Zhiyang Guo | Sliding Mode Control | Best Scholar Award

Published on by Innovation Technologist

Dr. Zhiyang Guo | Sliding Mode Control | Best Scholar Award

Doctoral Candidate at Dalian Maritime University, China

🌊 Dr. Zhiyang Guo is a passionate researcher in maritime engineering and control systems, currently pursuing a Ph.D. at Dalian Maritime University. With expertise in sliding mode control and autonomous vehicles, he has published impactful research articles and holds patents in innovative maritime technologies. 🚤🔬 His work contributes to advancing the capabilities of unmanned and autonomous surface vehicles, driving innovation in the maritime industry. 🌟

Publication Profile : 

Scopus

Educational Background 🎓

Zhiyang Guo is currently a Doctoral Candidate at Dalian Maritime University, where he has been pursuing his Ph.D. since 2022. He previously completed his Master’s degree at the same institution from 2020 to 2022, specializing in cutting-edge maritime technologies.

Professional Experience đź’Ľ

Zhiyang Guo has demonstrated a strong commitment to advancing maritime engineering and control systems. His research focuses on unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), particularly in sliding mode control. He has contributed to the field through patent applications and publications in esteemed journals, showcasing his innovative approach to maritime maneuverability and control systems.

Research Interests 🔬

  • Sliding mode control
  • Unmanned surface vehicles (USVs)
  • Autonomous surface vehicles (ASVs)

Achievements and Innovations

Zhiyang has filed two patents (Application Numbers: 202410936299.5 and 202410936301.9) and authored influential journal articles, including:

  1. Turning and zigzag maneuverability investigations on a waterjet-propelled trimaran in calm and wavy water using a direct CFD approach (DOI: 10.1016/j.oceaneng.2023.115511)
  2. Predefined-time global recursive sliding mode control for trajectory tracking of unmanned surface vehicles with disturbances uncertainties (DOI: 10.1016/j.oceaneng.2024.119408)

Publications đź“š

  • Guo, Z., Zhang, J., Shang, Y., Zhang, L., & Chen, W. (2024). Predefined-time global recursive sliding mode control for trajectory tracking of unmanned surface vehicles with disturbances uncertainties. Ocean Engineering, 313, 119408.
  • Zhang, J., Guo, Z., Zhang, Q., Shang, Y., & Zhang, L. (2023). Turning and zigzag maneuverability investigations on a waterjet-propelled trimaran in calm and wavy water using a direct CFD approach. Ocean Engineering, 286, 115511.
  • Cui, Y., Wang, D., Guo, Z., Zhang, L., & Zhang, J. (2023). Maneuverability prediction for trimaran ship under the combined influence of wind and wave. Proceedings of SPIE – The International Society for Optical Engineering, 12756, 127564V.
  • Wei, Y., Zhang, J., Guo, Z., Shang, Y., & Zhang, L. (2023). Research on added waves resistance in misalignment paralleling of two ships. Proceedings of SPIE – The International Society for Optical Engineering, 12756, 127561G.

 

 

 

Manas Ranjan Sethi | ECE | Best Researcher Award

Published on by Innovation Technologist

Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

đź’Ľ Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

đź“ť Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.

 

 

 

Gonzalo PĂ©rez Serrano | Artificial photosynthesis | Young Scientist Award

Published on by Innovation Technologist

Mr. Gonzalo PĂ©rez Serrano | Artificial photosynthesis | Young Scientist Award

PhD at IMDEA Nanociencia, Spain

Gonzalo Pérez Serrano is an Assistant Researcher at IMDEA Nanoscience in Spain, specializing in ultrafast phenomena at the nanoscale. He holds a Master’s degree in Biotechnology from Universidad Autónoma de Madrid and a Bachelor’s degree in Biology from Universidad Complutense de Madrid. His research focuses on artificial photosynthesis, specifically the design of structural modulators for photosystems, where he studies the impact of protein dynamics on chromophore function.

Publication Profile : 

Orcid

 

🎓 Educational Background :

Gonzalo Pérez Serrano completed his undergraduate degree in Biology at the Universidad Complutense de Madrid in 2022. During his studies, he gained valuable experience at the Veterinary Health Surveillance Centre (VISAVET), specializing in molecular techniques such as genetic material extraction and purification, molecular diagnosis, and data analysis. His Bachelor’s thesis focused on the molecular detection of Rickettsia bacteria in ticks from wild animals in the Madrid region. After completing his Bachelor’s, Gonzalo pursued a Master’s degree in Biotechnology at the Universidad Autónoma de Madrid (2022-2023), where he conducted his Master’s thesis on the design of artificial photosystems, under the supervision of Dr. Sara Hernández Mejías at IMDEA Nanoscience.

đź’Ľ Professional Experience :

Following the completion of his Master’s, Gonzalo Pérez Serrano was appointed as an Assistant Researcher at IMDEA Nanoscience in October 2023. He has been involved in various significant research projects, including a project funded by the LaCaixa Foundation focused on photochemical energy conversion using biohybrids with proteins and nanoclusters, and another research grant from the BBVA Foundation on optimized photochemical conversion. His work spans artificial photosynthesis, light energy conversion, and nanotechnology, contributing to a deeper understanding of the mechanistic role of protein dynamics in chromophore function. Prior to his current position, he gained practical research experience as a trainee student at IMDEA Nanoscience while completing both his Bachelor’s and Master’s degrees.

📚 Research Interests : 

Gonzalo’s research interests are centered on the fields of artificial photosynthesis, light energy conversion, and nanotechnology. He focuses on the study of protein engineering and physical chemistry, with a particular emphasis on pump-probe spectroscopy. His work aims to advance our understanding of how proteins and chromophores interact to enhance light-harvesting efficiency. He is passionate about using cutting-edge techniques in nanoscience to develop sustainable and efficient systems for energy conversion. 🌱🔬💡

đź“ť Publication Top Notes :

  1. Serrano, G. P., EchavarrĂ­a, C. F., & Mejias, S. H. (2024). Development of artificial photosystems based on designed proteins for mechanistic insights into photosynthesis. Protein Science, 33(10). https://doi.org/10.1002/pro.5164