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

 

Ms. Yuri Kim | Data Monitoring | Best Researcher Award

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Ms. Yuri Kim | Data Monitoring | Best Researcher Award 

Ms. Yuri Kim, Korea University, South Korea

Yuri Kim is a Ph.D. candidate in Computer Science at Korea University, Seoul, South Korea, where she has been conducting research since September 2020 as a recipient of the ICT Elite Talent Development Program Scholarship. She holds a B.Sc. and M.Sc. in Computer Science from Eötvös Loránd University, Budapest, Hungary, where she graduated with distinction under the Stipendium Hungaricum Scholarship. Yuri has extensive experience in both academia and industry, having served as a lecturer at Korea University Graduate School of Education and Eötvös Loránd University, teaching advanced data structures and functional programming. She has also worked as a project manager at The Mihalik Group, leading the development of in-house business automation software, and as a student backend developer at Ericsson. Her research interests span natural language processing, machine learning, and financial modeling, with notable publications on stock trading recommendation systems, serendipity-based recommender systems, and Linked Open Data structures. She has been actively involved in entrepreneurial ventures, participating in the 2024 Korean I-Corps Program and the 2023 Innovation Startup School, focusing on AI-driven solutions. Proficient in Python, C, Go, and Clean, Yuri combines her expertise in programming with strong project management skills in Agile and Scrum methodologies.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Yuri Kim is an exceptional researcher in computer science, artificial intelligence, natural language processing (NLP), and recommender systems, demonstrating a strong academic and professional background. Her diverse expertise, high-impact research contributions, and interdisciplinary approach make her a highly suitable candidate for the Best Researcher Award.

🎓 Education

📍 Korea University, Seoul, South Korea
🔹 Ph.D. Candidate in Computer Science (2020.09 – Present)
🔹 🎖️ Recipient of ICT Elite Talent Development Program Scholarship
🔹 🏫 Research Assistant

📍 Eötvös Loránd University, Budapest, Hungary
🔹 B.Sc. & M.Sc. in Computer Science (Integrated Program) (2016.09 – 2019.08)
🔹 📊 GPA: 4.28 / 4.5 (Master’s) | 4.48 / 4.5 (Bachelor’s)
🔹 🎖️ Recipient of Stipendium Hungaricum (Hungarian Government Scholarship)

💼 Work Experience

📍 The Mihalik Group, Chicago, IL (Remote)
🔹 Project Manager (PM) (2023.08 – 2024.07)
🔹 🏗️ Developed in-house business automation software
🔹 📅 Planned & designed project phases, coordinated schedules
🔹 🔍 Managed resources & monitored project performance

📍 Korea University Graduate School of Education, Seoul, South Korea
🔹 Lecturer – Advanced Data Structures (2022.09 – 2023.02)
🔹 🖥️ Delivered lectures on Data Structures & Algorithms
🔹 📝 Designed quizzes, assignments, exams & projects
🔹 📚 Developed instructional materials

📍 Eötvös Loránd University, Budapest, Hungary
🔹 Lecturer – Functional Programming (Clean Language) (2018.02 – 2019.02)
🔹 🏛️ Taught Clean Functional Programming
🔹 ✍️ Designed evaluations, assignments, exams & projects
🔹 📖 Created instructional materials

📍 Ericsson, Budapest, Hungary
🔹 Student Backend Developer (2018.09 – 2019.01)
🔹 ⚙️ Developed & evaluated performance test cases using C
🔹 🛠️ Updated codebase to align with modern standards
🔹 🔍 Conducted code reviews & refactoring

🏆 Awards & Honors

🔹 2024 Korean I-Corps Program 🚀

  • AI-Based Personalized Makeup Consulting
    🔹 2023 Innovation Startup School 🏅
  • AI-Based Celebrity Memorabilia Donation & Auction Platform

🎖️ Achievements

📌 Publications & Research 📚
🔹 Developed a Rule-Based Stock Trading Recommendation System 📈
🔹 Designed a Serendipity-Incorporated Recommender System 🤖
🔹 Implemented a Linked Data Visualization System 🔗
🔹 Proposed methods for Enhancing Linked Open Data (LOD) 🛠️
🔹 Researched Interdisciplinary Applications for Functional Programming 🎨
🔹 Compared Clean vs. C Programming for Education 💻
🔹 Explored Distributed Computation Patterns in Go & RabbitMQ ☁️

