Mr. Chibuzo Okwuosa | Fault Diagnosis | Best Researcher Award

Mr. Chibuzo Okwuosa, Kumoh National Institute of Technology, South Korea

Mr. Chibuzo Okwuosa is currently pursuing his Ph.D. in Mechanical Engineering at Kumoh National Institute of Technology in Gumi, South Korea, where he previously earned his Master of Engineering, focusing on filter-based feature selection for stator winding fault classification in squirrel cage induction motors. Since 2021, he has served as a Research Assistant at the Defense Reliability Laboratory, contributing to innovative research initiatives, and is currently a researcher at the Research, Development and Prognostics (FD&P) center. His outstanding contributions to the field were recognized with the Excellence Paper Award at the 2023 Autumn Academic Conference. Prior to his academic career, Mr. Okwuosa gained valuable experience as a Relationship Officer at Keystone Bank Ltd in Nigeria, enhancing his client management and relationship-building skills.

Professional Profile:

Orcid

Suitability for the Best Researcher Award:

Mr. Okwuosa’s achievements in Mechanical Engineering, particularly his innovative work on fault detection systems, make him an ideal candidate for the Best Researcher Award. His research not only advances the state of the art in engineering diagnostics but also offers practical solutions to real-world problems faced by industries worldwide. Furthermore, his combination of academic rigor, practical application, and recognition through awards makes him stand out as a researcher with significant potential for future contributions.

Educational Background:

Mr. Chibuzo Okwuosa is currently pursuing his Ph.D. in Mechanical Engineering at Kumoh National Institute of Technology in Gumi, South Korea. He holds a Master of Engineering in Mechanical Engineering from the same institution, where his thesis focused on filter-based feature selection for stator winding fault classification in squirrel cage induction motors (SCIM). He also earned a Bachelor of Engineering in Agricultural Engineering from Imo State University in Nigeria.

Professional Experience:

Since 2021, Mr. Okwuosa has served as a Research Assistant at the Defense Reliability Laboratory at Kumoh National Institute of Technology, contributing to innovative research and development initiatives. He currently works as a Researcher at the Research, Development and Prognostics (FD&P) in Gumi, Korea.

Awards & Achievements:

Mr. Okwuosa received the Excellence Paper Award at the 2023 Autumn Academic Conference, recognizing his outstanding contributions to the field of mechanical engineering. 🏆📄

Career Transition:

Before his academic pursuits, he worked as a Relationship Officer at Keystone Bank Ltd in Nigeria, where he honed his skills in client management and relationship building.

Publication Top Notes:

“A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance”
- Published Year : 2024
“Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection”
- Published Year : 2024
“Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals”
- Published Year : 2024
“An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning”
- Published Year: 2023
“Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach”
- Published Year : 2023

Mr. Yidie Wang | AI for fault diagnosis | Best Researcher Award

Mr. Yidie Wang, Hunan University, China

Mr. Wang holds a Master’s degree in Engineering Mechanics from Hunan University and a Bachelor’s degree in Welding Technology and Engineering from Kunming University of Science and Technology. His research includes developing theoretical models and detection algorithms for road compaction using one-dimensional convolutional neural networks (CNNs) and designing decision-making algorithms for autonomous driving with deep reinforcement learning. His work extends to fault diagnosis using Attention mechanisms and unsupervised GAN-based algorithms, reflecting his expertise in AI applications for vehicle safety and reliability. Mr. Wang’s research interests span Machine Learning, Deep Learning, Reinforcement Learning, Data Mining, and Autonomous Driving, emphasizing his commitment to advancing technology in these critical areas.

Suitability for the Best Researcher Award:

Mr. Yidie Wang demonstrates a strong research background in areas that are highly relevant to the criteria for the Best Researcher Award. His research interests in Machine Learning, Deep Learning, and Reinforcement Learning, coupled with his expertise in Fault Diagnosis, Autonomous Driving, and Multi-Sensor Fusion, align with cutting-edge fields in AI and autonomous systems.

Educational Background:

Mr. Wang completed his Master’s degree in Engineering Mechanics from Hunan University. His coursework covered advanced topics such as Continuum Mechanics, Nonlinear Vibration Mechanics, and Topology Optimization Techniques, all of which are highly relevant to his research. His Bachelor’s degree in Welding Technology and Engineering from Kunming University of Science and Technology also provided a strong foundation in Mechanical Engineering, Programming, and Applied Mechanics.

Research Experience:

Mr. Wang has undertaken significant research projects, such as developing a theoretical model and detection algorithm for continuous compaction of road subgrade. His use of one-dimensional convolutional neural networks (CNNs) to classify and predict compaction results demonstrates his ability to apply advanced machine learning techniques to real-world problems.

In his Master’s thesis, Mr. Wang focused on autonomous driving, specifically designing decision-making algorithms for lane changing, obstacle avoidance, and following behavior using deep reinforcement learning. His innovative approach to fault diagnosis using an Attention mechanism and unsupervised GAN-based algorithms further underscores his expertise in AI applications for vehicle safety and reliability.

Research Interests:

Mr. Wang’s research interests align well with cutting-edge technologies, including Machine Learning, Deep Learning, Reinforcement Learning, Data Mining, and Autonomous Driving. His work in Fault Diagnosis and Multi-Sensor Fusion for Autonomous Driving highlights his focus on critical, high-impact areas within AI and engineering.