Katarzyna Sieradzka | Digital Technologies | Research Excellence Award

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Dr. Katarzyna Sieradzka | Digital Technologies | Research Excellence Award

Casimir Pulaski Radom University, Poland

Dr. Katarzyna Sieradzka is a researcher and lecturer at the Department of Economics, Faculty of Economics and Finance, Casimir Pulaski University of Radom, with a PhD in Economics obtained in 2003. She is the author of numerous scientific articles and a co-author of several scientific monographs, reflecting her sustained contribution to economic research and academic scholarship. She has actively participated in many national and international scientific conferences, where she has shared research findings and engaged in interdisciplinary academic dialogue. Her research contribution to the field of entrepreneurship development focuses on identifying and analyzing the key determinants that shape business growth and long-term performance. In particular, her work examines the roles of innovation, competitiveness, corporate social responsibility, and digitalization in modern enterprises. She investigates how technological adoption, organizational capabilities, and strategic management approaches influence firm performance, adaptability, and resilience in dynamic economic environments. A significant aspect of her research explores the role of corporate social responsibility in strengthening stakeholder relationships and enhancing sustainable business practices. She also analyzes how innovation contributes to value creation and the development of competitive advantage across different sectors. Additionally, her studies address the transformative impact of digital tools on business models, decision-making processes, and operational efficiency. Through an integrated analytical approach, her research provides practical and theoretical insights into entrepreneurship dynamics. Her work supports evidence-based decision-making for managers and policymakers. Overall, her academic activities contribute to promoting sustainable entrepreneurship and inclusive economic development.

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Assoc. Prof. Dr. Lei Wang | Automatic Control Systems | Best Researcher Award

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Assoc. Prof. Dr. Lei Wang | Automatic Control Systems | Best Researcher Award

Wuxi University, China.

Dr. Wang Lei is an Associate Professor at Wuxi University, specializing in intelligent control systems. With a strong background in artificial intelligence applications in automation, he has led over 10 major research projects, published more than 30 peer-reviewed papers, and holds 20+ patents and 11 software copyrights. He has international training experience in Thailand, Taiwan, Poland, and the UK, enriching his global academic perspective.

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🎓 Education

Dr. Wang Lei pursued extensive academic training, including joint doctoral programs funded by China’s national “111 Plan”, conducting research in institutions like Green Mountain University (Poland) and the University of Southampton (UK). His master's studies included training at Prince Songkhla University (Thailand) and Yunlin University of Science and Technology (Taiwan).

💼 Experience

Currently an Associate Professor at Wuxi University, Wang Lei has spearheaded numerous provincial and national research projects, including collaborations with the Ministry of Education, Wuxi Science and Technology Bureau, and the National Natural Science Foundation of China. His editorial roles include reviewing for journals like International Journal of Robust and Nonlinear Control and Security and Communication Networks.

🔬 Research Interests

His research focuses on the application of artificial intelligence in automatic control systems, covering areas such as iterative learning control, dynamic observers, fuzzy systems, and actuator fault tolerance.

🏆 Awards & Patents

Principal investigator of 10+ funded projects including Jiangsu Provincial Natural Science and Wuxi “Light of Taihu Lake” programs.

Holder of 20+ patents, including “A trajectory tracking method for non-repetitive time-varying systems”.

Recognized with support from national initiatives such as the “111 Plan”.

📚 Notable Publications

🆕 2025

Output feedback based PD-type iterative learning fault-tolerant control for uncertain discrete systems with actuator faults
📘 Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
🔗 DOI: 10.1177/09596518241263003
👥 Yanxia Shen, Wei Zou, Lei Wang

🔬 2024

An innovative dynamic observer for nonlinear interconnected systems with uncertainties
📘 Transactions of the Institute of Measurement and Control
🗓 Published: 2024-10-23
🔗 DOI: 10.1177/01423312241274007
👥 Nan Ji, Lei Wang, Xinggang Yan, Dezhi Xu

Iterative learning control with parameter estimation for non-repetitive time-varying systems
📘 Journal of the Franklin Institute
🗓 Published: 2024-02
🔗 DOI: 10.1016/j.jfranklin.2024.01.011
👥 Lei Wang, Ziwei Huangfu, Ruiwen Li, Xiewen (Sitman) Wen, Yuan Sun, Yiyang Chen