📌 Projects 🔍
🔹 SEC 13D/G & 13F Report Tracking Platform (2024.12) 📊
🔹 Text-to-Speech-Based Audiobook Auto-Generation (2024.12) 🎙️
🔹 AI Avatar-Based Motion Slide Auto-Generation (2024.12) 🖼️
🔹 DART Stock Large Shareholder Report Crawling System (2024.10) 📑

Publication Top Notes:

A Rule-Based Stock Trading Recommendation System Using Sentiment Analysis and Technical Indicators

Introduction to programming Using Clean

Assoc. Prof. Dr. Olimpiu Stoicuta | Online Monitoring Awards | Best Researcher Award

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Assoc. Prof. Dr. Olimpiu Stoicuta | Online Monitoring Awards | Best Researcher Award

Assoc. Prof. Dr. Olimpiu Stoicuta, University of petrosani, Romania

Olimpiu-Costinel Stoicuța, is an Associate Professor in the Department of Control Engineering, Computers, Electrical Engineering, and Power Engineering at the University of Petroșani. He holds a Ph.D. in Electrical Engineering from the Technical University of Cluj-Napoca, with a specialization in sensorless vector-controlled systems for induction motors. With a strong academic background that includes a Bachelor’s degree in Automation and Applied Informatics and a Master’s in Automatic Control of Processes Techniques, he has contributed extensively to research in fields such as automatic control systems, system theory, optimization techniques, and data science. Prof. Stoicuța has authored numerous publications and serves on the editorial boards of several international journals. He is a member of professional organizations, including IEEE and the Robotics Society of Romania, and has received accolades such as the Bologna Professor award and the ALUMNI prize for young researchers.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Dr. Olimpiu-Costinel Stoicuta is a highly suitable candidate for the Best Researcher Award based on his exemplary academic and professional achievements. Here are the key factors supporting his nomination:

📘 Education

  • 1996–2001: B.S. in Systems and Computer Science, Automation and Applied Informatics, University of Petrosani 🎓
  • 2004–2006: M.S. in Automatic Control of Process Techniques, University of Petrosani 🛠️
  • 2003–2010: Ph.D. in Electrical Engineering, Technical University of Cluj-Napoca 🧑‍🔬
    • Thesis: “Contributions to the Study of the Stability of Sensorless Vector-Controlled Systems with Induction Motor”
    • Supervisor: Prof. Teodor Pana 👨‍🏫

👨‍💻 Work Experience

  • 2001–2007: Graduate Assistant, University of Petrosani
  • 2007–2012: Assistant Professor, University of Petrosani
  • 2012–2015: University Lecturer, University of Petrosani
  • 2015–Present: Associate Professor, University of Petrosani

💼 Expertise and Research Interests

  • Sensorless vector control of electrical drives ⚡
  • System identification and automatic control systems 🖥️
  • Modeling, simulation, and optimization techniques 🔄
  • Data science, system theory, and special electrical drives 📊

🖥️ Technical Skills

Programming Languages: MATLAB/Simulink, Visual C, Visual Basic, MathCad 🧮

Operating Systems: Windows, Linux 🐧

Software Tools: Abaqus CAE, FEMM, CodeComposer, EViews, GX IEC Developer 7 🛠️

Publication top Notes:

Small speed asymptotic stability study of an induction motor sensorless speed control system with extended Gopinath observer

CITED:21

Controllers tuning for the speed vector control of induction motor drive systems

CITED:18

Design of an extended Luenberger observer for sensorless vector control of induction machines under regenerating mode

CITED:16

Visible light wireless data communication in industrial environments

CITED:15

Underground channel model for visible light wireless communication based on neural networks

CITED:13

Mr. Sahngzhe Sun | Monitoring Awards | Excellence in Research

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Mr. Sahngzhe Sun | Monitoring Awards | Excellence in Research 

Mr. Sahngzhe Sun, Wuhan University, China

Shangzhe Sun is a researcher affiliated with Wuhan University, specializing in computer vision, deep learning, and unmanned aerial vehicle (UAV) technology. His expertise includes 3D image processing, point clouds, LiDAR data analysis, and intelligent unmanned systems. Sun has contributed to significant advancements in UAV-based applications, particularly in power transmission line detection, insulator defect detection, and real-time 3D mapping. His notable works include “DCPLD-Net: A diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-Borne LiDAR data,” published in the International Journal of Applied Earth Observation and Geoinformation, and collaborative projects like OR-LIM and LUOJIA Explorer for exploration and mapping. Through his research, Sun aims to improve UAV capabilities in high-precision mapping, surveillance, and defect detection, contributing to the safety and efficiency of power transmission facilities and intelligent mapping.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Excellence in Research Award: Shangzhe Sun 

