Tae Wan Kim
Seoul National University · 工学
研究室紹介
Professor Tae Wan Kim's research lab focuses on sustainable construction innovation and bio-inspired materials science. The lab investigates modular and offsite construction methodologies to enhance project performance, safety, and efficiency in urban environments, while also exploring gecko-inspired adhesion mechanisms for advanced surface engineering. A key emphasis is placed on understanding the interplay of critical success factors in modular construction and developing high-performance catalysts for clean energy applications.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15A series of platinum catalysts supported on ordered mesoporous carbon (CMK-3) with different Pt loadings from 1 to 10 wt% have been prepared, and their catalytic activities for hydrogen production viaaqueous-phase reforming (APR) of ethylene glycol (EG) have been investigated. Characterization by X-ray powder diffraction, transmission electron microscopy, N2 sorption, and CO chemisorption techniques reveal that an ordered mesostructure, high surface area, large pore volume, and uniform mesopore
Helping others can have a positive effect on both the giver and the receiver. However, supporting someone with depression can be complicated and overwhelming. To address this, we proposed a Facebook-based social bot displaying depressive symptoms and disclosing vulnerable experiences that allows users to practice providing reactions online. We investigated how 55 college students interacted with the social bot for three weeks and how these support-giving experiences affected their mental health
Skid-steer vehicle can generate a large traction force, which is especially good for navigation on a rough terrain. However, the turning motion is so sensitive to slippage effect that designing a controller is still challenging problem. Also, the motion of the vehicle is affected not only by wheel motion, but also by the road properties and the characteristics of wheel control. With this in mind, we employ a model predictive control (MPC) with an on-line model learning. The velocity model, which
Cholic acid-conjugated methylcellulose-polyethylenimines (MCPEI-CAs) were synthesized and characterized for drug delivery systems. Their synthesis was confirmed by 1H NMR and FT-IR analysis. Induced circular dichroism result with Congo red showed that methylcellulose (MC) and polyethylenimine-grafted cationic derivative (MC-PEI) would have helical conformation and random coil structure, respectively. It was found that MCPEI-CAs could form positively charged (>30 mV Zeta-potential) and spheric
Reaction time, driver sensitivity, and time headway are traffic parameters that are critical for understanding and modeling traffic stability and wave propagation. Both theoretical and empirical relationships between these three parameters are examined with vehicle trajectory data. A few clear and distinct relationships are identified, and driver reaction time is found to be closely related to time headway, especially in the deceleration phase. Time headway can be accurately predicted when react
Path tracking control for autonomous vehicle using model predictive control (MPC) algorithm maintains maneuverability by calculating a sequence of control input which minimizes a tracking error. The weakness of this method is that the performance of MPC may decrease significantly when the priori prediction model is not accurate. Therefore, it is important to keep the vehicle stable when MPC having model error. This paper uses an on-line model-based reinforcement learning (RL) to decrease the pat