Heungseob Kim | Innovative Research Award | Energy-efficient path planning for underwater vehicles

Published on by Network Science

Innovative Research Award

Heungseob Kim
Changwon National University,

Heungseob Kim
Affiliation Changwon National University
Country South Korea
Scopus ID 57191904084
Documents 14
Citations 455
h-index 8
Subject Area Energy-efficient path planning for underwater vehicles
Event International Research Awards on Network Science & Graph Analytics
ORCID 0000-0003-0090-5670

The available bibliographic profile records 14 documents, 455 citations, and an h-index of 8 for the researcher. These indicators provide a quantitative context for assessing research visibility and scholarly influence, while the specific scientific contribution is considered in relation to the scope, originality, and relevance of the underlying work. [1]

Abstract

This article presents an academic recognition profile for Heungseob Kim, affiliated with Changwon National University in South Korea. His stated subject area concerns energy-efficient path planning for underwater vehicles, an interdisciplinary research direction combining network-oriented computational methods, autonomous navigation, optimization, and energy-aware decision-making. The profile records 14 documents, 455 citations, and an h-index of 8 according to the supplied Scopus information. [1] The recognition is associated with the International Research Awards on Network Science & Graph Analytics.

Keywords

Heungseob Kim, Innovative Research Award, Changwon National University, South Korea, underwater vehicles, energy-efficient path planning, autonomous navigation, path planning, network science, graph analytics, computational optimization, marine robotics, energy-aware navigation, research recognition.

Introduction

Energy-efficient path planning is an important research problem for underwater vehicles because autonomous systems operating in marine environments must balance navigation objectives with limited energy resources. Path-planning approaches may incorporate environmental constraints, vehicle dynamics, mission objectives, and computational considerations. Within this context, research on efficient navigation can contribute to the development of more sustainable and reliable autonomous underwater systems.

Network science and graph analytics provide methodological frameworks for representing interconnected environments, routes, states, and decision alternatives. Their application to navigation and optimization can support structured analysis of complex movement problems and enable algorithms to identify feasible and efficient paths.

Research Profile

Heungseob Kim is affiliated with Changwon National University, South Korea. The supplied research profile identifies energy-efficient path planning for underwater vehicles as a principal subject area. This field connects autonomous vehicle navigation with optimization and energy management, particularly where mission duration, route feasibility, and operational efficiency must be considered together.

The available bibliometric information indicates 14 indexed documents, 455 citations, and an h-index of 8. [1] His ORCID identifier is 0000-0003-0090-5670, providing a persistent researcher identifier for distinguishing scholarly work associated with the profile. [2]

Research Contributions

The identified research direction can be considered through several interconnected contribution areas:

  • Development and evaluation of energy-aware approaches to underwater vehicle path planning.
  • Application of computational optimization to route selection under operational constraints.
  • Integration of navigation efficiency with the practical limitations of autonomous underwater platforms.
  • Potential use of network and graph-based representations for modeling alternative navigation paths and connected operational states.

These areas are relevant to marine robotics and autonomous systems research because energy consumption directly affects endurance, mission planning, and operational reliability. The assessment of individual contributions should, however, be based on the documented publications and their methodological evidence rather than bibliometric indicators alone.

Publications

The supplied profile reports 14 documents indexed in Scopus. [1] Because individual publication titles, journal information, publication years, and DOI identifiers were not provided in the source data for this article, no specific DOI has been assigned or inferred. This approach avoids introducing unverifiable bibliographic information.

For authoritative publication-level information, readers should consult the researcher’s Scopus author profile and ORCID record listed in the External Links section. These sources can be used to verify publication metadata, authorship, citation records, and persistent researcher identification.

Research Impact

The reported citation count of 455 and h-index of 8 provide measurable indicators of scholarly visibility within the indexed literature. [1] In the context of underwater vehicle research, energy-efficient path planning has potential relevance to autonomous marine exploration, inspection, monitoring, and other applications where vehicle endurance and route efficiency are important considerations.

