Kyung-Soo Kim
Korea Advanced Institute of Science and Technology · Engineering
About the Lab
Professor Kyung-Soo Kim's research lab specializes in advanced control systems, intelligent sensing, and embedded systems with a focus on real-time signal processing, robust control design, and human-centered intelligent technologies. The lab develops innovative solutions in tactile perception systems, video watermarking for digital rights management, and nonlinear state estimation for energy storage systems such as lithium-ion batteries. Key research directions include sliding mode control with performance optimization, disturbance rejection in electric motor drives, and perceptually transparent multimedia security systems. The lab emphasizes practical applicability, combining theoretical rigor with real-world implementation in automotive, robotics, and multimedia applications.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Abstract As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various t
Commercial markets employ digital right management (DRM) systems to protect valuable high-definition (HD) quality videos. DRM system uses watermarking to provide copyright protection and ownership authentication of multimedia contents. We propose a real-time video watermarking scheme for HD video in the uncompressed domain. Especially, our approach is in aspect of practical perspectives to satisfy perceptual quality, real-time processing, and robustness requirements. We simplify and optimize hum
We consider a novel method for designing the sliding mode that minimizes the quadratic performance while keeping a pole-clustering constraint. Our approach is based on the manipulations of linear matrix inequalities (LMIs) imposed by the design objectives. For this purpose, we newly propose LMI conditions for the quadratic performance optimization and the pole-clustering problem, respectively, in a full order state. Then they are combined in the LMI framework that is typically devised for the sl
This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlin
This paper presents a robust current tracking controller for permanent magnet synchronous motors (PMSMs) with a performance recovery property for electric power steering (EPS) applications. The contributions of this work are twofold. First, a disturbance observer (DOB) is designed to compensate the disturbances arising from the model–plant mismatches while reducing the closed-loop sensitivity. Second, a current controller is designed to improve the current tracking performance in the frequency d
Scales are used to reduce the conservatism encountered in most multiobjective approaches to control design. The most general case (i.e., matrix scales) results in a nonconvex problem, though the use of scalar scales leads to convex searches in the analysis and state feedback problems. Output feedback synthesis and other extensions are discussed. Numerical examples are provided to show the effectiveness of the suggested approach. The article considers, in particular, H/sup /spl infin// control.
Research Areas
Dive deeper into Kyung-Soo Kim's research on Nubint
Open this lab's papers in the app to read with AI, summarize, and cite in your writing.