Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman ๐Ÿ‡ฌ๐Ÿ‡ง is a globally recognized expert in signal processing and condition monitoring ๐Ÿ”. He currently serves as Chair Professor and Director at the University of Huddersfield ๐Ÿซ. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing ๐Ÿ”ง. A Fellow of multiple prestigious organizations ๐ŸŒ, Prof. Gelmanโ€™s international collaborations span across Europe, Asia, and the USA ๐ŸŒ. His innovations have advanced aerospace and medical diagnostics โœˆ๏ธ๐Ÿงฌ. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies ๐Ÿ”ฌ๐Ÿ› ๏ธ.

Professional Profile

SCOPUS

Education and Experienceย 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) ๐ŸŽ“, with BSc (Hons) and MSc (Hons) degrees in engineering ๐Ÿ“˜. He is a British citizen ๐Ÿ‡ฌ๐Ÿ‡ง. Since 2017, he has been a Professor and Chair at the University of Huddersfield ๐Ÿ›๏ธ. Prior to that, he served at Cranfield University (2002โ€“2017) as Chair in Vibro-Acoustical Monitoring ๐Ÿ”Š. His distinguished academic journey includes visiting professorships in China ๐Ÿ‡จ๐Ÿ‡ณ, Denmark ๐Ÿ‡ฉ๐Ÿ‡ฐ, Poland ๐Ÿ‡ต๐Ÿ‡ฑ, Spain ๐Ÿ‡ช๐Ÿ‡ธ, Italy ๐Ÿ‡ฎ๐Ÿ‡น, and the USA ๐Ÿ‡บ๐Ÿ‡ธ. Prof. Gelman combines deep technical expertise with global educational outreach ๐ŸŒ๐Ÿ‘จโ€๐Ÿซ.

Professional Developmentย 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society ๐ŸŒ. He is a Fellow of BINDT, IAENG, IDE, and HEA ๐ŸŽ–๏ธ, and an Academician of the Academy of Sciences of Applied Radio Electronics ๐Ÿง . He chairs award and honors committees for top acoustics and vibration institutions ๐Ÿ…. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others ๐ŸŽ“, he mentors emerging researchers globally ๐ŸŒŽ. Prof. Gelmanโ€™s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education ๐Ÿ“ˆ๐Ÿ”ง.

Research Focusย 

Prof. Gelmanโ€™s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems ๐Ÿ”๐Ÿ”Š. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing โœˆ๏ธ๐Ÿฅ๐Ÿญ. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data ๐Ÿง ๐Ÿ“Š. His interdisciplinary approach benefits both industry and academia ๐ŸŒ๐Ÿ”ฌ. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring ๐Ÿงฌโš™๏ธ. His innovations contribute to safer, more efficient engineering systems across global platforms ๐ŸŒ๐Ÿš€.

Awards and Honorsย 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence ๐Ÿ…. These include the Rolls-Royce Innovation Award (2012, 2019) โœˆ๏ธ, William Sweet Smith Prize by IMechE ๐Ÿ› ๏ธ, and COMADIT Prize by BINDT for impactful contributions to condition monitoring ๐Ÿงฒ. He also received Best Paper Awards at CM/MFPT conferences ๐Ÿ“„ and recognition from the USA Navy and Acoustical Society of America ๐Ÿ‡บ๐Ÿ‡ธ๐Ÿ”Š. His European and UK fellowships support cutting-edge human capital projects ๐Ÿง ๐Ÿ‡ช๐Ÿ‡บ. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation ๐ŸŒ๐Ÿ‘จโ€๐Ÿ”ฌ.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21โ€“22), 3195โ€“3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.

 

Mr Seemant Tiwari | AI & ML | Best Researcher Award | 1086

Mr Seemant Tiwari | AI & ML | Best Researcher Award

Ph. D Student of Southern Taiwan University of Science and Technology, Tainan City, Taiwan,Taiwan

Seemant Tiwari graduated with a Bachelor of Technology, in Electrical Engineering from AKTU (formerly known as UPTU & GBTU), in Lucknow, Uttar Pradesh, India. He earned his Master of Technology, in Power Electronics, in May 2013 from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, in Chennai, Tamil Nadu, India. He obtained his Post Graduate Certificate Program, in Petroleum and Natural Gas Flow Measurement & Control Techniques, in November 2013 from the Fluid Control Research Institute, in Palakkad, Kerala, India. He has been working on a Ph.D. since September 2019 at the Department of Electrical Engineering (Renewable & Intelligent Power System Laboratory) at Southern Taiwan University of Science and Technology, in Yongkang District, Tainan City, Taiwan. Before joining STUST in Taiwan, he was a lecturer in the Electrical Department at R.B.S. Polytechnic, in Agra, Uttar Pradesh, India. His current research interests include wind speed forecasting, renewable energy forecasting, and electric load forecasting.

