Dr. Xiaofei Yang | Remote Sensing Awards | Best Researcher Award

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Dr. Xiaofei Yang | Remote Sensing Awards | Best Researcher Award

Dr. Xiaofei Yang, Guangzhou University, China

Dr. Xiaofei Yang is a lecturer at the School of Electronic and Communication Engineering, Guangzhou University, with a strong research background in artificial intelligence, remote sensing, image classification, and deep learning. He earned his Ph.D. in Computer Software and Theory from Harbin Institute of Technology in 2019 and completed postdoctoral research at the University of Macau, where he focused on hyperspectral image classification and 3D image reconstruction. Dr. Yang has authored 27 peer-reviewed publications, including 11 in IEEE Transactions journals—six as first author—and two Web of Science highly cited papers. His work has been presented at prestigious international conferences such as IJCNN, and he actively serves as a reviewer for top-tier journals including IEEE TGRS and TNNLS. His recent projects span cloud detection, terrain classification, plant disease diagnosis, and typhoon path prediction using deep learning. Recognized with the Innovation Scholarship by the Ministry of Industry and Information Technology in 2019, Dr. Yang continues to contribute to cutting-edge research in remote sensing and AI applications.

Professional Profile:

SCOPUS

ORCID

Summary of Suitability: Dr. Xiaofei Yang – Research for Best Researcher Award

Dr. Xiaofei Yang is an outstanding candidate for the Research for Best Researcher Award, recognized for his impactful contributions to artificial intelligence, remote sensing, and deep learning applications. With a strong academic foundation from the Harbin Institute of Technology and advanced research experience as a postdoctoral fellow at the University of Macau, Dr. Yang has emerged as a leading figure in intelligent image processing and computational modeling.

🎓 Education

  • Ph.D. in Computer Software and Theory
    Harbin Institute of Technology, Shenzhen, China
    March 2014 – October 2019

  • M.Sc. in Computational Mathematics
    Harbin Institute of Technology, Shenzhen, China
    August 2011 – January 2014

💼 Work Experience

  • Lecturer, Guangzhou University, China 🇨🇳
    March 2023 – Present

  • Postdoctoral Fellow, University of Macau 🇲🇴
    September 2021 – February 2023

    • Focus: Hyperspectral image classification using deep learning

  • Trainee, Zhuhai-UM Institute
    May 2021 – August 2021

    • Research on hyperspectral image classification

  • Postdoctoral Fellow, University of Macau
    September 2020 – April 2021

    • Research on 3D image reconstruction

  • Trainee, Peng Cheng Laboratory, Shenzhen 🇨🇳
    October 2019 – August 2020

    • Developed new open-source algorithm for image processing

🏆 Achievements

  • 📄 27+ publications in top journals and conferences, including:

    • 11 IEEE Transactions papers (6 as first author)

    • 2 papers highly cited by Web of Science

  • 🧠 Expert in:

    • Artificial Intelligence

    • Hyperspectral Image Classification

    • Remote Sensing

    • Deep Learning and Transformer Networks

  • 🗣️ Conference Presentations:

    • IJCNN 2019 (Hungary)

    • GSKI 2017 (Thailand)

  • 👨‍🏫 Teaching:

    • Courses at the University of Macau Master’s Program in deep learning and computer vision

  • 📚 Peer Reviewer for Top Journals:

    • IEEE TNNLS, TGRS, GRSL, Signal Processing Letters, and more

🥇 Awards & Honors

  • 🏅 Innovation Scholarship, Ministry of Industry and Information Technology (2019)

  • 🎓 Outstanding Graduate Student, Harbin Institute of Technology (2014)

Publication Top Notes:

Balancing supply and demand for ride-hailing: A preallocation hierarchical reinforcement learning approach

Global–local prototype-based few-shot learning for cross-domain hyperspectral image classification

MDFFN: Multi-Scale Dual-Aggregated Feature Fusion Network for Hyperspectral Image Classification

Spectral-Spatial Attention Transformer Network for Hyperspectral Image Classification

ACTN: Adaptive Coupling Transformer Network for Hyperspectral Image Classification

 

Prof. Dr. Len Gelman | Monitoring | Best Researcher Award

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Prof. Dr. Len Gelman | Monitoring | Best Researcher Award 