📊 2023

Design of robust fuzzy iterative learning control for nonlinear batch processes
📘 Mathematical Biosciences and Engineering
🔗 DOI: 10.3934/mbe.2023897
👥 Wei Zou, Yanxia Shen, Lei Wang

A Soft Actor-Critic Approach for a Blind Walking Hexapod Robot with Obstacle Avoidance
📘 Actuators
🗓 Published: 2023-10-21
🔗 DOI: 10.3390/act12100393
👥 Lei Wang, Li Ruiwen, Ziwei Huangfu, Yishan Feng, Yiyang Chen

Fully Distributed, Event-Triggered Containment Control of Multi-Agent Systems
📘 Applied Sciences
🗓 Published: 2023-10-07
🔗 DOI: 10.3390/app131911039
👥 Lei Wang, Guanwen Chen, Tai Li, Ruitian Yang

Slowness or Autocorrelation? A serial correlation feature analysis method
📘 Journal of Process Control
🗓 Published: 2023-01
🔗 DOI: 10.1016/j.jprocont.2022.11.010
👥 Qinghua Li, Zhonggai Zhao, Lei Wang

Mr. Chibuzo Nwabufo Okwuosa | Fault Detection | Best Researcher Award

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Mr. Chibuzo Nwabufo Okwuosa | Fault Detection | Best Researcher Award

Kumoh National Institute of Technology, South Korea.

Okwuosa Chibuzo Nwabufo is a Research Ph.D. Scholar at Kumoh National Institute of Technology 🇰🇷, South Korea, specializing in Mechanical Engineering. With a strong foundation in machine learning, deep learning, and real-time fault diagnostics, his work emphasizes bridging theoretical innovation with industrial application. Chibuzo is passionate about Prognostics and Health Management (PHM), Explainable AI (XAI), and digital twin technologies, aiming to create smart, AI-driven maintenance systems for next-generation industries.

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🎓 Education

Chibuzo earned both his Master’s and is currently pursuing his Ph.D. in Mechanical Engineering from Kumoh National Institute of Technology, South Korea. His academic focus has been consistently rooted in intelligent fault diagnostics, predictive maintenance, and real-time monitoring technologies.

💼 Experience

With over four completed and two ongoing research projects, Chibuzo has hands-on experience in both academia and industry. Notable projects include real-time diagnostics for diaphragm pumps, fault analysis in induction motors, and zinc phosphating coating processes. He has collaborated on industry-sponsored projects and led initiatives involving advanced data-driven solutions for predictive maintenance.

🔬 Research Interests

His key research domains include:

🔧 Prognostics and Health Management (PHM)

🤖 Machine Learning & Deep Learning

🧠 Explainable AI (XAI)

🌐 Digital Twin Technologies

⚙️ Real-time Fault Diagnostics

🏆 Awards & Grants

Chibuzo’s research has been supported by prestigious Korean government grants:

IITP Innovative Human Resource Development for Local Intellectualization

ITRC Program (MSIT, Korea)
These grants facilitated collaborations with industry leaders and funded cutting-edge research in diagnostics and manufacturing innovation.

📚 Selected Publications

🆕 Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems

📅 2025-04-13 | Sensors
🔗 DOI: 10.3390/s25082449
👨‍🔬 Contributors: Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora, Jang-Wook Hur
📌 Application of deep learning and high-resolution imaging for defect detection on challenging industrial surfaces.

⚙️ A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

📅 2024-10-09 | Journal of Sensor and Actuator Networks
🔗 DOI: 10.3390/jsan13050064
👨‍🔬 Contributors: Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Baek Cheon Shin, Jang-Wook Hur
📌 Fault detection in mechanical components using spectral features and XGBoost.

🔍 Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

📅 2024-02-29 | Electronics
🔗 DOI: 10.3390/electronics13050926
👨‍🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Intelligent transformer fault diagnosis using statistical signal analysis and feature engineering.

⚡ Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals

📅 2024-01-18 | Preprint
🔗 DOI: 10.20944/preprints202401.1371.v1
👨‍🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Preprint focusing on enhanced signal processing for electrical fault classification.