Profile

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Education

  • Bachelor of Technology (B.Tech.) in Electrical Engineering from AKTU (formerly UPTU & GBTU), Lucknow, India.
  • Master of Technology (M.Tech.) in Power Electronics from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India (2013).
  • Post Graduate Certificate Program in Petroleum and Natural Gas Flow Measurement & Control Techniques from the Fluid Control Research Institute, Palakkad, Kerala, India (2013).

Seemantโ€™s educational journey reflects a strong foundation in electrical engineering, complemented by specialized training in power electronics and fluid control techniques.

Experience

Before joining STUST in Taiwan, Seemant worked as a lecturer in the Electrical Department at R.B.S. Polytechnic, Agra, India. During his tenure, he focused on teaching electrical engineering fundamentals and guiding students in their practical understanding of the subject. His transition to academia in Taiwan marked a shift toward research, where he is now delving into predictive modeling for energy systems.

Awards and Recognition

Throughout his academic journey, Seemant has been recognized for his research efforts in renewable energy. He has received commendations for his innovative contributions to wind speed and electric load forecasting using AI techniques. His expertise in power systems has made him a valuable asset in his current research environment.

Academic and Professional

Seemant Tiwari graduated with a Bachelor of Technology, in Electrical Engineering from AKTU (formerly known as UPTU & GBTU), in Lucknow, UttarPradesh, India. He earned his Master of Technology, in Power Electronics, in May 2013 from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, in Chennai, Tamil Nadu, India. He obtained his Post Graduate Certificate Program, in Petroleum and Natural Gas Flow Measurement & Control Techniques, in November 2013 from the Fluid Control Research Institute, in Palakkad, Kerala, India

Areas of Research: AI & ML

Seemant has been working on a Ph.D. since September 2019 at the Department of Electrical Engineering (Renewable & Intelligent Power System Laboratory) at Southern Taiwan University of Science and Technology, in Yongkang District, Tainan City, Taiwan. Before joining STUST in Taiwan, he was a lecturer in the Electrical Department at R.B.S. Polytechnic, in Agra, Uttar Pradesh, India. His current research interests include wind speed forecasting,renewable energy forecasting, and electric load forecasting.

ย Publications:ย 

  • Big Data Analytics: Energy Forecasting Computational Intelligence Methods
  • Mathematics for Machine Learning
  • A Survey on Big Data Analytics for Load Prediction in Smart Grids
  • Industries Application of Type-2 Fuzzy Logic
  • Segmentation and Clustering of Time Series Data
  • Modification Metric of Class Document on Naรฏve Bayes for Sentiment Analysis of Online Learning Evaluation
  • Artificial Intelligence (AI) in the Sustainable Energy Sector
  • Implications of Machine Learning in Renewable Energy
  • Applications of Smart Technologies Regarding Promoting Energy Efficiency and Sustainable Resource Utilization
  • Approaches Involving Big Data Analytics Using Machine Learning
  • Electrical Load Forecasting Methodologies and Approaches
    • Conference Paper
    • Published in Eurasia Proceedings of Science, Technology, Engineering and Mathematics, 2022
    • DOI: 10.55549/epstem.1218629
  • Supervised Machine Learning: A Brief Introduction
    • Conference Paper
    • Published in Proceedings of the International Conference on Virtual Learning, 2022
    • DOI: 10.58503/icvl-v17y202218
  • Wind Speed Forecasting Methods for Wind Energy Generation
  • Concepts and Strategies for Machine Learning
    • Book Chapter
    • Published in Current Studies in Basic Sciences, Engineering and Technology, 2022
    • ISBN: 978-605-81654-2-7
  • Artificial Intelligence Implications in Engineering and Production
  • Various Models for Predicting Wind Energy Production

Conclusion

Given his focus on renewable energy, AI, and practical applications in energy forecasting, Mr. Seemant Tiwariโ€™s research has the potential to drive significant community-level impact, making him a fitting candidate for the Research for Community Impact Award.