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.

 

Jaecheol Ha | Computer Science | Best Researcher Award

Prof .ย  Jaecheol Ha | Computer Science | Best Researcher Award

Professor at Hoseo University , South Korea

Professor Jaecheol Ha is a seasoned academic with a Ph.D. in Electronics Engineering from Kyungpook National University and over 25 years of research and teaching experience. Currently a full professor at Hoseo University, he has also held academic positions at Korea Nazarene University and was a visiting researcher at Purdue University, USA. His research focuses on critical areas such as AI security, mobile network security, hardware security, and side-channel attacksโ€”fields of growing importance in today’s digital world. As the honorary president of the Korea Institute of Information and Cryptography (KIISC), he demonstrates recognized leadership in the cybersecurity research community. While his academic background and research interests are highly relevant, more information on his publication record, research impact, and mentorship contributions would further strengthen his case. Nonetheless, based on the available information, Professor Ha presents a strong and credible profile for the Best Researcher Award, particularly in the domain of cybersecurity.

Professional Profileย 

Education๐ŸŽ“

Professor Jaecheol Ha has a solid academic foundation in electronics engineering, having earned his Bachelorโ€™s (BE) in 1989, Masterโ€™s (ME) in 1993, and Ph.D. in 1998 from Kyungpook National University in the Republic of Korea. His progression through all three degrees at a single institution reflects a consistent and focused commitment to his field of study. Kyungpook National University is recognized for its strong engineering programs, providing him with a rigorous education and research training environment. His doctoral studies likely laid the groundwork for his later specialization in areas such as AI security and hardware-based cryptographic methods. This strong educational background has supported his successful academic career, enabling him to contribute meaningfully to research and teaching. His education not only equipped him with deep technical knowledge but also prepared him to take on leadership roles in academic and research institutions, both domestically and internationally.

Professional Experience๐Ÿ“

Professor Jaecheol Ha has extensive professional experience spanning over two decades in academia and research. He is currently a full professor in the Division of Computer Engineering at Hoseo University in Asan, Republic of Korea, where he plays a key role in teaching and research. Prior to this, from 1998 to 2006, he served as a professor in the Department of Information and Communication at Korea Nazarene University in Cheonan. His academic career reflects a strong commitment to education and research in the fields of computer engineering and cybersecurity. In 2014, he broadened his international experience by working as a visiting researcher at Purdue University in the United States, further enhancing his global academic perspective. In addition to his teaching and research roles, he currently serves as the honorary president of the Korea Institute of Information and Cryptography (KIISC), a position that highlights his leadership and influence in the Korean cybersecurity research community.

Research Interest๐Ÿ”Ž

Professor Jaecheol Haโ€™s research interests lie in the critical and rapidly evolving field of cybersecurity, with a focus on AI security, mobile network security, hardware security, and side-channel attacks. His work addresses some of the most pressing challenges in digital security, particularly as emerging technologies like artificial intelligence and mobile communication continue to expand. By exploring vulnerabilities in hardware and communication systems, as well as developing methods to protect against side-channel attacks, his research contributes to building more resilient and secure digital infrastructures. His interest in AI security reflects a forward-thinking approach, recognizing the increasing integration of AI in sensitive systems and the corresponding need for robust protective measures. Through his work, Professor Ha seeks to bridge theoretical understanding with practical applications, providing solutions that can be implemented in real-world systems. His research not only supports academic advancement but also contributes to national and global efforts to strengthen cybersecurity.

Award and Honor๐Ÿ†

Professor Jaecheol Ha has received recognition for his contributions to the field of cybersecurity through his leadership role as the honorary president of the Korea Institute of Information and Cryptography (KIISC). This prestigious position reflects his respected status within the academic and research communities, as well as his long-standing commitment to advancing knowledge in information security. While specific awards or honors are not listed, his appointment to such a significant role within a national institute suggests a high level of trust and acknowledgment by his peers. It highlights his influence in shaping research directions and policies in cryptography and cybersecurity in Korea. His professional journey, including his international research collaboration at Purdue University, also indicates recognition of his expertise beyond national boundaries. These honors affirm his impact as a leader and researcher, underscoring his suitability for further accolades such as the Best Researcher Award in his field of specialization.

Research Skill๐Ÿ”ฌ

Professor Jaecheol Ha possesses a wide range of research skills that are crucial for tackling complex problems in the field of cybersecurity. His expertise spans several critical areas, including AI security, mobile network security, hardware security, and side-channel attacks. With a deep understanding of both theoretical and practical aspects of these fields, he is skilled at identifying vulnerabilities in systems and developing innovative solutions to mitigate them. His ability to bridge the gap between cutting-edge research and real-world applications demonstrates his strong problem-solving capabilities. Additionally, his international research experience, particularly as a visiting researcher at Purdue University, indicates a high level of adaptability and collaboration in global research environments. His leadership as honorary president of the Korea Institute of Information and Cryptography (KIISC) further highlights his ability to mentor, guide, and foster collaboration among researchers, strengthening his research skills in both individual and team-based contexts.