Prof. Dr. Len Gelman, The University of Huddersfield, United Kingdom

Professor Len Gelman is a distinguished academic and researcher in the fields of Signal Processing, Condition Monitoring, and Maintenance. He holds a PhD and Doctor of Science (Habilitation) degrees and is a Fellow of several prestigious institutions, including the British Institute of Non-Destructive Testing (BINDT), IAENG, IDE, and HEA. Since 2017, Professor Gelman has served as the Professor and Chair in Signal Processing and Condition Monitoring/Maintenance at the University of Huddersfield, where he is also the Director of the Maintenance Centre for Efficiency and Performance Engineering. Prior to this, he was a Professor at Cranfield University (2002-2017), where he established a leading research programme in vibro-acoustical condition monitoring. Professor Gelman has received numerous accolades, including the UK Rolls-Royce Innovation Award (2019), the COMADIT Prize (2017), and the Best Paper Award at the International Condition Monitoring/Maintenance Conference (2016 and 2013). With extensive experience in both academia and industry, he has developed pioneering technologies for damage detection in turbines and aircraft engines, with significant contributions to Rolls-Royce, Dresser-Rand, and Scottish Southern Energy. Professor Gelman has built strategic international partnerships with top universities and research centres across the globe, including institutions in China, Korea, the USA, and Europe. He has supervised numerous postdoctoral fellows and researchers and is renowned for his leadership in vibro-acoustical condition monitoring, a field in which he has secured over £7.3M in research grants.

Professional Profile:

SCOPUS

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Professor Len Gelman is an outstanding researcher whose extensive contributions to signal processing, condition monitoring, and maintenance engineering position him as a leading figure in his field, making him an ideal candidate for the Best Researcher Award. His innovative work has consistently benefited both industry and society, earning him significant recognition and awards.

Education 🎓

  • BSc (Hons), MSc (Hons) in Signal Processing and Condition Monitoring/Maintenance

  • PhD, Doctor of Science (Habilitation) in Vibro-Acoustical Monitoring/Maintenance

Work Experience 💼

  • 2017-present
    Professor and Chair in Signal Processing and Condition Monitoring/Maintenance
    Director of the Maintenance Centre for Efficiency and Performance Engineering
    University of Huddersfield, UK

  • 2002-2017
    Professor and Chair in Vibro-Acoustical Monitoring/Maintenance
    Cranfield University, UK

Achievements 🏆

  • Led research in condition monitoring and maintenance for complex systems.

  • Built the novel “Vibro-acoustical condition monitoring of complex mechanical systems” research program at Cranfield University.

  • Recruited over 90 MSc students from various international universities for MSc studies at Cranfield.

  • Successfully gained £7.3M in research grants for research projects involving leading companies like Rolls-Royce, Caterpillar, and Shell.

  • Established strategic international partnerships with world-class universities and research centres around the globe. Monitoring

Awards and Honors 🥇

  • UK Rolls-Royce Innovation Award (2019)

  • COMADIT Prize for significant contributions to condition monitoring/maintenance (2017)

  • Rolls-Royce Engineering Award for Innovation (2012)

  • EC Fellowship Award (2015) – European Social Fund-Human Capital Operational Programme

  • Oxford Academic Health Science Network Award (2014)

  • Best Paper Award at CM/MFPT 2016 and CM/MFPT 2013

  • William Sweet Smith Prize from the UK Institution of Mechanical Engineers (2010)

  • USA Navy Award for helicopter fault diagnosis methodologies (1998)

  • Acoustical Society of America Award (1998)

Professional Recognition 🌟

  • Chairman of several international committees, including:

    • International Institute of Acoustics and Vibration (USA) (2014-2016)

    • International Society for Condition Monitoring/Maintenance (2011-2017)

    • European Federation of NDT (2014-present)

  • Editorial Board Member for renowned journals:

    • “Insight” NDT and Condition Monitoring

    • “Electronics” (MDPI)

    • “Energies” (MDPI)

    • “Prognostics and Health Management”

    • IEEE Fellow (Recognized as a leading professional in the field)

Publication Top Notes:

Novel Investigation of Influence of Torsional Load on Unbalance Fault Indicators for Induction Motors

Vibration analysis of rotating porous functionally graded material beams using exact formulation

Novel instantaneous wavelet bicoherence for vibration fault detection in gear systems

Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions

Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Novel fault identification for electromechanical systems via spectral technique and electrical data processing

Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions

Novel higher-order spectral cross-correlation technologies for vibration sensor-based diagnosis of gearboxes

Novel vibration structural health monitoring technology for deep foundation piles by non-stationary higher order frequency response function

 

Prof. Mehdi Behzad | Monitoring | Lifetime achievement Award

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Prof. Mehdi Behzad | Monitoring | Lifetime achievement Award 