Shangzhe Sun, affiliated with Wuhan University, specializes in computer vision, deep learning, UAV-based imaging, and intelligent unmanned systems. His work demonstrates a strong focus on innovative research for real-time, drone-based data collection, which has significant applications in infrastructure inspection, mapping, and autonomous navigation systems.

Education:

  • Ph.D. in Computer Vision and Deep Learning (Expected or obtained by 2024)
    Wuhan University, China
    Specialization: Computer Vision, UAV-based systems, LiDAR data processing, point cloud mapping, and intelligent unmanned systems.

Work Experience:

  • Researcher/Graduate Research Assistant
    Wuhan University
    Focused on computer vision, deep learning, and UAV applications for remote sensing and geospatial data processing. Contributed to significant research projects on UAV LiDAR applications, defect detection in power transmission, and collaborative mapping.
  • Research Collaborator (Likely Role)
    Collaborated with various co-authors and institutions on projects involving LiDAR-based object detection, multimodal sensor integration, and UAV mapping.

Shangzhe Sun’s recent publications, including works on insulator defect detection, real-time UAV 3D point clouds, and UAV-based exploration, reflect a strong research background in UAV applications and geospatial data analysis. Additional work experience may be in academia or research settings, given the specialized topics of his publications.

Publication top Notes:

CITED:18
CITED:2
CITED:2
CITED:2
CITED:1

Mr. Mazhar Abbas | Monitor Awards | Best Researcher Award

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Mr. Mazhar Abbas | Monitor Awards | Best Researcher Award 

Mr. Mazhar Abbas, Hainan University, China

Hafiz Muhammad Mazhar Abbas, son of Muhammad Bilal, hails from Mouza Chattani, Tehsil Mailsi, District Vehari, and is currently residing at Room #15-325, International Student Building, Hainan University, Haikou, China. He holds a Master’s degree (M.Sc. Hons.) in Agriculture with a specialization in Agronomy, graduating in 2019 from the University of Agriculture Faisalabad (UAF) with a CGPA of 3.60/4.00. Prior to this, he earned his Bachelor’s (B.Sc. Hons.) in Agriculture Agronomy from UAF in 2017 with a CGPA of 3.56/4.00. His academic journey began with his F.Sc. (Pre-Med) in 2011 and Matriculation in 2009. Mazhar Abbas has also gained practical experience in tunnel farming, soil and water conservation, and managing crop production farms. His skills extend to agricultural management functions, contributing to his expertise in agronomy.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award:

Hafiz Muhammad Mazhar Abbas demonstrates significant expertise and contributions in the field of agronomy and environmental management, particularly in rice production and biochar modification for soil and plant resilience. His qualifications and research accomplishments, along with his publications, make him a strong contender for the Best Researcher Award. Below is an evaluation based on his qualifications, publications, and experience.

Educational Qualifications:

  1. M.Sc. (Hons.) Agriculture – Agronomy
    • Institution: University of Agriculture, Faisalabad (UAF)
    • Passing Year: 2019
    • GPA: 3.60/4.00
  2. B.Sc. (Hons.) Agriculture – Agronomy
    • Institution: University of Agriculture, Faisalabad (UAF)
    • Passing Year: 2017
    • GPA: 3.56/4.00
  3. F.Sc. (Pre-Medical)
    • Institution: Multan Public Higher Secondary School (MPHSS)
    • Passing Year: 2011
    • Marks: 869/1100
  4. Matriculation
    • Institution: Hawks Secondary School
    • Passing Year: 2009
    • Marks: 940/1050

Work Experience:

  1. One-week Tunnel Farming Training Course – Completed in 2016.
  2. Practices for Soil and Water Conservation – Engaged in activities focused on sustainable agricultural practices.
  3. Farm Management for Crop Production – Experience in managing farms, focusing on efficient crop production techniques.
  4. Managing Functions – Involved in organizing and managing agricultural functions and activities.

Publication top Notes:

CITED:9
CITED:2