Conclusion๐Ÿ’ก

Professor Jaecheol Ha appears to be a well-qualified and experienced researcher with a strong focus on cybersecurity, leadership experience, and international exposure. These factors support his eligibility for a Best Researcher Award, especially if the focus is on long-term contribution and domain impact.

However, to make a fully confident endorsement, it would be ideal to see quantitative evidence of research excellence โ€” such as high-impact publications, citations, or funded projects. If such data exists and supports the narrative, then he is a strong and suitable candidate for this award.

Publications Top Notedโœ

  1. Title: SSIM-Based Autoencoder Modeling to Defeat Adversarial Patch Attacks
    Authors: Seungyeol Lee, Seongwoo Hong, Gwangyeol Kim, Jaecheol Ha
    Year: 2024
    Citations: 1
  2. Title: Implementation of Disassembler on Microcontroller Using Side-Channel Power Consumption Leakage
    Authors: Daehyeon Bae, Jaecheol Ha
    Year: 2022
    Citations: 6
  3. Title: Deep Learning-based Attacks on Masked AES Implementation
    Authors: Daehyeon Bae, Jongbae Hwang, Jaecheol Ha
    Year: 2022
    Citations: 1
  4. Title: Performance Metric for Differential Deep Learning Analysis
    Authors: Daehyeon Bae, Jaecheol Ha
    Year: 2021
    Citations: 26

 

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Prof. Dr. Wei Fang, Nanjing University of Information Science & Technology, China

Prof. Dr. Wei Fang is a Professor in the Department of Computer Science at Nanjing University of Information Science & Technology, China, and a member of the State Key Laboratory for Novel Software Technology, Nanjing University. He holds a Ph.D. and M.Sc. in Computer Science from Soochow University. Wei was a visiting scholar at the University of Florida in 2015-2016. His research interests include Artificial Intelligence, Big Data, Data Mining, and Meteorological Information Processing. He has led several research projects funded by the National Natural Science Foundation of China and is an active reviewer for international journals. Wei is a senior member of the CCF and ACM.

Professional Profile:

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ORCID

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Summary of Suitability for Best Researcher Award โ€“ Prof. Wei Fang

Prof. Wei Fang of Nanjing University of Information Science & Technology stands out as a highly meritorious candidate for the Best Researcher Award. With a solid academic foundation, national and international research exposure, and extensive contributions in Artificial Intelligence, Big Data, Computer Vision, and Applied Meteorology, his work bridges theoretical innovation with real-world application.

๐ŸŽ“ Education

  • Ph.D. in Computer Science โ€“ Soochow University, China

  • M.Sc. in Computer Science โ€“ Soochow University, China

๐Ÿ“š Visiting Scholar โ€“ University of Florida, USA (Faculty of Computer Science, Sept 2015 โ€“ Sept 2016)

๐Ÿ’ผ Work Experience

  • ๐Ÿ‘จโ€๐Ÿซ Professor, Department of Computer Science, NUIST

  • ๐Ÿงช Affiliated with the State Key Lab for Novel Software Technology, Nanjing University

  • ๐Ÿค Program Committee Member for multiple international conferences

  • ๐Ÿ“ Reviewer for various international journals

  • ๐ŸŒ International Research Scientist

๐Ÿ† Achievements & Honors

  • ๐Ÿง  Recognized for impactful research in:

    • Artificial Intelligence ๐Ÿค–

    • Big Data & Cloud Computing โ˜๏ธ๐Ÿ“Š

    • Computer Vision ๐Ÿ‘๏ธ

    • Applied Meteorology ๐ŸŒฆ๏ธ

  • ๐Ÿ”ฌ Project Leader of national and industrial research projects funded by:

    • National Natural Science Foundation of China

    • Guodian Nari Nanjing Control System Co., Ltd.

    • Baoshan Iron and Steel Co., Ltd.

  • ๐ŸŽ–๏ธ Senior Member of CCF (China Computer Federation) & ACM

  • ๐Ÿ“ˆ Cited in SCI-indexed journals

Publicationย Top Notes:

A rapid learning algorithm for vehicle classification

CITED: 562

A Method for Improving CNN-Based Image Recognition Using DCGAN.

CITED: 230

Efficient feature selection and classification for vehicle detection

CITED: 220

A survey of big data security and privacy preserving

CITED: 117

Survey on research of RNN-based spatio-temporal sequence prediction algorithms

CITED: 100