŸ«

Since March 2010, Guoliang Wang has been a Professor at Liaoning Petrochemical University, specializing in automation and control engineering. 🏫 He served as Associate Dean of the Department of Automation (2013-2014), leading academic and research initiatives. 🌍 His postdoctoral research at Nanjing University of Science and Technology (2011-2016) explored stochastic system applications and control theory advancements. πŸ”¬ Over the years, he has led multiple research projects, consulted on industrial automation solutions, and contributed to major technological advancements. πŸ’‘ His work has resulted in 86 peer-reviewed journal publications, 9 patents, and significant contributions to adaptive dynamic programming. πŸš€ As a member of various professional associations, he actively collaborates with international researchers and institutions. His expertise spans Markov jump systems, stochastic modeling, fault detection, and AI-driven automation strategies

Research Interests πŸ”¬

Guoliang Wang’s research spans modeling and control of Markov jump systems, stochastic system applications, and AI-driven automation. πŸ€– His work in fault detection, diagnosis, and big data-driven prediction has led to practical advancements in system optimization. πŸ“Š He has proposed novel stabilizing controllers, developed reinforcement learning-based optimization models, and improved system performance through convex optimization techniques. πŸ” His expertise in stochastic control extends to image processing, predictive analytics, and adaptive dynamic programming. πŸ“‘ His research contributions have significantly enhanced system stability and reduced computational complexity in industrial automation. πŸ’‘ Through collaborations with global researchers, he continues to push the boundaries of automation, AI, and smart control systems. πŸš€ His work integrates theoretical insights with real-world applications, ensuring a lasting impact on engineering and technology. 🌍

Guoliang Wang has been recognized for his outstanding contributions to automation and control engineering. πŸ† He received the prestigious Liaoning Province Natural Science Academic Achievement Second Prize for his groundbreaking research. πŸ… His achievements include being a member of elite research committees such as the Youth Committee of the Chinese Association of Automation and the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee. πŸŽ–οΈ He has been an invited speaker at international conferences and has received commendations for his work in optimizing stochastic system control. πŸ“œ His research impact is further reflected in his editorial board membership at the Journal of Liaoning Petrochemical University. ✍️ With numerous patents, consultancy projects, and high-impact research, he continues to receive nominations and accolades in automation, AI, and control system optimization

Publications πŸ“š

  • Sampled-Data Stochastic Stabilization of Markovian Jump Systems via an Optimizing Mode-Separation Method

    IEEE Transactions on Cybernetics
    2025 |Β Journal article
    CONTRIBUTORS:Β Guoliang Wang;Β Yaqiang Lyu;Β Guangxing Guo
  • Stabilization of Stochastic Markovian Jump Systems via a Network-Based Controller

    IEEE Transactions on Control of Network Systems
    2024-03 |Β Journal article
    CONTRIBUTORS:Β Guoliang Wang;Β Siyong Song;Β Zhiqiang Li
  • Almost Sure Stabilization of Continuous-Time Semi-Markov Jump Systems via an Earliest Deadline First Scheduling Controller

    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    2024-01 |Β Journal article
    CONTRIBUTORS:Β Guoliang Wang;Β Yunshuai Ren;Β Zhiqiang Li
  • Stability and stabilisation of Markovian jump systems under fast switching: an averaging approach

    Journal of Control and Decision
    2023-10-02 |Β Journal article
    CONTRIBUTORS:Β Guoliang Wang;Β Yande Zhang;Β Yunshuai Ren

Lichen Shi | Mechanical Engineering | Best Researcher Award

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