Dr. Seungwoo Jeong | Robotics SLAM | Research Excellence Award

Published on by Robotics and Automation

Dr. Seungwoo Jeong | Robotics SLAM | Research Excellence Award

Korea Railroad Research Institute | South Korea

Dr. Seungwoo Jeong is a Machine Learning and Control Engineering specialist with strong expertise in robotics, automation, and intelligent transportation systems. He has extensive research and industry experience, contributing to advanced logistics, factory automation, and robot R&D projects that improved efficiency, safety, Robotics SLAM and system reliability. He has published widely in leading mechanical engineering journals and has led projects reducing error rates and operational costs. He holds a PhD in Mechanical Engineering from Yonsei University, with prior graduate training from the University of California, Davis, and Ajou University.

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Featured Publications


The experiments of wearable robot for carrying heavy-weight objects of shipbuilding works


– IEEE International Conference on Automation Science and Engineering, 2014

Behavior tree driven multi-mobile robots via data distribution service (DDS)


– International Conference on Control, Automation and Systems (ICCAS), 2021

Behavior tree-based task planning for multiple mobile robots using a data distribution service


– IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2022

Attaching sub-links on linear actuators of wearable robots for payload increase


– IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2014

Mr. Emmanuel Ebikabowei Enemugha | AI-Based Robot Perception | Best Researcher Award

Published on by Robotics and Automation

Mr. Emmanuel Ebikabowei Enemugha | AI-Based Robot Perception | Best Researcher Award

University Malaya Department of Mechanical Engineering | Nigeria

Mr. Enemugha Emmanuel Ebikabowei is a dedicated mechanical engineer and researcher, currently a Ph.d. Candidate in mechanical engineering at the University of Malaya, Malaysia, with specialization in computational fluid dynamics, gas-turbine performance, pump-impeller blade design, and energy systems optimization. He also serves as a lecturer in the department of mechanical engineering at Nigeria maritime university. His publication record on researchgate lists 5 documents with 69 reads, though his citation and h-index metrics are not publicly indicated. His scholarly work includes notable contributions such as a hybrid optimization of mixed-axial flow pump impellers using taguchi method, genetic algorithms, AI-Based Robot Perception and neural networks. additionally, He has conducted experimental analyses on firewood combustion efficiency for sustainable cooking in bayelsa state, Nigeria. His research is shaping the future of efficient pump systems and clean energy solutions for both industrial and community-scale applications.

Profile: Orcid

Featured Publications

Enemugha, E. E., Ab Karim, M. S. B., & Nik Ghazali, N. N. B. (2025). Hybrid optimisation of mixed-axial flow pump impellers parameter using Taguchi, genetic algorithms, and artificial neural networks. Next Research.

Enemugha, E. E., & Munuakuro, A. E. (2025). Experimental analysis of firewood combustion efficiency and fuel consumption patterns for sustainable cooking in Bayelsa State, Nigeria. International Journal for Research in Applied Science and Engineering Technology, 13(4).

Enemugha, E. E. (2025). The effects of impeller blade count on centrifugal pump performance and efficiency under different operating conditions: A comparison of numerical prediction. International Journal for Research in Applied Science and Engineering Technology, 13(4).

Bratua, I., Burubai, W., & Enemugha, E. E. (2025). Comparative analysis of fuelwood weight loss and energy efficiency in Bayelsa State, Nigeria. World Journal of Advanced Engineering Technology and Sciences, 14(3).

Enemugha, E. E., Ab Karim, M. S., & Nik Ghazali, N. N. (2025). Comprehensive optimization of centrifugal pump performance through the integration of the Taguchi method and polynomial regression models. Global Journal of Engineering and Technology Advances, 22(2).