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About

Jungeun Kim is a researcher whose work spans robotics, intelligent information retrieval, and decision-making under uncertainty. A key contribution is the dynamic modeling of nonlinear two-wheeled robots using a data-driven approach, where Kim developed a system identification method from fundamental nonlinear kinematics, validated in a Simulink environment across varied operating conditions—a foundational step for agile, self-balancing robotic platforms (17 citations). In information retrieval, Kim proposed an effective method to enhance focused crawlers by leveraging Google’s search capabilities, improving the precision of domain-specific web data collection (13 citations). More recently, Kim has advanced the theory of fuzzy decision-making by introducing the q-rung complex diophantine neutrosophic normal set, a powerful generalization of q-rung orthopair fuzzy sets that better captures complex, uncertain information in multiple-attribute decision-making problems (9 citations). This work demonstrates a rare ability to bridge practical engineering challenges with rigorous mathematical frameworks. With a growing citation footprint across these diverse domains, Kim’s research is notable for its methodological innovation and real-world applicability, making significant strides in both robotic control and computational intelligence.