Prof. Mehdi Behzad, Sharif University of Technology, Iran

Professor Mehdi Behzad is a distinguished academic and expert in mechanical engineering at the Sharif University of Technology, Tehran, Iran. He earned his Ph.D. from the University of New South Wales, Australia, in 1995, with a specialization in rotor dynamics and coupled vibrations. With over three decades of academic and industrial experience, Professor Behzad has led pioneering research in vibration analysis, condition monitoring, and fault diagnostics of rotating machinery. He has supervised more than 90 M.Sc. and 11 Ph.D. theses, contributed extensively to national industrial projects, and developed intelligent software solutions for signal processing and machinery health assessment. His professional service includes chairing major national conferences on condition monitoring and maintenance, as well as delivering keynote lectures at international forums such as the CM2024 in Oxford, UK. Professor Behzad’s contributions span academic teaching, applied research, and industrial consultancy, making him a leading figure in the field of vibration analysis and mechanical systems diagnostics.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Lifetime Achievement Award

Prof. Mehdi Behzad is a distinguished academic and industry expert whose lifelong dedication to mechanical engineering, particularly in the field of vibration analysis and rotor dynamics, exemplifies the qualities honored by the Lifetime Achievement Award. His career spans over three decades of impactful teaching, groundbreaking research, industrial collaboration, and academic leadership.

👨‍🎓 Education

📍 Ph.D. in Mechanical Engineering
University of New South Wales, Sydney, Australia – May 1995

  • 🌀 Thesis: Transfer matrix analysis of rotor systems with coupled lateral and torsional vibrations

  • 🧮 Courses: Finite elements, vibration, frequency analysis, lubrication

  • 🧑‍💻 Developed vibration analysis software using Riccati transfer matrix

  • 📄 Published 3 papers on rotor dynamics

📍 M.Sc. in Mechanical Engineering
Sharif University of Technology, Tehran, Iran – May 1989

  • 📘 Thesis: Transfer Function and stability of electrohydraulic servo systems

  • 🧪 Repaired an electrohydraulic servo system for experiments

  • 📚 Advanced studies in control, dynamics, nonlinear vibration

📍 B.Sc. in Mechanical Engineering
Isfahan University of Technology, Iran – Feb 1986

  • 🔧 Broad mechanical engineering training including dynamics, turbomachinery, heat transfer

🧑‍🏫 Academic & Teaching Experience

📍 Professor – Sharif University of Technology (1994–2025)

  • 👨‍🔬 Supervised 90+ M.Sc. and 11 Ph.D. theses

  • 📘 Taught undergrad & grad courses in vibration, rotor dynamics, control, mathematics

  • 🛠 Developed curricula & practical labs

  • 🧑‍🏭 Founded training centers, oversaw solid mechanics lab & naval division

  • 📜 Organized nationwide Condition Monitoring & Fault Diagnosis conference (2007–2024)

🧪 Research & Industrial Experience

📍 University of New South Wales (1990–1995)

  • 📊 Built and used data acquisition systems

  • 🔁 Solved numerical issues in transfer matrix methods

  • 📝 Wrote reports for Sydney Electricity & Pacific Power

📍 Sazeh Consultant, Tehran (1988–1990)

  • 🛠 Vibration analysis for industrial structures

  • 🧾 Created guidelines for thermal stress, piping design, and actuator testing

📍 Industrial Consultant (1996–2024)

  • 🏭 Completed 50+ major vibration and condition monitoring projects

  • 🔍 Diagnosed faults in turbines, compressors, cement mills, pumps, and more

  • 🖥 Developed intelligent diagnostic software

  • 🌊 Assessed vibration in hydropower & petrochemical plants

  • 🚂 Involved in projects with railways, powerplants, and petrochemical complexes

🏆 Achievements, Awards & Honors

🎤 Keynote & Invited Speaker

  • 📍 20th International Conference on Condition Monitoring and Asset Management (CM2024), Oxford, UK

    • 🗣 “Challenges in Condition Monitoring”

    • 🎙 “Vibration Features for Machinery Condition Monitoring”

🏅 Leadership Roles

  • 🎖 Chairman of Iran Maintenance Association (2007–2012)

  • 🧩 Research Deputy, Sharif University – Mechanical Eng. Dept.

  • 🎓 Director, University Center for Training (since 2010)

📘 Curriculum Innovator & Educator

  • 🛠 Founded and led numerous industrial courses & workshops on:

    • Vibration Analysis Levels 1 & 2

    • Rotor Dynamics

    • API 687 Repair Technologies

    • Reliability Centered Maintenance

    • Shaft Alignment

Publication Top Notes:

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