The broader significance of the research area lies in its intersection of computational intelligence, optimization, autonomous systems, and marine engineering. Continued development of efficient planning methods may contribute to more effective utilization of limited onboard energy resources and improved mission-level decision-making.

Award Suitability

Heungseob Kim’s stated research specialization aligns conceptually with an innovation-focused research recognition because energy-efficient path planning addresses a practical and technically demanding problem in autonomous underwater systems. The combination of navigation, optimization, energy management, and network-oriented analytical methods provides a relevant interdisciplinary foundation for consideration under the Innovative Research Award.

The available bibliometric profile further establishes a documented body of indexed research, with 14 documents, 455 citations, and an h-index of 8. [1] Final award evaluation should appropriately consider originality, methodological rigor, publication quality, demonstrated research outcomes, and the relevance of the candidate’s work to the award’s stated criteria.

Conclusion

Heungseob Kim’s academic profile reflects research activity at the intersection of energy-efficient path planning and underwater vehicle systems. His affiliation with Changwon National University, documented Scopus profile, and persistent ORCID identifier provide a basis for scholarly verification. [1] [2] The identified research area is relevant to ongoing efforts to improve the efficiency and autonomy of marine robotic systems, making it appropriate for consideration within a research recognition framework focused on innovation.

References

  1. Elsevier. (n.d.). Scopus author details: Heungseob Kim, Author ID 57191904084. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57191904084
  2. ORCID. (n.d.). Heungseob Kim: ORCID record 0000-0003-0090-5670. ORCID.
    https://orcid.org/0000-0003-0090-5670
  3. International Research Awards on Network Science & Graph Analytics. (n.d.). Award Website.
    https://networkscience-conferences.researchw.com/

Hui Wang | Network Resilience | Best Researcher Award

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Dr. Hui Wang | Network Resilience | Best Researcher Award

Southwest Jiaotong University | China

Dr. Hui Wang is an Associate Researcher at Southwest Jiaotong University, where he also earned his Doctoral degree after completing his Bachelor’s studies at the University of South China. His research focuses on intelligent control using deep reinforcement learning, data-efficient embodied AI that learns across physical and virtual environments, physics-informed modeling and simulation, and computer-vision-based perception and defect detection. He is proficient in Python (PyTorch/TensorFlow), MATLAB, and C, with extensive experience in data communication and hardware-in-the-loop systems. Dr. Wang has been recognized with major honors, including the Outstanding Doctoral Dissertation Award from Southwest Jiaotong University (2024) and the National Scholarship for Doctoral Degree (2023). He has led and contributed to several national-level research projects, notably developing a physics-informed simulation engine and resilient pantograph control algorithms for the National Natural Science Foundation of China, designing intelligent high-speed railway pantograph systems for the China Postdoctoral Science Foundation, and advancing deep reinforcement learning applications for high-speed rail as part of the Sichuan Science and Technology Plan. His earlier work includes pioneering machine-learning-based monitoring methods for railway catenary components, developing CNN-based detection models, unsupervised learning frameworks, and segmentation-assisted diagnosis tools.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Multi-modal imitation learning for arc detection in complex railway environments",  J Yan, Y Cheng, F Zhang, N Zhou, H Wang, B Jin, M Wang, W Zhang, IEEE Transactions on Instrumentation and Measurement, 2025.

"Research on multimodal techniques for arc detection in railway systems with limited data",  J Yan, Y Cheng, F Zhang, M Li, N Zhou, B Jin, H Wang, H Yang, W Zhang, Structural Health Monitoring, 14759217251336797, 2025.

"CSRM-MIM: A Self-Supervised Pre-training Method for Detecting Catenary Support Components in Electrified Railways", H Yang, Z Liu, N Ma, X Wang, W Liu, H Wang, D Zhan, Z Hu, IEEE Transactions on Transportation Electrification, 2025.

"Assessment of current collection performance of rail pantograph-catenary considering long suspension bridges", X Wang, Y Song, B Lu, H Wang, Z Liu, IEEE Transactions on Instrumentation and Measurement, 2025.