🧠 An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

📅 2023 | IEEE Access
🔗 DOI: 10.1109/ACCESS.2023.3266865
👨‍🔬 Contributors: Chibuzo Nwabufo Okwuosa, Jang-Wook Hur
📌 Machine learning-based fault classification in squirrel cage induction motors under low-load conditions.

 

 

 

Prof. Dr. Javier Ruiz-del-Solar | Robotics | Best Researcher Award

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Prof. Dr. Javier Ruiz-del-Solar | Robotics | Best Researcher Award

Universidad de Chile, Chile.

Javier Ruiz-del-Solar S. is a Chilean Full Professor at the Department of Electrical Engineering, Universidad de Chile, and Director of the Advanced Mining Technology Center. With a deep passion for robotics, AI, and mining tech, he has made remarkable contributions to academia and industry, mentoring the next generation of engineers and researchers.

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🎓 Education

Prof. Dr. Javier Ruiz-del-Solar is a distinguished academic with a strong foundation in electrical and electronic engineering. He earned his Doctor-Engineer degree from the Technical University of Berlin, Germany in 1997. Prior to that, he obtained his M.Sc. in Electrical Engineering in 1992 and his undergraduate degree in Electrical Engineering in 1991 from the Universidad Técnica Federico Santa María, Chile. His academic trajectory reflects a solid commitment to excellence in engineering and research.

🧑‍🔬 Experience

Prof. Dr. Javier Ruiz-del-Solar has held numerous prominent academic and editorial positions throughout his career. He serves as a Full Professor at the Universidad de Chile, where he also directs the Advanced Mining Technology Center. His editorial contributions include being an Associate Editor for the IEEE Transactions on Cognitive and Developmental Systems (2017–2023) and a current Advisory Board Member of the Journal of Field Robotics (2022–2024). He also served as an Associate Editor for the Journal of Intelligent and Robotic Systems (2008–2017). In addition, Prof. Ruiz-del-Solar has been the Chairman of the IEEE Latin American Robotics Council since 2003, contributing significantly to the development of robotics research and collaboration across the region.

🔍 Research Interests

His work lies at the intersection of:

🤖 Robotics & Autonomous Systems

🧠 Artificial Intelligence & Deep Learning

⛏️ Mining Technology and Automation

🏆 Awards & Honors

🏅 Member, Chilean Academy of Engineering (since 2009)

🥇 Best Paper Award, RoboCup Symposium (2004, 2015, 2017)

🚀 RoboCup @Home Innovation Award (2007, 2008)

🎤 IEEE Distinguished Lecturer (2008–2009)

👨‍🏫 Best Teacher Award, Universidad de Chile (2007)

📚 Selected Publications

🛠️ Autonomous & Collaborative Mining Systems

1. The Road to the Mine of the Future: Autonomous Collaborative Mining

Journal: Mining (April 2025)
DOI: 10.3390/mining5020025
Key Themes:

Vision of fully autonomous, cooperative mining systems.

Integrates robotic systems, multi-agent collaboration, and intelligent decision-making.

Emphasis on safety, productivity, and sustainability in future mining operations..

🤖 Reinforcement Learning in Mining

2. Autonomous Loading of Ore Piles with Load-Haul-Dump Machines Using DRL

Journal: Expert Systems with Applications (March 2025)
DOI: 10.1016/j.eswa.2024.125770
Highlights:

Application of deep reinforcement learning to optimize ore loading in underground mining.

Demonstrates efficiency gains and reduced human intervention.

3. Control of Heap Leach Piles Using Deep Reinforcement Learning

Journal: Minerals Engineering (July 2024)
DOI: 10.1016/j.mineng.2024.108707
Highlights:

Uses DRL to optimize irrigation and aeration in heap leaching.

Potential for smarter, real-time process control in hydrometallurgy.

🍒 AI and Robotics Beyond Mining

4. Cherry CO Dataset: Detection, Segmentation, and Maturity Recognition

Journal: IEEE Robotics and Automation Letters (June 2024)
DOI: 10.1109/LRA.2024.3393214
Focus:

High-quality dataset for precision agriculture using computer vision.

Supports cherry detection and ripeness classification.