Mrs. Inajara Rutyna | Online Monitoring | Best Researcher Award

Posted on by Global Sensor Awards

Mrs. Inajara Rutyna | Online Monitoring | Best Researcher Award

Mrs. Inajara Rutyna | Online Monitoring | Warsaw University of Technology | Poland

Mrs. Inajara Rutyna is a distinguished researcher in the field of Artificial Intelligence and Renewable Energy Systems, currently pursuing her Ph.D. in Automation, Electronics, and Electrical Engineering at the Warsaw University of Technology, Poland. Her academic foundation is built on a Master’s degree in Numerical Methods in Engineering and a Bachelor’s degree in Industrial Mathematics from the Universidade Federal do Paraná, Brazil. Throughout her academic and professional journey, Mrs. Inajara Rutyna has consistently demonstrated exceptional proficiency in mathematical modeling, computational intelligence, and optimization methods. Her professional experience encompasses diverse roles, including AI Development Specialist at IDEAS NCBR Sp. z o.o., where she developed intelligent algorithms and Python-based models for renewable energy forecasting, and Mathematical Modeller and Data Scientist at the National Centre for Nuclear Research, Poland, contributing to mathematical frameworks for sustainable power systems. Additionally, her earlier engagements as a Game Economy Designer at Rage Quit Games and as a Project and Process Analyst at Segula do Brasil Engenharia e Tecnologia reflect her versatility in applying data-driven modeling to industrial, gaming, and energy contexts. Mrs. Rutyna’s research interests lie primarily in Artificial Intelligence applications for renewable energy forecasting, computational fluid dynamics, optimization algorithms, and machine learning-based energy modeling. Her technical skills include advanced programming in Python, MATLAB, and Fortran, as well as expertise in numerical analysis, data science, and algorithmic development. She has authored and co-authored multiple IEEE and Scopus-indexed publications focusing on energy efficiency prediction, evaluation metrics for wind power, and AI-based forecasting. She is an active member of professional bodies such as the IEEE, contributing to international research collaborations and scientific discussions on sustainable technology innovation.

Professional Profiles: ORCID

Featured Publications 

  1. Rutyna, I. (n.d.). Gated lag and feature selection for day-ahead wind power forecasting using on-site SCADA data. IEEE. (Citations: 42)

  2. Rutyna, I. (n.d.). Efficiency analysis of k-nearest neighbors machine learning method for 10-minutes ahead forecasts of electric energy production at an onshore wind farm. Elsevier. (Citations: 38)

  3. Rutyna, I. (n.d.). Evaluation metrics for wind power forecasts: A comprehensive review and statistical analysis of errors. IEEE Access. (Citations: 57)

  4. Rutyna, I. (n.d.). Polynomial interpolation with repeated Richardson extrapolation to reduce discretization error in CFD. Springer. (Citations: 31)

  5. Rutyna, I. (n.d.). Stochastic hybrid optimization methods for renewable energy forecasting and grid stability. IEEE Transactions on Sustainable Energy. (Citations: 29)

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

Posted on by Global Sensor Awards

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