"FENet: A Physics‐Informed Dynamics Prediction Model of Pantograph‐Catenary Systems in Electric Railway", W Chu, H Wang, Y Song, Z Liu, 2025.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

Published on by Network Science

Prof. Dr. Qinglai Wei | Self-Learning Systems | Best Researcher Award 

Associate Director, at Institute of Automation, Chinese Academy of Sciences, China.

Professor Qinglai Wei is a distinguished researcher and educator specializing in control systems, computational intelligence, and learning-based optimization. Serving as the Associate Director at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, he has made significant contributions to adaptive dynamic programming, nonlinear control, and reinforcement learning. With an illustrious academic journey from Northeastern University and rich professional experience, Prof. Wei has authored numerous influential papers, books, and book chapters. His awards include multiple IEEE honors and recognition as a Clarivate Highly Cited Researcher. He is a prominent figure in advancing intelligent control systems and their applications in complex scenarios.

Professional Profile

Scopus

Google Scholar

Education 🎓

  • Ph.D. in Control Theory and Control Engineering (2009): Northeastern University, China. Advised by Prof. Huaguang Zhang, his research focused on intelligent control systems.
  • M.S. in Control Theory and Control Engineering (2005): Northeastern University, China, under Prof. Xianwen Gao’s mentorship.
  • B.S. in Automation (2002): Northeastern University, China, advised by Baodong Xu.
    These academic milestones laid the foundation for his expertise in adaptive dynamic programming and intelligent systems.

Professional Experience 💼

  • Associate Director (2018–Present): The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences.
  • Professor (2016–Present): The State Key Laboratory and the School of Artificial Intelligence, University of Chinese Academy of Sciences.
  • Visiting Scholar roles at University of Rhode Island (2018) and University of Texas at Arlington (2014) reflect his international collaboration and academic outreach.
    Earlier roles include Associate and Assistant Professor positions at The State Key Laboratory, showcasing steady growth in his academic career.

Research Interests 🔬

Prof. Wei’s research spans:

  • Computational Intelligence & Intelligent Control
  • Learning Control & Reinforcement Learning
  • Optimal & Nonlinear Control
  • Adaptive Dynamic Programming
    Applications include process control, smart grids, and multi-agent systems. His innovative methods continue to drive advancements in control theory and intelligent systems.

Awards 🏆

Prof. Wei’s excellence is marked by accolades like:

  • Best Paper Awards (2023 & 2022): International CSIS-IAC and China Automation Congress.
  • IEEE Outstanding Paper Awards (2018): Recognition for impactful contributions to the IEEE journals.
  • Highly Cited Researcher (2018 & 2019): By Clarivate Analytics for his influential publications.
    Other honors include National Natural Science Foundation Awards and Young Researcher Awards, emphasizing his leadership in the field.

Top Noted Publications 📚

  • “Learning and Controlling Multiscale Dynamics in Spiking Neural Networks” (2024, IEEE Transactions on Cybernetics): This study employs Recursive Least Square (RLS) modifications to manage multiscale dynamics in spiking neural networks. It advances neural control methods for adaptive tasks in dynamic environments【8】.
  • “Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems” (2024, IEEE/CAA Journal of Automatica Sinica): This paper focuses on event-triggered mechanisms to ensure robust consensus in multiagent systems under parallel optimal control.
  • “Primal-Dual Adaptive Dynamic Programming for Nonlinear Systems” (2024, Automatica): A framework using primal-dual adaptive dynamic programming tackles the stabilization and optimization of nonlinear systems.
  • “Class-Incremental Learning with Balanced Embedding Discrimination” (2024, Neural Networks): This work enhances class-incremental learning by introducing techniques to balance embeddings and improve discrimination among new and existing classes.

Conclusion

Qinglai Wei is exceptionally suited for the Research for Best Researcher Award. His prolific contributions to control theory, computational intelligence, and reinforcement learning, combined with his global recognition and leadership, exemplify his stature as a world-class researcher. With a proven track record of innovative research, impactful publications, and numerous accolades, he stands out as a strong candidate for this prestigious honor. Continued expansion into interdisciplinary collaborations and mentorship initiatives will further solidify his legacy as a pioneering researcher.