박현욱 교수

Superhydrophobic surfaces have attracted much attention lately as they present the possibility of achieving a substantial skin-friction drag reduction in turbulent flows. In this paper, the effects of a superhydrophobic surface, consisting of microgrates aligned in the flow direction, on skin-friction drag in turbulent flows were investigated through direct numerical simulation of turbulent channel flows. The superhydrophobic surface was modeled through a shear-free boundary condition on the air

Image resizing is to change an image size by upsampling or downsampling of a digital image. Most still images and video frames on digital media are given in a compressed domain. Image resizing of a compressed image can be performed in the spatial domain via decompression and recompression. In general, resizing of a compressed image in a compressed domain is much faster than that in the spatial domain. We propose a novel approach to resize images with L/M resizing ratio in the discrete cosine tra

In this article, we first propose a deep reinforcement learning (RL)-based optimal decoupling capacitor (decap) design method for silicon interposer-based 2.5-D/3-D integrated circuits (ICs). The proposed method provides an optimal decap design that satisfies target impedance with a minimum area. Using deep RL algorithms based on reward feedback mechanisms, an optimal decap design guideline can be derived. For verification, the proposed method was applied to test power distribution networks (PDN

In this article, for the first time, we propose a transformer network-based reinforcement learning (RL) method for power distribution network (PDN) optimization of high bandwidth memory (HBM). The proposed method can provide an optimal decoupling capacitor (decap) design to maximize the reduction of PDN self- and transfer impedances seen at multiple ports. An attention-based transformer network is implemented to directly parameterize decap optimization policy. The optimality performance is signi

In this paper, for the first time, we propose a reinforcement learning-based optimal on-board decoupling capacitor (decap) design method. The proposed method can provide optimal decap designs for a given on-board power distribution network (PDN). An optimal decap design refers to the optimized combination of decaps at proper positions to satisfy a required target impedance. Moreover, a minimum number of decaps should be assigned for optimal decap designs. The proposed method is applied to the te

High-order harmonic generation (HHG) is investigated in rare-gas clusters as a function of the cluster size using 0.8 and 1.3 μm femtosecond lasers. A characteristic, species-dependent knee structure in the single particle response is observed. A 1D recollision model qualitatively reproduces this behavior and associates it to the degree of delocalization of the initial wave function. Small clusters are observed to have a higher efficiency than monomers but rapidly lose this advantage as the size

We report on a tunable intense femtosecond mid-infrared (mid-IR) light source carrying orbital angular momentum (OAM). Our setup is based on an optical parametric amplification system with an 800 nm pump shaped with a spiral phase plate. We confirm the anisotropic OAM transfer from the pump to the idler through stimulated difference frequency generation by measuring the diffraction patterns of a triangular aperture illuminated by the signal, pump, and idler beams. The tunability of the setup is

We report on the generation of harmonic-like photon upconversion in a LiNbO₃-based nonlinear photonic crystal by mid-infrared (MIR) femtosecond laser pulses. We study below bandgap harmonics of various driver wavelengths, reaching up to the 11th order at 4 μm driver with 13% efficiency. We compare our results to numerical simulations based on two mechanisms: cascade three-wave mixing and non-perturbative harmonic generation, both of which include quasi-phase matching. The cascade mode